1/n. Will there be any more profound, fundamental discoveries like Newtonian physics, Darwinism, Turing computation, QM, molecular genetics, deep learning?
Maybe -- and here's some wild guesses about what they'll be...
2/n.
Guess (1):New crypto-economic foundations of society. We might move to a society based on precise computational mechanisms:
a) smart contracts with ML oracles
b) ML algorithms that learn + aggregate our preferences/beliefs make societal decisions/allocations based on them
3/n. We see small specialized instances today (crypto/DeFi, AI-enabled ad auctions, prediction markets, recommender systems) but the space of possibilities is large and today's Bitcoin may not be very representative.
4/n. There can’t be a concise textbook for our current economic/political/legal system like one for QM or CS theory. But in a crypto-economic future, society would be founded on the math of computational game theory, CS theory, and machine learning.
5/n.
Guess (2): New Fundamentals in Bio, Neuro, and study of big, messy systems.
We’re currently using deep learning to produce predictive models of entangled, high-dimensional systems in biology, neuroscience/human behavior, climate/weather, stat-mech, economics, etc
6/n. In the next 10 years, these models will get much better. They’ll make predictions relevant to aging and health (bio), disease (bio), consciousness/self-awareness (neuro), drives/desires and values (neuro), etc.
7/n. DL models are mostly blackboxes — better prediction doesn’t mean an understandable, explanatory, elegant theory like Newtonian physics.
8/n. However, it’s plausible to me that better predictive DL models will sometimes yield elegant theories.
9/n. Either (a) we make *interpretable* DL models that yield the elegant theory directly, or (b) better predictions helps humans create the new theories, or (c) we produce strong evidence that *no elegant theory* is possible in the domain.
10/n. (For example, we already have evidence from machine learning that there’s no elegant theory of ImageNet, i.e. of recognizing common objects like cats, chairs, and houses from 2D photos).
11/n.
Guess (3) New Fundamental Metaphysics of the Universe.
Some basic questions: Why is there something rather than nothing? Why the laws of QM and not some other laws? Are we living in a simulation and if so what kind? ...
12/n. How do values/ethics interact with the fundamental nature of the universe? Is there other intelligent life in the (multi)verse?
I think we might build on some existing ideas and make a profound advance on these questions. I have in mind these ideas:
Postscript:
Will there be a new Newton/Einstein/Darwin figure, a single person who discovers a fundamental theory?
For Guess 1 (Crypto-economic Foundations), it’s possible that figures like Satoshi/Vitalik will get lots of credit (if the future is built on blockchain/ETH) but...
I expect there'll be many dispersed contributors.
For (2) New Fundamentals in Bio/Neuro, I think it will be a large group effort either like AlphaFold2 (one lab) or like the HGP (many labs).
The last guess, New Fundamental Metaphysics, is much more theoretical/philosophical...
and so one person could be a Newton/Einstein style figure. If you like working solo and want to be the next Einstein, try spending 10 years on a New Fundamental Metaphysics (deeply grounded in math/physics/computation)!
1.Language models could become much better literary stylists soon. What does this mean for literature? A highly speculative thread.
2. Today models have limited access to sound pattern / rhythm but this doesn't seem hard to fix: change BPE, add phonetic annotations or multimodality (CLIP for sound), finetune with RL from human feedback. GPT-3 is a good stylist despite handicaps! gwern.net/GPT-3#rhyming
3. There are already large efforts to make long-form generation more truthful and coherent (WebGPT/LaMDA/RETRO) which should carry over to fiction. RL finetuning specifically for literature will help a lot (see openai.com/blog/summarizi…, HHH, InstructGPT)
What are some domains of knowledge where big language models will be impactful?
Maybe domains with vast, messy stores of content that few humans master. E.g. 1. All US laws+regulations 2. Biological details of every beetle (>1M species) 3. All code in 787 (14M lines)
4. Function of all genes in all genomes (20k in humans) 5. Obscure human languages (Akkadian) 6. For a big company, what's the standard operating procedure for every staff role.
Let’s say there’s N items of interconnected knowledge in a domain. Even if humans can understand any *one* item better than a GPT-3-like model, the model can provide value by understanding N>100,000 items modestly well.
Education reform ideas, starting with least radical: 1. Outside USA, get rid of "early specialization" in high-school/uni and switch to US flexible, liberal-arts system 2. Outside UK, switch to UK-style short degrees (3 year BA, 1 year MA, 3 year PhD)
3. Expand coding, CS, AI, and data science through the whole education system. It’s the new “reading, writing, arithmetic." 4. Allow BA degrees by open examination (fee = wage for examiner to grade the papers). Allow PhD by open submission of thesis.
5. PhD not required to be academic (e.g. require 2-3 year masters instead as in old UK system)
(Getting more radical...) 6. Reduce age segregation in school and uni. Most important, normalize people starting uni (or uni-level colleges) aged 14-18.
Baseline models (GPT-3, GPT-J, UnifiedQA/T5) give true answers only 20-58% of the time (vs 94% for human) in zero-shot setting.
Large models do worse — partly from being better at learning human falsehoods from training. GPT-J with 6B params is 17% worse than with 125M param.
Why do large models do worse? In the image, small sizes of GPT3 give true but less informative answers. Larger sizes know enough to mimic human superstitions and conspiracy theories.
2/ Gerry Sussman. Hofstadter said Gödel invented LISP in proving the incompleteness theorem. Sussman shows the amazing breadth and elegance of LISP ideas. SICP, SICM, How to build robust systems. google.com/url?sa=t&rct=j…
3/ Eric Drexler. Engineering is neglected by philosophy departments (see Sussman also). Engines, Nanosystems, how engineering differs from science, CAIS. Disclosure: he's currently at the FHI (which is part of a phil dept). overcomingbias.com/2013/06/drexle… lesswrong.com/posts/x3fNwSe5…