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Every day now there are new AI models especially LLMs, which might warrant some consideration from a wide part of the human population. In a couple years we wi

by pama 3y ago

Every day now there are new AI models especially LLMs, which might warrant some consideration from a wide part of the human population. In a couple years we will have multiple new announcements per hour and we might need some earlier models to evaluate these new developments and test them. For Phind-70B in particular, I hope that lmsys will share a version that will be part of the human evaluation leaderboard so we get a rounded evaluation. But for code assistants there should be a totally separate impartial evaluation benchmark, ideally still human judged for another year or so but eventually maybe some way of having the models fighting out competitive coding battles that they can help create.

swatcoder 3y ago
> In a couple years we will have multiple new announcements per hour Models are research output. If 10 new models are being announced every day in a couple years, it would mean that generative AI research has failed to stabilize and produce a stable, reliable component ready for product engineering. And if that's where we are in a couple years, that's almost certainly a sign that the hype was misplaced and that money is chasing after itself trying to recoup sunk costs. That's a failure scenario for this technology, not what an AI-optimist (you otherwise seem to be one) should be anticipating.
- int_19h 3y ago
  That doesn't follow at all. It just means that there are still low-hanging fruits to pursue for better (smarter, faster, larger context etc) new models, but it doesn't say anything about the stability and usefulness of existing models.
- nickpsecurity 3y ago
  That’s not true. Both good science and market-driven engineering favor continued iterations on existing ideas looking for improvements or alternatives. We’re often exploring a giant space of solutions. Unlike many fields, the A.I. people are publicly posting many of their steps in this journey, their iterations, for review. While it brings lots of fluff, such openness dramatically increases innovation rate compared to fields where you only see results once or twice a year. Both people using cloud API’s and FOSS developers are steadily increasing effectiveness in both experimentation and product development. So, it’s working.
- pama 3y ago
  I referee for a lot of the top machine learning conferences and yes I am very optimistic about AI and its impact on humanity. The amount of exciting new papers in machine learning and AI was definitely on an exponential rise for a decade since about 2012 or so, and the total production has kept increasing even during the last couple of years when the submissions in some top annual conferences exceeded 10k. Not every paper results in a useable model but a higher fraction of papers come with code and pretrained weights over time. Many of these papers will never be read by many more than the reviewers and the group who wrote them and a couple friends, but it does not speak necessarily to the quality of the work itself or the potential impact it could have on every possible future if we found better ways to separate the useful information. As the exponential increase in total compute becomes more widely accessible there are exponentially more applications that are of broader interest and will have even bigger impact than nowadays. I don’t think that the model of reviewing 10s or 100s of thousands of papers in conferences, or playing the popularity contest on social media is going to be productive so we need better methods for advancing the useful ideas more quickly. (Case in point: the mamba state space model by Gu and Dao was rejected from a conference this winter, but it happened to be advertised enough at a keynote presentation by Chris Re with a packed audience at neurIPS23, so the model was picked up by a lot of people who used it and submitted applications that used it to the ICML conference already.) I also don’t think that some of the biggest companies have enough manpower, motivation and interest in going alone, though of course they can easily stay ahead of the game in specialized areas with their own resources.
ilove_banh_mi 3y ago
this is how the WWW started, one new website every other day, then a couple every few hours, then ...
- goatlover 3y ago
  Difference being the web was meant to grow as hyperlinked documents, not separate programs. It's not the same kind of thing. LLMs are more like apps being produced by different companies trying to capture walled gardens, and their open source counterparts.
  - jlokier 3y ago
    > LLMs are more like apps being produced by different companies trying to capture walled gardens, and their open source counterparts. I think the analogy to the web is stronger than that. For now the LLMs are mostly separate, but it won't be long before LLMs emerge that make API calls to other LLMs, sometimes over the internet. In due course, expect meta-LLMs to emerge that aggregate knowledge from other LLMs by talking to them, rather than by training on their data. Those meta-LLMs which optimise for competitive quality results will have to read the research as it comes out, and continually assess which other new LLMs are worth calling out to, and for which purposes. Eventually the API calls will become bi-directional requests to exchange knowledge and insights, i.e. multiple models talking to each other, continually learning.

by pama 3y ago

swatcoder 3y ago

> In a couple years we will have multiple new announcements per hour Models are research output. If 10 new models are being announced every day in a couple years, it would mean that generative AI research has failed to stabilize and produce a stable, reliable component ready for product engineering. And if that's where we are in a couple years, that's almost certainly a sign that the hype was misplaced and that money is chasing after itself trying to recoup sunk costs. That's a failure scenario for this technology, not what an AI-optimist (you otherwise seem to be one) should be anticipating.

int_19h 3y ago
That doesn't follow at all. It just means that there are still low-hanging fruits to pursue for better (smarter, faster, larger context etc) new models, but it doesn't say anything about the stability and usefulness of existing models.
nickpsecurity 3y ago
That’s not true. Both good science and market-driven engineering favor continued iterations on existing ideas looking for improvements or alternatives. We’re often exploring a giant space of solutions. Unlike many fields, the A.I. people are publicly posting many of their steps in this journey, their iterations, for review. While it brings lots of fluff, such openness dramatically increases innovation rate compared to fields where you only see results once or twice a year. Both people using cloud API’s and FOSS developers are steadily increasing effectiveness in both experimentation and product development. So, it’s working.
pama 3y ago
I referee for a lot of the top machine learning conferences and yes I am very optimistic about AI and its impact on humanity. The amount of exciting new papers in machine learning and AI was definitely on an exponential rise for a decade since about 2012 or so, and the total production has kept increasing even during the last couple of years when the submissions in some top annual conferences exceeded 10k. Not every paper results in a useable model but a higher fraction of papers come with code and pretrained weights over time. Many of these papers will never be read by many more than the reviewers and the group who wrote them and a couple friends, but it does not speak necessarily to the quality of the work itself or the potential impact it could have on every possible future if we found better ways to separate the useful information. As the exponential increase in total compute becomes more widely accessible there are exponentially more applications that are of broader interest and will have even bigger impact than nowadays. I don’t think that the model of reviewing 10s or 100s of thousands of papers in conferences, or playing the popularity contest on social media is going to be productive so we need better methods for advancing the useful ideas more quickly. (Case in point: the mamba state space model by Gu and Dao was rejected from a conference this winter, but it happened to be advertised enough at a keynote presentation by Chris Re with a packed audience at neurIPS23, so the model was picked up by a lot of people who used it and submitted applications that used it to the ICML conference already.) I also don’t think that some of the biggest companies have enough manpower, motivation and interest in going alone, though of course they can easily stay ahead of the game in specialized areas with their own resources.

ilove_banh_mi 3y ago

this is how the WWW started, one new website every other day, then a couple every few hours, then ...

goatlover 3y ago
Difference being the web was meant to grow as hyperlinked documents, not separate programs. It's not the same kind of thing. LLMs are more like apps being produced by different companies trying to capture walled gardens, and their open source counterparts.
- jlokier 3y ago
  > LLMs are more like apps being produced by different companies trying to capture walled gardens, and their open source counterparts. I think the analogy to the web is stronger than that. For now the LLMs are mostly separate, but it won't be long before LLMs emerge that make API calls to other LLMs, sometimes over the internet. In due course, expect meta-LLMs to emerge that aggregate knowledge from other LLMs by talking to them, rather than by training on their data. Those meta-LLMs which optimise for competitive quality results will have to read the research as it comes out, and continually assess which other new LLMs are worth calling out to, and for which purposes. Eventually the API calls will become bi-directional requests to exchange knowledge and insights, i.e. multiple models talking to each other, continually learning.