While studying AI (both ML and AGI), I've made the following notes, which, possibly, will be of interest to others.
- By "AI," we'll mark what is currently called "artificial intellect," but that's not intellect. This is just automation of solving certain tasks using specific machine decision-making algorithms - decision trees, neural networks, and some other mechanics. All those algorithms are based on data and require vast amounts of homogenous data for their operations and enhancements. It would be more appropriate to call this Data Science, yet the talk is not about science but about applications of this science. Hence AI. BUT this is not intellect as understood by humans.
- Why is Python so popular in AI? The answer is quite unexpected. Python as a programming language for large products is not suitable. But its benefit is a fairly rapid mastery. BASIC of modernity of a kind. So, ML is not for programmers but for data scientists who may or may not be able to program. For them, Python is easier to master. The fact that everyone says "Python has more libraries" is not a problem because .NET also has ML.NET and wrappers for TensorFlow, Keras, etc. That is, if someone needs to implement ML in .NET - no problems. Is it possible to access Python from .NET? You can try using IronPython or approach the task via pythonnet. Thus, if you want libraries in Python, then there are solutions.
- Various ML courses still teach primitive algorithms, "bricks." The nuance is that all these bricks have long been implemented in the above-mentioned libraries, and it is not algorithms that should be studied but when to use which algorithms. A modern data scientist will not write an own regression or some other primitive function - it's not just extra work (everything has already been written for us), but you are not able to use the implementation with ready-made bricks. It's like writing your SHA256 or AES128 - well, you wrote it, but then where to apply it, as all encryption libraries have and use their own implementations.
- A consequence of (3) - a modern data scientist doesn't do programming at all but performs composition of blocks (low-code and no-code technologies) as well as builds and optimizes data pipelines. That's what is called MLOps now.
- There exist two directions of operations and development of an AI specialist and AI business. The first one is the building of data pipelines, described in (4). That's a great job, well-paid and required by the market. But that is not programming. If you love "data" as an entity, then you'd like this job too. So, this work and this business are about data: the more data, the better. There are no genius ideas, disruptive technologies, sophisticated inventions, and similar breakthroughs. The foundations of ML are not rocket science: they have simple math and trivial data transformations. You will succeed in building a business here if you see data and know how to collect them and apply them to optimize certain business processes. To re-iterate, you need to possess huge amounts of data; only this gets you ahead of the competition. The second direction of action is the application of the ideas and capabilities of ML in solving vertical market tasks and problems. AI activities and AI businesses in this direction grow from narrow and specific scientific and technological needs. Here, you may have little data (or no data at all). But you may make a breakthrough when solving a scientific or technological problem. For this direction, you first of all need to be a specialist in some subject - chemistry, physics, biology (or you should have such a specialist), and then you'd search, which ideas and principles of ML and AI you can apply in this specialist's work. Everything between those two directions ("we can write code, and we want to apply this knowledge in AI"
most often doesn't have practical applications and a business benefit. Consequently, the risks of such a business are not justified by the possible commercial results. - Could one use ML and AI for solving tasks X, Y, Z? Most likely, no. Modern AI, based on large datasets and data streams, is hardly applicable when you have little or no data. For this, you need other technologies like AGI. Those are, unfortunately, put behind because they don't bring quick money to large players, capable of investing in those technologies.
(this post has been originally published on my personal page).
