Tim,

That use should be something that would benefit people. Good luck with it.


On Wed, 17 Jan 2024, 1:18 pm Tim Daly, <[email protected]> wrote:

> I have GPT systems running locally. It is possible to feed the systems
> text files for training. Once trained they can answer questions related
> to the training. It will be interesting to convert the book to text and
> train the system on the book. At minimum it might make an interesting
> "help" facility capable of responding to natural language questions.
>
> On Monday, January 15, 2024 at 10:05:54 PM UTC-5 [email protected]
> wrote:
>
>> Tim, notice though that the only intelligence here is what is provided by
>> human intelligence. None of the various systems have any inherent
>> intelligence.
>>
>> My youngest granddaughter has just turned 1 and I have been spending time
>> watching her develop intelligence and the difference between her and any of
>> the current systems is possibly on the order of a million orders of
>> magnitude with the systems being the primitive level.
>>
>> As I said above, these systems can have possible use cases but only if
>> humans are controlling them intelligently. It is a matter of finding
>> specific use cases that work for us.
>>
>> An appropriate analogy is summed up as: fire is a good servant but a
>> destructive master.
>>
>> On Tue, 16 Jan 2024, 1:00 pm Tim Daly, <[email protected]> wrote:
>>
>>> There is an interesting example of proof earlier in the video:
>>> https://youtu.be/3pb_-oLfWJ4?t=298
>>>
>>> Reinforcement learning (using the Bellman equation[0]) needs
>>> the ability to at least decide if it succeeds and can use information
>>> about success in intermediate steps to rapidly improve. It also
>>> needs a set of actions that can be performed at each step.
>>> Reinforcement learning has conquered gaming with this structure.
>>>
>>> LEAN provides a database of theorems, definitions, and tactics
>>> that can be applied at each step of a proof. It provides feedback
>>> about the local success of each action. It provides global success
>>> once the proof completes. Thus all of the elements needed by
>>> reinforcement learning are available in proof systems. LEAN's
>>> theorems are strongly typed.
>>>
>>> When training GPT-like systems, reinforcement training is the last
>>> step (see Karpathy's slide 3):
>>> https://karpathy.ai/stateofgpt.pdf
>>> It is straightforward to customize a GPT-like system with LEAN's
>>> mathematical knowledge to focus on proofs.
>>>
>>> We use group theory as our organizational skeleton. One of the steps
>>> to integrating LEAN and computer algebra is aligning LEAN's axiom
>>> structure with our category/domain structure (my current research).
>>> Once that is done then theorems, such as a commutative theorem,
>>> can be inherited and available to prove a function in a domain.
>>>
>>> We use Buchberger's Groebner basis algorithm. This algorithm has
>>> already been proven in Coq and is in use[1].
>>>
>>> It is only a matter of time before the axioms which support the Risch
>>> algorithm are proven. That will form the basis of proof of our current
>>> implementation.
>>>
>>> Because we are strongly typed and use a group theory scaffold we
>>> are in a unique position to formalize our algorithms.
>>>
>>> The combination of proof systems and computer algebra will be a
>>> large step in computational mathematics.
>>>
>>> Tim
>>>
>>>
>>> [0] Bellman Equation
>>> https://en.wikipedia.org/wiki/Bellman_equation
>>>
>>> [1] Connecting Gröbner Bases Programs with Coq to do Proofs in Algebra,
>>> Geometry and Arithmetics
>>> https://arxiv.org/abs/1007.3615
>>>
>>>
>>>
>>>
>>>
>>> On Monday, January 15, 2024 at 6:59:46 PM UTC-5 Dima Pasechnik wrote:
>>>
>>>> On Mon, Jan 15, 2024 at 10:45 PM Hill Strong <[email protected]>
>>>> wrote:
>>>> >
>>>> > No artificial stupidity (AI) will ever or should ever be considered
>>>> as [a trustworthy co-author in mathematical research]. At best, these
>>>> systems could be a possibly useful tool in the hands of those who know
>>>> their subject field. In the hands of everyone else they will be like young
>>>> children using power tools - a dangerous proposition at best and an utter
>>>> disaster at worse.
>>>> >
>>>> > There is far too much unthinking hype about the subject even by the
>>>> researchers in the field.
>>>>
>>>> Well, we all know Doron Zeilberger's co-author:
>>>> https://sites.math.rutgers.edu/~zeilberg/ekhad.html
>>>>
>>>> In this sense it's not new at all. More powerful, yes, but still...
>>>>
>>>> >
>>>> > On Tue, 16 Jan 2024, 12:10 am Tim Daly, <[email protected]> wrote:
>>>> >>
>>>> >> "When integrated with tools such as formal proof verifiers,
>>>> >> internet search, and symbolic math packages, I expect, say,
>>>> >> 2026-level AI, when used properly, will be a trustworthy
>>>> >> co-author in mathematical research, and in many other fields
>>>> >> as well" -- Terrance Tao
>>>> >> https://youtu.be/3pb_-oLfWJ4?t=358
>>>> >>
>>>> >> I might add that it would be important that the computer algebra
>>>> >> algorithms be proven correct.
>>>> >>
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>>>>
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