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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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.

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Currently, the development of artificial intelligence (AI) as an autonomous discipline is undergoing a decisive transition towards these goals. A sufficient level is already shown to bring concrete solutions to the general public and to fill functions previously reserved for the brightest minds.

It has also reached a point of maturity that allows a practical deployment of autonomous specialties, including deep learning.

The Foundations Of Deep Learning

Artificial intelligence as a scientific discipline serves multiple objectives that divide the field of knowledge into more or less autonomous areas. Recognition of three-dimensional objects has little to do with the problems posed by machine translation. But in both cases, algorithms derived from years of work in this scientific field are used.

From the beginnings of artificial intelligence, two fundamental orientations appeared in this field. One aimed to address the material and logical bases of consciousness. That is a complete and mechanical simulation of rational human thought. The other orientation sought to address specific problems to give them in each case a solution derived from automated data processing.

It goes without saying that the second orientation is the one with the most real technology applications at work. The creation of robots that somehow mimic the human mind is still a long way off. But it is another thing to tackle problems where reality can be broken down into numerically treatable data.

Machine learning imitates a set of tests within a parameterizable tightly closed system. With it, he looks for correspondences and relationships that allow predicting a future result. Work with decision trees, apply inductive logic programming, and any effective technique to read, classify and categorize large masses of data.

In these techniques, there is an economy of computing resources. It seeks to maximize the results with a minimum of process load and execution time. The algorithms that are part of the body of knowledge of these specialties aim for a system to find by itself correspondence generating new relationships or dependencies.

Deep learning is a subfield of more recent development, it has a theoretical consistency since 2010. The specificity of this orientation is not to guide systems with complex systems algorithms. It starts with simpler models that are applied to a real case to imitate its operation.

In the case of a complex execution board game like chess in the deep learning application, no instructions are given. It seeks to produce advantages in the game that approximate a victory. To do this, the rules of the game are followed, and by the brute force of trials, the system creates its own rules or criteria to search for the master moves. The good results in this and other practical cases make it to be tested in a multitude of problems presented in real life.

The usual way to produce decision models by patterns found within the system itself is called a neural network system. The name itself suggests the source of inspiration used as the basic scheme: the nervous system. However, this meeting point has its own implementation for each problem that arises.

Also Read: Artificial Intelligence (AI) In Marketing And Sales

Utilities And Main Uses Of Deep Learning

A very promising example of deep learning was that experienced by Google with AlphaZero. It is a large-scale computer system in terms of hardware that aims to teach the machine to play chess in hours at the highest level.

The capacities acquired by the system with this technology were later subjected to a confrontation with a conventional program in its conception but of great performance.

The tests carried out at the end of 2017 have been more than positive, the capabilities acquired by deep learning brilliantly surpassed those given by large teams of programmers to the aforementioned commercial program.

The areas of science where this specialty of artificial intelligence is most interesting are the following :

• In molecular biology the structural analysis of proteins.
• Asset and portfolio management in financial markets.
• Studies of fluid mechanics in aeronautical engineering.
• Discovery of pathological patterns in images taken by magnetic resonance systems.
• Climatic and historical climatology studies.
• Production of new materials and nanotechnology.
• Solving conjectures and mathematical problems.
• Security tests with cryptography algorithms.
• Cosmology and models of the structure of matter.
• Genetic research.

Current difficulties in the application of deep learning

The most productive techniques in artificial intelligence require high data processing capacity. The tests are usually run on machines tailored to the experiment you want to carry out. This leaves the possibilities of achieving goals with some economic and social impact in the hands almost exclusively of large technology companies.

However, the scientific culture acquires new tools to meet its most immediate objectives. It is also undeniable that the results end up being part of social life with special services and products at some stage of their conception or production.

The confluence of disciplines such as big data makes it easy to divide the current high requirements for classic problems into intermediate steps. The frameworks like Hadoop and Spark facilitate the handling of large volumes of information in various media.

Collaboration between different technologies is of the utmost importance when scientists move on the frontiers of science. The expected fruits will mark the coming years with a new scientific revolution that will make information the main raw material in the world.

The goal of reaching human perception

The partial successes with deep learning have reopened the theoretical possibility of simulating the form of human understanding in machines. The degree of difficulty of even this still distant goal is completely unknown. But it is a measure of the level reached today.

An important milestone is a feat carried out in 2012 by the team led by Andrew Ng by distinguishing a cat in a set of ten million video files. Skills like this will be available to millions of systems spread across every country in the world.

The security and control of social networks against users with unethical behavior goes through a review of the information that reaches them. The construction of intelligent systems would make it easier to get rid of the most negative effects on these instruments for citizen participation.

Image tagging can only be done automatically by recognizing the figures that are presented in them. The same for the written information contained in files with image settings. These operations are essential to detect trolls and put coercive measures on them to prevent their actions.

The direct applications of artificial intelligence technologies are mainly built for new needs. Singularly with born by the intervention of technology in society. But we are only at the beginning of a great change. Scientific production and the energy for new advances will have the invaluable support of these technologies in a cycle that has no end in sight.

Also Read: MACHINE LEARNING FOR COMPANIES: ADVANTAGES OF ARTIFICIAL INTELLIGENCE FOR YOUR BUSINESS

Conclusion

Artificial intelligence is in a mature moment that gives it a great role as a direct factor of production. The social and economic changes that will ensue are difficult to foresee. In this dynamic, deep learning is one of the most fruitful orientations. In just over eight years he has shown a great ability to attack unapproachable problems with other means.

A current difficulty that is difficult to tackle for artificial intelligence is machine translation. The last step with which machines could unambiguously understand human written or verbal commands. The complexity of new neural networks with the participation of some new technique may allow machines to communicate with human language.

Human perception is the necessary prelude to generate thought in symbolic language. Transferring this human capacity to a machine is not easy, nor is it known if it can be done indistinguishable. But when deep learning models try to scale a nervous system they only use a basic model.

Artificial intelligence is called to be present in all aspects of human life. From education to the conquest of space, any dimension of reality has an appointment with this technological instrument. It is advisable to be attentive to the news that will accompany the latest innovations in this field. It justifies that they have enough entity to completely transform the world we know.

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