Machine learning in trading: theory, models, practice and algo-trading - page 3746
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The publication of unsolved mathematical problems provided a tremendous stimulus for the development of new mathematical theories, the discovery of previously unknown branches of mathematics and other research findings. Mathematicians tried their hand at tackling well-known problems.
AI marketing is destroying this mechanism. Unfortunately, this is a fact. It was precisely the unsolved problem that brought enormous benefits to mathematics worldwide. It is a pity that this is not unfolding according to Perelman’s scenario, but to suit the purposes of AI marketing. A dark day for mathematics.
In my humble opinion, all means are valid when it comes to solving problems. The use of computers to advance pure mathematics already has a rich history. It has been half a century since the four-colour problem was solved in this way. A complete classification of simple finite groups would have been impossible without computers.
In itself, the emergence of new possibilities in this field with the development of AI can only be welcomed. But using this for PR that fills people’s heads with all sorts of absurd ideas is utterly repugnant.
The emergence of new opportunities in this area as a result of developments in AI can only be welcomed in itself.
It seems they’ve squandered 15 million USD on solving the Millennium Problem. That’s probably a record-breaking waste.
They’ve destroyed the growth of one of the most beautiful branches of mathematics for the sake of PR. And they’re ready to keep chipping away at the tree of maths. Blasphemy met with applause.
Perelman’s autistic approach to mathematical research no longer seems quite so abnormal, given his natural secrecy. Aggressive AI marketing is now creating gags as well...
It seems they’ve blown 15 million USD on solving the Millennium Problem. That must be a record-breaking waste.
They’ve destroyed the growth of one of the most beautiful branches of mathematics for the sake of PR. And they’re ready to carry on chipping away at the tree of maths. Blasphemy met with applause.
There’s also been a bit of a scandal over the possible theft of results by an OpenAI employee who was working on the problem privately (but using AI).
Perelman’s autistic approach to mathematical research no longer seems so abnormal, given his natural secrecy. Aggressive AI marketing is now even creating gags…
In my humble opinion, Perelman is autistic only in relation to mainstream society. His mathematical socialisation is perfectly normal. That is precisely why, in my view, the attempt to steal his results failed.
However, with the current level of AI development, the possibility of scientific results being stolen is reaching a whole new level. And it is completely unclear how one can stand up to corporate thieves – solidarity amongst scientists may well prove useless here.
I asked the AI chatbot to explain the Navier-Stokes equations:
What about OpenAI’s solution to the Navier–Stokes problem? Is it a genuinely original solution, or is it a compilation of the vast body of literature on the subject?
AI chatbot’s response:
It seems they’ve blown 15 million USD on solving the Millennium Problem. That must be a record-breaking waste.
The following figures are interesting for comparison.
The following figures are worth noting for comparison.
These are, however, very specific man-hours – those of unique specialists, the training of whom has required a great deal of resources, which must also be taken into account. Moreover, it takes not just one person, but several schools of thought spanning several generations.
In some respects, the difference between ordinary mathematicians and AI mathematicians is small – they both need the same ‘shoulders of giants’ to stand on, and so on.
In my view, the costs will be roughly comparable, but the big question is how to account for the infrastructure costs required to support both mathematicians and AI.
From a statistical point of view, it all depends on the distribution used. And here we might recall Taleb, who argues that some areas of human activity are described by normal distributions, whilst others are characterised by heavy-tailed distributions such as the Pareto distribution.
These are, after all, specialised man-hours – those of unique specialists whose training has required a great deal of resources, which must also be taken into account.
There’s no need, as we don’t factor in development costs for AI.
In that case, we simply need to calculate it based on average grant amounts. For two people solving the problem over the course of a year, that works out at around 0.2 million. That amounts to a nearly hundredfold advantage.
This could be attributed to the novelty of the technology, as was the case, for example, with aluminium, which was initially used for jewellery.
In that case, you simply need to work it out based on the average grant amounts. For two people who have solved the problem within a year, that works out at around 0.2 million. That works out as a nearly hundredfold advantage.
It’s better to use physical units. For example, in W*h. Or perhaps in ‘tokens’.
It’s better to use physical units. For example, in W·h. Or in ‘tokens’.
If measured in W·h, the difference is more than 2,000-fold. But if we take into account that energy from food is more expensive than from the mains, then it’s about 25-fold.
If we use ‘tokens’ as a rough measure, then it’s almost 5,000 times.
We’re waiting for artificial OI to reach the level of AI chatbots. It runs on glucose.