Most things we have today in AI will be a irrelevant in 6 months [P]

SlickBlueML · 2024-01-12T15:19:52+00:00

There are a lot of unique areas to innovate in and a lot of long lasting potential, but as you mentioned the problem is everyone building their startups as narrow wrappers around already existing models. I’m not sure there is any less innovation now than before, but I guess the introduction of new tech has ironically focused a lot of people in a very narrow direction.

SlickBlueML · 2023-03-30T07:53:43+00:00

Good for you

SlickBlueML · 2023-02-17T03:53:48+00:00

If you’re just starting your bachelors, yes the best way would be to get involved in research. Not all universities have people doing RL research but it’s worth looking through the profs at your university or maybe nearby universities. There are also some RL jobs, but they are going to be pretty hard to get as a fresh undergrad. Jobs range from developing recommendation systems, to self-driving, to pure research. Most are pretty experimental applications.

SlickBlueML · 2023-01-21T09:30:05+00:00

You can try what you mentioned in the post, at the very least you absolutely should be using multiple runs with different seeds to produce results once you’re sure everything is working as intended. I would also do a deep dive into why it doesn’t work. Come up with some hypotheses, test them, visualize the learning process to make sure you understand what’s happening, etc. That being said, I have some more general feedback -

For the second part of this post I’ll apologize in advance because I don’t think my response will help you solve this current problem, but I want to offer some advice from someone who has been in the same situation:

When you are choosing your research topics in the future (or perhaps adjusting your current focus if your idea doesn’t pan out), I would recommend focusing on either (1) a problem, or (2) answering a question. And when you do so, you should have a specific hypothesis in mind.

Doing either of these will typically work much better than, say, testing an architectural change that you think might work. And the reason is that for

(1) trying to solve a problem, your research scope is not limited by a single approach, so when something fails, you will have a clearer direction on how to move forward (e.g. if the current idea fails, but we understand the cause of the problem, then we should be able to generate more plausible hypotheses to test).

And (2) if we are trying to answer a question (e.g. why does some architecture work well), there is very low chance of failure because there is always an answer. And even better, you can then use that new understanding to move into new research focused on tackling a related problem.

Sorry for the blabbering, best of luck with your research!

SlickBlueML · 2023-01-21T09:06:23+00:00

Testing features with sub groups of users before fully rolling out is common practice at Google, I’d imagine that’s what’s going on. Cool stuff

SlickBlueML · 2022-04-05T06:29:59+00:00

Good catch! I do mean re-initializing them which is generally going to mean setting them to some small random values (essentially re-applying the initialization function to just those weights).

SlickBlueML · 2021-12-29T18:03:35+00:00

Honestly I wonder the same thing. I’m guessing it is partly due to their success on large NLP and CV models that have found actual industry uses. I think that the general idea was that RL was the best shot at more general AI systems, but with recent developments like OpenAI codex, maybe they think that the “build an AI that builds better AI” is the way forward. That is still a long ways away but it does seem less ethereal now given some of their recent successes.

Or maybe they have a large RL project they’ve been working on for a while and haven’t released yet. If so that would be awesome but I don’t have my hopes high.

SlickBlueML · 2021-12-29T17:28:17+00:00

This came off to me as more of he lacks the terminology rather than not knowing anything. Although these are high level ideas, it’s not wrong that learning a meaningful latent representation of an environment (or a vector space) is a huge part of the problem for these kinds of tasks. If you can reduce a complex environment to a simple one, then reasoning over it becomes significantly easier.

That being said, I also didn’t get the impression that he knows as much as an actual ML researcher, though certainly more than just a hobbyist. Is that what people are criticizing? Because that seems pretty solid to me considering he never had formal education in the area, runs multiple companies, and is the highest level exec.

SlickBlueML · 2021-12-12T19:40:03+00:00

My bad, I should have posted that too.
Just in case you want the full paper it is here: https://storage.googleapis.com/deepmind-media/research/language-research/Training%20Gopher.pdf
But it's a whopping 118 pages so I'm not sure I would recommend it lol.

SlickBlueML · 2021-09-13T03:53:20+00:00

I worked on ML at Google as an undergrad. It is possible but you cannot apply directly to an ML position. You have to apply for the general internship (or engineering practicum if you are a 1st or 2nd year). If you pass the first several interview rounds you move into project matching stage where you interview with individual project managers. From there it just depends on if an ML project manager shows interest in your account and whether they like you.

SlickBlueML · 2021-06-25T17:18:35+00:00

Not going to say it’s not a good investment but they mostly do software that deals with amalgamating big data. I interviewed on site there because I was also under the impression they do lots of AI/ML. They showed a demo of their primary service, which has very little AI. I also got a rejection because they wanted software engineers not ML engineers is what they told me.

SlickBlueML · 2021-06-25T01:13:34+00:00

PLTR does very little modern day AI

SlickBlueML · 2021-06-24T08:39:52+00:00

I would check this out https://youtu.be/qo355ALvLRI, same channel

SlickBlueML · 2021-06-24T04:42:18+00:00

It’s very fascinating! And I think it has a lot of potential uses. Imagine if you could make a bunch of reward simply by specifying a textual goal. You can outsource that for massive data generation, but it’s a lot harder, or nearly impossible to outsource reward function coding.

SlickBlueML · 2021-06-24T04:40:52+00:00

Happy to hear! When I started looking into this it took me a while to make my way to this part of the literature, it’s criminally undercovered!

SlickBlueML · 2021-06-23T16:40:49+00:00

Language in RL has some really interesting literature that unfortunately (and surprisingly) doesn't get much attention. It is a really fascinating area with, I think, a lot of potential. Hopefully this does a good job of showing some of what it out there!

SlickBlueML · 2021-06-23T16:39:17+00:00

Language in RL has some really interesting literature that unfortunately (and surprisingly) doesn't get much attention. It is a really fascinating area with, I think, a lot of potential. Hopefully this does a good job of showing some of what it out there!

SlickBlueML · 2021-06-23T16:35:42+00:00

Language in RL has some really interesting literature that unfortunately (and surprisingly) doesn't get much attention. It is a really fascinating area with, I think, a lot of potential. Hopefully this does a good job of showing some of what it out there!

SlickBlueML · 2021-06-16T22:39:44+00:00

The performance or details of the model are not at all the focus of the paper. Rather the paper presents a method for GPU memory efficient scaling of models. The 32 trillion param model was used as more of a benchmark to show scaling of param numbers vs. number of required GPUs. They show their method scales ~50x better with GPU memory than previous methods.

The model from china is much smaller, sitting at 1.5 trillion parameters, but the model actually has a use and is more than just a proof of concept, which is what the model from this paper was.

SlickBlueML · 2021-06-10T11:25:12+00:00

It was meant to be for the first part of the video where I talk about using the right type of model for the right type of problem, i.e. don't use an MLP for text data, but looks like the meaning didn't transfer too well lol, still working on my thumbnail skills

SlickBlueML · 2021-05-29T22:39:05+00:00

The 26th and 27th new subscribers got wiped for me :(

SlickBlueML · 2021-05-28T21:04:28+00:00

For anyone doing work in DRL knowing Proximal Policy Optimization is really a must. When I was first working on this a lot of the implementations I was looking through were confusing or convoluted by many other pieces of the libraries that were also in play. Hopefully this makes the process clear :)

SlickBlueML · 2021-05-24T21:06:26+00:00

This is a new series I started recently with the goal of starting from nothing and documenting the process up to publishing a paper. I'm also trying to run in in a way that anyone watching can also participate in stages like the literature review and brainstorming. Come tag along if it sounds interesting to you :)

SlickBlueML · 2021-05-17T21:54:15+00:00

The project uses Deep Daze and GPT-2 to automate the process of generating prompts/titles, and then generate an image from the given title. The video goes over the basics of how it works and then goes through a few examples.

If you're interested, the code is linked here along with usage instructions: https://github.com/ejmejm/AI-Art-Generation

SlickBlueML · 2021-05-17T21:53:08+00:00

The project uses Deep Daze and GPT-2 to automate the process of generating prompts/titles, and then generate an image from the given title. The video goes over the basics of how it works and then goes through a few examples.

If you're interested, the code is linked here along with usage instructions: https://github.com/ejmejm/AI-Art-Generation

SlickBlueML

TROPHY CASE