41–50 of 80 posts
In my experience, there are just a lot of "bad" AI/ML engineers who don't fundamentally understand what data can do, what ML algorithms can handle, and how to piece it together to produce something of value to the end user. A couple of these people on a team can torpedo a project. Worse are those who sabotage projects or are general pain points of hindering progress. These may be jaded people who don't believe that M…
a16z has a podcast where they explored gross margins a month back. The panel called out AI as an example of a software business that has a high likelihood of not having standard SaaS margins (Most of the panel thought this could be a limitation). The podcast is nice because I think it holistically explores gross margins in a way that you start to understand how it might impact AI as a viable primary business model an…
This does disadvantage smaller bootstrapped businesses though.
Good analysis and great of them to share their thinking. Does feel like this could have been a tweet that said the necessary condition for successful ML solution is applying it to a problem that has asymmetric upside. Great for telling people they should get tested for diseases, terrible for diagnosis. In the alerting first case, consequences of being wrong are no better than base rate as they wouldn't have been test…
Earlier quoted context omitted.
I've yet to see an "AI" that is not just memorizing data.
Then you haven't really looked. Most credible machine learning systems work well on unseen data, which by definition isn't memorizing.
In my experience, there are just a lot of "bad" AI/ML engineers who don't fundamentally understand what data can do, what ML algorithms can handle, and how to piece it together to produce something of value to the end user. A couple of these people on a team can torpedo a project. Worse are those who sabotage projects or are general pain points of hindering progress. These may be jaded people who don't believe that M…
Earlier quoted context omitted.
Then you haven't really looked. Most credible machine learning systems work well on unseen data, which by definition isn't memorizing.
Can you link an example you find to generalize particularly well?
It generalises to almost every face, and the ones it doesn't its failure mode is safe.
Or something like word embeddings. Works incredibly well, and most "failure" modes are around things like bias, where the behavior reflects the real world.
Or something like AlphaZero. Not only is every new game of Go it plays brand new, it learnt to play Chess without knowing the rules. That just isn't memorization.
https://deepmind.com/blog/article/alphazero-shedding-new-lig...
a16z has a podcast where they explored gross margins a month back. The panel called out AI as an example of a software business that has a high likelihood of not having standard SaaS margins (Most of the panel thought this could be a limitation). The podcast is nice because I think it holistically explores gross margins in a way that you start to understand how it might impact AI as a viable primary business model an…
This is a great feature of the AI space for startups - in the short term it reduces competition, in the long term it's not really a problem. If your business is break-even currently, it will be profitable in 2-3 years due to declining cost of compute. In 10 years the compute costs will fall by an order of magnitude and more efficient models will become available, making the economics closer to traditional SaaS. This…
Earlier quoted context omitted.
Perhaps. It seems it’s still an open question whether AI is just about memorizing your data, or can it actually make reliable decisions during previously unseen scenarios. Have we already observed, or collected, all that is possible in the “driving” world?
> It seems it’s still an open question whether AI is just about memorizing your data, No - it's not at all, and this is a well understood problem in building machine learning systems. There are some cases where this occurs but usually this is just overfitting. Good AI systems generalize well on unseen data.
Not only is every game of Go it plays and wins brand new (so no memorisation), the same system learnt to play Chess without knowing the rules, and plays in a "style .. unlike any traditional chess engine"
https://deepmind.com/blog/article/alphazero-shedding-new-lig...
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Correct me if I’m wrong but isn’t this fundamentally true for any team working on any project?
No, there’s more ambiguity in machine learning projects. When you develop a website, aside from the design, it works or it doesn’t. Whether some kind of ml product can work at all is often team dependent
This really isn’t true. There are websites and web apps that “work” but are really suboptimal from a performance and UX perspective. It’s possible to do this right, but it’s much easier to do a poor job. You end up with something that kind of works and may even be profitable but which is a boat anchor around your company compared to a better approach.