Data Scientists are data analytics experts who have the technical skills to solve complex
Alexandra Murzina, Machine Learning Engineer, Advanced Technologies Group, Positive Technologies
One of the most promising techniques inmachine learning is now reinforcement learning. By the way, the DeepMind system, which won StarCraft II, is based on it. This approach to learning, and then to using, is really more reminiscent of AI. Such a system is immersed in an environment that gives it responses to actions. This method is very similar to how you and I study, but sometimes it takes us years, but here there is an opportunity to significantly speed up the process through modeling and computer power.
For example, such a system will quickly learn "wellto drive a car. Of course, there will remain issues related to exceptional situations and the operation of the system in their conditions (due to the limitation of the tasks it solves). In the autumn of 2017, capsular neural networks caused no less noise: at that time they spoke as much as about a revolution in the world of deep learning. Today they almost forgot about them in public. In practice, still very popular are the busings and neural network architectures, which have already become the standard for solving certain problems. Such, for example, as the detection of objects in images or classification of images.
Theoretically, AI as a technology will come faster inthose areas where a person’s work consists mostly of routine actions, or in areas where decisions need to be made quickly based on a large amount of data. That is, the sensational news about how 600 traders were replaced by two hundred programmers can easily become one of ordinary events in the field of automation. Turning to the cybersecurity industry, such automation is likely, for example, in the area of virus analytics, or the detection of attacks. By the way, our team is working on a technology that allows machine learning to detect attacks on web applications in seconds.
In practice, predicting industries in which A.I.will be in demand on a large scale in full force and in the near future it will be difficult, since its use is associated with a lot of social and legal nuances. Let's say, autopilot: technologically it is already quite possible to transfer standard flights to autopilots, but how many passengers would trust their lives to an airplane without a person sitting at the controls? Or, say, medicine - there are many developments based on the use of AI in relation to this industry, but from the point of view of the legislative framework they cannot be used to the fullest and today they still remain at the conceptual level.
Yes, there is a lot of noise around the direction:at the same time, many experts prefer to immediately talk about solving specific problems, saving time and money, subject to the use of technology. In reality, these ideas stumble over the personnel issue: if several years ago there was talk about a shortage of programmers, then nothing has changed now, except that now we no longer just need, for example, Java programmers, but also complex engineers, who can understand and solve the problem, including using machine learning methods.
Feeling of market oversaturation with specialistsmachine learning is more deceptive than real. Yes, many people think that they can quickly learn this area by taking a couple of courses, but as a result, the market is oversaturated with specialists with an irrelevant background. However, machine learning is in most cases a tool for effectively solving a specific problem (well, only if you are not doing it for its own sake) and in order to choose the right tool you need to have extensive expertise in a specific field (in our case, information security) .
The history of machine learning today is moreIt resembles the situation with the “golden hammer” antipattern popular in the development environment, in which any problem is tried to be solved with the help of a single (albeit golden) hammer. Machine learning specialists who have completed a couple of courses apply their experience in a hundred cases out of a hundred, without being able to understand when another toolkit is needed — an additional one. Many of these freshly baked specialists are not developers and cannot go beyond the framework of Jupyter Notebook (a popular tool in ML), or lack adequate knowledge in other areas, which does not allow them to successfully use technology in any particular area to solve specific problems.
Alena Arykina, Data Scientist Sberbank PJSC
In machine learning there is a creative part,depending on the data and knowledge of the field, on the intuition of the developer and much more. And there are automatic things, where you need to find better parameters and rewrite a long-known code. The second part, as in any "mechanical" process, people tend to optimize everything, including through machine learning. If earlier mathematicians moved from “manual learning” to automatic one on the basis of such libraries as keras, today examples of such optimizations can serve as libraries for managing the state of datasets, preprocessing pictures and texts, even automatic detection of features of elements. Algorithms can be simple (cut off the endings of words) or complex (build special neural networks - autoencoders that compress data to any size), but a set of such training tools most often determines the quality of the final model, and therefore the skills of a datassentist.
Machine learning will eventually come later in allareas where it will be slowed down by legislation or human distrust: medicine or a car with an autopilot. We already see tremendous achievements in these areas - they are being used in other countries. But I am sure that in order to implement them with us and make them accessible to ordinary people, we will have to win more than one bureaucratic war.
Data Science is really very interesting. Every second my friend IT specialist already at least read about machine learning. Willy-nilly, you start to worry: won't there be too many datasintists? In addition, now they are trying to use machine learning in any IT project and for any tasks, not really imagining why such algorithms are needed there - it is fashionable. HYIP will pass and the number of vacancies will decrease. On the other hand, the question is whether those who really love Data Science will remain in the profession and not chase after fashion.
Tatyana Savelyeva, head of unstructured data group, Yandex.Taxi, author of the tldr_arxiv telegram channel
I don’t really like the word “AI” at all, becauseit is too general and ambitious, and often causes people to overestimate the level of technology. You know, there is such a joke: “How to distinguish ML from AI? ML is done in Python, and AI is done in PowerPoint.”
The first Data Science trend is the increasingpopularity of the subject area: companies are increasingly realizing that without processing a large amount of useful information in the future it will be difficult. There is a trend towards automation of machine learning: if 10 years ago you had to write all the methods to work yourself, now there are many convenient ready-made libraries.
But with the increasing ease of use of methodscurrent tools are changing more and more quickly - you need to constantly keep your finger on the pulse. There is a trend towards the use of neural networks: industrial conferences are publishing more and more articles related to this type of algorithms.
So machine learning is lastwill come to areas where there is little data or where they are not at all - for example, in this way you can hardly predict the place where the asteroid falls, or the time of the collision of the Moon with the Earth. It seems that machine learning is hard to implement in bureaucratic institutions - government agencies, medical institutions.
In any case, at some point the market willThere will be a large number of applicants for starting vacancies - junior specialists or interns, as the knowledge necessary for employment in such a position is becoming more accessible. But the demand for experienced specialists who have already implemented ML projects will grow, since it takes a lot of time and effort to gain this experience, and the number of machine learning tasks is growing faster than the number of people who have had time and were able to gain such experience.
Emily Drahl, Data Analysis Analytics, Mechanica AI, Head of Data Mining in Action
In machine learning one of the mostbright trends is the transition from its use as an assistive technology to full automation based on it. This is most clearly manifested in the automation of industrial production, agriculture and agribusiness, as well as the development of the concepts of smart city and smart home.
Now machine learning applicationsquite a lot and this is due to the current level of technology stack development, the level of our understanding of the field and a number of unresolved ethical issues. My personal top application is medicine, psychology and pedagogy. Here, it is not primarily about ancillary services (recommendation systems for diagnosing diseases or interactive systems), but about the full automation of processes through AI and ML.
I think the IT industry today is different becausetechnologies are developing very dynamically and if you stop keeping pace with these changes, then there is a very tangible risk of becoming an unclaimed specialist. This is one of the few areas where university graduates without experience can compete with experienced professionals.
Thanks to the dynamic market, work for those whoKeeps up with trends, will always be. But for those who are not ready to learn all their life, the difficult question remains to be solved: how to remain relevant. Here will help experience, professional outlook and knowledge of related (or not so!) Areas of activity.
The field of education is currently changingconceptually and, so to speak, faces not only schoolchildren and students, but also adult professionals with work experience. Having relevant education in the past, a sufficient amount of time and the proper level of perseverance, you can retrain yourself without significant financial investments and pass an interview at a minimum for an entry-level position in data analysis. This is one of the goals of online courses.
If we talk about universities, most of themexperiences a number of difficulties in teaching current technical disciplines: technologies change very quickly, it is necessary to attract practicing specialists, and they are not always ready to work in the format that the university implies. This is how leading IT companies come to the rescue, creating schools, opening departments at universities, conducting practical courses and internships, and also training yesterday’s graduates within the company at the start of work. Ultimately, I personally see the task of a university not only and not so much in releasing a ready-made specialist to the market, but in the fact that higher education should give a person a certain cultural, intellectual and emotional level, on which his professional life depends to a large extent rather than from knowledge of specific technologies.
Anna Voevodskaya, machine learning expert, Jet Infosystems
It seems to me that now they are applying more and more.reinforcement learning. The decision to learn, interacting with the environment, using rewards, actions and observations. One of the most famous examples of reinforcement learning is AlphaGo. Also, such training methods are used to simulate the movement of a person (the last competitions at NIPS were about RL), machines and other.
Machine learning is magic at its best.sense. Quite complex mathematics is applied specifically to your data, an in-depth analysis is done and a very accurate forecast is issued just for you. And everyone wants this magic for themselves: it makes money, and it’s good for their image - great.
As for the glut of the market, I'm in thisI do not believe. Good specialists are always hard to find. For example, Java did not appear two years ago, but senior is still hard to find in this area. And a good datasaytnist is generally like a unicorn: he knows and loves math, and kodit, and understands business metrics, and explains everything well. If we have an oversupply of such people at some point in the world, it will be nice. But this is some kind of utopia.