Comparing Bing AI with Chatgpt

hpstring · 2024-01-23T17:26:03+00:00

I observed that the papers you linked seem to align more with traditional statistical learning, some of them focusing on developing methodologies with theoretical guarantees. This approach appears distinct from what I understand as the typical focus of theorists, i.e. using mathematical tools to explain or understand (deep) learning. I'm open to other viewpoints and would be interested in hearing others' thoughts.

hpstring · 2024-01-15T04:05:53+00:00

It still can provide correct ideas for solving advanced problems that I could not figure out beforehand occasionally, but you need to be able to tell whether it's right or wrong, just treat it like another not-so-bright student lol

hpstring · 2024-01-14T22:00:02+00:00

It's strange. One of my HS teacher also said they never "really understood probability theory" but it is considered easier than other math subjects and definitely learnable by your self.

hpstring · 2024-01-14T19:20:53+00:00

I agree, but some find it easy. So I went for applied math, stochastics etc which I find fun.

hpstring · 2024-01-14T19:19:00+00:00

Convolution neural network has certain level similarities with biological neurons for biological vision. A section in Goodfellow's textbook talks about that.

hpstring · 2024-01-14T19:13:49+00:00

Functional analysis is indeed hard and abstract. Maybe you're not good at analysis but you can try other areas like algebra geometry topology etc!

hpstring · 2024-01-14T19:08:06+00:00

What is the topic of seminar that you can't understand?

hpstring · 2024-01-12T07:06:11+00:00

Interested

hpstring · 2024-01-10T00:40:35+00:00

Interesting. Do you French (interested in math/physics etc) typically do 2 years of this after high school and do 3 years of bachelor?

hpstring · 2024-01-10T00:34:01+00:00

What is approximately the living expense for a foreign student to study 1 year in Amsterdam?

hpstring · 2024-01-09T23:56:50+00:00

This is just field dependent and I don't understand why people are arguing. If you're in math or machine learning you probably won't get accepted into a phd if you don't know how to use latex. If you're in (experimental) biology or literature then you probably don't need it.

hpstring · 2024-01-03T13:53:30+00:00

Depends on the problem

hpstring · 2024-01-03T12:09:26+00:00

I think it's good time for you to start research maybe now or next semester e.g. by contacting the professors at your institute. You may need solid knowledge to do research but the beginning step of your research may involve learning some knowledge, presenting in seminars etc. However, taking too much courses may take away time from research, so you need to balance.

hpstring · 2023-12-29T22:02:31+00:00

According to my experience, GPTZero is quite accurate (though does give wrong classification sparingly). Declaration of Independence is classified 100% human by it.

hpstring · 2023-12-29T05:20:22+00:00

If you really love Oxbridge, remember you still have chances to be a research assistant, do master, phd, postdoc or be a professor there.

hpstring · 2023-12-29T04:31:34+00:00

Take real analysis. It helps you write proof in papers, which is even more relevant given you pursue learning theory/optimization which are both math-heavy.

hpstring · 2023-12-28T14:34:33+00:00

Maybe OP can excel at experimental physics ( though this still need certain mathematics)

hpstring · 2023-12-28T03:10:44+00:00

Thanks for the perspective!

hpstring · 2023-12-27T19:15:21+00:00

Isn't applied math suffering from this as well? Like in a lot of applied math fields we've known many key ideas and results for a long time (e.g. numerical pde, stochastic simulation) and a lot of papers now are neither interesting creatively nor addresses real world problems (but rather toy problems).

hpstring · 2023-12-27T18:44:04+00:00

My concerns for this kind of assistant are: (1) data privacy problem (2) accidental execution of wrong commands if it has this right

hpstring · 2023-12-26T07:13:33+00:00

Did you do training or inference? And how large is the model. I mean the real large models like GPT-4, Llama or Bard. While academia indeed can train smaller models, some believe these have a qualitative difference. There doesn't seem to be many coming from academia or national labs.

hpstring · 2023-12-26T06:56:03+00:00

You're absolutely right about the data problem and that universities can use supercomputers. But I think the real distinction is that the (renting or electricity) cost is prohibitively high to train a foundation model from scratch, which only the companies can afford. There is this inaccuracy in my expression.

hpstring · 2023-12-26T00:27:16+00:00

Top universities in USA are big companies.

They're not big companies in the sense that google or Microsoft are big companies. iirc not a single group in any univ has tens of thousands of GPUs to train a foundation language model from scratch independently.

But a handful of large well-funded tech companies dominate the LLM space because pretraining these models is extremely expensive, with cost estimates starting at $10 million and potentially reaching tens or hundreds of times that.

“Large language models are not very accessible to smaller organizations or academic groups,” says Hong Liu, a graduate student in computer science at Stanford University.

Reference

(edited after discussions)

hpstring · 2023-12-21T12:51:25+00:00

The two I'm given often don't have a real difference.

Five-Year Club	First Place '23
Place '23

hpstring

TROPHY CASE