AI’s Instagram Problem
deeplearning.ai
AI’s Instagram Problem
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Re: AI’s Instagram Problem
#2Stuff does happen though.
- Facebook enters the geolocation game (dowalla/foursquare/instagram)
- Google releases reviews to maps (yelp & co)
- Amazon starts it's own airline (Fedex)
- Microsoft adds ChatGPT to search (Kagi, Duckduckgo, etc..)
Sometimes it's hard to recover your startup when a big player does something to fill the void you were targeting.
Re: AI’s Instagram Problem
#3> AI develops so quickly that waves of new ideas keep coming: quantum AI, self-supervised learning, transformers, diffusion models, large language models, and on and on.
I wonder what’s wrong with us as a species. We love to run into one direction all together chasing the next big thing just until the next big trend comes along.
Quantum AI crypto LLMs for the win!
Re: AI’s Instagram Problem
#4There is a pretty big gap between what’s talked about at conference and blogs vs the actual, not very sexy, hard data science work that goes into doing “machine learning” at scale.
Re: AI’s Instagram Problem
#5> AI develops so quickly that waves of new ideas keep coming: quantum AI, self-supervised learning, transformers, diffusion models, large language models, and on and on. I wonder what’s wrong with us as a species. We love to run into one direction all together chasing the next big thing just until the next big trend comes along. Quantum AI crypto LLMs for the win!
> what’s wrong with us as a species
We ask for faster horses, when automobiles would get us where we want to go. It is a lack of imagination for what is possible in the future that keeps us risk-averse and focused instead on a quest for incremental improvements to the status quo.
This was the focus of the idealistic vision of the whole "disruption" meta-meme as it spread through Silicon Valley.
Not to say that maintenance and refinement aren't important, but innovation doesn't look like just adding more parameters and ways to scale massive computations.
The main concept that AI laypeople (even those who burn OpenAI credits on a hobbyist basis) are missing to really grasp the possibilities of these kinds of statistical analysis techniques is a deep understanding of the notion of inductive bias. For instance, Transformers are so powerful precisely because they have generalized that idea of inductive bias one level up by using multi-head attention to project onto many different linear spaces and attend to them differently in different contexts, rather than just filtering through effectively one linear perspective at a time as with your traditional dense NNs.
The kinds of innovations we have witnessed most recently are actually pretty small steps in the space of possible architectures, not to mention training methods, etc. For instance, predictive coding approximates gradient descent[1] which opens up all sorts of new architectures (feedback arch, modular/federated/local compositions, etc.) that are intractable with traditional backprop-based techniques unless you can manage the infrastructure around periodic global parameter/state syncs.
Re: AI’s Instagram Problem
#6> AI develops so quickly that waves of new ideas keep coming: quantum AI, self-supervised learning, transformers, diffusion models, large language models, and on and on. I wonder what’s wrong with us as a species. We love to run into one direction all together chasing the next big thing just until the next big trend comes along. Quantum AI crypto LLMs for the win!
Are these really separate phenomena though? I'd strongly argue that between self-supervised learning, transformers, diffusion models, and LLMs we're looking at phases of the same phenomenon, each building on top of the knowledge that came before.
Like vaccines - inactivated viruses isn't a different "wave" than mRNA, they are logical successors within the same intellectual pursuit.
Or Vulkan vs. OpenGL. They aren't separate "fads" that gained traction.
Or ARM vs. x86. They aren't "fads" so much as entirely reasonable evolutions of technologies based on the evolving knowledge of the field and its needs.
Not everything is a popularity contest.
Re: AI’s Instagram Problem
#7All AI projects are valuable, but it's annoying and demotivating to work hard on a unique and useful project and have no one read/use it because it doesn't stand out in the extremely rapidly-evolving ecosystem.
Most of my AI text-generation and image-generation projects and tools have already become obsolete by technology released in the past few months, and I've almost given up competing.
Re: AI’s Instagram Problem
#8> AI develops so quickly that waves of new ideas keep coming: quantum AI, self-supervised learning, transformers, diffusion models, large language models, and on and on. I wonder what’s wrong with us as a species. We love to run into one direction all together chasing the next big thing just until the next big trend comes along. Quantum AI crypto LLMs for the win!
That's definitely not "us as a species". I would say this is a specific effect of post-industrial, market-driven economy - and even there, probably restricted to specific communities such as entrepreneurs.
By and large, I believe, people are conservative and would prefer for things to stay as they were, unless the change solves a specific problem.
Sometimes people actively tried to resist "progress" and had to be forced into their luck by those in power. E.g.: https://en.wikipedia.org/wiki/Luddite#Government_response
Re: AI’s Instagram Problem
#9Stuff does happen though. - Facebook enters the geolocation game (dowalla/foursquare/instagram) - Google releases reviews to maps (yelp & co) - Amazon starts it's own airline (Fedex) - Microsoft adds ChatGPT to search (Kagi, Duckduckgo, etc..) Sometimes it's hard to recover your startup when a big player does something to fill the void you were targeting.
How does DDG and Kagi competes with ChatGpt ? In my understanding their “responses” above search results works like Google graph, but I’ll be happy to be corrected.
Re: AI’s Instagram Problem
#10> AI develops so quickly that waves of new ideas keep coming: quantum AI, self-supervised learning, transformers, diffusion models, large language models, and on and on. I wonder what’s wrong with us as a species. We love to run into one direction all together chasing the next big thing just until the next big trend comes along. Quantum AI crypto LLMs for the win!
New technology = race to figure out how to make the most money off of it. Not that complicated, nor does it have anything to do with "us as a species".