Machine Learning: A Definition

Machine Learning is one of the great technologies of artificial intelligence. It is a computer programming method that uses algorithms to allow computers to learn independently without net programming. To be more precise, Machine Learning is based on the exploitation of data, favoring pattern recognition. 

The First Algorithms

The first Machine Learning algorithms are not recent. Some were created in the 1950s, the Perceptron being their best known.

Goals

The objective of Machine Learning is straightforward: how to “teach computers to learn” and thus act as humans do by perfecting their way of learning and their knowledge autonomously over time? Where a traditional program makes precise instructions, a Machine Learning algorithm learns from its experience and improves its performance over time. The main objective is to see computers act and react without being programmed beforehand.

Machine Learning Big Data

The potential of Machine Learning is revealed, among other things, for Big Data, in situations where trends must be spotted from a large amount of diverse and varied data. Machine learning is preferred over traditional methods when such a large amount of data needs to be analyzed. This is thanks to its power in terms of speed and accuracy (the more data it has, the more accurate it becomes).

Machine Learning is essential for discovering patterns within the enormous databases available. It can extract data from complex information sources without human intervention.

Example of Using Machine Learning

The autonomous vehicle is an excellent example of Machine Learning. Indeed, such a vehicle has many cameras, radars and a lidar sensor. Equipment that allows:

• To use a GPS to pinpoint the vehicle’s location with great precision regularly.
• To analyze the section of road located in front of the car.
• And to detect moving or fixed objects located at the rear or sides of the car.

A central computer installed in the vehicle constantly collects and analyzes this information and classifies it in the same way as the human brain’s neural networks.

How Machine Learning Works

As early as the 2010s, Machine Learning quickly became the most widely used form of artificial intelligence. Using statistical algorithms, it teaches a machine to recognize an image, interpret a text, trade, forecast sales or even recommend products or content that correspond perfectly to the preferences of Internet users.

Operating a Machine Learning model requires four key steps. Typically, this process is handled by a Data Scientist.

Training Data or Training Dataset

The training dataset is the first tool used by business intelligence and information technology (IT) specialists. It requires consequent exploitation of a database. This is the first phase of machine learning for AI before the model concerned goes into production.

To develop this phase, one must choose and appreciate training data. Data will essentially aim to allow the Machine Learning model to acquire notions in solving its designed problems. Labeling this data to indicate to the model the characteristics to identify is possible. Otherwise, the model must spot and extract the recurring features from itself. The data must be meticulously prepared, organized, and cleaned to avoid machine learning model training failure. Otherwise, future predictions will be directly affected.

A Software Framework

You can use a Machine Learning framework to develop your own AI to exploit your data better. Because with the help of different frameworks, access to this technology has been more accessible over the years.

An Algorithm Adapted To The Expected Result.

This step involves selecting an algorithm to run on the training data set. The choice of algorithm depends on the type and volume of training data and the type of problem to be solved. This algorithm must therefore be compatible with the desired result (forecast, qualification of the content of an image, text, etc.).

The chosen algorithm requires training. It is an iterative process during which the weights and the bias will be modified to optimize the result’s precision. Thus trained, the algorithm represents the Machine Learning model.

A Deployment Environment

The last phase involves optimizing the model by applying it to new data. For example, a machine learning model to spot spam will be used on emails. On the other hand, a Machine Learning model of a robot vacuum cleaner will use data from real-world interactions like moving furniture or adding new objects to the room. Its performance and accuracy can thus increase over time.

According to several scientists, Machine Learning represents the only acceptable form of AI as it incorporates a central function of human intelligence: learning. But for others, it’s just a family of AI technologies to solve a finite number of problems.

Machine Learning Algorithms

Multiple and diverse Machine Learning algorithms are divided into two main categories: supervised and unsupervised, depending on whether or not it is necessary to label the data beforehand.

When it comes to supervised learning, the data used is already labeled. This is often the case with structured data from company management systems (e.g. credit repayment or not, mechanical breakdown or not). Therefore, the machine learning model knows what to look for (pattern, element…) in this data. Once the training is complete, the trained model can detect the same elements on unlabeled data.

Some examples of Machine Learning algorithms.

Regression Algorithms (Linear or Logistic)

Linear or logistic are the least powerful but most easily interpretable algorithms. They make it possible to understand the relationships between the data.

Represented classically in the form of straight lines on a graph, the role of linear regression is to determine the value of a variable to be predicted, also called the “dependent variable”, from the value of one or more other explanatory variables, also called “independent variables”. The terms dependent/independent come from the assumption that the dependent variable depends on the independent variables, which they do not depend on (the road accident rate depends on the consumption of alcohol and not the reverse). An example of linear regression is predicting a salesperson’s annual sales based on their level of education or experience. 

Logistic regression is used when the dependent variables are binary. When the dependent variables are more difficult to classify, other types of regression algorithms, such as the Support Vector Machine, are used.

The Decision Tree Algorithm

A decision tree is a decision support tool. The set of choices is represented in the graphical form of a tree, hence its name. We find the different possible decisions at the ends of the branches and the tree leaves. This tool can be readable and quick to execute but also automatically calculable by supervised learning algorithms.

Clustering Algorithms

Clustering is a machine learning method of grouping data points. Data clustering is a data analysis method used in unsupervised learning. Clustering algorithms are thus used to divide data into subgroups called clusters. The purpose of data partitioning is to divide a set of data into different homogeneous groups so that each subset has common characteristics, according to so-called proximity criteria.

Association Algorithms

They aim to discover the patterns and relationships between data and identify “if/then” relationships, called association rules. These are rules similar to those of Data Mining.

This is the case, for example, of the Apriori algorithm, which can be used by sales teams seeking to determine which product will be chosen with another by a customer. In practice, more advanced algorithms such as collaborative filtering (User-User, Item-Item), bandit algorithms on A/B testing or matrix factorization suggest other purchases when you browse a site. E-commerce.

Dimensional Reduction Algorithms

This is a very classic algorithm called Principal Component Analysis. Its purpose is to determine the directions where the data set has the most variances and to protect the data linearly in those directions.

Dimension reduction can also be made with reinforcement learning. Here, the algorithm learns by trying to reach its goal multiple times.

Also Read: Machine Learning And Deep Learning Increasingly Necessary For Companies

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Before explaining these two technologies, it is necessary to remember the definition of their origin: Artificial Intelligence. This resides in the capacity of a machine to process the data it captures, which in turn are the result of previous experiences. This processing is similar to the functioning of the human brain, which captures information and transforms it to generate knowledge.

What Are Machine Learning And Deep Learning?

First of all, machine learning, as a derivative of AI, involves the creation of algorithms that can modify themselves without human presence.

Machine learning is a data analysis method based on the premise that our systems learn from that data. Through such a method, plans will be able to identify patterns and make decisions without any intervention on our part.

Deep Learning

On the other hand, deep learning is a type of machine learning whose function is to train a system to learn by itself. This competition is possible by recognising patterns and executing tasks such as those that we human beings do. As a relevant fact, this branch of AI uses a specific class of algorithms called neural networks.

Although the algorithms are created and work similarly to machine learning, multiple layers of neural networks are responsible for individually providing a different interpretation of the data. These networks have the purpose of trying to imitate the function of the neural networks of our brain. This includes voice recognition, object detection, and image identification. Voice assistants such as Amazon’s Alexa or Apple’s Siri are based on this technology. Even biometric recognition systems for fingerprints, face, voice, etc., also have this type of technology.

Machine Learning And Deep Learning Have Different Capabilities.

A deep learning model is designed to continuously perform data analysis while maintaining a logical structure similar to a human being. To achieve this type of analysis, deep learning must use layers of algorithmic systems, those mentioned above, artificial neural networks. These networks are the ones that allow much more advanced knowledge than the basic machine learning models. Deep learning facilitates the automation of training processes and is capable of creating its criteria automatically, altogether dispensing with human intervention.

In short, machine learning and deep learning are almost the same, as they work in the same way but have different capabilities. Although the basic models of machine learning are continually evolving, their functions still require monitoring on our part. Suppose an artificial intelligence algorithm gives an incorrect prediction. As a result, we will have to intervene and apply the necessary adjustments. By having this model, an algorithm will be able to determine if a prediction is incorrect by utilizing its neural network.

Advantages Of Machine Learning and Deep Learning

Many organizations have basic or advanced Artificial Intelligence applications, and their use continues to spread.

Regardless of the productive sector or the size of these companies, implementing this technology helps solve common day-to-day problems to the most complex. For this reason, this technology has a very positive impact on efficiency and profitability.

In particular, companies that manage large amounts of data must rely on machine learning and deep learning; since these resources can be used in different areas, from finance and health to marketing and sales.

We can summarize part of the advantages of AI in these areas:

• Speed ​​in the management and processing of data and identification of relevant information.
• Ability to analyze consumer behavior with greater precision.
• Fraud detection and prevention, specifically in the banking and insurance sector.

Even machine learning and deep learning support making the right decisions in companies. Likewise, they increase the capacity for efficient and intelligent work, reducing the percentage of human error and adding competitive advantages.

How Can Machine Learning And Deep Learning Be Helpful in Our Company?

Machine learning and deep learning contribute decisively to our company obtaining scalability, more excellent performance, and cost and time savings. In addition, these technologies can also provide the following benefits:

• They personalized customer service. It allows analyzing user preferences so that personalized products can be offered automatically. In this way, the perception that customers have of our company is improved, thus enhancing loyalty. For example, platforms such as Netflix, YouTube and Spotify constantly use this technology to suggest other content based on what we have enjoyed. 
• Process automation. Of course, one of the most relevant contributions of the two technologies that we analyze is the automation of routine tasks. The latter absorbs a lot of time and effort from human talent and does not provide added value. Using machine learning, our systems can detect the processes they must deal with.
• Reduce errors. The automatic learning of the management systems applied in the organization means that the mistakes made are not repeated. The longer it stays in the system, the more resilient it will be.
• Preventive actions. Based on the above, machine learning tools can prevent bugs and errors. Artificial Intelligence can exclude any action that compromises or puts the development of products or services at risk.

Other Important Uses

• Cybersecurity. Undoubtedly, the contribution of this technology to the protection of networks, systems and terminals of organizations against risks of cyberattacks is significant. It should be noted that most malware uses similar code, so the use of machine learning can prevent their meddling.
• Fraud detection. Thanks to the technologies we are dealing with, it is possible to detect which transactions are legitimate and which are not accessible. It is even feasible to reveal the mismanagement of resources. Such a function is achievable when a pattern is assigned to financial movements.
• Medical diagnoses. When implemented in the technological tools of the health system, these technologies help insurers be more intuitive about possible health problems, depending on the frequency of medical consultations. Apart from that, these technologies offer more reasonable costs and recommend different medication options, among others.
• Improves the security and integrity of information. Cloud storage is another service that facilitates these two strands of AI.

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What do I do with artificial intelligence, machine learning, data science, and progress through digitization as a controller? – More than you think!

Companies worldwide are increasingly feeling the need to integrate new, data-based technologies to remain competitive. The use of these technologies implies far-reaching changes in the company’s internal handling of data, affecting control. You can check ProjectPro Machine Learning Projects to learn what kind of machine learning project is used by some biggest companies.

Do not be afraid of these changes, but seize the opportunity and make yourself indispensable for the upcoming transformation. Innovations in the use of data are difficult to implement without support from the specialist area. It is not uncommon for projects to fail due to a lack of a common basis for communication.

If not you as a controller, who is better suited to act as an interface between the department and data science? Their technical expertise is more in demand than ever because they are familiar with business practice and company data.

Actively Helping To Shape Progress

Prepare yourself in good time for future requirements and actively shape your company’s future! A first step in the right direction is to get a realistic picture of the job of a data scientist.

Build Up Knowledge – Assess Benefits

Brush up on your basic statistical knowledge from your school and university days! You can use various options for this:

Print And Online Media To Build Up Basic Knowledge

Numerous print and online media entertainingly convey the basics and largely do without mathematical jargon and complicated formulas. Familiarize yourself with how basic statistical techniques work. So you can have a say when it comes to correlations, regressions, classifications, and clustering methods.

Once you have established a basic understanding, you will soon understand machine learning, neural networks, and artificial intelligence (AI) principles. You will find that this is not rocket science or sheer magic.

Online Courses For Deeper Insights Into Practice

To delve deeper into practice, the Internet has a variety of free or inexpensive online courses available. These offer an easy introduction to coding with Python or R and other data science applications.

You do not have to complete retraining to become a data scientist, and a rough understanding of the instruments and the possibilities is sufficient. In this way, you reduce reservations and better assess the added value of data science. 

Promotion By The Employer

Coping with such a build-up of knowledge in addition to professional and private obligations is undoubtedly a challenge. Here, the employer must be made aware of further training measures. Actively claim your funding. Do not wait until the topic has taken you by surprise and suddenly you are confronted with data scientists as work colleagues.

If this is already the case, treat them with suspicion and interest. They can learn a lot from each other and benefit from them. If your employer offers further training on its initiative, you should take advantage of them. In this way, you do not get sidelined with new developments in the company.

How AI and Machine Learning Can Be Used In Controlling

As soon as you have recognized the potential of data science, you can actively help shape innovations and act as the linchpin for new projects. Machine learning and deep learning in controlling make everyday work easier and relieve you of annoying repetitive tasks.

Time-consuming activities that follow fixed procedures and rules and require a great deal of attention can often be automated relatively easily. Machine learning and AI have proven themselves many times in the finance and accounting departments and the creation of reports and dashboards.

As a controller, you do not have to fear a loss of importance in your job. As an expert, you have an exclusive understanding of the business processes based on the numbers. Combined with your acquired basic understanding of data science, you make yourself indispensable for your company. Only you can deliver solutions where algorithms fail.

In the meantime, you can concentrate on your core task as a controller and provide important impulses for planning and controlling company processes. In this way, you can locate the control part more strongly in control.

It is all the more important to drive change in your own company in these dynamic times. Therefore, the focus of our online conference Digital Finance & Controlling this year is on the successful digitization of the finance sector.

Get to know the DNA of a digital finance area and find out which software can support you in your processes. The event is now available on-demand.
Although algorithms are superior to people when it comes to the systematic processing of large amounts of data, they can only produce meaningful results based on fixed rules and unambiguous data. They are good at recognizing patterns of relationships and deriving rules from them but fail in unforeseen events that do not follow any structure.

The correct classification of such events and the corresponding reaction can only be mastered by actual intelligence. This is where you come into play as a “human in the loop.” Only you have a feel for when algorithms are wrong.

With your knowledge of the limits of technology, you protect your company from consequential decisions made due to blind trust in algorithms. Here, too, control by capable controllers is required.

How Does Machine Learning Work?

Gain a realistic idea of ​​machine learning and its possibilities! Free yourself from exaggerated expectations and gloomy future scenarios from science fiction!

Machine learning is currently the most prominent aspect of the sub-area of ​​computer science dedicated to imitating human behavior: artificial intelligence.

The initial attempt to achieve the set goal by programming complex rules soon reached its limits, as social behavior can only be mapped to a limited extent by static rules. Machine learning takes an innovative way to solve this problem.

With the help of special algorithms, this approach automatically derives rules from data for which results are already available. These rules can, in turn, be used to forecast potential results for data for which they are not yet available (predictive analytics).

Machine learning can therefore be understood as the automated programming of software solutions for data processing:

Also Read: What are Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?

What Is Deep Learning?

Deep learning works on the same principle as machine learning, with the difference that data is processed with so-called artificial neural networks. These neural networks extract and compress data into a form that makes it easier and faster for computers to access the information it contains.

The use of neural networks has proven itself in the processing of audiovisual data (speech, image, document, and video recognition) but is not limited to these types of data.

The idea for artificial neural networks for information processing was formulated as early as the late 1940s. Still, it has only been relatively recently that technological progress and the lower prices for high-performance computer processors have made it possible to use this technology cost-effectively.

Neural networks consist of layers of simple, functional units, so-called perceptrons, which receive signals and send out signals when threshold values ​​are exceeded.

To use a neural network to be referred to with the media-relevant term deep learning, there must be at least one additional layer (hidden layer) between an input and an output layer.

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Industrialization, Traceability and Verifiability In Machine Learning

In the first place, it started from the premise that, when implementing Machine Learning models, especially in the banking sector, “we need the models to be traceable, reproducible and verifiable”, as well as industrialized.

This industrialization makes it possible to standardize the processes that usually occur in all Machine Learning projects, to be agile while guaranteeing the three aspects mentioned above and reducing the cost of maintenance of the models.

The expert gave an example: “at the bank, we have to be able to answer why a person was denied a loan, tracing the path from the data to the score issued by the model.” To do this, it is necessary to know which version of the model is in production and what data was used or where the predictions were stored. Several versions of data are usually saved, associated with the models to cover the traceability and reproducibility part. Those are in production at all times.

On the other hand, verifiability is handled by a committee in which different bank areas intervene ( model owner, risks, legal, etc.). The Machine Learning model cannot go into production if the committee does not approve it. In addition, other business decisions are made: decision thresholds, when to launch or when to retrain the model. Check out this Best Machine Learning Course, taught by industry experts who have mastered this domain and have many years of experience in the industry.

Analysis and Design of The Machine Learning Model

As Experts explained, the design and development of a Machine Learning model are governed by a series of requirements: that it be simple, monitorable, interpretable, that it is not biased, that the input variables comply with the regulation and that it is adjusted to the case usage and operational restrictions.

All this means taking into account some aspects and addressing some challenges in the different phases of the process:

• Definition of the use case in which different areas are involved. Several fundamental questions are answered for the development of the model: what variables and what samples can be used, if there are legal restrictions that limit the use of the model, if the model is going to work in batch mode or real-time, as well as the technology necessary for it.
• According to the expert, the analysis of the target population is one of the phases that takes the longest. First, it is necessary to decide on which population the model is going to train and which one will be applied, with the possibility that it has not been historically dealt with. Then the availability of variables is studied, and the target is defined, which must be aligned with business and risks in terms of criteria, among other things.
• Data splitting or data division in the train, test and validation sets. It is decided how to make the cuts (temporarily, grouped or stratified), always keeping in mind that they are compatible.
• Possible preselection of variables. Although the selection of variables is still made on the training data, it is possible to make a distributed preselection to reduce the volume of data.
• Model training and predictions. Openbank has its flexible Auto-ML tool to adapt to the variety of use cases that are addressed. Here you have to know how to adjust the parameters to ensure traceability and reproducibility and avoid black boxes.
• Interpretability, for which they also have their tool. Once the model has been trained, an attempt is made to answer and explain, for example, why a particular score has been assigned to a client. In addition, this same tool can be applied to models that have not been implemented.
• Monitoring, of two types: the classic one that does business with its KPIs to make a standard follow-up of the improvements in the industry or, from a more technical point of view, aimed at measuring the so-called data shift.
• Possible biases. According to the expert, they can no longer afford to develop biased models, and she believes that it is necessary to define, from company policy, what type of fairness is to be achieved, using various strategies to maximize profit with restrictions.

As we can see, a Machine Learning project in the company cannot be limited to developing and training a helpful model. It is necessary to attend to a series of considerations before and during the process: for example, that the models fit the objective, but that they can also be generalized to be more efficient or not lose sight of legal or ethical issues.

Also Read: Clarifying The Concepts Of Various Technology Terms – Artificial Intelligence, Deep Learning, Machine Learning, Big Data, and Data Science

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Artificial Intelligence (AI)

AI was first discovered by John McCarthy in 1956 contains machines that can perform functions specific to the human mind. Although it is very general, it involves planning, language comprehension, knowledge of objects and sounds, learning, and problem-solving. AI can also be described as a computer, machine, or program that can perform tasks that normally require human intelligence. It can be anything from voice recognition, visual perception, or decision making. Human intelligence performed by machines simply.

Machine learning (ML)

Machine Learning is used to achieve AI and is a research area within AI that aims to develop machines’ ability to independently understand and handle large amounts of data. The technology is based on algorithms (instead of code) to analyze and learn from the data to make a decision.

Deep Learning (DL)

Deep Learning has “grown out of” Machine Learning. It is an artificial network where the algorithms work as when the neurons of the human brain communicate with each other, with the difference that there is a clear structure from the beginning. It is thanks to Deep Learning that there are self-driving cars, that we can see and prevent diseases and that, for example, Spotify and Netflix can give us accurate music and movie tips.
Example of the difference between Artificial Intelligence (AI) and Machine learning (ML)
Let it be said that you are facing an investment decision and ML can give you advice regarding, for example, an investment in a specific share. But AI can answer that you should not buy shares at all, but gold because AI “thinks for itself” and develops the underlying model.

An alternative description of AI, ML, and DL

There are also other definitions of AI, Ml, and DL where AI is the first wave, ML the second wave, and DL is the third. Examples of this can be that AI is used to recognize people’s emotions from images, ML algorithms bring the images into the system. DL (Deep Learning) would then recognize patterns in faces and emotions from these images.

Business systems and AI

Common areas where we find AI today are customer service and sales automation. A common goal for most companies is to improve customer satisfaction. Take Amazon Dash Replenishment, for example. When your printer from HP, Epson, Brother, or another manufacturer has some ink, a message is sent directly to Amazon to deliver new cartridges.

Also Read: Clarifying The Concepts Of Various Technology Terms – Artificial Intelligence, Deep Learning, Machine Learning, Big Data, And Data Science

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The End of Experimentation

Over the last few years, industries have been largely experimenting with AI/ML to find out how best to use it. As the technologies become more accessible, experimentation is gradually coming to an end. Healthcare, transportation, finance, and manufacturing brands alone are already moving past the experimentation stage and have adopted mass-scale adoption in a variety of different ways. SME brands in all sectors should be watching those bigger business models and taking note. The potential for ML and AI to level the playing field is just as impactful as social media was for marketing. That means that despite the inherent challenges, even the smallest of enterprises will be able to compete with the big names through the right applications of AI and ML.

Finding Team Members

One of the problems for business owners is that hiring tech specialists can be very challenging. That’s always down to a lack of understanding of what skills to look out for and the kinds of training applicants should have to perform AI/ML-related functions. That’s where research is going to come in. Tech buzzwords on a resume can look impressive, but without understanding what those terms mean, they can quickly lead to hiring mistakes that hold a company back from growth. While you should look for potential new employees with practical experience in AI and ML, formalized qualifications can be a lot more advantageous to watch for. Look for those applicants with Azure Machine Learning Studio training and who have used that training practically. The more you know what AI and ML can do for you, the easier it will be to fine-tune the relevant skills, training, and experience that you should be looking for.

Metadata – The New Gold

Big data has been called the ‘new oil’ for the last few years in almost every tech-related opinion piece and trend-spotting article. While the value of big data is indisputable, it’s metadata that will be joining the dots between big data and machine learning output. Labeling metadata is becoming the norm and is going to result in SMEs being better able to streamline those everyday business tasks and processes. One of the key challenges of big data is gleaning actionable insights from it. ML and AI are set to make big data more accessible, easier to read, and easier to action via labeled metadata that can transform any SME’s future.

Artificial intelligence and Machine Learning are not sci-fi concepts any longer. They are accessible technologies that even the smallest business models can leverage into high-value tools. As a result, they will only become more visible in workplaces around the world.

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Without a doubt, the most cutting-edge technological trend of recent years is everything related to artificial intelligence and data analysis. And it is that relatively recently there have been great advances in this field, which together with the availability of enormous amounts of data and increasing computing power are giving rise to all kinds of very interesting practical applications.

The problem comes when the terms related to the field become marketing empty words that in many cases are outright lies. It is very common to talk that this or that product uses artificial intelligence to achieve something and, sometimes, they are conventional algorithms making predictable decisions.

What is Artificial Intelligence?

Artificial intelligence (AI) was born as a science many years ago when the possibilities of computers were really limited, and it refers to making machines simulate the functions of the human brain.

AI is classified into two categories based on its capabilities:

• General (or strong) AI: that tries to achieve machines/software capable of having intelligence in the broadest sense of the word, in activities that involve understanding, thinking, and reasoning on general issues, on things that any human being can do.
• Narrow (or weak) AI: which focuses on providing intelligence to a machine/software within a very specific and closed area or for a very specific task.

Thus, for example, a strong AI would be able to learn by itself and without external intervention to play any board game that we “put before it”, while a weak AI would learn to play a specific game like chess or chess. Go. What’s more, a hypothetical strong AI would understand what the game is, what the objective is, and how to play it, while the weak AI, although it plays Go better than anyone else (a tremendously complicated game), will not really have a clue what it is doing.

One of the crucial questions when it comes to distinguishing an artificial intelligence system from mere traditional software (complex as it may be, which brings us to the jokes above) is that AI “programs” itself. That is, it does not consist of a series of predictable logical sequences, but rather they have the ability to generate logical reasoning, learning, and self-correction on their own.

The field has come a long way in these years and we have weak AIs capable of doing incredible things. Strong AIs remain a researcher’s dream and the basis of the scripts for many science fiction novels and films.

What is Machine Learning?

Machine Learning (ML) or machine learning is considered a subset of artificial intelligence. This is one of the ways we have to make machines learn and “think” like humans. As its name suggests, ML techniques are used when we want machines to learn from the information we provide them. It is analogous to how human babies learn: based on observation, trial, and error. They are provided with enough data so that they can learn a certain and limited task (remember: weak AI), and then they are able to apply that knowledge to new data, correcting themselves and learning more over time.

There are many ways to teach a machine to “learn”: supervised, unsupervised, semi-supervised, and reinforcement learning techniques, depending on whether the correct solution is given to the algorithm while it is learning, it is not given the solution, it is Sometimes you give or are only scored based on how well or poorly you do, respectively. And there are many algorithms that can be used for different types of problems: prediction, classification, regression, etc …

You may have heard of algorithms such as simple or polynomial linear regression, support vector machines, decision trees, Random Forest, K nearest neighbors … These are just some of the common algorithms used in ML. But there are many more.

But knowing these algorithms and what they are for (to train the model) is just one of the things that need to be known. Before it is also very important to learn how to obtain and load the data, do an exploratory analysis of the same, clean the information … The quality of the learning depends on the quality of the data, or as they say in ML: “Garbage enters, garbage comes out”.

Today, the Machine Learning libraries for Python and R have evolved a lot, so even a developer with no knowledge of mathematics or statistics beyond that of the institute, can build, train, test, deploy and use ML models for applications of the real world. Although it is very important to know all the processes well and understand how all these algorithms work to make good decisions when selecting the most appropriate for each problem.

What is Deep Learning?

Within Machine Learning there is a branch called Deep Learning (DL) that has a different approach when creating machine learning. Their techniques are based on the use of what are called artificial neural networks. The “deep” refers to the fact that current techniques are capable of creating networks of many neural layers deep, achieving unthinkable results a little more than a decade ago, since great advances have been made since 2010, together with large improvements in computing power.

In recent years Deep Learning has been applied with overwhelming success to activities related to speech recognition, language processing, computer vision, machine translation, content filtering, medical image analysis, bioinformatics, drug design … obtaining results equal to or better than those of human experts in the field of application. Although you don’t have to go to such specialized things to see it in action: from Netflix recommendations to your interactions with your voice assistant (Alexa, Siri, or Google assistant) to mobile applications that change your face … They all use Deep Learning to function.

In general, it is often said (take it with a grain of salt) that if the information you have is relatively little and the number of variables that come into play is relatively small, general ML techniques are best suited to solve the problem. But if you have huge amounts of data to train the network and there are thousands of variables involved, then Deep Learning is the way to go. Now, you must bear in mind that the DL is more difficult to implement, it takes more time to train the models and it needs much more computing power (they usually “pull” GPUs, graphics processors optimized for this task), but the problems are usually more complex as well.

What is Big Data?

The concept of Big data is much easier to understand. In simple words, this discipline groups the techniques necessary to capture, store, homogenize, transfer, consult, visualize, and analyze data on a large scale and in a systematic way.

Think, for example, of the data from thousands of sensors in a country’s electrical network that send data every second to be analyzed, or the information generated by a social network such as Facebook or Twitter with hundreds (or thousands) of millions of users. We are talking about huge and continuous volumes that are not suitable for use with traditional data processing systems, such as SQL databases or SPSS-style statistics packages.

Big Data is traditionally characterized by 3 V:

• The high volume of information. For example, Facebook has 2 billion users and Twitter about 400 million, who are constantly providing information to these social networks in very high volumes, and it is necessary to store and manage it.
• Speed: following the example of social networks, every day Facebook collects around 1 billion photos and Twitter manages more than 500 million tweets, not counting likes and many other data. Big Data deals with that speed data receiving and processing so that it can flow and be processed properly without bottlenecks.
• Variety: the infinity of different types of data can be received, some structured (such as a sensor reading, or alike ) and others unstructured (such as an image, the content of a tweet, or a voice recording). Big Data techniques must deal with all of them, manage, classify, and homogenize them.

Another of the great challenges associated with the collection of this type of massive information has to do with the privacy and security of said information, as well as the quality of the data to avoid biases of all kinds.

As you can see, the techniques and knowledge necessary to do Big Data have nothing to do with those required for AI, ML, or DL, although the term is often used very lightly.

These data can feed the algorithms used in the previous techniques, that is, they can be the source of information from which specialized models of Machine Learning or Deep Learning are fed. But they can also be used in other ways, which leads us to …

What is Data Science?

When we talk about data science, we refer in many cases to the extraction of relevant information from data sets, also called KDD ( Knowledge Discovery in Databases, knowledge discovery in databases). It uses various techniques from many fields: mathematics, programming, statistical modeling, data visualization, pattern recognition, and learning, uncertainty modeling, data storage, and cloud computing.

Data science can also refer, more broadly,  to the methods, processes, and systems that involve data processing for this extraction of knowledge. It can include statistical techniques and data analysis to intelligent models that learn “by themselves” (unsupervised), which would also be part of Machine Learning. In fact, this term can be confused with data mining  (more fashionable a few years ago) or with Machine Learning itself.

Data science experts (often called data scientists ) focus on solving problems involving complex data, looking for patterns in the information, relevant correlations, and ultimately, gaining insight from the data. They are usually experts in math, statistics, and programming (although they don’t have to be experts in all three).

Unlike experts in Artificial Intelligence (or Machine Learning or Deep Learning ), who seek to generalize the solution to problems through machine learning, data scientists generate particular and specific knowledge from the data from which they start. Which is a substantial difference in approach, and in the knowledge and techniques required for each specialization.

The post Clarifying The Concepts Of Various Technology Terms – Artificial Intelligence, Deep Learning, Machine Learning, Big Data, and Data Science appeared first on TechReviewsCorner.

Background to the creation

The history of bitcoin begins in 2008. Satoshi Nakamoto – or one or more developers who went public with this pseudonym – publish a concept for a new digital currency in a mailing list among cryptography fans that should work independently of banks. This font, titled ‘ Bitcoin: A Peer-to-Peer Electronic Cash System ‘, has been called a ‘manifest’ over time and, on nine pages, clearly shows how transactions can only be done using cryptographic encryption and without the trust previously required in the business partner or at least the implementing financial institution are possible.

First steps

It should take a few more months before Bitcoin finally discovered the light of its digital world; on January 3, 2009, the time had finally come. In the first so-called ‘Genesis’ block, the message ‘The Times 03 / Jan / 2009 Chancellor on brink of second bailout for bank’ was encoded, which should probably indicate with a wink at the desired position of the new currency against the conventional banking system Support among the general population was waning.

The train is rolling

Initially, there was little interest in Bitcoin among the general public, outside of cryptography circles, nobody could do anything with the keywords ‘blockchain’, ‘decentralized currency’ or ‘proof of work’. This changed abruptly in 2011: After the price of Bitcoin had leveled with the US dollar, many investors were listening and the first big money flowed into the new project. you can look at the bitcoin database by satoshi nakamoto.

Bitcoin arrives in public

Bitcoin will show enormous growth in the next few years – in addition to the number of traders who accept BTC, the number of so-called old coins will increase. Among these alternatives to the original Bitcoin is Ethereum, which comes with a modified concept and the possibility of executing new ‘smart contracts’.

The summit

2017 was a big year for Bitcoin. At the beginning of the year, the division into the original Bitcoin BTC and Bitcoin Cash BCH was pending, but in December completely different things hit the headlines: for the first time, the cryptocurrency reached a value of almost $ 20,000.

Also Read: Dangers Of Hotspots

The descent

After the spectacular all-time high, the air is out and in a no less rapid downturn, a not inconsiderable proportion of investors lose a fortune. After a short-term low of around 3,200 US dollars was reached, things started to move up and down slowly in a more positive direction, which is still more or less stable to this day.

The post How Long Has Bitcoin Been Around? – Overview appeared first on TechReviewsCorner.

Technology-based on combining images of the real world with virtual images through the camera of a mobile device or on the screen of a computer. Its purpose is to add virtual information to the reality and existing and aims to provide as much useful information as well as create excitement and admiration in products and services and is a marketing technique. For example, visualize how some furniture would look in our living room without having to buy it. 

Examples and more information

Microsoft has already made clear its intentions regarding augmented reality and the various uses it can have, using holograms. 

Virtual reality

It is the simulation in real-time of a virtual world developed by a computer or computer program.

The user is able to immerse himself in imaginary worlds where he represents physical objects and places with which he can interact. Normally the senses that are most focused on are sight and hearing creating unique experiences. 

Examples and more information

One of the objects that are capable of projecting this reality is the Oculus Rift glasses. 

Also Read: AI In ECommerce will achieve Huge improvement in customer satisfaction

The post What Exactly Mean By Augmented Reality & Virtual Reality appeared first on TechReviewsCorner.

This has put the attention of cybersecurity on the telecommuter, and on such applications. Zoom, the video conferencing application we referenced, went from supporting somewhere in the range of 10 million meetings every day in December 2019 to in excess of 300 million in April, which gives us an away from ​​the marvel.

During these weeks there has been a ton of discussion about security dangers, both at home or in the remote working environment, just as those related with various application vulnerabilities (and, once more, this is the situation with Zoom). All things considered, whose obligation is it in the field of cybersecurity?

Legitimate risk in cybersecurity

Raising duty regarding security issues is an incredible inquiry, however, the appropriate response is that it depends. It relies upon the relevant enactment, for instance. What number of workers truly comprehend that utilizing an apparatus can cause a security issue? What number are sufficiently prepared in the utilization of the innovation that is being conveyed? In what manner can more robotization be presented?

When there is the quick appropriation of innovation, as occurred during the pandemic, there isn’t in every case enough arrangement about security: it isn’t constantly done to the level legally necessary, as it were, which opens organizations to fines and weights of advertising in the event of an infraction.

Then again, the duty regarding security isn’t satisfactorily tended to in the authoritative documentation, neither with the innovation supplier nor with the representative. When in a rush, the lawful terms may not sufficiently address security dangers, including with regards to obligation, review rights, participation around announcing breaks … Sometimes this implies the hazard is (unreasonably) in the possession of the business.

The administration contract is where the reasonable degree of hazard must be built up for each gathering included. Also, with representatives, preparing and clear IT use approaches should help guarantee more prominent responsibility on your part.

The post Who Is Legally Responsible For Cybersecurity? appeared first on TechReviewsCorner.