AI University is pushing AI development to the masses

Rumors that AI will leave humans to work are a bit exaggerated. As history shows, such sentiments arise every time a new revolutionary technology appears: steam engines, railways, electricity and the Internet. And every time it turns out that the employment created with the help of these technologies is many times greater than all the lost jobs. New powerful neural networks will not be an exception. Firstly, their opportunities in themselves open up limitless scope for creating new professions and projects for people of their interests. Secondly, someone still needs to interact with them: develop, train, test, set tasks, monitor security, and so on. AI will not set a task for itself, and will not evaluate the result of its implementation. And for those AIs that can set the task themselves, the more you need an eye and an eye.

You need to take seats on this train early. During life, adults and capable of catching a revolutionary technological transition is an event about which they say “such a chance falls only once in a lifetime.” Previously, it really was like this: in the 18th-20th centuries, technological transitions happened with a step of 50-100 years. But since the end of the 20th century, progress has accelerated significantly: those who came to the Internet in the 1990s, mobile development in the 2000s, invested in Bitcoin in the 2010s, secured a career and a source of income for a lifetime. But revolutionary transformations that will change everything, including already existing industries (the same Internet, mobile technologies, fintech, and so on) and affect all areas of activity, continue to occur like clockwork every decade. The wave of changes will cover everyone, so why not try to ride it?

AI for Everyone: How to Revolutionize Your Career

The advantages of technological transitions are that in the early stages, while the market and labor niche is not yet formed and occupied, it is open to almost everyone – both for people with relevant experience and for newcomers who are ready to jump from other employment (a story by a student of the University of Artificial intellect “How I Stopped Working as a Veterinarian and Became a Neuron Programmer“). Later, when the market is occupied, divided, and all jobs created and distributed, there will be no such opportunity. According to expert estimates, the AI ​​market will grow to two trillion dollars in 7 years.

https://www.statista.com/statistics/1365145/artificial-intelligence-market-size/

Now, at the stage when everyone in this field is still newbies, it’s time to master relevant professions and open AI businesses. By the way, AI will also inevitably change the same veterinary medicine, both in predictable ways – in the field of diagnosis, in the first place, and in completely unexpected ways that are yet to be invented. Therefore, the experience “from veterinarian to AI engineers” does not at all mean parting with one’s past profession, but can become a way to return to it on one’s own terms: for example, to open a business, outstripping competitors in offering radically new services.

And the same is true for any profession. For non-IT people, AI opens up even more opportunities, because the addition of AI to professions where digitalization has not yet played a key role can become the same revolutionary business revolution that the Internet and mobile phones have become for many industries. That is, it’s completely unnecessary to give up your calling and cynically go into AI “for a long ruble” for good. You can learn how to work with AI, and then return to your profession and change it.

Many veterinarians also sincerely love their profession.

Profession in AI from scratch: where to start?

Choose the specialization that is closest to you. The field of AI includes many different areas, such as computer vision; reinforcement learning; classical machine learning; genetic algorithms; integration into production; project management and custom sales and so on.

Get an education in a field related to AI. The time it takes to learn AI depends on which path you take: self-taught or through formal education.

Employers often want candidates for an AI position to have a university degree in computer science, mathematics, or another technical field. However, for some entry-level positions, an associate degree or even no degree may be sufficient. This is all the more unnecessary if you are looking to return with AI skills to your old industry or start your own business.

The duration of self-study depends on your prior knowledge, the number of free hours per week you can afford to dedicate to it, and the available learning resources. It can take anywhere from a few months to a year or more to get a solid grasp of AI concepts, programming languages ​​such as Python, mathematics, and various machine learning algorithms. Self-paced online courses, tutorials, and practical projects can speed up the learning process — fortunately, there are a lot of offers on the education market for teaching artificial intelligence, and then I will talk about my experience at the University of Artificial Intelligence.

The length of formal education is more standardized: earning a bachelor’s degree in computer science, data science or a related field will take ~four years, during which you will receive comprehensive training in AI and related subjects.

Take an internship to gain hands-on experience. An internship is a great way to get hands-on experience in AI and learn more about how the industry works. Many companies offer internships for students and graduates, so look for opportunities in your area. The most advanced AI course programs also include internship programs. IN Neural University internship program students implement projects for companies of absolutely different spheres and sizes.

neural-university.ru/internships

Apply for entry-level positions in AI. After you have received education and practical experience, you can start looking for a job in the field of AI. Start by looking for entry-level positions like junior machine learning engineer or data analyst to gain more experience and advance your career. In addition, it will help to develop the necessary connections, competencies and find a promising niche for your own business.

Or return to your area with new skills. If you were already an expert in some non-IT specialty, then an alternative to starting a career in AI from a grassroots position could be to return to your profession at the level of head of AI integration or even launch a business in your own unique niche. People at the intersection of non-IT professions with AI competencies will be rare at first – and there will be a huge demand for them. And AI will come in handy in absolutely any employment: from doctors to carriers, from museum work to mining.

Keep developing. The field of AI is constantly evolving, so it is important to keep learning and developing your skills. There are many online courses and projects that offer AI training for various skill levels and purposes. Some of these courses cover deep learning, machine learning, data science, tensorflow, neural networks, and predictive analytics.

Networking and activity in the AI ​​community. Being part of the AI ​​community can help you learn more about new technologies and trends, as well as meet other professionals in the field. Attend conferences and events, participate in online forums and groups to expand your network of contacts. There are free programs that AI schools offer to attract new students, such as the three-day intensive “University of Artificial Intelligence” for everyone who wants to try AI development. It lasts 3 days, during which you yourself will write as many as 9 neural networks. There will also be 3 homework assignments with verification and 1 interactive master class. The intensive is designed for learning from scratch, that is, for people with no experience at all in this direction.

Personal experience of studying at the University of Artificial Intelligence

In the process of choosing courses in AI development, I went through many different machine learning programs. They all started with topics such as mathematical analysis, terver, the theory of random processes, Markov chains, higher algebra with a full entry into tensor modeling – and this, they say, is only a small part of the knowledge required for the transition to, in fact, neural networks. It is clear that all this stretches out in terms of a year and a half, and in terms of money – from 200 thousand rubles at best. But I wasn’t going to write a dissertation. At the end of the course, all I needed was a portfolio of completed projects for my resume, plus a couple of tasks for my current work to solve.

The possibility of freelancing in the future also seemed promising: I found an order, quickly completed it, and quickly received payment. Having settled on the University of Artificial Intelligence, where there was everything necessary to develop ML models for most basic tasks without going into a complex, but completely impractical theory, I taught the first neuron in my life – image recognition for playing tic-tac-toe – somewhere through three hours after entering the training platform for the first time.

The terms of reference were: write a neural network yourself, which can become an integral part of the tic-tac-toe bot system. Using the prepared image database, create and train a neural network that recognizes two categories of images: tic and tac toe. Achieve over 95% recognition accuracy (accuracy)

Terms of Reference: Independently write a neural network that can become an integral part of the bot system for playing games "Tic-tac-toe".  Using the prepared image database, create and train a neural network that recognizes two categories of images: tic and tac toe.  Achieve over 95% recognition accuracy (accuracy)

Basic Machine Learning Course

Here is an example curriculum for the course “Data Science, Neural Networks, Machine Learning and Artificial Intelligence” that I am currently taking. It is divided into two blocks – basic and advanced.

base unit

  1. Syntax

  2. Numpy and Matplotlib libraries

  3. Introduction to neural networks. Line Layer (Dense)

  4. Training, validation and test sets. Retraining of neural networks

  5. Convolutional Neural Networks

  6. Modules. Neural network integration on DEMO PANEL

  7. Text processing with neural networks

  8. Recurrent and one-dimensional convolutional neural networks

  9. Pandas and Matplotlib libraries

  10. Solving the Regression Problem Using Neural Networks

  11. Time series processing with neural networks

  12. Processing Audio Signals with Neural Networks

  13. Autoencoder architecture

  14. Image segmentation

  15. Creating a simple web server and configuring operation parameters

  16. requests library. Calling the model via API

advanced block

  1. Variational autoencoders

  2. Generative Adversarial Networks

  3. Word processing. Sequence-to-sequence model

  4. Word processing. Mechanism Attention (Networks with attention)

  5. Word processing. Mechanism Transformers

  6. Reinforcement training. Introduction. Q-learning algorithm

  7. Reinforcement training. Political learning methods, Reinforce algorithm

  8. Reinforcement training. Benefit networks, improved Q-learning algorithm.

  9. Object Detection Model YOLOv3

  10. Object Detection Models YOLOv4, RetinaNet

  11. Object detection. Object Tracking Technology

  12. Genetic algorithms. Introduction. Basic principles

  13. Genetic algorithms. Selection of neural network hyperparameters

  14. Data clustering algorithms

  15. Audio processing. Speech recognition (SpeechToText)

  16. Audio processing. TextToSpeech Technology

It should be noted here that for learning it is necessary to master the Python language. I owned it at a sufficient level initially, so the corresponding sections are not included in my list. If you do not own Python – it must be additionally included in the program – “25 Free Python Courses“.

A look into the future

AI, like Comrade Colt, will equalize everyone for a while. But, if you look at the past experience of the Internet revolution, the mobile revolution, and so on, everything that starts with fair competition of garage startups inevitably ends up with corporations and a divided market where no garage startup can break through. AI cannot escape this fate either: sooner or later, today’s AI startups will become tomorrow’s AI monopolies. And if this is inevitable, then, as they say, “you can not stop – lead.”

Arm yourself with paid and free resources, stock up on time and patience – and get started, the revolution will not wait.

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