Artificial intelligence is the intelligence that machines can exhibit. It differs, therefore, from natural intelligence, which is possessed by human beings. It is considered that research in AI properly began in 1956, following a conference held in 1956 at Dartmouth University.
Research in artificial intelligence has experienced many ups and downs due to investments, expectations surrounding this technology, and the development of the necessary computational capacity for research. There was a period, known as the 'winter of artificial intelligence,' between 1974 and 1980 when funding disappeared, and there were no advances in research.
Between 1980 and the late 1990s, the sector experienced various fluctuations in popularity accompanied by the corresponding appearance and disappearance of investment, which were resolved by achieving sufficient computing capacity.
A Bit of History
Since its beginnings, research in artificial intelligence has gone through countless ups and downs. Between 1956 and 1974, it enjoyed a golden age when scientists predicted that a computer with cognitive capacity equal to that of a human being would be achieved in a short time, leading to million-dollar investments in research. However, these estimates turned out to be incorrect, and expectations were not met, leading to the disappearance of investments. This period, between 1974 and 1980, is known as the 'winter of artificial intelligence.' In addition to financial problems, projects also faced limited computational and data storage capacity, which hindered the necessary processes and experiments.
From 1980 until the late 1990s, the field of artificial intelligence experienced fluctuations in its popularity, accompanied by the corresponding appearance and disappearance of investments. By the end of the 1990s, computers began to have enough capacity to make advances in the field. In fact, the computer used to play chess in 1997 was 10 million times more powerful than the one used for the same purpose in 1951.
A Change in Perspective
Since then, the perception of artificial intelligence has undergone a radical change. The power of computers and the availability of large amounts of data have enabled a series of significant advances, albeit in a different direction from the one pursued previously. Instead, progress has been made in the field of deep learning, neural networks, and machine learning, all of which are branches of artificial intelligence. Additionally, there are other areas of research such as predictive analytics, natural language recognition, and facial recognition.
The predominant fields within artificial intelligence now include deep learning, machine learning, neural networks, and bots. All of these are branches of artificial intelligence. Furthermore, there are other branches or sub-branches, such as predictive analytics, natural language recognition, and facial recognition.
Predictive analysis is a technique that is part of machine learning. Its goal is to use predictive models to identify patterns in historical and transactional data to predict future risks and opportunities. This allows organizations to prepare in advance. For example, in the context of a production chain, predictive analysis could foresee a machine failure, enabling the problem to be resolved before it causes a complete breakdown and thus avoiding or reducing production interruptions.
Netflix is a company that uses predictive analysis to improve its services, especially in its recommendation engine. Around 80% of Netflix users consume content recommended by the platform, which has contributed to reducing the service cancellation rate. Additionally, Netflix uses data on viewing behavior, such as the time of day and the amount of content watched, to enhance its recommendations.
Natural Language Understanding (NLU) is a discipline within Natural Language Processing (NLP). It is considered one of the most complex problems in artificial intelligence, known as "AI-hard" problems. NLU is gaining popularity due to its application in large-scale content analysis, whether in the form of structured or unstructured data and in large volumes.
An example of the use of this technology is virtual assistants like Alexa, Siri, or Google Assistant. For instance, Siri, Apple's assistant, can recognize commands through the training of its neural network. The system uses probability calculations to detect if the recorded audio signal matches the phrase "Hey, Siri," comparing it to the original model. When a certain threshold is reached, the system activates and responds to the user.
One of the fields of study that has recently received interest is machine learning, where the ability of a machine to learn and act without being explicitly programmed for it is studied. The term is highly popular in the field of artificial intelligence-related technologies. It involves the use of algorithms that allow machines to learn by imitating the way humans learn.
Machine learning, also known as machine learning, is now one of the booming technologies in the business world. This form of artificial intelligence offers several significant advantages to businesses, such as its analytical capabilities, which provide a valuable and autonomous source of information.
Predictive analysis offers immeasurable value to businesses, allowing them to predict market trends, make data-driven predictions, minimize risks, address problems before they occur, and make informed decisions.
In addition to prediction, machine learning algorithms are commonly used by companies to reduce errors in operational and management systems, enhance data security, strengthen the analytical capabilities of data analysis tools, and automate processes.
According to recent research, machine learning is among the most in-demand professions, and a recent study by Algorithmia has shown a significant increase in resources allocated to this technology in the business sector.
Machine learning is a discipline within artificial intelligence that allows machines to learn in a manner similar to humans, using mathematical algorithms. Thanks to this, machines can perform complex analyses without the need for specific programming.
The goal of this technology is to provide machines with advanced analytical capabilities, enabling them to solve problems without human intervention, through identification, classification, and prediction.
The concept of machine learning may seem like something out of science fiction, but it is already present in our current world.
Machine learning algorithms have the ability to identify patterns in large amounts of data, and with that information, they can draw conclusions and conduct analyses without needing to be explicitly programmed.
Machine learning is based on statistics and mathematics, and its aim is to provide automatic and intelligent solutions to complex problems by identifying and classifying patterns.
While at its core, machine learning is a mathematical technology, its applications are endless and are used in systems for predictive analysis, generating automatic responses, and in a wide range of other fields.
In essence, machine learning is a technology that allows machines to learn on their own and provides a wealth of valuable information through data analysis, and its impact is becoming increasingly significant in the business world.
Machine learning is a technology with a multitude of practical applications. Although it may seem futuristic, it is actually part of our everyday life.
Video and music streaming platforms like Netflix and Spotify use machine learning algorithms to provide personalized recommendations to their users. Virtual assistants like Alexa and Siri, which respond to human questions, are also clear examples of how machine learning is used. Furthermore, this technology is employed to improve search results on engines like Google, for the operation of robots and autonomous vehicles, for disease prevention, and for creating antivirus software that detects malicious software.
In the business world, machine learning has become a crucial technology, especially due to its predictive capabilities.
Predictive analysis is a valuable skill for businesses, as it allows them to anticipate market trends, make data-driven predictions, reduce risks, address problems before they occur, and make more informed decisions.
In addition to predictive analysis, companies often use machine learning algorithms to reduce errors in operating and management systems, strengthen data security, improve the analytical capabilities of data analysis tools, and automate processes.
As artificial intelligence has evolved and machine learning has become more prominent, various branches and types of algorithms within machine learning have emerged.
Machine learning is mainly divided into two models: supervised machine learning and unsupervised machine learning.
Additionally, there is a debate about whether deep learning is a subcategory of machine learning. Deep learning has advanced to the point where some consider it as an independent field of study.
On the other hand, the nature of machine learning projects can vary depending on how algorithms are applied or utilized. Through the necessary programming and coding, these algorithms can be adapted to virtually any process or operation.
Despite the growing excitement around machine learning, organizations must understand that its potential is directly linked to the quality and performance of the programmed algorithms. Much of what machine learning does involves statistical data analysis, and just like in any statistical analysis, the results depend on the logic applied in the analysis and, in this case, the work of programmers and developers, as well as the business logic implemented in the machine learning project.
Supervised machine learning refers to machine learning algorithms that "learn" from labeled data provided by humans. In this case:
1. Human intervention is required to label, classify, and input data into the algorithm. The algorithm generates expected output data since the input data has been previously labeled and classified by humans. There are two types of data that can be input into the algorithm:
2. Classification: Used to classify objects into different categories. For example, determining whether a patient is sick or if an email is spam.
Regression: Used to predict a numerical value, such as house prices based on different characteristics or hotel occupancy demand. Some practical applications of supervised learning include:
Predicting insurance claims costs for insurance companies.
Detecting bank fraud by financial institutions.
Forecasting machinery failures in a company.
In contrast to supervised learning, unsupervised machine learning involves no direct human intervention. Algorithms learn from unlabeled data and seek patterns or relationships among them. In this mode:
Unlabeled input data is used. No human intervention is required to classify the data. There are two types of algorithms used in unsupervised learning:
1. Clustering: Used to group output data into different clusters. For example, segmenting customers based on their purchasing patterns.
2. Association: Used to discover rules within a dataset. For example, identifying that customers who purchase a car also purchase insurance.
Unsupervised learning finds applications in various areas such as:
Supervised algorithms are those that can work with new data based on what they have learned in the past.
Within supervised machine learning algorithms, we find two types: classification algorithms and regression algorithms.
Unsupervised machine learning algorithms are capable of drawing conclusions from datasets without being previously trained.
When working with unsupervised machine learning algorithms, data is distributed into different places, and no datasets are provided. Nowadays, there are many types of machine learning algorithms that attempt to discover correlations without any external input, using only the raw data available.
Reinforcement learning allows automatic agents and software to automatically define ideal behavior within a specific context, taking into account feedback from their surroundings.
To obtain information about behavior, the machine only needs simple feedback, known as the reinforcement signal. This behavior can be learned once and continues to adjust and improve over time.
Reinforcement learning algorithms are designed to acquire knowledge from previous experience. In other words, these algorithms can address problems based on the results obtained in similar situations they have previously experienced.
Reinforcement Learning works by subjecting the machine to a series of challenges in which it must make a decision. If the decision is correct, the system receives a reward. After several challenges, the algorithm is capable of making correct decisions on its own, without the need for human intervention.
Classification and clustering are two methods of pattern identification used in machine learning. Although both techniques have certain similarities, the difference lies in the fact that classification relies on predefined classes to assign objects, while clustering identifies similarities among objects, grouping them based on these common characteristics that differentiate them from other groups of objects. These groups are known as "clusters."
In the field of machine learning, clustering falls under unsupervised learning; in other words, for these types of algorithms, we only have input data (unlabeled) from which we need to gather information without knowing the expected output in advance.
Clustering is used in projects for companies looking to find commonalities among their customers in order to identify groups and tailor products or services. If a significant percentage of customers share certain characteristics (age, family type, etc.), it can justify a particular campaign, service, or product.
On the other hand, classification belongs to supervised learning. This means that we have knowledge of the input data (labeled in this case) and the possible outputs of the algorithm. There is binary classification, which provides solutions to problems with categorical responses (such as "yes" and "no," for example), and multiclass classification, for problems with more than two classes, offering more open-ended responses, such as "excellent," "average," and "poor."
Classification is used in various fields, such as biology, Dewey Decimal classification for books, and email spam detection, among others.
At Bismart, we use both classification and clustering in our projects, which span various sectors. For instance, in the social services sector, we have employed clustering to identify groups of the population that use specific social services. Using data from social services, we have been able to cluster groups of people who use similar services based on their characteristics (number of dependents, degree of dependency, marital status, etc.). This has allowed us to anticipate the type of service a new social service user will need by comparing their attributes to those of existing clusters.
Classification is used when there is a need to identify users or customers to make decisions about future product launches or campaigns. For example, at Bismart, we carried out a project for the insurance sector in which the client needed to classify customers based on their likelihood of making claims, enabling the classification of insurance policies based on the predicted number of accidents. This allows the company to select customers with a lower number of claims and exclude those with a high number.
Data Acquisition: Data can be obtained from various sources, including the web, databases, audio-to-text transcriptions, etc.
Analysis with Algorithms: A human team collects and analyzes the data before passing it through an algorithm that extracts relevant information.
Decision Making: The algorithm provides a result that is used as a basis for business decision-making in accordance with company guidelines.
Before starting a machine learning project, it is important to consider whether the problem requires artificial intelligence. This implies the existence of a significant amount of relevant data. It is crucial to avoid basing a project on insufficient or low-quality data, as this effort would result in a waste of time.
It's important to note that machine learning algorithms identify patterns in data but do not reason. Therefore, they should be used as a solid foundation for decision-making.
Although machine learning algorithms can learn on their own, human supervision is always required. The machine can process graphics, numbers, etc., but it always needs human interpretation to provide value and logic to the results from a business perspective.
When should machine learning be used to solve a problem?
When writing logical software is difficult and requires a significant amount of time and resources.
When there are large amounts of data available.
When the problem fits the structure of machine learning. A machine learning problem should have a clearly defined target variable, such as customer classification or accident prediction. The sufficiency and adequacy of available data should also be assessed to determine if they are suitable for predicting the desired outcome.
Using machine learning requires careful consideration of expectations. In most cases, algorithm results serve as a tool for decision-making and subsequent actions but do not always directly translate into solutions. However, there are exceptions like Netflix, where results are presented as recommendations on the platform without human intervention. In these cases, it's essential to evaluate the impact of potential errors in the results. For example, an error in recommending a TV series is different from an error in predicting a traffic accident.
Machine learning may not be effective at identifying random events since it relies on pattern recognition. Therefore, when faced with a random event, the algorithm may not know how to react as there is no pattern to associate it with. Hence, if the problem to be solved includes many chance occurrences, other solution methods may need to be considered.
Even if the algorithm performs well and provides an effective solution, sometimes the results can be challenging to interpret. Some algorithms, like decision trees, are easier to understand and allow you to see which variables are most relevant, but others may provide complex and hard-to-interpret results even if they are accurate for the objective. The high complexity of the reasons behind the algorithm's results can make it difficult for a human to understand why a particular conclusion was reached.
Machine learning is not the best choice if you don't have enough labeled data to train the algorithm. Labeled data enables the algorithm to learn patterns and provide accurate predictions.
Furthermore, a machine learning project can be slow and require a high tolerance for errors. It's essential to keep in mind that the machine can make mistakes, although the goal is always to minimize the margin of error.
When is machine learning the best solution?
When scalability is a challenge, as machine learning excels at working with large amounts of data.
When customized results are needed, as machine learning uses our own data to produce specific outcomes for our business
When is machine learning the best solution?
Next, we will review some of the best machine learning platforms for businesses and users who are not experts in data science. These tools automate the entire machine learning process, from data preparation to model training, evaluation, and implementation in production.
Azure Machine Learning is part of Microsoft's extensive range of Big Data tools and supports both supervised and unsupervised machine learning algorithms, as well as deep learning algorithms.
This machine learning platform is very comprehensive and offers different levels of usability for users with varying skill levels. It is specifically designed to help businesses create value through machine learning and is one of the most efficient and quick options for creating and deploying machine learning models.
Azure ML allows users to code in Python or R, work with machine learning models in other programming languages using the SDK, and also work without the need for coding or with minimal code using Azure ML Studio. Additionally, the tool promotes collaboration, is easily integratable, and allows users to create, train, and monitor machine learning and deep learning models in a straightforward manner.
Azure Machine Learning is compatible with other frameworks like TensorFlow, PyTorch, or Scikit-learn, meaning that models developed in these frameworks can be imported into Azure ML without the need for code changes.
Scikit-learn is the most popular machine learning package in Python due to its simplicity and a wide range of use cases. It supports common machine learning algorithms such as decision trees, linear regression, random forests, k-nearest neighbors, support vector machines (SVM), and stochastic gradient descent.
Scikit provides tools for analyzing models, including the confusion matrix, to evaluate the performance of each model.
Scikit-learn is an ideal environment for those who want to get started in the world of machine learning and begin with simple tasks before moving on to more complex options like Azure Machine Learning.
IBM Watson offers a wide range of machine learning products that allow you to easily access data from different sources without sacrificing confidence in the predictions and recommendations generated by your artificial intelligence models.
The brand offers a wide range of AI capabilities focused on enterprise use, not only enabling the creation of machine learning models but also offering a set of tools to accelerate value through pre-built applications.
Amazon SageMaker is a fully managed machine learning service, although it is focused on users with knowledge of data science. The platform enables data scientists and developers to create and train machine learning models quickly and easily, and deploy them directly into production environments.
Additionally, SageMaker provides an integrated Jupyter Notebook instance that makes it easy to access data for exploration and analysis without the need to manage a server. The platform also provides optimized generic machine learning algorithms to run efficiently on large datasets in a distributed environment.
Like Azure Machine Learning, SageMaker natively supports the most popular machine learning and deep learning frameworks.
MLflow is an open-source platform that manages the entire machine learning lifecycle, including experimentation, deployment, and a central model registry. It can be integrated and used with all machine learning libraries and programming languages
In the 21st century, in 2011, a branch of machine learning known as deep learning (DL) emerged. The popularity of machine learning and advances in computing power allowed the rise of this new technology. Although the concept of deep learning is similar to that of machine learning, it uses different algorithms. While machine learning works with regression algorithms or decision trees, deep learning uses neural networks that function similarly to the biological neural connections in our brains. In both cases, having quality and reliable data is crucial to ensure effective operation.
Data consolidation, data integration through an ETL or SSIS process, and data management are essential to guarantee the success of machine learning or deep learning.
Machine Learning refers to mathematical algorithms that enable machines to learn in a similar way to how humans do. However, machine learning is not limited to just algorithms but also encompasses the approach used to address the problem. It is essentially a way to achieve artificial intelligence.
Deep Learning (DL) is a subset of machine learning. In fact, it can be considered the most recent evolution of machine learning. It is an automated algorithm that mimics human perception, inspired by the functioning of our brain and the interconnection between neurons. DL is the technique that comes closest to how humans learn.
Most deep learning methods use a neural network architecture. For this reason, it is often referred to as "deep neural networks." The term "deep" refers to the multiple layers that make up these neural networks.
What is the difference between the two?
In simple terms, both machine learning and deep learning mimic the way the human brain learns. The main difference between machine learning and deep learning lies in the types of algorithms used, as deep learning employs more advanced neural networks and is closer to human learning. Both technologies can learn in a supervised or unsupervised manner.
In the business world, deep learning offers a wide variety of applications and uses that vary depending on the needs of each sector.
Here are some of the most common uses:
Image and Video Recognition: Deep learning algorithms have significantly improved the quality and accuracy of text, object, logo, and landmark detection algorithms. Deep learning-based computer vision technology has increased accuracy in facial recognition, visual search, and reverse image search.
Voice Recognition: Also known as Speech-to-Text (STT) or Automatic Speech Recognition (ASR), this technology converts spoken words into text. ASR is used in many sectors, such as healthcare and the automotive industry.
Manufacturing: Deep learning algorithms enhance the accuracy of industrial systems and devices. For example, it can be used to send automatic alerts about production issues, power industrial robots and sensors, or analyze complex processes.
Entertainment: Deep learning algorithms drive a wide range of entertainment systems, including content personalization, streaming, and adding sound to silent movies.
Retail and e-commerce: Some e-commerce applications already use deep learning to improve the shopping experience and customer experience with voice-activated purchases. Additionally, intelligent robots in stores, future trend predictions, and personalized recommendations are also common uses of deep learning in retail.
Health: Deep learning is used for computer-aided disease detection and diagnosis, sometimes offering performance superior to human experts. Furthermore, deep learning algorithms help improve medical research and drug discovery.
The business world has access to six of the best deep learning (AI) applications and software. These include CNTK, TensorFlow, PyTorch, the Cloudinary Video AI API, among others.
CNTK is an open-source tool specifically designed for commercial-grade deep learning. CNTK allows for the easy combination of common models such as CNN, RNN, LSTM, and feed-forward DNN.
TensorFlow is an open-source framework developed by Google for building and executing deep learning and machine learning algorithms. TensorFlow constructs processes using the concept of a computational graph and can run on CPUs, GPUs, and TPUs.
PyTorch is a deep learning and machine learning framework based on Python and Torch. PyTorch is easy to use and supports distributed training. PyTorch allows for model export using ONNX and is highly compatible with open-source platforms.
The Cloudinary Video AI API is an example of a machine learning solution that doesn't require data science knowledge. The API can be integrated into any web application and enables the processing and management of video content handled by AI.
In conclusion, these deep learning applications and software are useful for automating processes, conducting predictive analysis, and managing AI-driven content in the business world.
Graphics Processing Units (GPU)
GPUs are composed of a large number of processing cores working simultaneously. They are specialized in multiple computation and simultaneous execution of operations, which makes them an extremely efficient tool for deep learning algorithms. In addition, they offer a wide memory bandwidth, typically 10 to 15 times larger than a conventional CPU.
Field Programmable Gate Arrays (FPGAs)
FPGAs are integrated circuits in which the internal network can be reprogrammed depending on the task to be performed. They are an attractive alternative to ASICs, which require a lengthy design and manufacturing process. FPGAs can offer better performance than GPUs, just as an ASIC designed for a specific purpose will always outperform a general-purpose processor.
High Performance Computing (HPC)
HPC systems are highly distributed computing environments that use thousands of machines to achieve massive processing power. They require high component density and special power and cooling requirements. Deep learning algorithms that require high computational power can take advantage of HPC hardware or HPC services offered by cloud providers such as AWS and Azure.
At Bismart, we organized an event that covered the scope of technology in clinical management. During the event, various perspectives were shared on the application of technology in clinical management. On one hand, the potential of artificial intelligence to change the functioning of social services was explained through a success case: the pilot test of a predictive model to optimize social service benefits, taking into account population segmentation, the social services catalog, social services archives, budgets, cost per service, etc. In this way, the administration can ensure that resources are allocated to those who need them most, even if they don't request them.
On the other hand, José Manuel Simarro from the Spanish System of Pharmacovigilance of Human Medicines focused on the application of technology for analysis and research. Big Data and Artificial Intelligence accelerate data processing and, therefore, reduce the time required for research.
To conclude, Josep M. Picas pointed out that 40% of diagnoses are incorrect. If the same doctors who fail 40% of the time are teaching in universities and responsible for transferring medical knowledge, it is very likely that the new generations of healthcare professionals will make the same mistakes. Artificial intelligence in this field becomes a support tool that can improve diagnoses. Now, several doctors may have disparate views on the same case. However, with artificial intelligence, the machine will always provide a 100% correct diagnosis objectively.
Thus, Artificial Intelligence has come to stay, not so much as a tool to replace what we already have but to enhance it and ensure its efficiency. One way to achieve this is through folksonomy.
Artificial Intelligence in Improving Care for the Elderly
The aging population means there is an increase in the proportion of people over 65 without a corresponding growth in the working-age population, which is responsible for sustaining the pension system.
This phenomenon presents various challenges to society. On one hand, there are economic problems, such as the need to finance the pension system and healthcare and social care costs for the elderly. On the other hand, there are direct problems affecting the elderly, such as chronic diseases, comorbidities, loneliness, and loss of physical and mental capabilities. These problems worsen with age, and even though we are living longer, there is no guarantee that these years will be of good quality.
This issue is already a concern today, but according to the WHO, the number of elderly people (over 60 years old) is expected to double by 2050 and triple by 2100. Globally, the population aged 60 and over is growing faster than any other age group. Therefore, it is crucial to start taking measures and policies to improve the quality of life for the elderly and prevent a negative impact on the economy. Elderly people could participate in paid or unpaid work, but in both cases, they would receive some form of compensation. It has been shown that people who work after retirement age develop physical and mental deterioration symptoms much later than those who retire earlier. This could also reduce healthcare and social care spending in this age group.
In this regard, artificial intelligence can help alleviate the effects of an aging population.
It is evident that implementing such a system is not easy. In the short term, it will involve costs, and not all elderly people can or want to work.
Currently Implemented Systems
Municipalities are taking steps to improve care for the elderly. For example, the Barcelona City Council has implemented two important projects to improve the lives of elderly people.
The first is the MIMAL project, which offers a teleassistance device for people with cognitive impairments. This device allows caregivers to locate the elderly person at any time through geolocation, increasing their safety and autonomy.
The second project is Vincles BCN. This social innovation project aims to improve the well-being of elderly people who feel lonely and strengthen their social relationships through the use of technology. Elderly people are provided with a device and an application that allows them to communicate with their family and others, stay informed about community activities, and more.
Additionally, Bismart organized the event "The Power of Machine Learning" in Barcelona and Madrid, where over 100 leaders from public and private organizations could learn about the potential of tools like Machine Learning, Big Data, Stream Analytics, and Power BI.
During the event, Bismart experts presented real cases that demonstrated the ability to predict crime using cutting-edge technologies. Specifically, they showed how, through predictive analysis and the use of Azure Machine Learning, they could predict crime occurrences in districts of the city of Chicago.
The approach is based on the analysis of historical crime data, combined with correlated variables such as lunar phases, weather, lighting, socio-demographic data, and socio-economic data, among others. These data are used to train models in virtual machines, applying algorithms that learn from past mistakes and new trends. Additionally, real-time data can be added through Stream Analytics to obtain updated results on the probability of crimes in different areas and times.
This example shows how the application of Machine Learning and predictive analysis can contribute to building safer cities. Furthermore, tools like Stream Analytics and Social Media Analytics can also be useful for detecting cyber terrorist attacks.
Currently, these technologies, such as Machine Learning, Azure Stream Analytics, and Big Data, are more accessible to companies of all sizes through Microsoft's Azure cloud platform. These tools enable predicting consumer behavior, discovering market trends, analyzing prices, and detecting fraud and crimes. Finally, event attendees were invited to challenge Bismart to develop pilot projects using predictive analysis, Machine Learning, or Stream Analytics with their own data.
The implementation of artificial intelligence in companies encompasses a wide range of applications, which sometimes makes it difficult to grasp its scope due to its abstract nature. Therefore, we consider some examples of how companies from different sectors are using artificial intelligence in their operations.
Applications of Artificial Intelligence in the Entertainment Sector: The entertainment industry has witnessed numerous advancements thanks to Artificial Intelligence (AI). Some notable applications include facial recognition, virtual and augmented reality for creating immersive experiences, and motion tracking, among others.
One renowned example of AI in the entertainment industry is Netflix's recommendation algorithm. This leading streaming platform not only offers an extensive catalog but also possesses one of the most effective recommendation algorithms. By utilizing machine learning techniques, Netflix provides personalized recommendations that generate an annual value of $1 trillion in customer retention. Moreover, it is estimated that 80% of the platform's views come from these recommendations.
Artificial Intelligence in the Automotive Sector: The automotive industry is widely recognized for its extensive use of Artificial Intelligence. Not only has it been a pioneer in its application, but AI has become a fundamental element for companies in this sector.
A clear example of AI in the automotive sector is the integration of robots in assembly lines. It is now commonplace to see robots performing tasks that were previously carried out by humans, such as molding aluminum blocks into vehicles, painting cars, or assembling parts.
In addition to the assembly line, AI has had a significant impact on the development of assisted driving. AI algorithms are used to recognize objects, and this is evident in functions such as parking assistance, where modern vehicles can detect proximity to objects and emit alerts. In the future, assisted driving is expected to expand further to enhance road safety and reduce traffic accidents.
Artificial Intelligence in the Manufacturing Industry: Artificial Intelligence has made significant advancements in the manufacturing sector by simplifying and improving the accuracy of operations. It allows companies to program inventory more efficiently, detect and prevent errors, resolve problems more quickly, and overall optimize the entire manufacturing process.
Artificial Intelligence in Marketing: Marketing is one of the business areas that will experience significant evolution thanks to Artificial Intelligence in the near future. Currently, it is commonly used to automate content generation and optimize customer data collection. However, one of the most promising applications is content personalization, recommendations, and experiences.
Marketing plays a crucial role in direct interaction with customers. In this regard, AI plays a strategic role as marketing, especially digital marketing, moves towards communication and experiences tailored to consumer preferences.
It is evident that Artificial Intelligence has transformed various business areas in many aspects. Although there were initial concerns about its impact on human intelligence, it has been proven over time that AI brings significant benefits to both businesses and other aspects of our lives.
Most enterprise data is not fully exploited, which is a significant problem. Approximately 95% of useful data remains unexploited.
It's astonishing to think that companies choose to utilize only 5% of the available information. However, the real problem lies in the inability to access the remaining 95%.
The main reason for this is that business data lacks proper structure, which means it's disorganized. When data is unstructured, it becomes extremely difficult to find the information needed. Fortunately, there is a solution to this problem: text analytics solutions, specifically intelligent folksonomy.
With the exponential growth of information to process, text analysis systems have become increasingly relevant. Nowadays, companies and organizations need to be able to process data beyond traditional formats, including text written in everyday language that we humans use to communicate.
So, what is text analytics?
Text analytics technologies are those that have the ability to process data in an unstructured format, specifically in the form of written text. These text analysis systems are capable of extracting high-quality information from any type of text.
These technologies are part of artificial intelligence and use algorithms that can identify patterns in unstructured texts. This capability is extremely relevant because it is estimated that 80% of the information relevant to organizations is found in unstructured data, mostly in the form of text.
The good news is that there are numerous text analysis systems available today. However, not all of them have the same capabilities or are suitable for the same purposes. Therefore, it is essential to understand how these technologies work and their differences.
In general terms, text analysis systems are based on one of two methods: taxonomy and folksonomy. The main difference between the two lies in the fact that taxonomy requires prior organization of information using predefined tags to classify content, while folksonomy is based on natural language tagging.
It is important to take these differences into account when selecting a text analysis system, as each approach has its own advantages and challenges. By understanding the capabilities and features of these systems, companies can make the most of valuable information hidden in unstructured data, thereby improving their decision-making and gaining meaningful insights for their growth and success.
There are several tools and software available to perform text analysis. Some of these tools are presented below:
SAS: SAS is a software used to extract valuable insights from unstructured data, such as online content, books, or feedback forms. In addition to guiding the machine learning process, it can automatically reduce generated topics and rules, allowing for tracking changes over time and improving results.
QDA Miner's WordStat: QDA Miner is a tool that enables the analysis of qualitative data. It utilizes the WordStat module for text analysis, content analysis, sentiment analysis, and business intelligence. It provides visualization tools to interpret the results, and its correspondence analysis helps identify concepts and categories in the text.
Microsoft's Cognitive Services Suite: This suite offers a set of artificial intelligence tools that facilitate the creation of intelligent applications with natural and contextual interaction. While not exclusively a text analysis program, it incorporates elements of text analysis in its ability to analyze speeches and language.
Rocket Enterprise's Search and Text Analytics: This tool provides security and ease of use for text analysis. It is particularly useful for teams with limited technological expertise, as it allows for quick information retrieval in large amounts of data.
Voyant Tools: Voyant Tools is a popular text analysis application among digital humanities scholars. It provides a user-friendly interface and the ability to perform various analysis tasks, such as visualizing data in a text.
Watson Natural Language Understanding: Watson, developed by IBM, offers the text analysis system called Watson Natural Language Understanding. It utilizes cognitive technology to analyze text, including sentiment and emotion evaluation.
Open Calais: Open Calais is a cloud-based tool that helps label content. Its strength lies in recognizing relationships between different entities in unstructured data and consequent organization. While it cannot analyze complex sentiments, it can help manage unstructured data and convert it into a well-organized knowledge base.
Folksonomy Text Analytics: Bismart's Folksonomy software utilizes intelligent tags based on generative artificial intelligence (IAG) and Large Language Model (LLM) machine learning models to filter unstructured data files and locate specific information. This approach eliminates the need to manually define tags and categories, allowing for real-time adaptation to different uses. It is user-friendly and fast, making it perfect for collaborative projects.
These tools offer various capabilities and approaches to text analysis, so it is important to assess specific needs and select the one that best suits each case. Folksonomy, the smarter branch of text analytics.
Folksonomy is a data organization method that utilizes tags. Users label the content with their own words or categories, allowing them to explore all the tags created with natural language and the numerous associated descriptions.
A significant breakthrough in the analysis of unstructured data is the Large Language Model (LLM). This model utilizes artificial intelligence to analyze large volumes of real-time information. What makes LLM advantageous is its ability to comprehend natural language, making it easier to identify patterns and trends in the data. The combination of LLM and folksonomy can be a powerful tool for analyzing unstructured data in the fields of health and clinical research, as it allows for a deep understanding of the natural language used in these areas. Additionally, automating the creation of the master entity through folksonomy streamlines and enhances the efficiency of data analysis, which is particularly useful in clinical situations that require real-time decision-making. To summarize, the combination of LLM and folksonomy can be a valuable tool for research and clinical practice.
One of the key advantages of folksonomy is its ability to work with unstructured data. Until recently, only structured information could be used for data analysis, which means information that had been prepared for computer processing. However, information in text, audio, etc., formats could not be treated in this manner and required manual processing. The problem is that, according to Gartner, 95% of the value of information lies in unstructured data.
Previously, to analyze this unstructured information, it was necessary to create a master entity that would allow for the classification of information within the text. However, this master entity had to be created manually, which was a laborious and error-prone process. Additionally, it is possible that the entity does not encompass all the valuable information present in the data. In many cases, creating the master entity required more effort than manually analyzing the documents.
An additional advantage of folksonomy is that it does not require a master entity, as it simultaneously analyzes all documents based on weighting rules according to the grammatical category of words, allowing for the automatic identification of the most relevant terms. In this way, the master entity is created automatically. This working approach is known as bottom-up, as opposed to the top-down approach of manual entity creation. The bottom-up approach also allows for data discovery, meaning that relevant terms in the data that were not previously known can be identified and would not have been included in the master entity if it had been created manually. The same applies in reverse: concepts that are not present in the documents will not appear in a master entity created through folksonomy.
The folksonomy is built from the bottom up, meaning that users themselves add tags to the content, rather than having predefined tags that may not fit the actual available content. This allows for a broad view of all available data instead of trying to discover what is there. Additionally, there is no strict hierarchy, making it flexible to use. Although standard folksonomy is a useful tool, it can sometimes present problems and confusion due to the lack of linguistic control. Many people may use different words to describe the same content, or the system may not distinguish between acronyms like "ONCE" and a word with its own meaning and different from the acronyms, such as the number "eleven".
Ambiguity can be a problem, which prevents knowledge extraction from unstructured data.
This is why new intelligent folksonomy systems have emerged to help identify precise information. This new generation of folksonomy leverages technological advancements to provide intelligent tags to your data, making significant progress in solving many problems associated with folksonomy. Despite these changes, folksonomy still maintains its focus on natural and intuitive language.
Folksonomy Text Analytics
Bismart's intelligent folksonomy software addresses many of the problems and confusion caused by the lack of linguistic control in standard folksonomy. It is now possible to accurately identify the information you need, even when you have a large amount of data.
This software allows for the combination of synonyms, differentiation of homonyms, addition of technical or custom dictionaries, and even reduction of tags using a blacklist. Its intelligent algorithms also take into account errors and duplicate content.
It is a user-friendly tool, with options such as a drag-and-drop menu for synonyms, as well as an advanced search engine. Its implementation is quick and easy, and it can be restructured in real-time to adapt to your needs. There are multiple structuring options available to tailor the tool to different requirements.
With this software, you can quickly leverage the knowledge from your unstructured data through a bottom-up approach, without the need for manually creating and defining tags and structures.
Standard folksonomy provides a wealth of valuable information, and Bismart's Folksonomy Intelligence allows you to extract useful insights from this information.
How do we apply Folksonomy Text Analytics in the healthcare field?
In the clinical setting, large volumes of data are generated daily, including medical discharges and histories. This data contains valuable information for healthcare administration and professionals. However, extracting this information manually is impossible due to the volume of
The folksonomy is built from the ground up, meaning that it is the users themselves who add the tags to the content, rather than having predefined tags that may not fit the actual available content. This allows for a broad view of all the available data instead of trying to discover what is there. Additionally, there is no strict hierarchy, making it flexible to use. While the standard folksonomy is a useful tool, it can sometimes present problems and confusion due to the lack of linguistic control. Many people may use different words to describe the same content, or the system may not distinguish between acronyms like "ONCE" and a word with its own meaning and different from the acronym, like the number "eleven".
Ambiguity can be a problem, preventing knowledge extraction from unstructured data.
That is why new intelligent folksonomy systems have emerged to help identify accurate information. This new generation of folksonomy leverages technological advances to provide intelligent tags to your data, which is a significant step forward in resolving many problems associated with folksonomy. Despite these changes, folksonomy still maintains its focus on natural and intuitive language.
Bismart's intelligent folksonomy software tackles numerous issues and uncertainties caused by the lack of linguistic control in standard folksonomy. It now enables precise identification of the information you need, even when dealing with vast amounts of data.
This software offers the ability to combine synonyms, differentiate homonyms, add technical or customized dictionaries, and even reduce tags using a blacklist. Its intelligent algorithms also take into account errors and duplicate content.
It is a user-friendly tool, with options such as a drag-and-drop menu for synonyms, as well as an advanced search engine. Its implementation is quick and simple, and it can be restructured in real-time to adapt to your needs. There are multiple structuring options available to tailor the tool to different requirements.
With this software, you can quickly harness the knowledge of your unstructured data through a bottom-up approach, without the need for manual creation and definition of tags and structures.
Standard folksonomy provides a wealth of valuable information, and Bismart's Folksonomy intelligence allows you to extract useful insights from that information.
In the clinical field, vast amounts of data are generated daily, including medical admissions and discharges, as well as medical records. These data contain valuable information for healthcare professionals and administration. However, manually extracting this information is impossible due to the sheer volume of data and the fact that it is written in unstructured natural language. To tackle this challenge, the nephrology department at Hospital del Mar in Barcelona, in collaboration with the Ferrer group, partnered with Bismart to implement a text analysis project that would enable efficient information extraction.
The goal of the project was to understand the thousands of medical discharges available and extract relevant clinical knowledge. To achieve this, Bismart provided the hospital with the Folksonomy tool, capable of extracting information from unstructured data in various formats, such as text, image, video and audio.
The nephrology department had generated over 1600 hospital discharge documents in a period of three years. These documents presented the additional challenge that each doctor used different abbreviations for the same tests, diseases, or medications. Therefore, it was necessary to have a tool that could identify these words as synonyms.
The benefits that the Bismart Folksonomy project has provided to the Hospital del Mar and the Ferrer Group include the extraction of knowledge from unstructured information, intelligent recommendations, acceleration in the generation of medical knowledge, and reduction of variability. Specifically, the tool allowed for the identification of synonyms and implications, management of keywords through tags, and classification of certain words and terms into black or white lists, among others.
Furthermore, the tool has improved decision-making that benefits patients and the system, and has facilitated the training of healthcare professionals. This training has been based on three types of big data:
Thanks to this tool, healthcare professionals have been able to understand clinical practice and its variability, make informed decisions based on real-time information, determine the population's epidemiology, generate clinical research hypotheses, conduct observational studies, predict clinical cases before they occur, automatically extract patient variables based on search criteria, and establish non-obvious correlations.
Previously, without Folksonomy, discovering this information required a tedious and costly manual process that could take weeks. Doctors had to read and analyze thousands of documents and manually establish relationships between them.
After applying data normalization and quality processes with Bismart's Folksonomy, doctors were able to answer their questions in a few hours. This was possible thanks to the extraction of knowledge from the unstructured data of medical records available in the hospital. For example, the results of objective 1 revealed that 39.91% of patients admitted to nephrology were diabetic, a total of 651, of which 89 were being treated with metformin.
Folksonomy is a powerful tool that can greatly benefit any corporation, and you might already be utilizing it without even realizing it. It offers a unique way to organize data and digital content, where the consumers themselves contribute by adding classes or tags to identify specific content. Users provide a plethora of descriptive information using natural language. You may have come across it being referred to as social bookmarking or social tagging.
Have you recently made use of this tool? Let's see if the first example triggers your memory.
Have you ever used a hashtag on Twitter, Instagram, Facebook, or Pinterest? Then you have already used folksonomy!
All of these sites use hashtags. They make it easy for users to find relevant content; all you have to do is click on a tag to see more content tagged the same way.
Anyone can incorporate any hashtag they want. There are no rules, so you can come across a wide variety of content from "#cute" to "#catsofinstagram".
The first one, "#cute", is an example of a general taxonomy that can be applied to many different types of content. On the other hand, "#catsofinstagram" only refers to one type of content (cat images on Instagram) and is therefore an example of limited taxonomy.
This website is a classic example of folksonomy, although it has disappeared nowadays. It was a social bookmarking page that allowed users to mark interesting websites as favorites and share them.
Each user could tag their highlighted pages with any words they wanted. So, if someone wanted to search for articles on a specific topic, they just had to type in the corresponding tag to get a list of the most recent bookmarks with that tag.
The page also allowed users to add a hot list and a page with the latest articles, making it even easier to find relevant content. It also offered certain general categories, such as "art and design," where users could navigate to find interesting content.
Another similar page, also disappeared, is 43 Things (although the page can still be seen, it's not the same).
Flickr, the community for sharing images, was one of the first to embrace folksonomy with the use of tags. Users upload photos and then add the tags they prefer to describe them. Once the photos are uploaded, other users can also add tags. Additionally, a photo can be tagged with a location.
One feature of Flickr is that it uses these tags to help users find more photos they may like. The page highlights trending tags at the moment and dedicates a section to the most popular tags of all time. With just one click, users can find hundreds or thousands of images they enjoy.
Although folksonomy offers various functions to help users find interesting information on social networks and Flickr, it can also be used for more serious purposes, such as improving medical services.
Academic folksonomies are a powerful tool for researchers because they can facilitate the organization of large sets of information and simplify the search. Medical researchers use programs like Bibsonomy and CiteULike to generate metadata quickly and economically without compromising quality. They can extract information from both texts and images.
With some of these programs, users can create their own sets and organize them, as well as share them with other users. Just like in Flickr, it's easy to discover more relevant content with little effort.
Another known program was Connotea, but it closed in 2013.
Folksonomy is very useful, but sometimes it can become a bit chaotic. It's easy to feel overwhelmed by the number of tags that users use, which can cause confusion. That's why Bismart has developed intelligent folksonomy software.
This application is easy to use and is designed to facilitate the extraction of knowledge from unstructured data. It allows separating equivalent terms, relating synonyms, reducing tags, and adding custom dictionaries. The intelligent algorithms of this software inspect errors and duplicate content. It works with any type of content and can do everything, from managing web pages to large databases of medical information.
Natural Language Understanding (NLU) is a sub-branch of natural language processing that has gained popularity for its use in analyzing large-scale content. NLU enables the discovery of audiovisual content, whether it comes from structured or unstructured data, in significant volumes.
The technology of voice and speech recognition has evolved tremendously in recent decades. It first emerged in the 1950s with Bell Laboratories' Audrey system, which could understand numbers. This was followed by IBM's Shoebox system, which could process 16 English words. Since then, speech recognition systems have reached a remarkably high level of technological complexity.
Currently, voice recognition systems are available on all smart devices and have the ability to understand continuous speech, distinguish voices, and comprehend multiple languages and a vast array of words. The applications for this technology have evolved, from its original use in professional and work environments to its integration into everyday entertainment and household activities.
The possibilities offered by voice recognition technology are numerous. It is used in the customer service industry to direct calls and manage large volumes of users. Biometrics are now being introduced in this field to detect voice tones and speaking patterns, allowing for user authentication, prevention of fraud in banking transactions, and identity theft, as well as assisting individuals who may have difficulties with conventional activities.
Recently, home devices incorporating this technology have emerged. Examples include Amazon's Echo, which utilizes Alexa for user communication, Apple's HomePod, and Google's Home. These devices have features that can be activated through voice commands, enabling a multitude of tasks such as ordering a taxi or contacting a primary care physician.
Furthermore, voice recognition technology is growing in the field of research. According to Google Trends (via Search Engine Watch), voice searches increased 38 times from 2008 to 2016.
One of the most transformative uses is dictation, which significantly reduces the time spent on text writing and audio transcription. Numerous applications and programs have emerged that rely on this dictation function, such as Dragon Naturally Speaking, Braina, and Sonix.
These programs are highly beneficial for transcribing oral texts, interviews, and other oral and written content that professionals such as journalists or content writers deal with. However, voice structuring offers even more possibilities.
At Bismart, we utilize voice recognition for some of our projects. For example, Folksonomy Text Analytics can work with audio files to find the information you need. This eliminates the need to spend time and resources listening to and transcribing audiovisual documents to extract all the information they may contain. It is especially useful when dealing with a vast amount of documents that would be impossible to process manually.
Organizing and tagging data and digital content is a task that can be approached in different ways, with two of the most common methods being folksonomy and taxonomy. Although both techniques aim to solve the same problem, there are significant differences between folksonomy and taxonomy, primarily in their approach.
Taxonomy is a structured and hierarchical way of classifying information based on its similarities. Categories are established by the person who creates or owns the content, and its goal is to facilitate access to the material. It is commonly used in organizing websites and content repositories.
However, taxonomy presents certain challenges. On one hand, it can be costly and time-consuming. Furthermore, the language used may be confusing for end users and may not necessarily reflect their needs. Sometimes, creators do not use clear or effective tagging systems, making it difficult for users to find information.
Folksonomy, on the other hand, relies on tags applied by users who consume the content rather than its creator. Instead of following a pre-established hierarchy, users apply tags they find most useful for organizing information, using the language they prefer.
This approach can be seen on platforms where users can tag content, such as Flickr, where users can apply tags in natural language.
Folksonomy can be a powerful tool when many users tag the same piece of information. Companies can leverage this information to improve content structuring and help users find what they are looking for. Additionally, it is flexible and user-friendly.
Despite solving some of taxonomy's issues, folksonomy also has its disadvantages.
One of them is the lack of organization, as its operation can become chaotic. For example, different people may tag the same color in different ways: one as "teal," another as "turquoise," and another as simply "blue" or "green." This can result in too many different tags for a single piece of content.
Furthermore, these tags can be ambiguous due to the lack of strict standards to follow.
Another issue is the presence of abbreviations and acronyms, which can create confusion with similar topics or words. For example, folksonomy might struggle to differentiate whether "ONCE" refers to the Organización Nacional de Ciegos Españoles (National Organization of Spanish Blind People) or the number that follows ten. It can also have difficulty with synonyms or technical terms.
While standard folksonomy is a useful tool, its lack of linguistic control leads to numerous problems and confusion. This means that extracting new perspectives and knowledge from unstructured data is not always possible. That's why we have developed intelligent folksonomy to help you find the precise information you need.
These cutting-edge tagging systems utilize the latest technological advancements to provide intelligent labels for your data, automating the creation and definition of tags and structures.
This significant advancement in solving the problems of folksonomy allows for the preservation of its natural and intuitive language.
An example of such systems is our Folksonomy Text Analytics software, which is built on intelligent folksonomy. This powerful software allows for the identification of synonyms, differentiation of homonyms, and the addition of technical or custom dictionaries tailored to your specific needs. It also offers the capability to reduce tags using a blacklist and its intelligent algorithms take into account errors and duplicate content.
It is undeniable that artificial intelligence is causing a revolution in the current world. Undoubtedly, the year 2023 will be remembered as the year when ChatGPT was introduced, a machine learning and deep learning model that has brought artificial intelligence to the forefront of public opinion.
The reality is that artificial intelligence is transforming the way businesses operate and generating new ways of doing business. According to Forbes, ChatGPT has become the fastest-adopted enterprise technology in history.
One of the most significant advantages of AI is its wide range of applications, making it useful for all types of businesses. Each industry, of course, will apply artificial intelligence differently based on its activities, although there are practical cases where AI is beneficial for any sector.
One of the sectors that can make the most of artificial intelligence is the service sector, particularly the hotel industry. Although many hotel chains are already using AI, even unknowingly, this sector has the potential to harness it even further by exploring areas that have not been sufficiently explored. Artificial intelligence is an abstract technology with a wide range of potential applications, which can make it challenging to understand its scope. Nevertheless, it is one of the most promising technologies of the last century.
In today's highly competitive business environment, hotel companies need to stay one step ahead of the competition to avoid losing market share. Investing in innovative technologies like artificial intelligence can even become an added value to the service a hotel offers to its customers.
Next, we will explore some of the possible applications of artificial intelligence in the hotel industry.
Occupancy Prediction: Predictive analysis, a key branch of artificial intelligence, is utilized by hotel chains to forecast occupancy during specific periods. This capability aids in anticipating staffing needs, resource requirements, and supply demands, as well as predicting service demand and sales points.
Operations and maintenance management: Artificial intelligence plays a crucial role in the overall management of hotels, spanning various areas such as staff scheduling, energy usage monitoring, and predictive maintenance. These applications enhance efficiency and service quality, prevent management errors, and drive cost reduction.
Dynamic pricing strategy: Predictive analytics enables the implementation of a dynamic pricing strategy based on competitor behavior and projected demand. It also aids in predicting inflation and the price of consumer goods, optimizing the hotel's procurement processes.
Customer service chatbots: AI-powered chatbots enhance guest experiences by providing quick and convenient assistance with reservations, payments, and post-stay support. This not only fosters customer loyalty but also increases satisfaction levels.
It is important to note that chatbots do not replace human attention in the hotel industry, but rather enhance the guest experience by providing 24/7 assistance and handling common requests. This allows the staff to focus on providing personalized attention and service.
Sentiment Analysis: Natural Language Processing (NLP) is a prominent application of artificial intelligence that enables sentiment analysis. By analyzing textual content such as online reviews, AI algorithms can determine whether the expressed emotions are positive, negative, or neutral.
Sentiment analysis has multiple applications, including evaluating customer opinions on social media, monitoring brand reputation, and detecting trends in public opinion.
Tailored recommendations: AI analyzes guest data, including room preferences, past activities, previous bookings, and social media opinions, to offer personalized recommendations. For example, suggesting nearby restaurants that cater to guests' dietary preferences or recommending activities based on their interests. Personalizing the customer experience is crucial for guest loyalty and standing out from the competition. Increasingly, guest satisfaction is based on intangible aspects related to the experience they are provided.
Voice recognition and facial recognition: AI utilizes voice and facial recognition to personalize the guests' experience. For instance, a facial recognition system can detect when a guest arrives and tailor the welcome screen with relevant information. Additionally, voice recognition allows guests to interact with the hotel in a natural and personalized manner.
These are just a few examples of how artificial intelligence is revolutionizing the hotel industry. Its versatility and potential for improvement continue to expand, providing opportunities to optimize management and offer more efficient and personalized services to guests.
Hotel chains are increasingly adopting artificial intelligence (AI) as a tool to improve both operational efficiency and guest experiences. By harnessing the power of AI, these chains can optimize various internal aspects such as inventory management and staff scheduling, saving time and reducing costs in their processes.
Moreover, AI offers the ability to personalize the guest experience by providing tailored recommendations, utilizing AI-powered chatbots, and incorporating technologies like facial and voice recognition. By implementing these AI-based solutions, hotel chains can enhance customer satisfaction and foster guest loyalty, ultimately contributing to overall business success.
With the continuous advancement of AI technology, it is expected that hotel chains will continue to embrace these solutions and seize new opportunities to further enhance their operations and deliver increasingly personalized and satisfying guest experiences.
Artificial Intelligence (AI) is an integral part of the business culture and has made a significant impact across various industries. From more accurate data analysis to the implementation of machine learning solutions, AI has greatly improved the efficiency and profitability of many global companies.
The hotel industry is no exception to this trend. More and more hotels are embracing AI-based technologies to automate their day-to-day operations. AI can be utilized in numerous ways by hotel chains, but in this particular case, we will focus on the use of predictive analysis and machine learning to optimize hotel occupancy rates.
1. Machine learning to improve customer experience
To improve the customer experience in the hotel industry, predictive analytics and machine learning are key technologies. These technologies can help you personalize the experience of each guest who visits your hotel. Customers today expect to receive personalized treatment based on their personal preferences, needs and expectations, and working with Big Data makes this possible.
Predictive analytics relies heavily on historical data to predict future customer behavior. In this way, patterns in a customer's past behavior can be detected and provide them with the best room, services and personalized offers. In some cases, predictive analytics can even anticipate customer needs before the customer knows it. It is critical to analyze how certain types of guests behave after check-in and adjust services accordingly for future customers.
2. Adaptive Pricing Strategies
AI can also assist in implementing adaptive pricing strategies in the hotel industry. Many customers seek affordable rooms and services, but this may not always be the most profitable option for the company. By utilizing data analysis, a better understanding can be gained of the price expectations for different types of rooms and services.
Consulting with a data analysis expert is recommended to optimize the current pricing strategy. Subsequently, it is crucial to gather and analyze information on customer response to these prices. It is important to note that adaptive pricing strategies are directly linked to market fluctuations and customer expectations, therefore requiring regular updates and adjustments.
It is essential to create personalized experiences for customers, and customer service is a key aspect of this. With the assistance of artificial intelligence, we can significantly enhance customer service and provide better service to our future customers.
In addition to customer service, marketing is also crucial. Fortunately, predictive analytics and artificial intelligence are the perfect technologies to take our marketing strategy to the next level. Any effective marketing strategy must be based on data collection and analysis, as information about our customers, products and services, and the market is crucial for carrying out more effective marketing actions.
5. Occupancy and Demand Forecasting
Lastly, but certainly not least, we can utilize data analysis to predict our hotel's occupancy rate and demand. There are various artificial intelligence-based tools that allow us to forecast how many rooms will be occupied during a specific period, which, in turn, helps us anticipate the amount of staff we will need, necessary supplies, and more. Additionally, these technologies can also prevent management errors.
Predictive analysis also enables us to anticipate the overall demand for our services. By understanding the approximate demand rate, we can better cater to our customers, optimize resources, and increase profitability per guest.
Bots, whether you call them web bots, chatbots, AI bots, robots or any other name, are fast becoming the future of the Internet and business. Many experts in the business world are discussing how the best bots, configured with machine learning, artificial intelligence and Big Data, will be the foundation of the businesses of the future. Currently, bots can significantly improve customer service lines, but in the future, they could even write code instead of humans. This artificial intelligence technology has become an essential tool for businesses, as it is very easy to interact with them and the best bots are already available on many chat platforms. Today, it is possible to talk to bots on Facebook, Telegram, WhatsApp, Skype and Slack, and Facebook has even added analytics support for messaging bots, giving developers a wide range of tools to use.
Currently, there are a large number of bots available, and here are 10 of the best bots on the market today so that you can get to know them.
Mitsuku: This chatbot is one of the most advanced and popular options available online. It was designed to provide entertainment and assistance to users, making it a top choice for engaging with a bot.
Replika: EThis artificial intelligence bot is designed to provide emotional support and engage in meaningful conversations with its users.
Tars: This bot is an excellent choice for businesses as it can assist in automating repetitive tasks and enhancing customer support.
ManyChat: This bot provides a mass chat solution that allows businesses to interact with their customers through Facebook messages.
Haptik: This bot offers a highly advanced customer service solution that empowers businesses to enhance their service and efficiently resolve issues.
MobileMonkey: This bot offers a chat marketing solution that enables businesses to enhance customer interaction and boost sales.
Meya: This bot is a comprehensive business automation solution that empowers companies to create personalized and efficient solutions.
BotStar: This bot is an advanced solution for automating customer service that enables businesses to enhance their service and efficiently resolve issues.
Admitad Chatbot: A marketing and analytics bot that assists advertisers in enhancing their advertising campaigns and increasing their revenue.
Bank of America: A financial services bot that enables users to access their accounts, make transfers, and check their account statements in real-time.
These are just a few examples of the top bots available today. With the evolution of AI technology and the increasing demand for chatbot solutions, we can expect to see a continuous growth in the quantity and quality of bots available in the market.
In today's world, businesses that fail to embrace technology run the risk of falling behind. Technological innovation is crucial for maintaining competitiveness in the market. However, staying at the forefront of technology requires a significant investment that not all companies are willing to make, even though it can have a tremendous positive impact in the long run.
So how can I convince my management of the need to invest in artificial intelligence? Before planning a strategy, it is important to identify who makes the key decisions. It may be our direct manager or it may need to convince someone else in the company hierarchy. We must determine who has the decision-making power and who their influences are, i.e. who can influence or even determine the final decision.
To convince the company to invest in artificial intelligence, it is important to know what objectives the key decision maker has. It is important to align our proposal with their objectives to ensure that they are in line with the company's objectives. For example, if one of their goals is to increase conversions or reduce processing time, we should highlight how our proposal can solve this problem.
Furthermore, it is crucial to consider the return on investment when making the proposal. Therefore, it is essential to present the impacts of implementing the artificial intelligence solution across all departments of the company, including potential challenges and negative aspects. This approach ensures that the proposal is received objectively and transparently, with benefits for the entire organization.
If possible, conducting a small pilot project is a great idea to allow superiors to experience and evaluate firsthand the results and ease of implementing the artificial intelligence solution.
It is also important to note that applying an existing artificial intelligence solution may be more cost-effective than developing a customized solution from scratch. Initially, we can propose the implementation of existing algorithms and, if positive results are proven, consider the option of a tailored solution in the future.
These are some steps you can follow to persuade the company to invest in artificial intelligence and harness its advantages.
Artificial Intelligence (AI) has evolved far beyond being a mere ornament or something exclusive to technology experts. Every day, new applications for AI are being discovered, and researchers are creating models to evaluate the reliability of the data generated by AI, showcasing the increasing demand for AI solutions in the near future.
In addition to assisting in important decisions, AI can also be of great help in everyday situations. In the current healthcare crisis, for example, AI can help companies improve their organization and efficiency.
With the rise of remote work, we find ourselves constantly connected to our computers, often juggling multiple tasks at once. It can be challenging to stay on top of everything, which is where virtual assistants like Cortana can come in handy. These virtual assistants become smarter with frequent use and can help businesses and freelancers navigate the digital shift.
AI also has the potential to improve team communication, especially in high-traffic industries such as supermarkets and delivery services. GPS tracking tools and route optimization are crucial for keeping mobile workers safe and efficient. AI can track information like the driver's location and route, enhancing communication efficiency and keeping everyone informed.
Enhancing Security and Personalization
Social media metrics can provide useful insights into consumer behavior, but comprehensive understanding requires analysis. Artificial Intelligence (AI) can combine multiple factors such as user opinions, actions, and demographic data to generate charts and predict trends. This will be particularly valuable in the coming months as many consumers are becoming more cautious with their finances.
Undoubtedly, any discussion about customer data must address cybersecurity. In today's digital world, a single data breach can have a cascading impact on consumer privacy. These situations put companies in a delicate position. To safeguard customer privacy, computer programs utilizing artificial intelligence can be employed to detect fraudulent links and unknown email addresses, conduct regular security checks, and establish secure authentication processes for customers.
Microsoft has a book on artificial intelligence describing how artificial intelligence can be leveraged to deliver value to customers through several success stories from multiple companies.
In their e-book, Microsoft highlights one of the key benefits of working with their Azure Artificial Intelligence platform: a significant portion of the difficult work is already done. Developers can take advantage of services with customized and pre-trained models instead of having to create their own models from scratch. These models can be modified to better suit the specific needs and goals of the clients.
Within this e-book, Microsoft shares various case studies of companies that have implemented AI technology to enhance their competitiveness. These real-life examples showcase how Azure Artificial Intelligence can inspect and classify large data warehouses, enabling searches and access to information without the need for manual processing. It can also create interactive and engaging training environments through virtual and augmented reality, improve speed and accuracy in data registration, provide up-to-date and actionable information to field workers, and accelerate criminal investigations while protecting data privacy and security, among other capabilities.
If you want to delve deeper into the success stories explained in the book, download the e-book below:
As the world advances, the realm of artificial intelligence (AI) is becoming an essential component in the day-to-day operations of many businesses. Not only does AI enhance a company's understanding of its customers, but it also optimizes business processing. Whether in the retail, banking, or manufacturing sector, AI can provide you with the answers you need to stay competitive.
Businesses are leveraging AI to improve the efficiency of their customer service systems, conduct complex analysis and forecasting, and automate various business processes, among other applications. Some of the most relevant areas where AI is being utilized include logistics, healthcare, cybersecurity, retail, e-commerce, and human resource management.
Choosing the right AI solution is crucial for a company's success. While AI can help enhance processes, it is essential to consider the costs associated with integrating the technology. It is important not to implement AI without conducting necessary testing and ensuring that the selected solution or software integrates effectively with all the company's systems and proves useful to all departments.
Here are some of the most common uses of artificial intelligence in business.
Housing Price Forecast
One of the most surprising yet highly relevant uses in the housing sector is price forecasting. AI allows us to easily calculate housing prices using predictive algorithms or machine learning pattern recognition. The system explores a large database of home prices in different neighborhoods with detailed information about current residents, which helps identify the best buyer for the property.
Demand forecasting is another key aspect of AI use in businesses, especially in sectors like retail and tourism. Predictive analysis and machine learning are essential technologies for hotel companies, which use them to predict occupancy and optimize their resources, as well as to adapt to demand. AI can also help avoid management errors.
Most successful companies already have a chatbot. This AI-based technology allows for 24/7 customer engagement and provides quick assistance even outside of employees' working hours.
Currently, the market is flooded with online chatbots that can be customized to meet each company's needs. This way, advanced customer service can be offered that meets customer expectations.
AI is also crucial for customer experience in businesses. This technology enables companies to better understand their customers and automate processes to optimize customer service, increase revenue, and reduce operational costs without compromising the customer experience.
Intelligent Recommendation Systems
Another significant use of AI is intelligent recommendation systems that businesses use to develop cross-selling and up-selling strategies, as well as to increase the value of purchases and revenue generated from sales.
Throughout its official history since 1956, when John McCarthy coined the term for the first time, artificial intelligence has made remarkable progress. Not only has the technology itself evolved, but its scope in businesses and various areas of our everyday life has also expanded.
Currently, artificial intelligence plays a crucial role in the business environment, and it is projected that companies will increasingly invest in this technology in the coming years. According to estimates, the global AI market will reach $641 billion by 2028.
Despite the clear benefits that artificial intelligence offers in business, many people still struggle to grasp its concept and understand exactly what it entails, what it can do, and how it is used. Given that it is an abstract technology with multiple ramifications, such as bots, machine learning, and folksonomy, it can be challenging to create a clear mental image of what artificial intelligence is and how it can be applied to improve the performance of business activities.
With this in mind, we will now present some examples of common use of AI in business, covering five different sectors.
Artificial Intelligence in the Entertainment Sector: There are numerous applications of AI in the entertainment industry, including facial recognition, virtual and augmented reality for creating immersive experiences, and tracking filming. However, one of the most notable examples is Netflix's algorithm. This personalized recommendation algorithm based on machine learning is responsible for an annual value of $1 trillion in customer retention and generates 80% of the platform's views.
Artificial Intelligence in the Automotive Sector: The automotive industry is one of the most practical applications of AI, and the technology is already deeply ingrained in its DNA. One example of its use is the automation of tasks in the assembly line with robots. Additionally, AI is being implemented in assisted driving to enhance road safety and reduce the number of accidents.
Artificial Intelligence in Healthcare: AI is widely utilized in the pharmaceutical industry for drug production and assisted robotic surgeries. Furthermore, folksonomy, an AI technology, allows for the analysis of natural language texts and working with unstructured data in clinical research. It automates documentation processes and uncovers previously unknown information. Bismart, as a Microsoft Power BI company, offers a folksonomy solution called Folksonomy Text Analytics that has been implemented in several significant medical projects.
Artificial Intelligence in the Manufacturing Industry: AI has greatly enhanced the manufacturing sector by streamlining work processes and increasing operational precision. It empowers companies to efficiently manage inventory, identify errors, resolve issues more effectively, and overall improve the manufacturing process.
Artificial Intelligence in Marketing: Marketing is one of the business areas that benefits the most from AI, enabling personalized advertising and improved audience segmentation. Moreover, it can also be utilized for data analysis and enhancing the effectiveness of advertising campaigns.
More and more companies are taking advantage of artificial intelligence and machine learning to carry out test automation tasks. Automating the testing process and improving quality means significant savings in time and money. However, there are some important aspects to consider when using AI and machine learning for test automation. Let's review the 6 factors to keep in mind.
AI is responsible for creating intelligent machines that can perform tasks that normally require human intelligence. Thus, machine learning was born when humans decided that it was more effective to teach machines to learn on their own from the information they gather rather than programming them to perform specific tasks. Thanks to the development of the Internet and neural networks, it is possible to code machines to think and understand in a similar way to humans. By connecting machines to the Internet, they have access to an unlimited amount of information, which enhances their learning capabilities.
Today, machine learning is used, among other things, to automate testing processes and improve software quality. In recent years, machine learning has had a major impact on software testing. Today, a software test can be either manual or automated. Test automation reduces the amount of tasks required for manual testing and saves developers from having to constantly review documents.
Many testers and QA teams are incorporating test automation in their companies, as machine learning helps manual testers simplify their tasks and achieve higher quality output in less time.
For this reason, it is important for manual testers to learn how to perform automated testing. This testing method helps save time and money, increases test coverage, improves test accuracy and decreases the amount of work for the QA team. In addition, test automation facilitates collaboration between developers and the testing team.
Visual Testing (UI) is a crucial activity in software quality control, where developers evaluate the appearance and performance of an application for the end user. It is essential to know the patterns that can be recognized by machine learning, as these may have a greater ability to detect defects, whether functional or cosmetic.
For the visual control of web or mobile applications, the use of deep learning is more suitable than a traditional machine vision system, as it offers more accurate and faster results. In addition, in situations where human intervention may be considered dangerous, one can rely on the creation of automated tests with machine learning that avoid manual work and automatically detect visual errors.
API Testing, on the other hand, is a software test that allows communication and data exchange between systems. It has the advantage of detecting defects in an API better than UI testing, and is easier to automate, as it resists changes in the application. However, it requires a high degree of technical knowledge and a wide range of tools to obtain complete test coverage. With AI, manual UI testing can become automated API testing that does the heavy lifting.
Having domain knowledge is crucial for performing any type of software testing, whether manual or automated, as artificial intelligence helps improve application testing. Advanced test automation tools help develop code and test scripts, and with AI, machines can write flawless code on their own. However, it is important to understand how the application will work and how it will affect the organization. Scaling defects in the application according to their severity is crucial, especially when using AI in test automation.
Having domain knowledge is crucial for conducting software testing, whether it's done manually or automated. This knowledge enables more effective testing of applications.
Writing test scripts can be challenging, especially when using languages like Java, Python, or C#. However, there are advanced test automation tools available that assist test practitioners in developing code and test scripts. Furthermore, artificial intelligence allows machines to write error-free code.
It is important to note that while AI can be beneficial in test automation, understanding how the application works and how it will impact the organization is essential. For instance, automated test results often include defects, so it is important to escalate the defect and determine if it is trivial, significant, or critical.
Spidering is a popular technique used to create automation test scripts, allowing for the examination of any web application using artificial intelligence and machine learning technologies to automatically scan and gather data.
Over time, these tools build a dataset and create patterns for the application during test execution. This enables the identification of potential issues by comparing them with the previously established dataset and patterns whenever the tool is used again.
However, it is important to note that some results may be inconsistent. Therefore, it is necessary for an expert with domain knowledge to verify whether the problem detected by machine learning is indeed an error.
In summary, spidering with AI is valuable in understanding which parts of an application should be tested, and machine learning simplifies the task. However, it is essential to have the validation of an expert to ensure the accuracy of the results.
Determining the number of tests required after a code change can be a challenge for software testers. However, AI-driven test automation tools can accurately predict whether an application needs multiple tests or not.
Implementing artificial intelligence in the testing process offers two key benefits. Firstly, it enables the elimination of unnecessary tests, resulting in significant time savings. Additionally, by evaluating the overall system performance without constantly repeating test scripts, the need for manual supervision of the process is reduced.
Robotic Test Automation
Robotic Process Automation (RPA) is a powerful tool that enables the execution of repetitive tasks autonomously, without the need for human intervention. With RPA, businesses can efficiently and accurately perform repetitive tasks, increasing productivity and reducing costs.
RPA utilizes artificial intelligence and image processing technology to automate data collection and management, and it can operate across various platforms, including web, desktop, and mobile applications. Furthermore, effective regression testing can be conducted by configuring test data.
The key benefits of using RPA in test automation include scalability and flexibility, the elimination of the need for writing test code, and increased accuracy in results. In summary, RPA is an effective solution for accelerating and enhancing testing processes, making them more accessible for IT teams.
A research study titled "When Will AI Exceed Human Performance?" (Grace et al.) suggests that machines could surpass humans in any task within just 45 years, and that in 120 years, all human jobs could be automated.
Some of these changes may happen sooner than expected. For instance, according to the researchers, machines could translate languages, write high school-level essays, and operate trucks within the next ten years (in 2024, 2026, and 2027, respectively).
While these are just predictions, there is no doubt that advancements in technology, including artificial intelligence, will drastically transform the workforce.
The Future is Now: Artificial Intelligence and Its Impact
Although artificial intelligence may seem like something out of science fiction movies, it is actually a reality that has been around for a long time. The term was coined in 1956 at the Dartmouth Conferences, but as early as the 13th century, philosopher Ramon Llull attempted to create a machine that used logical reasoning to generate knowledge.
Today, artificial intelligence is much more advanced. While it may not be exactly what we see in Hollywood, it is a powerful tool with many practical applications that solve a wide range of problems.
Here are a few examples of its use in real-life situations:
The Impact of AI on Work: an Evolving Debate
Artificial Intelligence (AI) is a topic of great interest, with fascinating possibilities as well as controversies and debates about its disadvantages. In a 2014 speech, Elon Musk characterized AI as the "greatest existential threat" to humanity. As Tom Standage states in The Economist, "anything you can do, AI can do better." However, a study by Grace et al. suggests that while this claim is currently an exaggeration, it is a real possibility for the near future of work.
Uncertainty about how AI will affect the labor market is creating pressure in many sectors. According to a report by PricewaterhouseCoopers, it is estimated that 30% of jobs in the UK could be replaced by automation in the next 15-20 years, especially those with low levels of education. The concern is especially around jobs that require driving as autonomous vehicles become a reality. A White House study predicts that 3.1 million jobs will be automated in the future, especially those related to heavy transportation.
On the other hand, the AI of this century possesses advanced abilities that are rapidly transforming our way of life. However, many of the concerns surrounding AI are similar to those that arose in the past during times of rapid change, such as the Industrial Revolution. Standage emphasizes the importance of remembering that many of these concerns have emerged before and have been resolved.
Similar to the past, AI has the potential to create new jobs and help society address many challenges. Jeff Greene and Vivek Wadhwa have highlighted the advantages of AI, including more efficient energy usage, reduced living costs, and increased flexibility for employees. The same PwC report that indicated 30% of jobs are at risk also demonstrated that AI will boost spending and productivity.
Improve, not replace
Artificial intelligence has arrived to stay, and many experts are exploring ways to utilize AI to enhance our work rather than jeopardize our jobs.
In an interview with Marginalia magazine, Steve Ardire, a software startup advisor, expressed his belief that the future will involve "people and machines working together to improve their work," and he adds, "if your job is not routine, artificial intelligence becomes a digital assistant."
In other words, artificial intelligence can be used as a tool to complement and, in some cases, even enhance work.
The healthcare and social services sector is an area where artificial intelligence is already being utilized as a tool to address problems. One example is the tool created by Bismart that reduces the number of patients readmitted to hospitals. Several studies have shown that artificial intelligence is more effective than doctors in interpreting radiographs and diagnosing issues, allowing doctors more time to focus on solving complex problems that require creativity.
And what about all those driving jobs that will be replaced by AI? As Toby Walsh, an Australian professor of artificial intelligence, points out, over 95% of traffic accidents are caused by human errors. Therefore, by eliminating the human factor in driving, roads will become much safer.
What does the future hold for us?
Governments must take action to retrain the workforce and align it with the demands of emerging technologies, bridging the gaps in skills and knowledge, especially for those who are at higher risk of being affected by automation, such as drivers. Political leaders will have to make crucial decisions on practical and ethical issues related to artificial intelligence, such as data storage and privacy. While Bill Gates has proposed a tax on robots, Elon Musk believes that a universal basic income is the answer.
It is undeniable that the labor market will experience significant changes due to artificial intelligence and other technological advancements. However, if artificial intelligence is used responsibly, it can provide us with many opportunities to improve our lives and address important problems.
The interest in machine learning and deep learning in the business field is constantly increasing. According to a research study by MarketsandMarkets, the machine learning market is expected to grow by 44.1% over a span of 6 years, from $1.03 billion in 2016 to $8.81 billion in 2022. This growth can be attributed to the generation of data and technological advancements, which are key factors driving the market. Furthermore, technologies like Azure Machine Learning are gaining more prominence in businesses.
To understand the difference between machine learning and deep learning, it is important to note that both are approaches to artificial intelligence based on complex mathematical algorithms. These algorithms enable machines to learn from data in a similar way to humans. Algorithms are used in a wide range of operations and business activities across various fields.
Currently, algorithms are present virtually everywhere, and there is a great demand for developing and applying algorithms that surpass those of the competition, as well as the desire to uncover the secrets of the latest Instagram algorithm.
But what exactly is an algorithm? According to Google, an algorithm is an ordered set of systematic operations that allows for problem-solving or calculations. In practice, an algorithm can be described as a mathematical formula or a set of formulas applied to technological tools to enable them to perform desired tasks.
Eduardo Peña, a professor at the Faculty of Informatics at the Complutense University of Madrid, explains that the work of computer programmers involves translating real-world problems into a language that machines can understand. In summary, algorithms are fundamental to programming and enable machines to execute specific tasks efficiently.
The Importance of Understanding Algorithms in the Enterprise Environment: Lessons from Vincent Warmerdam
The use of algorithms in the business environment is becoming increasingly common to optimize operations and functionality. These algorithms are data-driven and rely heavily on human intervention. Data scientists and engineers develop algorithms with the intention of solving problems and improving tasks performed by machines, technology tools and platforms. However, in many cases, despite brilliant algorithms, business problems are not solved or the expected results are not obtained.
What is the reason for this?
To understand it, let's go back to the beginning. Although algorithms are often portrayed as a kind of magical wand with supernatural powers, or even as an evil entity that penetrates our minds and reveals all our secrets, the reality is different. While algorithms can solve complex problems and perform tasks that were once extraordinary, they cannot do it alone.
Continuing with a mathematical analogy, the algorithm is just the formula. To solve a mathematical problem, the first step is to understand it and then deduce which formula to apply. Applying the wrong formula will not solve the problem. This does not mean that the formula itself is incorrect, it is just being applied incorrectly.
The same goes for algorithms. Vincent Warmerdam, co-founder of PyData and an expert in algorithms and machine learning, addresses this issue in his talk titled "The profession of solving (the wrong problem)". Through several personal stories, Warmerdam highlights that the algorithm itself is not the solution. What really solves business problems are all the elements surrounding the algorithm: the databases, data quality, data analysis, A/B testing, the right approach to the problem, and most importantly, what he calls "natural intelligence."
Warmerdam shares a story from his time as a student where he applied statistics to a real problem. He was working at a theater that was considering expansion. By analyzing the theater's annual attendance data, he noticed that attendance was decreasing year after year. He concluded that the theater should not proceed with the expansion because attendance was still declining. His professor was impressed and gave him the highest grade, and his superiors at work also praised his discovery. Problem solved, right?
However, a few weeks later, Warmerdam realized that the theater was always full and felt very hot due to the large influx of people. He realized that he had not solved the problem correctly. He had applied the wrong formula. Attendance at the theater was not decreasing because people were stopping going, but because there simply wasn't enough space for more people. During the first few years, the theater experienced steady growth until it reached maximum capacity. From that point on, attendance stopped increasing. Warmerdam had not correctly framed the problem, applied the wrong formula, and therefore did not solve the problem, despite the accolades he received from his superiors.
After that first encounter with algorithms, Warmerdam persevered and built a successful career as an expert in machine learning and algorithms. His extensive experience in the field has taught him that the same thing that happened to him in high school with statistics happens in the business world with algorithms. Algorithms alone are not the ultimate solution, but it is crucial to consider all the aspects surrounding the algorithm, such as databases, data quality, analysis, testing, and above all, human intelligence.
The Challenges of Algorithm Usage
Vincent Warmerdam firmly believes that algorithms are not the ultimate solution to problem-solving and can even worsen the situation when applied incorrectly. What is concerning is that on multiple occasions, he himself has celebrated apparent resolutions to business problems after developing an algorithm, only to realize days or weeks later that the problem remained unresolved and they were celebrating a false victory they had proclaimed themselves.
This is where the problem lies with algorithms. The world tends to believe that an algorithm has the power to solve anything. Vincent cites several of his colleagues at work who, when faced with any problem, immediately propose, "Let's create a super algorithm to solve this!" Without even comprehending the problem, analyzing the data, or verifying its accuracy.
An additional example of failure due to blind faith in algorithms is the infamous stock market incident known as the 'Flash Crash'. On May 6, 2010, stock market algorithms caused a 1,000-point drop, nearly 9% of the shares, without any apparent reason. Minutes later, everything returned to normal, and the points regained their original state. However, to this day, nobody can explain why it happened or what exactly occurred. Even the creators of the algorithm were unable to determine why that situation had occurred, proving that none of them fully understood the process or what was behind the algorithm. This supported Warmerdam's suspicions that artificial intelligence cannot be intelligent without natural intelligence, or in other words, without human intervention.
In this sense, while machine learning, deep learning, and algorithms have made significant advancements in the business world, it is crucial for entrepreneurs, scientists, and data engineers to be aware that algorithms alone do not solve problems. Applying the correct formulas to the wrong problem can lead to a false sense of victory that ultimately ends in defeat in the long run.
It is no secret that artificial intelligence is one of the most influential technologies of the 21st century. In fact, the technology consulting firm Plural Sight has identified it as the most relevant technology of 2021 in its annual report, and Gartner has pointed out that artificial intelligence engineering is the eighth most significant technological trend of the year.
Here are some of the most important AI projects of recent years, which demonstrate that the uses of this technology are limitless and that, if used properly, artificial intelligence can be a powerful ally in solving or mitigating social problems.
The evolution and progress of artificial intelligence are unstoppable. The latest AI system developed by Cerebras Systems, an American company, once again demonstrates that there is still much untapped potential.
The company has announced the development of an AI-based system for training models and devices that can handle the same number of parameters as the human brain, marking a milestone in the history of artificial intelligence.
The human brain has around 100 trillion neural connections that process information and enable us to learn new things. Until now, most artificial intelligence systems have had only about 1% of the processing capacity of the human brain.
Cerebras Systems is creating an AI-based computing system that will be capable of handling up to 120 trillion parameters, surpassing even the capacity of the human brain.
Hospital del Mar anticipates the progression of COVID-19 patients admitted to the hospital.
In times of a health crisis like the one we are currently experiencing, artificial intelligence can be of great help. A collaboration between Hospital del Mar, Ferrer, and Bismart showcases this potential.
During the peak of the pandemic, the research team at Hospital del Mar in Barcelona embarked on an innovative project that utilized folksonomy, a branch of AI, to analyze and identify common characteristics among COVID-19 patients.
This project emerged from the implementation of Bismart Folksonomy, an AI solution based on natural language processing, which allowed the Hospital del Mar team to identify shared traits among COVID-19 patients and detect patterns in the virus's behavior without requiring extensive time for data collection and analysis.
3. An algorithm that solves complex problems on its own.
In September of this year, quantum physicist Mario Krenn established a new research group at the Max Planck Institute for the Science of Light in Germany. The purpose of this group is to harness the power of artificial intelligence in quantum physics experiments.
The team of researchers is already hard at work on the development of the first AI algorithm that will extract the core concepts from solutions to highly complex scientific problems.
Back in 2016, Krenn created an AI algorithm named Melvin, which was capable of creating highly complex entangled states involving multiple photons. Melvin's remarkable achievement was the ability to develop these states without any specific instructions.
Now, Krenn has introduced an enhanced version of Melvin called Theseus. This deep learning algorithm surpasses the power of its predecessor and promises to be the shining star of the new research group at the Max Planck Institute.
4. The Implementation of Artificial Intelligence in the COVID-19 Vaccination Campaign
The COVID-19 vaccination campaign is undoubtedly the largest and most significant health project currently. The production and distribution of vaccines require an unprecedented logistical and production effort that must be carried out at an accelerated pace without compromising the quality of the material.
Stevanato Group, an Italian company and one of the leading providers of vials and syringes for COVID-19 vaccination worldwide, has embraced artificial intelligence, cloud computing, and mixed reality to enhance its supply chain efficiency. The company has implemented Microsoft 365 and Microsoft Teams as collaboration and coordination tools among different teams, significantly reducing interruptions in the supply chain.
Furthermore, Stevanato Group has leveraged machine learning to optimize its data strategy, resulting in improved efficiency and speed in quality testing. By incorporating artificial intelligence, the company can ensure an optimal supply of high-quality vaccines without any risks.
5. Cleaner Beaches
Climate change poses one of the greatest challenges to society today, and the accumulation of cigarette butts in natural environments is a concerning factor. According to National Geographic, over 5 trillion cigarette butts are discarded worldwide each year, negatively impacting ecosystems like beaches.
In an effort to reduce the environmental impact of cigarette butts on beaches, TechTIcs employees Edwin Bos and Martijn Lukaart have developed BeachBot. This robot utilizes artificial intelligence to detect and collect cigarette butts in the sand, including those that are buried. Equipped with installed cameras, BeachBot can identify the waste, collect it, and deposit it into its interior for proper disposal.
TechTIcs has created a collaborative system to engage society in the fight against marine pollution on beaches. Anyone can send images of waste found on the beach to help BeachBot perform its job more effectively.
6. Advancing Medicine with Artificial Intelligence
Artificial intelligence is becoming a key technology in the medical field, and its impact on solving health-related problems is increasing.
In 2021, a team of researchers from Google Research and Mayo Clinic created a groundbreaking AI algorithm called 'Baseline Profile Curve Identification'. This algorithm aims to improve the care of patients suffering from movement disorders and epilepsy, who require devices for electrical brain stimulation.
Brain stimulation, through electrical discharges, allows researchers to study the behavior of brain connections in these patients. However, analyzing the interaction of brain networks is a complex process, as the recorded signals are intricate and the possible measurements are limited.
With the new AI algorithm, Mayo Clinic researchers have managed to simplify and optimize this process. By using this algorithm, scientists can discover which regions of the brain interact with each other and more effectively place electrodes for electrical stimulation devices. This will enhance the care of these patients and contribute to research in the field of health.
In an increasingly data-driven environment, advanced analytics has become a key technology for companies seeking a significant competitive advantage. Utilizing advanced data analysis methods, advanced analytics uncovers more innovative, specific, and evolved business insights. In today's business world, data analytics has become essential for organizations that want to understand their operations and gain valuable information about both their internal functioning and competitor landscape in order to make informed decisions.
Data analytics plays a crucial role in customer understanding and the development of more efficient customer service strategies. However, as the amount of data generated by companies continues to exponentially increase, there is a growing need to employ more advanced analytics techniques to obtain higher-value insights that cannot be achieved through traditional data analysis.
It is in this context where the concept of "Advanced Analytics" becomes relevant. Advanced analytics is a discipline that combines statistical, mathematical and technological methods to deepen data analysis and obtain greater strategic value. These more sophisticated techniques make it possible to explore patterns, trends and complex relationships in the data, providing a deeper and more detailed view of business problems and opportunities.
Advanced analytics encompasses a wide range of techniques, including machine learning, data mining, artificial intelligence, and predictive modeling, that enable the discovery of hidden insights, prediction of future trends, and evidence-based decision-making. By applying these techniques, businesses can gain a competitive advantage by identifying new growth opportunities, optimizing operations, improving customer experiences, and anticipating market changes.
In summary, advanced analytics has become a fundamental pillar for companies that want to strategically leverage their data and gain significant competitive advantages in an increasingly data-driven business environment. By harnessing the techniques and tools of advanced analytics, organizations can unlock the potential of their data and gain valuable and insightful insights to drive growth and business success.
What is Advanced Analytics? Advanced Analytics, as the name suggests, refers to a type of data analysis that goes beyond traditional approaches. It is a set of techniques and tools that use advanced methods to uncover hidden patterns, predict future outcomes, and provide a deeper, more strategic understanding of information.
One distinctive feature of Advanced Analytics is the use of artificial intelligence capabilities, such as sophisticated algorithms and complex mathematical models, for prediction. Unlike conventional data analysis, which focuses on describing and analyzing past events, Advanced Analytics aims to understand why certain events occurred and what is most likely to happen in the future.
Predictive Analysis: This type of analysis utilizes statistical techniques and machine learning to forecast future events or behaviors based on historical data. By applying predictive models, companies can anticipate emerging trends, make strategic decisions, outpace the competition, or predict customer behavior.
Data Mining: Data mining is another key technique within Advanced Analytics. It involves uncovering hidden patterns and relationships in large datasets using advanced algorithms. This allows organizations to gain valuable insights into customer behavior, identify improvement opportunities, and optimize their business processes.
Text Analysis: With the growth of unstructured data such as emails, social media, and customer reports, text analysis has become crucial. This technique employs advanced language models to analyze large amounts of text and extract valuable information, including sentiments, opinions, recurring themes, and relevant entities.
Social Network Analysis: Social networks play a significant role in companies' interaction with their customers and in obtaining relevant data about their behavior and consumption habits. Social network analysis within Advanced Analytics focuses on examining data generated on social platforms to discover interaction patterns, influence, and user behavior. This helps organizations better understand their audience, tailor their marketing strategies, and make decisions based on online feedback.
Big Data Analysis: Big Data analysis focuses on the management and analysis of large volumes of structured and unstructured data. It utilizes techniques and tools to process, store, and analyze data on a large scale. This discipline enables organizations to extract relevant information from diverse sources and use it to make strategic decisions and gain a competitive advantage.
In summary, Advanced Analytics is a set of techniques and tools that go beyond traditional data analysis. It uncovers valuable insights, predicts future trends, and provides a deeper understanding of data. By applying these techniques, companies can make informed decisions, stay ahead of changes, and gain a competitive edge in a data-driven business environment.
What benefits does Advanced Analytics bring to companies?
Effective implementation of Advanced Analytics techniques for analyzing corporate data brings significant benefits. These benefits help organizations make more informed decisions and drive their growth and digital transformation. Here are some of the key benefits of Advanced Analytics at the enterprise level:
Despite its complexity and the need for highly specialized professionals, data mining is moving closer and closer to business environments. Its growing importance in the business world is generating a new demand: the understanding of what data mining is and what it is used for by business people. In this article, we will explore the basic concepts that every entrepreneur should know about data mining.
Data mining has become a widely adopted practice in the business world. According to an article published in Forbes magazine in January 2021, data mining has become one of the top priorities for chief information technology officers (CIOs) for 2021. Kim Hales, vice president of IT at NRG Energy in Texas, noted in Forbes that data mining is essential to "capture and integrate an even broader set of data as part of decision-making processes." As a result, data mining is now an integral part of the process of defining business strategies and making decisions.
The challenge in the relationship between data mining and business strategies lies in the complexity of data mining procedures, which require highly technical profiles and can be difficult to understand for non-experts in data engineering. However, business people do not need to master data mining processes, but they do need to understand what it is, what it is for and how it can improve business productivity.
What is Data Mining?
Data mining is the process of uncovering hidden patterns and correlations in data using statistical and mathematical techniques. It involves analyzing large volumes of data using data mining algorithms and pattern recognition technologies to convert data into actionable information, identify patterns, predict trends, and establish rules and recommendations. Data mining employs unconventional approaches to pattern recognition and can reveal patterns and trends that cannot be discovered through traditional queries due to data complexity or relationships.
In the business realm, data mining was developed to enable entrepreneurs to extract valuable insights from large datasets without the need for mathematicians or statisticians.
In summary, data mining is a mathematics-based process that allows the discovery of previously hidden information. It is used to obtain insights that support better business decision-making and represents an advanced way to drive data-driven decisions.
Factors to consider before investing in data mining: Data mining procedures are complex, involve multiple stages, and can easily lead to errors. Therefore, it is important not only to have them performed by trained professionals but also to have specific technologies that include user-friendly graphical interfaces to increase productivity and prevent errors. Additionally, it is crucial to validate the discovered patterns and ensure their applicability in real business situations. Otherwise, technicians may identify patterns that do not contribute value to improving business activities.
Today, the market offers numerous technological tools that facilitate data mining processes through graphical interfaces that simplify procedures and enhance productivity, such as Microsoft SQL Server Data Mining, a comprehensive data mining solution that combines multiple tools in an environment specifically designed to work with data mining models.
In addition to uncovering exclusive information and enriching knowledge to make more informed business decisions and develop more effective strategies and actions, data mining can be applied to a wide variety of specific initiatives and business intelligence strategies. For example, it can be used to:
Predictions, such as estimating sales or forecasting server load and downtime.
Customer segmentation and behavior prediction: By analyzing correlations in the data, it is possible to identify affinities between groups of customers and categorize them based on the products or services they have acquired. Future purchases can also be anticipated, and the frequency and amount of their acquisitions estimated.
Probability models: Data mining has the ability to calculate probabilities and predict risks, allowing for the prevention or mitigation of potential problems and the adjustment of business operations based on the identified probabilities.
Recommendation generation: Data mining can be used to discover connections between the products and services that make up a company's offering, facilitating the implementation of cross-selling and upselling strategies, as well as the identification of products that tend to be sold together.
Improvement of the customer experience: Data mining can be applied to identify points of satisfaction and dissatisfaction along the customer journey and uncover the needs, preferences, and problematic areas of customers.
4 Success Stories in which Data Mining Boosted Business Profitability and Efficiency
Far from being an abstract concept, data mining has proven to be the key to success for some organizations that have experienced a significant increase in productivity through valuable insights. An article published in Forbes in 2018 highlights 4 success stories driven by data mining:
The first case tells the story of a retailer who, through data mining, identified which customers had the potential to become long-term customers and which did not. This allowed them to optimize their marketing strategy and business efforts, tailoring them to the customer lifecycle. What this retailer achieved through great effort is precisely what Bismart ABC Client Analysis offers, an easily implemented technological solution that automates this process and classifies profitable, non-profitable, strategic, and growth potential customers, while evaluating portfolio diversification and concentration.
An insurance company used data mining to identify which offices managed certain types of claims more efficiently. This information allowed them to recognize best practices in handling these types of claims and apply them to other branches. As a result, the company was able to reduce costs and provide faster and more effective service to its customers.
A law enforcement agency employed data mining to analyze the rules used in prioritizing police cases. The analysis revealed that case prioritization was being done completely randomly and without clear criteria. After this discovery, the institution was able to improve case assignment by replacing the previous system with a more efficient and productive one.
Finally, a chemical manufacturer utilized data mining to identify warning signs in chemical spills. This allowed them to implement preventive measures, reduce costs and capital expenses, and establish new environmental protection standards.
These examples illustrate how data mining has become a powerful tool in the business world. It enables companies to gain deep insights, known as business "insights," in various functional areas related to their operations. Additionally, it helps better understand existing actions, strategies, and processes with the goal of improving them. Technology has allowed companies to apply scientific research methodologies to optimize their business activities.
The Top 7 Free Tools for Data Mining
It's no secret that data has the potential to drive significant improvements in business outcomes. Accessing data gives us the opportunity to identify areas for improvement and pathways to optimization. However, the process of data collection can be laborious and inefficient at times. Fortunately, there are numerous data sources that can expedite this process, along with the practice of data mining, a strategy that is gaining prominence in the business world.
Below are some of the most popular data mining tools.
Xplenty is a platform that simplifies data analytics preparation and reception for businesses. It stands out for its wide range of features and its excellent combination of hardware and software.
It offers the flexibility to build your own data pipeline and provides both zero-code and low-code options. Furthermore, it distinguishes itself with its robust customer support system. Due to its customizable feature set, Xplenty becomes a perfect data mining platform for any type of business.
If you're looking to obtain data more visually and leverage data visualization capabilities, Weka is the ideal tool. This platform offers various visual tools that enhance data analysis. Moreover, it has superior data mining capabilities compared to the previous tool. It's worth noting that data should be in a flat format to fully utilize this platform. With regression, visualization, and data processing functions, Weka provides optimal software for carrying out data mining processes.
As the name suggests, Rapid Miner stands out as one of the leading data mining platforms. Built in Java, it offers numerous options focused on business analysis and business intelligence (BI).
Despite the speed at which it provides data, Rapid Miner executes the process with minimal errors. This is due to the available frameworks in Rapid Miner, which are easy to use for both beginners and those without experience in data mining.
Rapid Miner includes Rapid Miner Studio, Rapid Miner Radoop, and Rapid Miner Server, offering a wide range of opportunities for data collection, processing, and mining, allowing businesses to expand their knowledge generation capabilities.
Teradata, also known as the Teradata database, is another ideal data mining option for companies seeking data mining solutions. This platform specializes in collecting specific information about sales, customer preferences, and product placement. Teradata stands out as one of the best data mining tools focused on sales, making it the perfect choice for optimizing sales processes.
Unlike most data mining platforms written in Java, Orange is developed in Python. This tool offers a comprehensive solution that combines visual and conventional data analysis. What sets it apart are the "widgets" that allow for visualizing analytics and predictive algorithm models. These widgets facilitate data interpretation and tables with verifiable characteristics.
If you're drawn to visual designs and fresh aesthetics, Orange is a potential choice for data processing in your company.
Revolution is characterized by its user-friendliness and effective presentation of statistical and visual data. It makes data collection easy and enables more advanced analysis. Its graphics and visual elements are crisp and high-quality.
However, Revolution does not excel in intensive data mining, as it focuses more on analysis than on extracting complex data. Therefore, if you're seeking in-depth analysis, Revolution can assist with a more superficial approach.
Dundas is a platform that can be used by multiple employees within a company simultaneously. With its "central data platform" feature, businesses can provide access to relevant data for all employees. Dundas includes interactive dashboards that simplify data management.
This data mining platform is innovative and particularly suitable for large-scale companies with a considerable workforce.
Since its launch in November 2022, ChatGPT has made a significant impact on public conversation, quickly becoming one of the fastest-adopted technologies in history.
It's important to note that generative artificial intelligence should not be confused with "general artificial intelligence," a more abstract concept used to describe an artificial intelligence that can match or surpass average human intelligence, which has recently sparked controversy.
According to McKinsey, the public version of ChatGPT reached 100 million users in just two months, democratizing a technology that has existed for over a century, such as artificial intelligence.
The rapid growth of generative artificial intelligence can be attributed to its accessibility. Prior to ChatGPT's entry into the market, artificial intelligence already had a strong presence in the technology industry but was mainly reserved for experts in the field. However, ChatGPT changed this paradigm by making generative artificial intelligence accessible to anyone.
For the first time, users can leverage this technology without the need for knowledge in machine learning, which forms the foundation of generative artificial intelligence.
All of this is possible because generative artificial intelligence chatbots use base models, which are neural networks trained with large amounts of unstructured and unlabeled data. These base models are highly versatile and can be applied to a wide variety of tasks, unlike previous artificial intelligence models designed for a specific task.
However, this versatility comes with the drawback that generative artificial intelligence tends to be less precise due to its breadth, increasing the likelihood of obtaining incorrect or less rigorous results.
Generative artificial intelligence is a branch of AI that focuses on developing systems and models capable of generating original and innovative content. It utilizes multimodal machine learning techniques and neural networks to learn and imitate or create new instances based on similar data provided during training.
The essence of generative AI lies in its models' ability to understand the underlying structure of a dataset and subsequently generate new instances that adhere to that same structure. This enables generative AI models to create a variety of content, including images, text, music, and video.
One commonly used technique in generative AI is generative adversarial networks (GANs). GANs consist of two types of neural networks: a generator and a discriminator. The generator is responsible for creating new data instances, while the discriminator evaluates the authenticity of these instances. Both networks are trained simultaneously, with the generator attempting to deceive the discriminator and the discriminator improving its ability to distinguish between generated and real instances.
What are the key differences between generative artificial intelligence and other types of artificial intelligence?
The main distinction lies in the ability of generative artificial intelligence to create original content in unstructured formats, such as text and images.
Generative artificial intelligence operates through artificial neural network models, commonly referred to as foundational models, that are trained using deep learning, a branch of machine learning. While deep learning has been around since the early 2000s, the recent models applied in generative AI have notable differences compared to previous models.
The standout feature of these new models lies in their ability to be trained on extremely large and diverse sets of unstructured data, unlike previous deep learning models that often relied on more limited and specific datasets.
This difference brings a significant increase in the versatility of the new models, allowing them to perform multiple tasks simultaneously and generate new content. For example, while previous deep learning models could, for instance, identify objects in an image or make predictions based on image information, the new generative AI models can do both tasks and additionally generate original content.
What makes these new deep learning models applied in generative AI special is their ability to accumulate knowledge and discern patterns and relationships in the data from the extensive training sets used. This is what enables ChatGPT to answer questions and generate original content, and allows DALL-E 2 and Stable Diffusion to create images from descriptions.
The inherent versatility of generative AI opens the door to a wide range of applications that previous deep learning models couldn't address.
Generative artificial intelligence has a wide range of applications in the business environment, focusing on task automation and optimization. It goes beyond the capabilities of ChatGPT and can perform multiple functions within a company, including classification, editing, summarization, answering questions, and creating new content.
These capabilities have significant potential to generate value in the business sphere, transforming the way operations are carried out across all areas and processes of an organization.
It is important to note that generative AI technology is constantly evolving, which means that as it advances, new applications can be identified in the business domain. Currently, its most relevant applications include task automation, workflow optimization, and specific request responses.
However, it is crucial to acknowledge that the versatility of generative AI also comes with risks. Therefore, companies looking to adopt this technology must do so responsibly and implement risk mitigation measures.
The risks associated with generative artificial intelligence must be addressed from the beginning of its implementation. These risks include:
1. Bias: Generative AI models can generate algorithmic biases due to imperfect training data or incorrect decisions in their development.
2. Intellectual Property (IP): Using training data and results generated by generative AI models can raise issues of copyright infringement, trademark infringement, patents, and other legal intellectual property rights.
3. Privacy: There is a risk that generative AI may reveal users' private information in its results, as well as the possibility of creating and disseminating malicious content such as deepfakes or misinformation.
4. Security: Generative AI can be employed to enhance the sophistication of cyberattacks and can be manipulated for malicious purposes.
5. Reliability: Generative AI models may offer inconsistent responses to the same questions, making it difficult to evaluate their accuracy and reliability.
6. Social and Environmental Impact: The development and training of generative AI models can have negative social and environmental consequences, including an increase in carbon emissions.
In summary, generative artificial intelligence offers numerous opportunities to enhance business efficiency and productivity, but it also presents challenges that must be responsibly and proactively addressed to ensure ethical and safe use of this technology.
Last Tuesday, on May 23rd, Microsoft announced two exciting additions to Power BI: Microsoft Fabric and Copilot.
Microsoft Fabric represents Microsoft's approach to building a highly adaptable data architecture around Power BI. On the other hand, Copilot provides Power BI users with advanced artificial intelligence capabilities based on machine learning and natural language processing (NLP).
Copilot is the latest addition in the field of artificial intelligence for Power BI. This component is essentially a large-scale multimodal artificial intelligence model based on natural language processing. We can compare it in simple terms to Power BI's "ChatGPT" or "DALL-E".
With the addition of Copilot to Power BI, users can now ask questions about the data and create visualizations and DAX measures simply by providing a brief description of what they want. In a Copilot presentation video, we can see that it works similarly to ChatGPT, through a conversational chat interface. Additionally, the video gives us a glimpse of some of the impressive capabilities of Copilot.
Azure OpenAI Service
Microsoft has introduced Azure OpenAI Service, a powerful artificial intelligence solution that incorporates the most advanced algorithms in the market. Here, we present the capabilities, models, and advantages of this innovative service.
Azure OpenAI Service is an artificial intelligence offering developed in collaboration between Microsoft Azure and OpenAI. This platform provides companies and developers with access to cutting-edge artificial intelligence models, including GPT-3.5, Codex, and DALL•E 2. These models enable the creation of advanced applications and the resolution of highly complex problems.
Microsoft's vision is to provide a comprehensive artificial intelligence service, giving companies access to the world's most advanced models, backed by the robust security measures integrated into Microsoft Azure.
Azure OpenAI Service runs on the highly optimized artificial intelligence infrastructure of Microsoft Azure and offers enterprise-level functionalities. This service is the perfect choice for companies and organizations that require scalable and high-performance artificial intelligence solutions.
Potential Integration between Excel and ChatGPT
The partnership between Microsoft and OpenAI has the potential to make a significant impact in the business intelligence software market. The potential integration of ChatGPT with Excel could transform Microsoft's most popular tool into the leading BI technology for many companies, attracting more users to Power BI and surpassing the competition.
In January 2023, it was reported that Microsoft was experimenting with integrating a version of GPT into its Word, PowerPoint, Outlook, and Excel applications. While specific details of the integration are not known, it is speculated that GPT could enhance the autocomplete function and provide more precise queries to Microsoft tool users.
There are also rumors that Microsoft plans to incorporate the popular chatbot, ChatGPT, into its products, including Excel.
The integration of Excel and ChatGPT has the potential to revolutionize the way the tool is used. Excel could generate text based on simple natural language instructions, allowing users to analyze their data directly within Excel spreadsheets.
This step could benefit Microsoft in an increasingly competitive business intelligence market. Instead of posing a threat to Power BI, the improvement and addition of new capabilities to Excel could attract more users to Power BI and other Microsoft suite technologies. Companies not only evaluate a tool's capabilities but also its integration within the existing ecosystem and ease of use with other tools. In this regard, Microsoft's ecosystem of business tools is robust.
The integration of ChatGPT with Excel could be a significant advantage for Microsoft, increasing its popularity and expanding its market share. With ChatGPT's artificial intelligence capabilities, Excel could become a more powerful and user-friendly BI tool than any other available. If GPT is integrated into other MS Office products like Outlook and PowerPoint, users could enjoy a completely new experience without having to acquire multiple tools.
Chat GPT-4, also known as GPT-4, is a large-scale multimodal model that harnesses the capabilities of artificial intelligence and natural language processing (NLP) to provide natural language responses to a wide range of questions. It possesses various other abilities, such as programming, translation, text correction, article generation, spreadsheet creation, and more.
The acronym "GPT" stands for Generative Pre-trained Transformer, a deep learning technology that employs artificial neural networks to generate text in a human-like manner.
According to OpenAI, the capabilities of Chat GPT-4 closely resemble those of humans. While it may be less proficient than humans in certain real-world scenarios, it demonstrates human-level performance in numerous professional and academic benchmark tests.
How to Use Chat GPT-4:
Currently, GPT-4 is only available in the paid subscription ChatGPT Plus, while the free version of ChatGPT continues to utilize GPT-3.5.
GPT-4 will also be accessible as an API, allowing developers to integrate it into the creation of applications and services.
The easiest way to start using GPT-4 for free is through Bing Chat. Following the announcement of the collaboration between Microsoft and OpenAI, it was confirmed that Bing's free-to-use chatbot is already powered by GPT-4.
For users subscribed to ChatGPT Plus, using Chat GPT-4 is identical to using GPT-3.5: simply express your needs and wait a few seconds for the algorithm to provide a response.
GPT-4: New Capabilities:
OpenAI has highlighted that GPT-4 offers significant improvements in three key areas: creativity, handling visual information, and context breadth.
Regarding creativity, GPT-4 excels in creative projects and collaboration, including music composition, scriptwriting, technical writing, and adapting to a user's writing style.
In terms of context breadth, GPT-4 can process up to 25,000 words of user text and interact with web page content through user-provided links, facilitating the creation of extensive content and conversations.
A standout feature of GPT-4 is its ability to process images, allowing users to pose questions or requests related to images.
OpenAI claims that GPT-4 is safer than GPT-3.5, with a 40% increase in objective responses and an 82% reduced likelihood of responding to inappropriate content requests.
Limitations of Chat GPT-4:
OpenAI acknowledges that GPT-4, like previous versions, still faces challenges related to social biases, generating incorrect responses, and handling inappropriate requests.
The term "generating incorrect responses" refers to false responses that GPT-3 and GPT-3.5 used to produce when lacking precise information. GPT-4 improves in this aspect, with a 40% higher likelihood of providing accurate responses compared to GPT-3.5, but it can still make inaccuracies.
Differences between Chat GPT-4 and Chat GPT-3.5:
While explicit details about the changes in GPT-4 are not provided, the emphasis is on its improvements compared to GPT-3.5 in terms of processing speed, ability to handle up to 25,000 words, and enhanced comprehension of detailed instructions. GPT-4 is also considered smarter, less prone to errors, and more resistant to inappropriate requests.
In addition to these improvements, GPT-4 introduces new functionalities, including its capacity to comprehend images, enhanced programming capabilities, and the ability to excel in more complex exams such as the Uniform Bar and Biology Olympiad. It also demonstrates a better understanding of humor and the ability to explain why something is funny.
The concept of the Metaverse originated in Neal Stephenson's science fiction novel "Snow Crash" in 1992. In this groundbreaking work, the term was used to describe a virtual world of equal importance to the physical world. The protagonist, Hiroaki, a pizza delivery driver in the physical reality and a samurai in the Metaverse, confronts a cyber threat in this virtual realm and battles the villain behind it.
Since its inception in science fiction literature, the Metaverse has gained significance in popular culture and has piqued the interest of major technology companies like Facebook, Microsoft, and Nvidia, as well as the general public.
Today, the Metaverse is envisioned as a virtual world expected to expand in the digital realm and resemble the physical world. This entails not only aesthetics but also the possibility for individuals to live out their daily lives and engage in social interactions similar to those in the physical world.
To make this vision a reality, technological advancements are required to enable total immersion in the digital environment. Virtual reality (VR) devices, artificial intelligence, and platform interoperability are key elements in this evolution. Artificial intelligence would be utilized to expand the analytical and metaphysical capabilities of the digital environment.
Interoperability, which involves the seamless coexistence of multiple systems, software, machines, and digital services, is essential for the functioning of the Metaverse. Additionally, significant data exchange will be necessary, necessitating the expansion of data science and data analysis, as well as tools like ETL processes and data warehouses to integrate and manage large amounts of information. Collectively, these technologies and concepts are pivotal in bringing to life the idea of a Metaverse where people can live, work, socialize, and engage in everyday activities similar to those in the physical world.