Marc Andreessen on the atomization of AI
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Marc Andreessen on the atomization of AI
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Re: Marc Andreessen on the atomization of AI
#2The common saying is "more data usually beats a better algorithm". However, this is only for short term progress. Long term progress in the field of AI clearly requires better algorithms, and doing more with less data is exactly the kind of problem that a startup in the field could solve with a clever idea.
That said, these startups will have to be extremely research heavy — how does one draw the most talented researchers to work for a small group of people with minimal pay over working for a large company with a high salary and all the computational resources you might need? And what kind of product could theoretical research produce in the short term that would keep a startup afloat long enough to come up with ground breaking ideas?
Re: Marc Andreessen on the atomization of AI
#3I think your second question is the biggest thing. If you're developing some groundbreaking technology or doing heavy research you face a bunch of gnarly questions. How to convince investors when the climate favors revenue and customers over technological promise? How to attract users or customers to some subset of what you're doing while you build the larger thing? How to explain to everyone what you're doing if, as in research, you don't have a 100% clear idea of where you're going to end up?
Re: Marc Andreessen on the atomization of AI
#4The common saying is "more data usually beats a better algorithm". However, this is only for short term progress. Long term progress in the field of AI clearly requires better algorithms, and doing more with less data is exactly the kind of problem that a startup in the field could solve with a clever idea. That said, these startups will have to be extremely research heavy — how does one draw the most talented resear…
Tons of resources dont matter if they aren't looking in the right direction. A small shop can make huge progress just need to think "okay they're all doing it this way", and then do the opposite most crazy thing ever. Honestly we dont have the hardware yet. AI needs something groundbreaking before it turns into more than a clever analytical tool.
Re: Marc Andreessen on the atomization of AI
#5The common saying is "more data usually beats a better algorithm". However, this is only for short term progress. Long term progress in the field of AI clearly requires better algorithms, and doing more with less data is exactly the kind of problem that a startup in the field could solve with a clever idea. That said, these startups will have to be extremely research heavy — how does one draw the most talented resear…
Vanilla deep learning is so far ahead of what most companies use for analytics, that very little research is needed to offer a product with significant advantages over what's in practice now. In fact, I'd say most ML/DL/AI startups don't need to do research at all. They just need to spread more widely what exists now.
I disagree with Andreessen that Google's Tensorflow propaganda on Udacity is "opening the kimono" or having a large effect on AI. TF isn't better than pre-existing tools in many ways. And the ways that Google uses it aren't really transferable in an online course, since Google does have the data, architectures, infrastructure and talent -- some of which it can't share by definition, and some of which it won't share, for obvious reasons.
The smartest startups outsource research to universities and government funded bodies. It's a way of derisking themselves as investments. That's no different for AI. Theoretical research and product development are almost (but not quite) totally different things. Which is something that graduate students who think they want to do a startup should remember, because if they love research, they probably won't have much time for it.
[Disclosure: I'm associated with a rival DL library that I won't plug here.]
Re: Marc Andreessen on the atomization of AI
#6The common saying is "more data usually beats a better algorithm". However, this is only for short term progress. Long term progress in the field of AI clearly requires better algorithms, and doing more with less data is exactly the kind of problem that a startup in the field could solve with a clever idea. That said, these startups will have to be extremely research heavy — how does one draw the most talented resear…
Tons of resources dont matter if they aren't looking in the right direction. A small shop can make huge progress just need to think "okay they're all doing it this way", and then do the opposite most crazy thing ever. Honestly we dont have the hardware yet. AI needs something groundbreaking before it turns into more than a clever analytical tool.
Yes, the data and the algorithms don't really matter unless you have the right product. Otto apparently was a group of Googlers who thought the product was wrong, and went for the narrower self-driving trucks market (before being acquired).
I imaginne that the winner of the self-driving car race will be determined by the product and not the technology per se. Tesla, Google, Uber, comma.ai, etc. all have quite different conceptions of what the product is.
Re: Marc Andreessen on the atomization of AI
#7The common saying is "more data usually beats a better algorithm". However, this is only for short term progress. Long term progress in the field of AI clearly requires better algorithms, and doing more with less data is exactly the kind of problem that a startup in the field could solve with a clever idea. That said, these startups will have to be extremely research heavy — how does one draw the most talented resear…
You're right on many points. My observation is, when it comes to deeper Ai that trends towards AGI, the private sector seems lost in terms of the big ideas and applications. The VCs are also lost and have no idea on how to appreciate fundamentally groundbreaking R&D development ventures.
To be honest, most of the funding and deeply interesting work for ground breaking Ai research lies in the public and defense sector. They have more of an idea and vision for what Ai can be applied to than the private sector. Leaps and bounds beyond.
In the private sector, I get asked questions like 'Can it be used to predict if someone will click on an ad'. In the public sector, I get a 40 page technology acquisition pdf outlining a forward thinking application of Ai.
People forget that self driving cars arose from Darpa research. The latest innovations in computer security utilizing Ai came from a Darpa competition. IBM's neuromorphic chip : darpa funding. Darpa and other defense programs also have a slew of Ai development projects open to researchers.
Ultimately I find this a bit ridiculous given how vocal some individuals in the tech field are about the 'dangers of Ai'. They harp and harp about what could happen if more capable Ai gets developed but you don't see their money anywhere near the groups doing deeper development in Ai.
Lastly.. Yes, better solutions and more deeply inspired approaches to Ai will trump hoards of data and compute power any-day. Thank goodness for the public sector pushing forward fundamental research and development in Ai.
Re: Marc Andreessen on the atomization of AI
#8The common saying is "more data usually beats a better algorithm". However, this is only for short term progress. Long term progress in the field of AI clearly requires better algorithms, and doing more with less data is exactly the kind of problem that a startup in the field could solve with a clever idea. That said, these startups will have to be extremely research heavy — how does one draw the most talented resear…
You're right on many points. My observation is, when it comes to deeper Ai that trends towards AGI, the private sector seems lost in terms of the big ideas and applications. The VCs are also lost and have no idea on how to appreciate fundamentally groundbreaking R&D development ventures. To be honest, most of the funding and deeply interesting work for ground breaking Ai research lies in the public and defense sector…
> Ultimately I find this a bit ridiculous given how vocal some individuals in the tech field are about the 'dangers of Ai'. They harp and harp about what could happen if more capable Ai gets developed but you don't see their money anywhere near the groups doing deeper development in Ai.
...because if you think AGI is dangerous, you should fund groups trying to develop it? I am puzzled by the presentation of this proposition as if it is an absolutely obvious sequitur requiring no defense, such that failure to follow through on it is "ridiculous".
Re: Marc Andreessen on the atomization of AI
#9The common saying is "more data usually beats a better algorithm". However, this is only for short term progress. Long term progress in the field of AI clearly requires better algorithms, and doing more with less data is exactly the kind of problem that a startup in the field could solve with a clever idea. That said, these startups will have to be extremely research heavy — how does one draw the most talented resear…
You're right on many points. My observation is, when it comes to deeper Ai that trends towards AGI, the private sector seems lost in terms of the big ideas and applications. The VCs are also lost and have no idea on how to appreciate fundamentally groundbreaking R&D development ventures. To be honest, most of the funding and deeply interesting work for ground breaking Ai research lies in the public and defense sector…
Have you heard of the OpenAI initiative?
Re: Marc Andreessen on the atomization of AI
#10I think your second question is the biggest thing. If you're developing some groundbreaking technology or doing heavy research you face a bunch of gnarly questions. How to convince investors when the climate favors revenue and customers over technological promise? How to attract users or customers to some subset of what you're doing while you build the larger thing? How to explain to everyone what you're doing if, as…
You just summed up my entire life for the past year. My answer: be a known entity that can get a ton of cash before you have a product. That seems to be the best way. Otherwise you grind it out really hard like I/we are doing. It's exceptionally difficult.