To collect and analyze existing data. For example, scientists can thus test a hypothesis, narrow the search area, and perform routine operations. In some studies, you can not grow cultures in a Petri dish, but use a mathematical model, rucing the research time, and leave cultivation to confirm the hypothesis. Or, with the help of train AI, to recognize tumors, to find some non-obvious patterns. But is generate new areas of knowlge or look for fundamentally new solutions. They can analyze the available data, but synthesizing new knowlge from this is still very limit. – Rather, when we come to a real AI, capable of thinking independently.
How to understand what scientific projects
Last year a project came to our foundation that want to Wuhan Mobile Phone Number List answer the question – is it possible to mathematically assess that an artist has fallen into a state of inspiration? Is it possible to evaluate this process, reproduce it in numbers? Unfortunately of the area fund at that time. But let’s imagine that such a study turn out. In fact, we would be one step closer to an AI that can create something. Not according to a text query compil by a person, but to generate independently what he wants to see. If we transfer this to science, the scientist also finds himself in a certain state when he is engag in scientific activities.
The topic turn out to be outside the scope
He may not call it inspiration, but it is something close BT Lists to him. When we can understand, model and digitize such processes, then it is possible. But now, again, AI technologies play a supportive role in science. Freaks or geniuses — to invest in when they are only at the idea stage? – We can not always assess the prospects and the possibility of something. Even the best minds can’t. For example, two-dimensional materials in the last century seem something impossible. In the 1930s, eminent theoretical physicists.