Recent comments in /f/MachineLearning

goolulusaurs t1_j8evwnj wrote

Reply to comment by dustintran in [D] Quality of posts in this sub going down by MurlocXYZ

I remember being here in 2017 also and I definitely recall the quality of the post being much higher. Even looking at the sidebar, most of the high quality AMAs from prominent researchers where prior to 2018. Now I often see posts that I would classify as relevant, correct or high quality get downvoted, and posts that seem misinformed or incorrect get upvoted. Personally I blame the reddit redesign for deemphasizing text and discussion in favor of lowest common denominator stuff like eye catching images and video.

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AdamAlexanderRies t1_j8eppdi wrote

Reply to comment by daking999 in [D] Quality of posts in this sub going down by MurlocXYZ

ChatGPT's mostly a cool toy, but there are some tasks it's genuinely useful for. I use it to explain complex topics, write code, brainstorm ideas, and for fun creative writing exercises. I've only tried the free version, but I am seeing mostly disappointment about the pro version.

Definitely check it out for at least curiosity's sake.

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currentscurrents t1_j8em94v wrote

Reply to comment by That_Violinist_18 in The Inference Cost Of Search Disruption – Large Language Model Cost Analysis [D] by norcalnatv

Samsung's working on in-memory processing. This is still digital logic and Von Neumann, but by putting a bunch of tiny processors inside the memory chip, each has their own memory bus they can access in parallel.

Most research on non-Von-Neumann architectures is focused on SNNs. Both startups and big tech are working on analog SNN chips. So far these are proof of concept; they work and achieve extremely low power usage, but they're not at a big enough scale to compete with GPUs.

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marcus_hk t1_j8ejn0n wrote

Reply to comment by Reasonable_Ad_6572 in [R] [N] Toolformer: Language Models Can Teach Themselves to Use Tools - paper by Meta AI Research by radi-cho

Which part do you disagree with here:

My unwavering opinion on current (auto-regressive) LLMs

  1. They are useful as writing aids.
  2. They are "reactive" & don't plan nor reason.
  3. They make stuff up or retrieve stuff approximately.
  4. That can be mitigated but not fixed by human feedback.
  5. Better systems will come

https://twitter.com/ylecun/status/1625118108082995203?s=20

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TMills t1_j8eh61k wrote

Reply to [D] Is a non-SOTA paper still good to publish if it has an interesting method that does have strong improvements over baselines (read text for more context)? Are there good examples of this kind of work being published? by orangelord234

It doesn't need to be sota in an absolute sense, but it should be interesting in an empirical way. If the model is small, it needs to benchmark against other small models. If it's efficient it should compare against other efficient models. If you just like it aesthetically, or think it's clever, then you need to think about what that cleverness buys you and evaluate it in that dimension.

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ksatriamelayu t1_j8ebhn4 wrote

Reply to [R] [N] Toolformer: Language Models Can Teach Themselves to Use Tools - paper by Meta AI Research by radi-cho

Keep in mind that our current theories in Neuroscience broadly agrees something similar is going on with mammalian, even reptilian brains. Hell, maybe even worm brains.

There's autonomous systems everywhere that calls each other for updates and in some certain brains, enough complexity that something that can called thinking occurs.

Practically, offloading calculations to a python REPL, machine translation to GTranslate API call, and knowledge search to Wikipedia corpus is going to let LLMs do what they do best - mask users intent and generate believable enough corpus. Let the facts stay factual and the hallucination stay hallucination.

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