Optimizing Data-to-Learning-to-Action

Optimizing Data-to-Learning-to-Action, sequel to The Learning Layer. Data-to-Learning-to-Action® & Learning Layer® are ManyWorlds® brands!

[Steven Flinn @stevenflinn]

These debates all ultimately converge to Searle's Chinese Room Argument. As I discussed 13 years ago, what Searle's thought experiment leaves out is a capability for learning, which GPT-4 and Sora have. But Searle's meme still infects many. https://t.co/6J6lZXxXjL

[Jim Fan @DrJimFan]

Shane Legg, cofounder of DeepMind, explains the importance of adding Search to Neural Networks.

Background: "Move 37" was a pivotal play by AlphaGo during the 2nd of its 5-game series against Go Champion Lee Sedol. AlphaGo placed a stone in a highly unconventional position that even confused experts, who first deemed it an error. Yet as the game unfolded, the move proved to be strategically brilliant, opening up opportunities that only became clear much later.

In a sense, Search increases intelligence at inference. The AI can take more time to deliberate without altering its parameters, and dynamically tradeoff efficiency with deeper thinking.

Video credit: @dwarkesh_sp podcast w/ @ShaneLegg

[Steven Flinn @stevenflinn]

AI is transforming science, which provides the foundation for much of overall innovation in the economy, thereby representing an additional driving force on innovation most generally (already being accelerated by AI). https://t.co/0bRzPQZi7O Example: https://t.co/xAmu3tsCdK

[Optimizing Data-to-Learning-to-Action @LearningLayer]

“With bigger models, you get better performance, but we don’t have evidence to suggest that the whole is greater than the sum of its parts.” https://t.co/io8XHPCfm1

[Steven Flinn @stevenflinn]

Delighted that the two startups for which I provide technology and IP guidance are partnering to deliver the next-generation #machinelearning-driven media processes to media clients right now.

[Bryan Caplan @bryan_caplan]

AI enthusiasts have cried wolf for decades.

GPT-4 is the wolf. I've seen it with my own eyes.
https://t.co/r9H8hC9TxA

[Steven Flinn @stevenflinn]

It's amusing how the homework threat is always top of mind when it comes to #gptchat, etc. Rather than the threat to the relevancy of what is being taught and graded.

[Optimizing Data-to-Learning-to-Action @LearningLayer]

Artificial innovation: "human experts 'favored' some amino acids over others, sometimes leading them to incorrect choices. Also, the computer program correctly pointed to some proteins with qualities that didn’t make them obvious choices for self-assembly" https://t.co/fYRixSpUbp

[Steven Flinn @stevenflinn]

Human-->Machine-->Human innovation synergy https://t.co/cMvxZxeqa8

[Optimizing Data-to-Learning-to-Action @LearningLayer]

"bring imagination to life and create one-of-a-kind videos full of vivid colors, characters, and landscapes. The system can also create videos from images or take existing videos and create new ones that are similar." https://t.co/LIJuwZyQAW

[Optimizing Data-to-Learning-to-Action @LearningLayer]

"researchers have shown that the hippocampus, a structure of the brain critical to memory, is basically a special kind of neural net, known as a transformer, in disguise" https://t.co/NNDTw5A8qi

[Optimizing Data-to-Learning-to-Action @LearningLayer]

The robots have come:“We’re watching the death of artistry unfold before our eyes”https://t.co/SsjF1POw3O

[Steven Flinn @stevenflinn]

@emollick In other words, identify and resolve the limiting constraint. This is universal, yet is too often unrecognized (which leads to misinvestment) and applies to all learning or innovation processes. https://t.co/5DbKuKcchr

[Ethan Mollick @emollick]

An idea from the history of science to generate startup ideas or ways to help in a crisis: find the reverse salient.

A reverse salient is the technology or process that is holding back development of the whole system (like ⚡️car batteries did). Solving the salient unlocks change

[Tom Goldstein @tomgoldsteincs]

Just how much have language models grown in the last 4 years? Let's have a look. In 2018, the puny BERT “large” model premiered with a measly 354M parameters. It can be trained on a single 8xA100 node in 5 days. That costs $2K on AWS - almost free by LLM standards! 🧵

[Amanda Askell @AmandaAskell]

Humans ascribe sentience to ELIZA but not cows. So when people say "humans over-ascribe sentience", they are correct - our rate of false positives is high. And when people say "humans under-ascribe sentience", they are also correct - our rate of false negatives is also high.

[Steven Flinn @stevenflinn]

The Fundamental Equation of Economics (and life).

For any project, activity, or most broadly, action:

Total Value = Direct Value + Learning Value

https://t.co/qpl6k1Ns2C

[Optimizing Data-to-Learning-to-Action @LearningLayer]

Be decision-driven not data-driven! Business value is ultimately driven by data-to-learning-to-action processes, and the way to optimize them is by working backward from the decision, not working forward from the data. https://t.co/ZhUUTLdlLE

[Yann LeCun @ylecun]

By the time a human child turns 5, she has seen the equivalent of 800 million "frames" of video + audio + touch to learn how the world works through Self-Supervised Learning.
Much of it is acquired actively.

5 years * 365 days * 12 hours * 3600 seconds * 10 fps = 788.4 million

[Steven Flinn @stevenflinn]

In Feb-Mar 2020 most people underestimated COVID because the human brain doesn't intuitively register exponential growth. Are people likewise generally underestimating the time to #AGI?