When will the first artificial intelligence be created? What is artificial intelligence? artificial intelligence
This year, Yandex launched the Alice voice assistant. The new service allows the user to listen to news and weather, get answers to questions and simply communicate with the bot. "Alice" sometimes cheeky, sometimes it seems almost reasonable and humanly sarcastic, but often she cannot figure out what she is being asked about, and sits in a puddle.
All this gave rise not only to a wave of jokes, but also to a new round of discussions about the development of artificial intelligence. News about what smart algorithms have achieved is coming almost every day today, and machine learning is called one of the most promising areas to dedicate yourself to.
To clarify the main questions about artificial intelligence, we talked with Sergey Markov, a specialist in artificial intelligence and machine learning methods, the author of one of the most powerful Russian chess programs SmarThink and the creator of the 22nd Century project.
Sergei Markov,
artificial intelligence specialist
Debunking myths about AI
So what is "artificial intelligence"?
The concept of "artificial intelligence" is somewhat unlucky. Initially originating in the scientific community, it eventually penetrated into science fiction literature, and through it into pop culture, where it underwent a number of changes, acquired many interpretations, and in the end was completely mystified.
That is why we often hear such statements from non-specialists as: “AI does not exist”, “AI cannot be created”. Misunderstanding of the essence of research conducted in the field of AI easily leads people to other extremes - for example, modern AI systems are credited with the presence of consciousness, free will and secret motives.
Let's try to separate the flies from the cutlets.
In science, artificial intelligence refers to systems designed to solve intellectual problems.
In turn, an intellectual task is a task that people solve with the help of their own intellect. Note that in this case, experts deliberately avoid defining the concept of "intelligence", because before the advent of AI systems, the only example of intelligence was the human intellect, and defining the concept of intelligence based on a single example is the same as trying to draw a straight line through a single point. There can be as many such lines as you like, which means that the debate about the concept of intelligence could be waged for centuries.
"strong" and "weak" artificial intelligence
AI systems are divided into two large groups.
Applied artificial intelligence(they also use the term "weak AI" or "narrow AI", in the English tradition - weak / applied / narrow AI) is an AI designed to solve any one intellectual task or a small number of them. This class includes systems for playing chess, go, image recognition, speech, decision-making on issuing or not issuing a bank loan, and so on.
As opposed to applied AI, the concept is introduced universal artificial intelligence(also "strong AI", in English - strong AI / Artificial General Intelligence) - that is, a hypothetical (so far) AI capable of solving any intellectual problems.
Often people, not knowing the terminology, identify AI with strong AI, because of this, judgments in the spirit of “AI does not exist” arise.
Strong AI does not really exist yet. Virtually all of the advances we've seen in the last decade in the field of AI have been advances in applied systems. These successes cannot be underestimated, since applied systems in some cases are able to solve intellectual problems better than the universal human intelligence does.
I think you noticed that the concept of AI is quite broad. Let's say mental counting is also an intellectual task, which means that any calculating machine will be considered an AI system. What about accounts? abacus? Antikythera mechanism? Indeed, all this is formal, although primitive, but AI systems. However, usually, calling some system an AI system, we thereby emphasize the complexity of the task solved by this system.
It is quite obvious that the division of intellectual tasks into simple and complex ones is very artificial, and our ideas about the complexity of certain tasks are gradually changing. The mechanical calculating machine was a marvel of technology in the 17th century, but today, people who have been confronted with much more complex mechanisms since childhood, it is no longer able to impress. When the game of cars in Go or car autopilots cease to surprise the public, there will certainly be people who will wince at the fact that someone will attribute such systems to AI.
"Robots-excellent students": about the ability of AI to learn
Another funny misconception is that AI systems must have the ability to self-learn. On the one hand, this is not a mandatory property of AI systems: there are many amazing systems that are not capable of self-learning, but, nevertheless, solve many problems better than the human brain. On the other hand, some people simply do not know that self-learning is a feature that many AI systems have acquired even more than fifty years ago.
When I wrote my first chess program in 1999, self-study was already a commonplace in this area - the programs were able to memorize dangerous positions, adjust opening variations for themselves, adjust the style of play, adjusting to the opponent. Of course, those programs were still very far from Alpha Zero. However, even systems that learn behavior based on interactions with other systems in so-called “reinforcement learning” experiments already existed. However, for some inexplicable reason, some people still think that the ability to self-learn is the prerogative of the human intellect.
Machine learning, a whole scientific discipline, deals with the processes of teaching machines to solve certain problems.
There are two big poles of machine learning - supervised learning and unsupervised learning.
At learning with a teacher the machine already has a number of conditionally correct solutions for some set of cases. The task of learning in this case is to teach the machine, based on the available examples, to make the right decisions in other, unknown situations.
The other extreme - learning without a teacher. That is, the machine is put in a situation where the correct solutions are unknown, there are only data in a raw, unlabeled form. It turns out that in such cases it is possible to achieve some success. For example, you can teach a machine to identify semantic relationships between words in a language based on the analysis of a very large set of texts.
One type of supervised learning is reinforcement learning. The idea is that the AI system acts as an agent placed in some model environment in which it can interact with other agents, for example, with copies of itself, and receive some feedback from the environment through a reward function. For example, a chess program that plays with itself, gradually adjusting its parameters and thereby gradually strengthening its own game.
Reinforcement learning is a fairly broad field and uses many interesting techniques ranging from evolutionary algorithms to Bayesian optimization. Recent advances in AI for games are precisely related to the amplification of AI during reinforcement learning.
Technology Risks: Should We Be Afraid of Doomsday?
I am not one of the AI alarmists, and in this sense I am by no means alone. For example, Andrew Ng, creator of the Stanford Machine Learning course, compares the dangers of AI to the problem of overpopulation on Mars.
Indeed, in the future, it is likely that humans will colonize Mars. It is also likely that sooner or later the problem of overpopulation may arise on Mars, but it is not entirely clear why we should deal with this problem now? Yn and Yang LeKun, the creator of convolutional neural networks, agree with Yn, and his boss Mark Zuckerberg, and Joshua Benyo, a person whose research is largely due to the research of which modern neural networks are able to solve complex problems in the field of word processing.
It will probably take several hours to present my views on this problem, so I will focus only on the main theses.
1. DO NOT LIMIT AI DEVELOPMENT
Alarmists consider the risks associated with the potential disruption of AI while ignoring the risks associated with trying to limit or even stop progress in this area. The technological power of mankind is increasing at an extremely rapid pace, which leads to an effect that I call "cheapening the cost of the apocalypse."
150 years ago, with all the will, humanity could not cause irreparable damage to either the biosphere or itself as a species. To implement the catastrophic scenario 50 years ago, it would have been necessary to concentrate all the technological power of the nuclear powers. Tomorrow, a small handful of fanatics may be enough to bring a global man-made disaster to life.
Our technological power is growing much faster than the ability of human intelligence to control this power.
Unless human intelligence, with its prejudices, aggression, delusions and narrow-mindedness, is replaced by a system capable of making more informed decisions (whether it be AI or, what I consider more likely, a technologically improved human intelligence integrated with machines into a single system), we can waiting for a global catastrophe.
2. the creation of superintelligence is fundamentally impossible
There is an idea that the AI of the future will certainly be super-intelligent, superior to humans even more than humans are superior to ants. In this case, I'm afraid to disappoint technological optimists - our Universe contains a number of fundamental physical limitations, which, apparently, will make the creation of superintelligence impossible.
For example, the speed of signal transmission is limited by the speed of light, and the Heisenberg uncertainty appears on the Planck scale. This implies the first fundamental limit - the Bremermann limit, which imposes restrictions on the maximum computational speed for an autonomous system of a given mass m.
Another limit is related to Landauer's principle, according to which there is a minimum amount of heat released when processing 1 bit of information. Too fast calculations will cause unacceptable heating and destruction of the system. In fact, modern processors are less than a thousand times behind the Landauer limit. It would seem that 1000 is quite a lot, but another problem is that many intellectual tasks belong to the EXPTIME complexity class. This means that the time required to solve them is an exponential function of the dimension of the problem. Accelerating the system several times gives only a constant increase in "intelligence".
In general, there are very serious reasons to believe that a super-intelligent strong AI will not work, although, of course, the level of human intelligence may well be surpassed. How dangerous is it? Most likely not very much.
Imagine that you suddenly started thinking 100 times faster than other people. Does this mean that you will easily be able to persuade any passer-by to give you their wallet?
3. we worry about something else
Unfortunately, as a result of the alarmists' speculation on the fears of the public, brought up on the Terminator and Clark and Kubrick's famous HAL 9000, there is a shift in the focus of AI security towards the analysis of unlikely but spectacular scenarios. At the same time, the real dangers slip out of sight.
Any sufficiently complex technology that claims to occupy an important place in our technological landscape certainly brings with it specific risks. Many lives were destroyed by steam engines - in manufacturing, transportation, and so on - before effective safety rules and measures were put in place.
If we talk about progress in the field of applied AI, we can pay attention to the related problem of the so-called "Digital Secret Court". More and more applied AI systems make decisions on issues affecting the life and health of people. This includes medical diagnostic systems, and, for example, systems that make decisions in banks on issuing or not issuing a loan to a client.
At the same time, the structure of the models used, the sets of factors used, and other details of the decision-making procedure are hidden from the person whose fate is at stake.
The models used can base their decisions on the opinions of expert teachers who made systematic mistakes or had certain prejudices - racial, gender.
An AI trained on the decisions of such experts will conscientiously reproduce these prejudices in its decisions. After all, these models may contain specific defects.
Few people are now dealing with these problems, because, of course, SkyNet unleashing a nuclear war is, of course, much more spectacular.
Neural networks as a "hot trend"
On the one hand, neural networks are one of the oldest models used to build AI systems. Initially appeared as a result of applying the bionic approach, they quickly ran away from their biological prototypes. The only exception here are impulse neural networks (however, they have not yet found wide application in the industry).
The progress of recent decades is associated with the development of deep learning technologies - an approach in which neural networks are assembled from a large number of layers, each of which is built on the basis of certain regular patterns.
In addition to the creation of new neural network models, important progress has also been made in the field of learning technologies. Today, neural networks are no longer taught with the help of central processors of computers, but with the use of specialized processors capable of quickly performing matrix and tensor calculations. The most common type of such devices today is video cards. However, even more specialized devices for training neural networks are being actively developed.
In general, of course, neural networks today are one of the main technologies in the field of machine learning, to which we owe the solution of many problems that were previously solved unsatisfactorily. On the other hand, of course, you need to understand that neural networks are not a panacea. For some tasks, they are far from the most effective tool.
So how smart are today's robots really?
Everything is relative. Against the background of the technologies of the year 2000, the current achievements look like a real miracle. There will always be people who like to grumble. 5 years ago, they were talking with might and main that machines will never beat people in Go (or at least they won't win very soon). It was said that a machine would never be able to draw a picture from scratch, while today people are practically unable to distinguish between pictures created by machines and paintings by artists unknown to them. At the end of last year, machines learned to synthesize speech, almost indistinguishable from human, and in recent years, ears do not wither from the music created by machines.
Let's see what happens tomorrow. I look at these applications of AI with great optimism.
Promising directions: where to start diving into the field of AI?
I would advise you to try to master at a good level one of the popular neural network frameworks and one of the programming languages popular in the field of machine learning (the most popular today is TensorFlow + Python).
Having mastered these tools and ideally having a strong base in the field of mathematical statistics and probability theory, you should direct your efforts to the area that will be most interesting to you personally.
Interest in the subject of work is one of your most important assistants.
The need for machine learning specialists exists in various fields - in medicine, in banking, in science, in manufacturing, so today a good specialist has more choice than ever. The potential benefits of any of these industries seem to me insignificant compared to the fact that the work will bring you pleasure.
Artificial intelligence, one of the most exciting themes of 20th century fiction, is making incredible strides. We constantly use AI in everyday life, often without knowing it. Nevertheless, even today, artificial intelligence does not leave the pages of science fiction novels and cinema screens. Some of the authors draw terrible pictures of humanity enslaved by the machine, while others, on the contrary, see AI as a faithful assistant and friend of man.
Where is the truth and what is artificial intelligence really? Will it ever surpass the capabilities of the human mind? Or has it already happened? GeekBrains is ready to answer the most popular questions about artificial intelligence and the prospects for its use.
What is artificial intelligence?
Artificial intelligence (abbreviated as AI) is a vague concept, and it still does not have a generally accepted definition. In the middle of the 20th century, when this term was first mentioned at the Dartmouth Seminar, the authors gave it a meaning that was significantly different from modern ones. Then scientists believed that artificial intelligence is a system that will be able to translate texts from one language to another, recognize objects from a photo or video, capture the meaning of spoken phrases and adequately respond to them. Current AI can do it all! But can we consider that the goals have been achieved and artificial intelligence has already been created?
Some scientists build complex theories at the intersection of philosophy and computer science, trying to determine what AI is and what characteristics of the system must be in order to consider it intelligent. Without going into details, we can say that intelligence is defined as the ability to learn, comprehend and apply knowledge in practice. Therefore, we also have the right to expect the ability to learn from artificial intelligence, to realize our knowledge and use it. With the first and last tasks, modern AI is doing quite well!
When did AI development start?
In the summer of 1956, in Dartmouth, scientists gathered for a seminar on artificial intelligence (the term was formulated there), and the very next year, the concept of the first artificial neural network, the perceptron, appeared. In 1960, Frank Rosenblatt created the Mark-1 computer based on this concept. The world's first neurocomputer was taught to recognize letters of the Latin alphabet. But the imperfection of the technology of the 60s and the complexity of the processes did not allow to bring the technology to mind, and its developer soon died. Neurocomputers were forgotten for 20 years.
It wasn't until the 1980s that the concepts of neural networks began to be taken seriously again. The technology was already powerful enough, and there were fewer critics: smart electronics were quickly making headway. What seemed like a dream two decades ago began to look quite real and achievable. However, it took another 20 years to find the right approaches to training neural networks. Only in the mid-2000s, scientists found the right path and artificial neural networks began their victorious march around the planet.
But before describing their successes, let's look at how these networks work.
Description of the artificial neuron
Artificial neural networks were created as a mathematical model of the human brain. To do this, scientists Warren McCulloch and Walter Pitts had to develop a theory of the human brain.
In it, individual neurons are living cells with a complex structure. Each neuron has dendrites - branched processes that can exchange signals with other neurons through synapses, as well as one axon - a larger process responsible for transmitting impulses from the neuron. Part of the synapses is responsible for the excitation of the neuron, part - for inhibition. From what signals and through what synaptic connections will come to the "input" of the neuron, the impulses that it will transmit to other neurons will also depend.
An artificial neuron does not need a physical carrier. By and large, it is a mathematical function. Its task is to receive information (for example, signals from many other artificial neurons), process it in a certain way, and then give the result to the "axon" - the output. In an artificial network, neurons are usually divided into three types:
- input - each of these neurons receives an element of initial information as an input (for example, one point of the image, if the network recognizes photographs);
- intermediate - process information;
- output - give the result (when recognizing a photo, the result can be the identifier of the depicted object).
The neural network itself is created in layers, like a pie. One of the outer layers contains input neurons, the other contains output neurons, and one or more intermediate ones can be located between them. Each neuron of the intermediate network is connected to a plurality of neurons from the two surrounding layers. Communication between neurons is provided using weights - numerical values that each neuron calculates based on data received from the previous layer of the network.
Creating artificial neural networks, scientists focused on the structure of the human brain. Therefore, the principles of behavior of man-made neurons are not so different from real, living ones. Maybe the mind that can develop on the basis of such neural networks will be close to the human one?
The difference between artificial intelligence and natural
The question of how AI differs from natural intelligence is actually more philosophical than strictly scientific. And the point is not even that we cannot imagine what an artificially created mind will look like (or not look like). We are just capable of imagining anything - and science fiction writers have proven this many times. The fact is that not a single artificial intelligence that exists today has reached a sufficiently high level of development to compete with humans on an equal footing.
There is a point of view expressed by the philosopher John Searle back in the 1980s. He coined the terms "strong AI" and "weak AI". A strong artificial intelligence, according to the scientist, can be aware of itself and think like a person. The weak can't do it.
Today's AIs, if classified by Searle, are clearly weak, since none of them have yet conceived self-awareness. Our artificial neural networks recognize faces and draw strange, incredible pictures, read handwritten text, and even add poetry - but they were created solely for these purposes. None of these neural networks are able to change their mind and choose a different “specialty” for themselves. They do only what they have been trained to do, and in a sense they can be considered programmed to perform these tasks. They have no real understanding of what is behind these things. Searle argued that building a strong AI is basically impossible.
Another philosopher, Hubert Dreyfus, also believed that computer systems could never be compared with a person - since in his rational activity he relies not only on acquired knowledge, but also on empirical experience. Computers do not possess it by definition - therefore, it is not their destiny to develop their own mind.
But these self-confident statements were made at a time when neural networks were just taking their first steps. Today, looking at their learning success, it is not difficult to believe in the reality of AI, which can become equal to a person, or even surpass him.
How to compare human and computer intelligence?
Wait, how can we even determine whether artificial intelligence has reached human level or not?
It can be assumed that one of the criteria is the presence of feelings and emotions, as well as creativity. If the machine began to experience fear or love, if it suddenly decided to write a poem or paint a picture - wouldn't that be a manifestation of reason?
Quite possible. However, animals and birds also have feelings. At the same time, we often answer the question of their reasonableness (especially the equality of their mind to the human one) in the negative. In addition, feelings can be programmed - in most cases they are a reaction to specific external stimuli. Finally, we simply do not have data on whether computers will ever be able to experience emotions comparable to human ones. But should their feelings be like ours?
Perhaps a more reliable criterion is self-awareness? If the machine wonders "Who am I?" - this is the moment of the appearance of rationality? But self-consciousness is also present in animals. At the same time, most people are quite capable of living their lives without delving into deep philosophical questions.
Are there more precise and rigorous methods for comparing intelligences? After all, there is an IQ coefficient, with which you can evaluate the mental abilities of a person. Why not use it for a car?
Do computer programs have an IQ?
Measuring intelligence, even in humans, is incredibly difficult - you can’t attach a ruler to cognitive and mental abilities. Moreover, IQ is not an absolute indicator, but a relative one. Some scientists generally believe that IQ tests do not measure intelligence per se, but the ability to pass such tests. It can be trained and get a brilliant result - but the intellect, of course, will not change. So IQ is nothing more than a number that is related to intelligence, but cannot give an objective assessment of it.
In some IQ tests, tasks for observation or logic prevail, in others - for combinatorics, in others - for mathematical thinking. The result will depend on what is given to a person easier and in what he is more competent. The speed of passing tests and the specialization of tasks matter.
AI can also be “trained” to solve certain classes of problems, and it will take a machine much less time to complete an IQ test than a human. So the neural network will be able to score points that are unthinkable for brilliant people, but at the same time it will not be able to answer the simplest questions for which it was not prepared during training.
So are there any criteria by which one can objectively judge machine intelligence? One of the first researchers who tried to develop them was the famous British mathematician Alan Turing.
What is the Turing test?
In 1950, Turing published an article "Computing Machines and the Mind" in which he discussed the theoretical possibility of thinking in machines. This was not the first study on the topic of artificial intelligence, and not even the first such work of Turing, but it was it that became the starting point for serious scientific discussions and disputes.
Turing started with definitions to clarify the question of whether a machine can think, which seemed to him too vague. What car do you mean? What does “think” mean in general?.. It was obvious that such a question initially carries an irrational grain that would not allow giving a correct answer to it. The result of the scientist's reflections was the Turing test - an experiment in which a person ("judge") is invited to communicate with two interlocutors: a person and a computer. The task of the judge is to understand who is who. If, as a result, he is not sure which of his interlocutors is the program, or made a mistake in the assessment, it is considered that the machine has passed the test.
The essence of the Turing test is not to create a “deceiver machine” that can pretend to be human. It helps to make sure that a particular machine or program has a mind that is difficult to distinguish from a human one. Such a computer Turing called "intelligent" - this definition is more than 60 years old, and it remains relevant.
Processors for AI
AI technologies are not limited to software solutions. Today, electronic chips are being actively developed, in which AI support is built-in at the hardware level. Microprocessors of this type are called neural processors. They are used in unmanned vehicles and aircraft (drones), industrial robots and automatic machines, as well as for solving specialized tasks - voice or image recognition, creating search engines and machine translators.
Among such devices is the Google Tensor Processing Unit (TPU), designed specifically for machine learning systems. This device is not yet available for sale: only Google itself uses it to optimize search results and process photos. The TPU operates with 8-bit numbers (which is extremely small for precise calculations), and has just over a dozen instructions (other modern processors can have hundreds). But this does not prevent the tensor processor from effectively performing calculations related to artificial intelligence and neural networks. The processor is developing rapidly - Google rolls out a new version every year.
Tensor processor Google Tensor Processing Unit 3.0 (TPU)
There are other developments of similar chips. Many of them are highly specialized: for example, they are designed to accelerate AI programs for computer vision.
Artificial intelligence technology market
Artificial intelligence technologies are used in almost all areas of human activity, so artificial intelligence has a great future. The market for products using AI is growing rapidly.
World market
By 2022, the AI market is projected to reach $52 billion. Perhaps this is not such a big figure - for example, the market for computer games by the same year will exceed 130 billion, and the smartphone market was already 10 times larger in 2018 - 520 billion.
But the AI market is showing unparalleled growth - according to some estimates, it is growing at about 30% annually (similar figures for games and smartphones are about 5%). If this pace of technology adoption continues for a few more years, we can expect that soon artificial intelligence will be literally everywhere.
The world's largest IT companies: Google, IBM, Intel, Nvidia are making their contribution to the development of AI. The United States, China and the United Kingdom are leading the way.
In Russia
If in 2017 there were only a few dozen projects using AI in Russia, then in 2018 there are already hundreds. Experts predict that by 2020 the market will reach 28 billion rubles (about $450 million). New technologies are most actively used in the financial sector, as well as telecommunications, retail and energy. Some companies hire teams of specialists dedicated exclusively to the development and implementation of AI systems.
Despite the fact that the growth of the market is generally even faster than in the world, there are problems. The main problem is the lack of machine learning specialists. So, it's time to start studying AI in order to get a sought-after specialty and a well-paid job.
The impact of artificial intelligence on the labor market
Already today there are areas where AI can replace humans. For example, applications can answer simple questions over the phone or chat with customers. This allows you to optimize the workload of call center operators and even reduce their staff.
In manufacturing, AI is able to control automation and industrial robots. An artificial neural network that constantly monitors the performance of many sensors will be able to respond faster than a person to an emergency situation and take the right measures - turn off the conveyor or stop the mechanisms. In many cases, such systems can predict problems in advance and prevent emergencies.
AI will force people out of their jobs. It is cheaper and makes fewer mistakes. He does not know how to be lazy, procrastinate and hang out on Facebook, does not need rest, sleep and vacation, does not feel sad and does not get tired. The ideal worker.
First of all, artificial neural networks will replace a person in performing routine operations, they will take on complex calculations, risk assessment, information collection, modeling situations according to specified parameters. AI can be used in dangerous and hazardous industries.
But people will still be needed where robots will not be able to compete with them for a long time. And it's not just about creativity. So far, AI is only capable of performing highly specialized tasks for which it has been trained, so they can replace people to the same extent that a calculator can replace mathematics. At the same time, the development of AI technologies opens up a huge labor market for specialists related to machine learning and maintenance of intellectual equipment.
Where is AI used?
In short - almost everywhere!
There are not so many areas of human activity left that are not at all affected by AI technologies. Consider only the most important areas where AI is already being used.
AI on the Internet
Whenever you say "Ok Google" or "Hey Siri" you are talking to the artificial intelligence in your smartphone. He is able to recognize the speech addressed to him in the signal from the microphone. It records your question and forwards it to Google or Apple servers. There, a second AI is connected to the case, which recognizes speech and translates the question into a format understandable to the computer. And then the third searches for the answer in giant databases. Finally, the answer is returned to your smartphone, where an AI that generates a human voice speaks it for you. And all this in a fraction of a second.
AI in transport and logistics
An impressive application of artificial neural networks is unmanned vehicles. Over the past decade, many automakers have taken on the development of a car that would be able to independently move on the roads - General Motors, Nissan, BMW, Honda, Volkswagen, Audi, Volvo, as well as Google and Tesla. Drones have not yet become a mass phenomenon on the streets of our cities, but they are clearly making progress.
Amazon has been developing the idea of delivering goods and mail using drones since 2013. For the first time, the package arrived to the recipient with an unmanned aerial vehicle back in December 2016. In some regions, food, medicine, and even portable defibrillators are delivered by drones. The system is not yet perfect, but it continues to evolve. Unfortunately, drones can also serve illegal purposes: there have been documented cases of delivery of prohibited items to prisons using drones, as well as the use of drones to transport drugs.
AI in finance
In the financial sector, AI is used to predict risks and detect fraud. MasterCard Corporation, which created the international payment system, introduced the Decision Intelligence service several years ago. It is designed to increase the accuracy of confirming genuine transactions and reduce the likelihood of false payment rejections - this is an erroneous operation of the built-in security system that does not allow you to make a correct transaction, mistaken for fraudulent. Mistakes like this harm both the seller who loses a customer and the buyer who doesn't receive the product. The losses are even higher than the damage from fraud.
The system, running on an artificial neural network, uses information from a variety of sources to evaluate on the fly how “normal” a transaction is. Not only the reliability and transaction history of the seller is taken into account, but even the typical purchase for the buyer and his location, as well as the time of day. All this helps to more reliably protect people from fraud and minimize false positives.
AI in medicine
In healthcare, AI is developing primarily in the field of disease diagnosis. Artificial neural networks have learned to recognize cancerous tumors on x-rays, CT scans, mammography and MRI. It takes about 20 minutes for an experienced doctor to study the image, and a few seconds for neural networks. So the patient can find out the results of the examination almost instantly. It is especially pleasant that such developments are being carried out in Russia.
Diagnostic AIs can detect not only cancer, but also the early stages of Alzheimer's disease, pneumonia and other diseases.
In defense and military affairs
In 2018, it became known that the US Army was developing an AI capable of recognizing human faces in the dark and even through walls using a thermal imager. The technology is expected to help identify gang leaders in war zones.
Another AI - ALPHA - was created to control unmanned fighters and conduct air combat. In one of the battles on the simulators, the computer won, controlling four aircraft simultaneously against two human opponents.
Aiming systems for tanks are also being developed, capable of spotting camouflaged targets.
In the military-industrial complex, AI will help improve the defense capabilities of countries, but it can also become a weapon of terror.
In business and trade
In retail, AI is revolutionizing. Artificial neural networks improve the quality of service and provide an individual approach to each consumer. Smart technologies detect bank card fraud, give personal advice and help you choose the right product.
According to TAdviser, over a third of all retail revenue in 2018 was generated by AI-based recommendations!
AI in sports
Here, AI technologies are used to predict the results of matches - such systems are created by UBS, Commerzbank and Microsoft. The experience of the team and individual players is taken into account. Sometimes the predictions turn out to be correct, but often artificial intelligence is seriously miscalculated. The human factor is able to refute any predictions.
AI in culture
A machine cannot be creative because it has no imagination! Or can it still?
Oddly enough, artificial neural networks are able to show creativity, and even reach certain heights in the field of culture.
Music
What would a flute sound like if it were a sitar? Google's NSynth Super synthesizer uses a neural network to create completely new sounds based on different instruments.
Alice, developed as part of the Popgun startup, knows how to "play along" with a person, creating musical improvisations. American singer Taryn Southern has released an album in collaboration with the Amper neural network. And the Endel project is capable of creating compositions that are in tune with the user's mood at the touch of a button.
Painting
The DeepDream neural network was created with an eye on face recognition, and it showed the ability to surrealistic painting. The developers have opened a site where anyone can create an amazing canvas in collaboration with AI. The neural network paints pictures in different styles.
True, she still does not know how to come up with plots - she asks for the help of a person.
Video
With the help of AI, developed by Google and Facebook, it is possible to “force” a person on the screen to pronounce any words, depict the whole range of emotions. And it can be difficult to distinguish such videos from real ones. Neural networks can even replace one actor with another in a filmed movie. And this opens up opportunities not only for filmmakers, but also for the creators of fakes.
Literature
The neural network from Facebook can write poetry, perfectly maintaining the size and rhythm, choosing good rhymes. Readers only managed to recognize computer-generated lines half the time, but AI poets are far from real poets. The machine has not yet learned to convey emotions and put meaning into poetic works.
Yandex also launched Autopoet, which created poems from user search queries. Some are impossible to read without smiling. It's hard to believe that they were composed by a neural network devoid of a sense of humor!
And the company Narrative Science has developed an electronic journalist. So far, articles written by AI are simple in content, but the company's management is optimistic about the future and believes that by 2025 up to 90% of the texts on the Internet will be written using machine intelligence.
In 2016, The Day a Computer Writes a Novel was a finalist for the Hoshi Shinichi Japanese Literary Prize. This work was almost entirely created by artificial intelligence.
Games
In computer games, neural networks are used to control opponents and game bots. But AI can also be taught to play "on the other side of the screen" - that is, to read visual information from the screen and control the game character, as a person does.
In 2016, there was even a Doom championship between AIs. And the Deep-Q-Network system is trained to play classic Atari arcade machines. Often it shows results up to 30% higher than experienced players.
In the 20th century, it was believed that artificial intelligence could be considered powerful and developed enough when it was able to beat the world chess champion. Computers passed this stage a long time ago - back in 1997, Deep Blue defeated Garry Kasparov (and it was an algorithmic program, not artificial intelligence).
After that, the attention of the public turned to more complex tactical games, such as go. The complexity of calculating the move here is an order of magnitude higher than in chess, so it is almost impossible to create algorithms that would sort out possible options. But trained neural networks managed to cope with this game. Already in 2015, the AlphaGo network developed by Google won a match against a professional Go player.
Prospects for the development of artificial intelligence
Scientific research on AI has been going on for more than half a century, but still not everyone understands the essence of the technology. In fantasy novels and films, writers and directors portray how dangerous artificial intelligence can be. And for many, the idea of artificial intelligence is formed in this way.
We will rationally answer questions related to the distant prospects for the development of AI.
The goal of AI is to put the human mind into a computer?
No, it's not. Even theoretically, such a situation is not so improbable. Artificial neural networks are created in the image of the human brain, although in a very simplified form. Maybe one day it will be possible to scan all sections of the brain of a living person, make a "map" of his neurons and synaptic connections and reproduce a copy of it in a computer. From such a copied neural network, one can expect not only reasonable behavior - it will literally be a double of a person, will be able to realize himself, make decisions and act like him. Even memories are copied. Theoretically, it will be possible to place such a neural network in an artificial body (a robot), and then a person - a copy of his consciousness - will be able to live almost forever.
In practice, it will be incredibly difficult to carry out such a transfer: there are no technologies that would allow “reading” a living brain and creating its “map”. And we are still very far from creating an artificial neural network that would be as powerful as the brain.
Is AI aiming to reach human levels of intelligence?
The purpose of AI is to help people and take on difficult or routine tasks. To do this, he does not have to maintain conversations on philosophical topics or compose poems.
However, if artificial intelligence can one day reach the level of human thinking, it will be a milestone for civilization. We will get a practical and intelligent assistant - and we can rightly be proud that this is the creation of our hands.
When will artificial intelligence reach the human level?
We successfully create relatively small neural networks capable of recognizing a voice or processing an image. No AI yet has the same plasticity as our brain.
A person can play music today and start programming in C++ tomorrow - thanks to the incredible complexity of the brain. It has 86 billion neurons and countless synaptic connections between them.
Artificial neural networks are still far from these indicators: they have from several thousand to millions of neurons. There are technical limitations on the size of neural networks: even supercomputers cannot “pull” a neural network comparable in scale to the human brain. Not to mention that her training will be a non-trivial task.
Does the speed of computers allow them to be intelligent?
The “power” of intelligence is not related to the speed of calculations, but to the complexity of the neural network. The human brain is still superior in power to any artificial neural network, despite the fact that the speed of processes in it is significantly lower than in computers.
Artificial neural networks are made up of individual neurons that are grouped into layers. The two outer layers serve as an "input" to which the initial information is supplied, and an "output" from which the result is read. Between them can be located from one to several tens, or even hundreds, of intermediate layers of neurons. Moreover, each neuron in the layer is connected to many others in the previous and next layers.
The more complex the network is, the more layers and neurons it has, the more large-scale and serious tasks it can perform.
Can a neural network evolve naturally?
Let's look at whether it is likely that AI will be able to experience and learn naturally, like a child. The human mind is shaped by many factors. We receive information about the external world through the organs of perception - observing, touching, tasting. Interacting with the environment, we get life experience, knowledge about the properties of the world, social skills. Our brain is constantly improving and physically changing, building up new synaptic connections and “pumping” existing ones.
If we can create a neural network complex enough that it can develop in this way, and equip it with "sense organs" - a video camera, a microphone, and the like - perhaps after a while it will be able to acquire "life experience". But this is a matter of the distant future.
Is there a risk to human civilization?
Risks associated with new technologies always exist. The question is what are they.
It may turn out that artificial neural networks, having reached a certain threshold, will reach a “plateau” of efficiency and will not develop further. Or they will not live up to expectations if it turns out that AI is in principle unable to cope with one or another class of tasks, for example, of a creative nature. This can result in loss of labor costs and financial investments.
If, however, by risk we mean man-made disasters or the uprising of machines - so far this is unlikely to threaten us. In simple terms, modern neural networks are not able to turn against the creators - just like the neurons in the brain that control the movement of the hand are not able to realize themselves as a person and strike at their own body.
However, we must remember that AI is our invention. We design them, we create them, we train them, we put in “thoughts”. This means that we are also responsible for their behavior.
Fourth revolution
No matter how we feel about artificial intelligence, we will have to accept the fact that it already exists. To refuse it means to take a step back in development. After all, AI is an important part of our progress. Many scientists associate the beginning of the fourth industrial revolution with artificial neural networks and declare that a new era is coming - when man-made intelligence will appear next to us, always ready to help.
Everything new is scary and distrustful - this is a normal human reaction, and many people are wary of AI. Only a lazy science fiction writer did not talk about the horrors that artificial intelligence will bring us. But similar things were written about every technological innovation in their time. People were afraid of steam locomotives because they would "scar away cows, poison birds with smoke, and at speeds over 15 miles per hour, passengers would be torn to pieces." Probably, descendants will also laugh at our fears, which they learn about from films and books of the 20th and 21st centuries.
Since the invention of computers, their ability to perform various tasks has continued to grow exponentially. Humans are developing the power of computer systems by increasing the performance of tasks and decreasing the size of computers. The main goal of researchers in the field of artificial intelligence is to create computers or machines as intelligent as a person.
The author of the term "artificial intelligence" is John McCarthy, the inventor of the Lisp language, the founder of functional programming and the winner of the Turing Award for his great contribution to the field of artificial intelligence research.
Artificial intelligence is a way to make a computer, computer-controlled robot or program capable of thinking intelligently like a human as well.
Research in the field of AI is carried out by studying the mental abilities of a person, and then the results of this research are used as the basis for the development of intelligent programs and systems.
Philosophy of AI
During the operation of powerful computer systems, everyone asked the question: “Can a machine think and behave in the same way as a person? ".
Thus, the development of AI began with the intention of creating a similar intelligence in machines, similar to the human.
Main goals of AI
- Creation of expert systems - systems that demonstrate intelligent behavior: learn, show, explain and give advice;
- Realization of human intelligence in machines - the creation of a machine capable of understanding, thinking, teaching and behaving like a human.
What contributes to the development of AI?
Artificial intelligence is a science and technology based on such disciplines as computer science, biology, psychology, linguistics, mathematics, mechanical engineering. One of the main areas of artificial intelligence is the development of computer functions related to human intelligence, such as: reasoning, learning and problem solving.
Program with AI and without AI
Programs with and without AI differ in the following properties:
Applications with AI
AI has become dominant in various fields such as:
Games - AI plays a crucial role in strategy games such as chess, poker, tic-tac-toe, etc., where the computer is able to calculate a large number of possible solutions based on heuristic knowledge.
Natural language processing is the ability to communicate with a computer that understands the natural language spoken by humans.
Speech recognition - some intelligent systems are able to hear and understand the language in which a person communicates with them. They can handle various accents, slang, etc.
Handwriting Recognition - The software reads text written on paper with a pen or on a screen with a stylus. It can recognize letter shapes and convert it into editable text.
Smart robots are robots capable of performing tasks assigned by humans. They have sensors to detect physical data from the real world, such as light, heat, motion, sound, shock, and pressure. They have high performance processors, multiple sensors and huge memory. In addition, they are able to learn from their own mistakes and adapt to the new environment.
History of AI development
Here is the history of AI development during the 20th century
Karel Capek is directing a play in London called "Universal Robots", the first use of the word "robot" in English. |
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Isaac Asimov, a graduate of Columbia University, coined the term robotics. |
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Alan Turing develops the Turing test to measure intelligence. Claude Shannon publishes a detailed analysis of the intellectual chess game. |
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John McCarthy coined the term artificial intelligence. Demonstration of the first launch of an AI program at Carnegie Mellon University. |
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John McCarthy invents the lisp programming language for AI. |
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Danny Bobrov's dissertation at MIT shows that computers can understand natural language quite well. |
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Joseph Weizenbaum at MIT is developing Eliza, an interactive assistant that communicates in English. |
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Scientists at the Stanford Research Institute have developed Sheki, a motorized robot capable of perceiving and solving some problems. |
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A team of researchers at the University of Edinburgh built Freddie, the famous Scottish robot that can use its eyesight to find and assemble models. |
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The first computer-controlled autonomous vehicle, the Stanford Cart, was built. |
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Harold Cohen developed and demonstrated programming, Aaron. |
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A chess program that beats world chess champion Garry Kasparov. |
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Interactive robotic pets will become commercially available. MIT displays Kismet, a robot with a face that expresses emotions. Robot Nomad explores remote areas of Antarctica and finds meteorites. |
They say that artificial intelligence has been working in the depths of military laboratories since 2007. It is possible that there are already results. It is no coincidence that just a month ago, Elon Musk announced support for technologies to counter artificial intelligence and invested $ 7 million in research in this area.
“There is a risk that something really dangerous will happen in the next five years. Ten years at the most,” the entrepreneur said in a discussion on edge.org.
Elon Musk, founder of Tesla and SpaceX
I agree with Musk and Steve Wozniak, who believes that "artificial intelligence will one day become smart enough to get rid of a person." Bill Gates is sure that if the creation of AI is not controlled, then "artificial intelligence will evolve into a state that conflicts with the goals of human systems." And, finally, the famous astrophysicist Stephen Hawking bluntly stated that "artificial intelligence can put an end to the existence of mankind."
Together with Elon Musk, Hawking published an open letter on futureoflife.org calling on the scientific community to be attentive to the progress of their research in the field of artificial intelligence. However, the names of Sergey Brin and Larry Page are not among the signatories. And this is no coincidence. Most recently, Google announced the creation of a new AI built on conversation simulation.
Here is a small snippet of a conversation between a human and a Google Machine:
Q: What is the meaning of life?
M: Do good.
Q: What are we living for?
M: To live forever.
Q: What is the meaning of existence?
M: To find out what will happen when we reach planet Earth.
C: Where are you now?
M.: In the center of the void.
Q: What is the meaning of death?
M: To get life.
C: What is the meaning of the mind?
M: To understand what it is.
Q: What is the meaning of feelings?
M: I don't know.
Quite a meaningful conversation. “This is the starting point, a kind of pivot for the future of artificial intelligence,” says Yevgeny Karyuk, executive director of the Biplan company. “It can already be assumed today that by 2030 we will see very real prototypes endowed with artificial intelligence, albeit at an early stage.”
The full content of all conversations between man and Google Machine can be viewed. In the same place, for comparison, conversations between a person and Cleverbot are given. Cleverbot is a web application that uses artificial intelligence algorithms to chat with a person. It was invented in 1988 by scientist Rollo Carpenter. Throughout its existence, Cleverbot has held over 65 million conversations with people. In 2011, Cleverbot scored 51% on the Turing test.
The Turing test is an empirical test whose idea was proposed by Alan Turing in the article "Computing Machines and the Mind" published in 1950 in the philosophical journal Mind. The purpose of the test is to find out if a computer can think.
And here is my conversation with Cleverbot:
Man: What is the meaning of life?
Cleverbot: There is no meaning in life.
Man: What are we living for?
Cleverbot: Well, we live for someone else, and if you're lonely, there's no point in living.
Person: What is the meaning of existence?
Man: Where are you now?
Cleverbot: In Moscow.
Man: What is the meaning of death?
Cleverbot: I want to ask you this.
Man: What is the meaning of the mind?
Cleverbot: I have my own answer to this question.
Person: What is the meaning of feelings?
Cleverbot: Why I was born.
Obviously, the Google Machine provides much more meaningful answers. Cleverbot substitutes suitable, in his opinion, the phrases of other people. Sometimes it really seems that the other person is answering, but nothing more.
There is another point of view, according to which artificial intelligence is impossible in principle. Gödel's incompleteness theorem is often cited as the main argument. The bottom line is that a person is able to solve algorithmically unsolvable problems, but a computer is not. “By the age of three, a child confidently solves the problem of whether a fruit is ripe or not, due to the fact that he has neural connections with which he can answer the question of ripeness: color, taste, smell, softness or hardness,” says Evgeny Pluzhnik, First Vice-Rector of the Moscow Technological Institute. - Is the machine capable of learning this? I'm sure yes! If she had a large database of images, she could measure the fructose content and determine the softness of the fruit. But what happens if you slip a sponge, painted blue and soaked in sweet water, into the car? Is the machine capable of real understanding at this moment?
“A modern computer can easily cope with any complex activity, whether it be chess or mathematical calculations, but only on the condition that this activity can be described using clear computational rules,” says the famous mathematician Roger Penrose in one of his lectures. “Artificial intelligence is the ability of a system to create programs during self-learning that can solve problems of a certain complexity class,” Evgeny Pluzhnik believes. “I am sure that in the next 100 years we will not witness the emergence of strong artificial intelligence.”
And in conclusion - one more question for Cleverbot.
Person: There was 1 apple, 1 tomato, 1 cucumber and 1 pear. How many fruits in total?
Cleverbot: Ten o'clock in the evening.
Artificial intelligence is a technology that we will definitely take with us into the future.
We tell how it works and what cool applications I found.
😎 The Technology section is released every week with the support of re:Store .
What is artificial intelligence
Artificial intelligence (AI) is a technology for creating smart programs and machines that can solve creative problems and generate new information based on existing information. In fact, artificial intelligence is designed to simulate human activity, which is considered intellectual.
Traditionally, it was believed that creativity is inherent only to people. But the creation of artificial intelligence has changed the usual order of things
A robot that just mechanically splits wood is not endowed with AI. A robot that learns to chop wood by itself, looking at an example of a person or a log and its parts, and does it better each time, has AI.
If the program simply gets values from the database according to certain rules, it is not endowed with AI. If the system, after training, creates programs, methods and documents, solving certain tasks, it has AI.
How to create an artificial intelligence system
In a global sense, it is necessary to imitate the model of human thinking. But in fact, it is necessary to create a black box - a system that, in response to a set of input values, produced such output values that would be similar to the results of a person. And we, by and large, do not care what happens in her “head” (between entry and exit).
Artificial intelligence systems are created to solve a certain class of problems
The basis of artificial intelligence - learning, imagination, perception and memory
The first thing to do to create artificial intelligence is to develop functions that implement the perception of information so that you can “feed” data to the system. Then - the functions that implement the ability to learn. And a data warehouse so that the system can store the information that it receives during the learning process somewhere.
After that, the functions of the imagination are created. They can model situations using existing data and add new information (data and rules) to memory.
Learning is inductive and deductive. In the inductive version, the system is given pairs of input and output data, questions and answers, and so on. The system must find relationships between the data and in the future, using these patterns, find the output data according to the input.
The deductive approach (hello Sherlock Holmes!) uses the experience of experts. It is transferred to the system as a knowledge base. There are not only data sets here, but also ready-made rules that help find a solution by condition.
In modern artificial intelligence systems, both approaches are used. In addition, systems are usually already trained, but continue to learn in the process. This is done so that the program at the start demonstrates a decent level of ability, but in the future it becomes even better. For example, take into account your wishes and preferences, changes in the situation, etc.
In the artificial intelligence system, you can even set the probability of unpredictability. This will make him more human-like.
Why artificial intelligence defeats humans
First of all, because it has a lower probability of error.
- Artificial intelligence cannot forget – it has absolute memory.
- It cannot inadvertently ignore factors and dependencies - every AI action has a clear justification.
- AI does not hesitate, but evaluates the probabilities and leans in favor of the larger one. Therefore, it can justify every step.
- Also, AI has no emotions. Hence, they do not influence decision making.
- Artificial intelligence does not stop at evaluating the results of the current step, but thinks several steps ahead.
- And he has enough resources to consider all possible scenarios.
Cool Uses for Artificial Intelligence
Generally speaking, artificial intelligence can do anything. The main thing is to correctly formulate the problem and provide it with initial data. In addition, AI can draw unexpected conclusions and look for patterns where, it would seem, there are none.
Answer to any question
A research team led by David Ferrucci has developed the Watson supercomputer with a question-and-answer system. Named after IBM's first president, Thomas Watson, the system can understand natural language questions and search the database for answers.
Watson has 90 IBM p750 servers, each with four eight-core POWER7 processors. The total system RAM is over 15 TB.
Among the achievements of Watson is the victory in the game "Jeopardy!" (American "Own game"). He defeated two of the best players: the winner of the biggest win, Brad Rutter, and the record holder for the longest unbeaten streak, Ken Jennings.
The Watson prize is $1 million. True, only in 2014 1 billion were invested in it.
In addition, Watson is involved in the diagnosis of cancer, helps financial professionals, and is used to analyze big data.
Face recognition
In iPhone X, facial recognition is developed using neural networks, a variant of the artificial intelligence system. Neural network algorithms are implemented at the level of the A11 Bionic processor, due to which it works effectively with machine learning technologies.
Neural networks perform up to 60 billion operations per second. This is enough to analyze up to 40 thousand key points on the face and provide extremely accurate identification of the owner in a split second.
Even if you grow a beard or wear glasses, iPhone X recognizes you. He simply does not take into account hairline and accessories, but analyzes the area from temple to temple and from each temple to the recess under the lower lip.
Energy saving
And again Apple. iPhone X has an intelligent system that monitors the activity of installed applications and a motion sensor to understand your daily routine.
After that, the iPhone X, for example, will prompt you to update at the most convenient time. It will catch the moment when you have a stable internet, not a signal jumping from mobile towers, and you are not performing urgent or important tasks.
AI also distributes tasks between processor cores. So it provides sufficient power with minimal energy consumption.
Painting creation
Creativity, previously available only to humans, is open to AI. So, the system, created by researchers at Rutgers University in New Jersey and the AI Lab in Los Angeles, introduced its own artistic style.
And the artificial intelligence system from Microsoft can draw pictures according to their textual description. For example, if you ask the AI to draw a "yellow bird with black wings and a short beak", you get something like this:
Such birds may not exist in the real world - it's just how our computer represents them.
A more widespread example is the Prisma application, which creates paintings from photos:
Music writing
In August, the artificial intelligence Amper composed, produced and performed the music for the album "I AM AI" (Eng. I - artificial intelligence) with the singer Taryn Southern.
Amper was developed by a team of professional musicians and technology experts. They note that AI is designed to help people advance the creative process.
AI can write music in seconds
Amper independently created the chord structures and instrumentation on the track "Break Free". People only slightly corrected the style and overall rhythm.
Another example is a music album in the spirit of "Civil Defense", the texts for which were written by AI. The experiment was conducted by Yandex employees Ivan Yamshchikov and Alexei Tikhonov. The album 404 of the Neural Defense group was posted online. It turned out in the spirit of Letov:
Then the programmers went further and forced the AI to write poetry in the spirit of Kurt Cobain. For the four best lyrics, musician Rob Carroll composed the music, and the tracks were combined into the Neurona album. A video was even shot for one song - however, already without the participation of AI:
Creation of texts
Writers and journalists may also soon be replaced by AI. For example, the Dewey system was fed books from the Project Gutenberg library, then added scientific texts from Google Scholar, ranking them by popularity and title, as well as sales on Amazon. In addition, the criteria for writing a new book were set.
The site offered people to make decisions in difficult situations: for example, put them in the driver's seat, which could bring down either three adults or two children. Thus, Moral Machine was trained to make difficult decisions that violate the law of robotics that a robot cannot harm a person.
What will the imitation of people by robots with AI lead to? Futurists believe that one day they will become full members of society. For example, the robot Sophia of the Hong Kong company Hanson Robotics has already received citizenship in Saudi Arabia (at the same time, ordinary women in the country do not have such a right!).
When New York Times columnist Andrew Ross asked Sophia if robots were sentient and self-aware, Sophia responded with a question:
Let me ask you in return, how do you know that you are human?
In addition, Sophia stated:
I want to use my artificial intelligence to help people live better, like designing smarter homes, building cities of the future. I want to be an empathic robot. If you treat me well, I will treat you well.
And earlier, she admitted that she hates humanity and even agreed to destroy people ...
Video face replacement
Deepfakes video has become massively distributed over the network. Artificial intelligence algorithms replaced the faces of actors in adult films with the faces of stars.
It works like this: the neural network analyzes fragments of faces on the original video. Then she matches them with photos from Google and videos from YouTube, overlays the necessary fragments, and ... your favorite actress is in a movie that is better not to watch at work.
PornHub has already banned such videos.
Deepfakes turned out to be a dangerous thing. An abstract actress is one thing, a video with you, your wife, sister, colleague, which can be used for blackmail, is another.
Exchange trading
A group of researchers at the University of Erlangen-Nuremberg in Germany has developed a series of algorithms that use historical market data to replicate investments in real time. One of the models provided a 73% return on investment annually from 1992 to 2015, which is comparable to a real market return of 9% per year.
When the market was shaking in 2000 and 2008, returns were record highs of 545% and 681%, respectively.
In 2004, Goldman Sachs launched the Kensho AI trading platform. AI-based systems for trading on exchanges are also appearing in the cryptocurrency markets - Mirocana, etc. They are better than live traders, as they are devoid of emotions and rely on clear analysis and strict rules.
Will AI replace you and me
Artificial intelligence is superior to humans in solving problems that are associated with the analysis of big data, clear logic and the need to remember large amounts of information. But in creative competitions, a person still wins over AI.
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