41–50 of 123 posts
Reminder: Most of the seminal accomplishments of this era's AI wave were actually developed in the 70s-90s. Yes, even GANs and RL. This industry has been riding on the NVIDIA welfare program for the past 10 yrs. How long until the hardware gets maxed out? http://people.idsia.ch/~juergen/deep-learning-miraculous-yea...
Reminder: Most of the seminal accomplishments of this era's AI wave were actually developed in the 70s-90s. Yes, even GANs and RL. This industry has been riding on the NVIDIA welfare program for the past 10 yrs. How long until the hardware gets maxed out? http://people.idsia.ch/~juergen/deep-learning-miraculous-yea...
Maybe the correct way to measure advance in AI is Turing Awards.
Maybe call it:
Cybernetic Research (CR)
Computational Cognition (CC)
Statistical Reasoning (SR)
Computational Reasoning (CR)
I think the total economic impact of AI will be greatest for tasks that output high-dimensional data, such as GANs. For the simple reason that it can replace a lot more human labor. A great many jobs could be augmented with such tech. Furthermore, I think the results from GPT-2 and similar language models show that researchers have found a scalable technique for sequence understanding. They are likely to just work be…
Does GPT-2 really "understand" anything? I feel like this is pretty quickly going to devolve into a semantic argument, but having interacted with some trained GPT-2 models, it seems to produce only what Orwell would have called duckspeak[0]. There's very clearly no mind behind the words, so it's hard for me to credit it with understanding. [0] http://www.orwelltoday.com/duckspeak.shtml
Maybe it makes more sense to talk about predicting the behavior of a system independent of the composition of the system.
I'm not sure about the pace of progress in research, but as an ML engineer at a startup who has been following developments, even if AI research stalls out completely, we've been given a huge set of amazing tools to apply to all kinds of technical problems for years to come. I also think that even in the absence of massive breakthroughs, there's still plenty of work to be done during a "winter" in filling in the gaps…
Can you give some examples of applications that you think will have big impacts? I see places where current AI techniques can make incremental improvements but I just don't see any applications that really seem game changing. The ones that come closest tend to be dystopian unfortunately, like most applications of facial recognition.
Right inventory at right location in right quantity is essential to e-commerce and our ai is helping out a lot in this regard
I've been waiting for the hype and marketing to collapse for a few years. Probably the fastest way to tarnish public perception of AI would be to keep pushing "AI-enhanced" products in front of the consumer as has been done. These things tend to demo well and have a nice cool factor for the first fifteen minutes or so, but after any kind of prolonged usage the limitations and rough-edges come up quick.
This is brand new technology. It's going to take a few years to reliably productionize - and most of the applied solutions will look nothing like the research. Many real world problems are going to combine multiple neural nets into systems with specific applications and there's a lot of detail to work out. The hype may collapse in the short term, but that's only because many of the first movers are stereotypical tech…
A machine learning model trained to understand Maxwell's Equations can in principle be used perform said simulations, resulting in probably an order or more of magnitude increase in simulation speed. Getting this to work well will reduce the time (and cost) it takes to design optical sensors, radar for autonomous vehicles, smartphone antennas, MRI machines, and more.
Having said that, it would require a lot of heaving lifting to pull this off to achieve near-physical accuracy for real-world physics problems.
A cursory search on Google for "arxiv deep learning electromagnetics" returns results of proofs of concept in this direction.
In the sense of AGI, it's all been hype. We are in an ML summer and have been for the past few years. But "deep learning" is nothing more than that, nothing to do with AGI, we're not approaching an AGI winter except for people who were daft enough to fall for the hype. There have been no advances in AGI in decades, it's already winter, and we've long been in it.
In terms of research and innovation, yes, but in economic terms, it has not even begun. There is still huuuge VC and government money being pumped into anything with AI on it. The last AI winter started when the financiers discovered the disconnect between the money they put in and delivery on promises.
AI went from an obscure hard CS field that only a few graybeards at MIT knew anything about, to this worldwide meme. Before the default thing your grandmother would tell you to study in college was business. Now your grandmother would tell you to study AI. I'm seeing a lot of people enter this space with the vague goal of getting rich quick. This is the same cohort that jumped into tech in the late 90s and the real estate market in the mid-2000s. It's not the AI of the Norvig and Marvin Minsky days.
I couldn't be more bearish about AI. I still love it though. I won't stop studying it when it becomes not cool anymore.
Reminder: Most of the seminal accomplishments of this era's AI wave were actually developed in the 70s-90s. Yes, even GANs and RL. This industry has been riding on the NVIDIA welfare program for the past 10 yrs. How long until the hardware gets maxed out? http://people.idsia.ch/~juergen/deep-learning-miraculous-yea...
We need yet another set of big breakthroughs because just adding more computing capacity is not going to carry the boom. Maybe the correct way to measure advance in AI is Turing Awards.
Earlier quoted context omitted.
This is brand new technology. It's going to take a few years to reliably productionize - and most of the applied solutions will look nothing like the research. Many real world problems are going to combine multiple neural nets into systems with specific applications and there's a lot of detail to work out. The hype may collapse in the short term, but that's only because many of the first movers are stereotypical tech…
I used to do electromagnetic modeling using finite element methods (though now a product manager for AI software infra) and it would to take me on the order of hours to days or weeks to model wave interaction with real-world objects. A machine learning model trained to understand Maxwell's Equations can in principle be used perform said simulations, resulting in probably an order or more of magnitude increase in simu…