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Artificial Intelligence 2019

we will tell people who are not related to software engineering
about today's machine intelligence and its use
Main course topics
  • comparison of machine and biological intelligence, how to think about machine intelligence
  • intelligence-stack: the main platforms of machine intelligence and their development prospects
  • application of machine intelligence in engineering, management, science
  • entrepreneurial perspectives of machine intelligence
What this course is about?

What is so surprising about the current rapid development of AI
In artificial intelligence, it was unexpectedly modeled not logical component of the human mind, but the an intuitive understanding. It turned out that neural networks make it possible to solve the problems of recognizing images, voice, situations, detecting abnormalities, but not as logical machines, rather in a completely different way: building multi-level abstractions. It all started recently: this breakthrough was noticed by humanity in 2012, only seven years ago. We will show what follows from this unexpected event.
Is AI capable of creating
Yes, machine intelligence can create new things these days. And it creates it out of chaos, while using evolution, while using competition with itself. And creates no worse than man would do. Including art. Including engineering. We will explain how this works - without formulas and mathematics.
Why artificial intelligence has not yet taken over the world
Why it is ridiculous to say both "man has taken over the world" and "machine intelligence has taken over the world." Why it is impossible to think of machine intelligence as a specific person, and how to think about it. Coexistence of machine and human intellects. Openness and evolution instead of "taking over the world."
Why artifica intelligence is talked about so much, yet it is not visible?
Of course, artificail intelligence is already visible: machine translation, speech recognition and speech synthesis. There are already cars that do not require a driver, although few were seen in our areas. Usually the cycle from investing into a complex product until its' entering the market for mass use is three years. Invested in the second quarter of 2019 alone, $ 7.4bn, the use of those money will be clearly visible in three years, in 2022, when the products will enter the market. The course will tell you more about what project get sponsored.
What you will gain from this course
Except overall mind clarity
What is the right way to think about artificial intelligence and what to expect from it
You will distinguish between strong / general artificial intelligence and weak / narrow artificial intelligence. You will not treat a computer program like a person. You will cease to compare machine intelligence only with the highest collective achievements of human intelligence and notice that it is cheaper than mushrooms and therefore you do not need to expect much from it for such money.
What money is spinning in the field of machine intelligence
How much does it cost to use (output) for a neural network and how much does it cost to train a neural network. Why is the entry barrier so low (the cost of a video card), and why only large companies
can afford breakthrough developments. What investments go into artificial intelligence, and whether the peak of their popularity has passed (no, far from being passed. It's like investing in people: it will never be enough! And the bill goes already to billions of dollars per quarter).
How artificial intelligence will affect jobs

The premise that artificial intelligence will steal people's jobs is wrong: the sum of people's work is not constant. Machines robbed people of almost all agricultural and a significant part of factory labor, yet the level of employment in the world did not become many times less. About artificial intelligence you need to think in about the same way. And enjoy the extra weekend a week.
The course is recommended for those who do not have enough time to follow the news of artificial intelligence, but want to keep up with the latest achievements. There will be no mathematical formulas and examples of computer programs in the course. If you ask about human intelligence, then it should be least interesting to know about the biochemistry of the brain or about what specific parts the brain consists of. What should be interesting is rather how intelligence created rock festivals, skyscrapers and regular space flights to Mars. This is how we'll talk about machine intelligence: it is meant for people who would like to use it, not develop its algorithms and equipment.


Course Program
1. Intelligence: machine and biological
The future is already here, only it is unevenly distributed. Where did human intelligence come from? Artificial intelligence: "what computers do not know how to do." What is the essence of today's breakthrough in machine intelligence? Everything is fast. Deep learning: deep abstractions. Why just now? Sutton Thesis, March 13, 2019. Singularity. Examples of today (not the future). NLP - Natural Language Processing. Goal: capuchin-like. Research speed: growing according to Moore's law. How the research works. Dexterity, embodied intelligence. Grounding and grounding and embodied intelligence. Creativity and competitiveness (adversarial architectures). Open endedness. There is no "intelligence", then what is cool? How to relate to AGI. The concept of cyber identity. Cyber identity and work with attention. Robots take away work? Whose virtual assistants are these? Adversarial attacks, deepfakes and so on. Technology neutrality.
2. Интеллект-стек
Systems Intellect Stack Levels. Emergence. Stack of machine intelligence platforms. Machine Learning Platforms. The main algorithm. An alternative stack of deep learning. Small connectivity: the key to development and improvement. Life cycle metaphors of "competency" (including machine). Language models. Data corral (data wrangling), plumbing data (data plumbing). The innate priors problem. Modeling, programming, designing, ontologization, formalization are all one. Machine learning and the level of cognitive architecture. Prospects.

Системные уровни интеллект-стека. Эмерджентность. Стек платформ машинного интеллекта. Платформы машинного обучения. Главный алгоритм. Альтернативный стек глубокого обучения. Малая связность: ключ к развитию и совершенствованию. Метафоры жизненного цикла "компетенции" (в том числе машинной). Языковые модели. Загон данных (data wrangling), сантехника данных (data plumbing). Проблема innate priors. Моделирование, программирование, проектирование, онтологизирование, формализация — это всё одно. Машинное обучение и уровень когнитивной архитектуры. Перспективы.
3. Applications
Dreams of humanity (how to measure progress?). Active horizons ("how life works"): AI applications wherever there are human application. Investing in AI. Forbs: 50 AI startups review (September 17, 2019). Today's frontier: cars. Invest today, results tomorrow. What do cheap quality cameras provide? Unexpected service and surveillance. Machine translate. Scientific discoveries. The Internet of Things (IoT), BigData and AI are closely related to predictive analytics. Robotic factories (dark factories). Hull engineering. Code and AI models as another type of media: stores and clouds. Take ideas from machine intelligence into pedagogy. Main problems. Life cycle of engineering technology: an example of AI. The dilemma of the innovator.
Still unsure about the course?
in the chat you can ask colleagues and lecturers questions that interest you and make an informed decision after you receive an answer to your question

(if you can't use the link, you can find the chat via the search: @welcomeSSM)
You really want but cannot join us physically? Then join us online!
Pick type of enrollment on the page that appear after you click on the button "I want to join group".
Course instructor
Anatoly Levenchuk
Head of Science of Systems Management School
Engaged in machine learning and artificial intelligence, when it was not even trending (since 1977).
What's next?
  • Do nothing. Just accept the knowledge you gathered from the course and stop worrying about the fate of mankind Machine intelligence will help to solve many of the problems, but people won't be left with no work (although it is unlikely that their current work will still be out there).
  • Your occupation will have to be changed anyways, because machine intelligence is omnipresent and spreads rapidly.
  • Invest in yourself: take some courses that will help you feel more confident during the inevitable changes. Systems Management School programs are at your service.
  • Get interested and start using machine intelligence in your work.
Not ready yet to enroll in our courses? Well, we created something very useful and absolutely free for you: project analysis check-list.