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Artificial intelligence now beats some of the best human forecasters

79 points ddp26 2 hours ago 67 comments

xgulfie 2 hours ago | parent

Hasn't this been true for like 40 years

296012 2 hours ago | parent

That is too bad for The Economist. Exor N.V and Agnelli might replace some pundits at The Economist.

ddp26 1 hour ago | parent

The Economist has actually published other human forecasts many times, e.g. Metaculus or Good Judgment forecasts. They do year-end forecasts too.

Whether they draw on AI or other humans seems immaterial to the quality of their reporting.

hank1931 1 hour ago | parent

AI won't replace Ann Wroe at The Economist. It is difficult to appreciate until you've read a few, but Ann Wroe's approach transformed The Economist's obituary section into one of the most widely read features in international journalism.

dgellow 1 hour ago | parent

Cramer is infamous for being a terrible forecaster, and still has a large audience. Which tells you there is more at play than being good at forecasting, you also have to sell a good story

glimshe 2 hours ago | parent

Product idea: a LLM trained separately from mainline LLMs that anticipate market trends by analyzing how mainline LLMs will invest. As retail investors will probably use mainline AI for decisions going forward , one could get an edge.

"The AI-driven Market Hypothesis"

Please let me know where I should pick up my Nobel prize.

graypegg 2 hours ago | parent

Then the next person needs an LLM trained to predict the LLM trained to predict the mainline LLM.

It's derivatives all the way down

in_absentia 1 hour ago | parent

"No one could have anticipated the market crash of 2028."

pydry 1 hour ago | parent

"You're absolutely right!"

codebastard 2 hours ago | parent

Would you not then also copy the investments? Or are you trying to inverse the trades by an unpredictable time factor reasoning that thanks to AI the underlying stock is over- or underpriced?

in_absentia 1 hour ago | parent

A lot of algorithmic trading is short-term, essentially trying to guess what other parties may be selling or buying so that you can front-run them and then collect a fee. Kinda like ticket scalping, except we accept it and have a retro-justification for why it's good ("improving liquidity").

Or, in the best case, you're trying to mine signals few days before earnings or some other big story and bet on the directional outcome of that.

Fully-algorithmic long-term trading is of dubious benefit simply because that's driven to a much greater extent by geopolitics and macroeconomic trends, unforeseen scandals, successful product launches, and so on. As an example, you can believe that AR / VR is the future; I don't disagree. And in 2013, you might have inferred that Google is working on a revolutionary miniature AR headset. But you would not have made money if you bet on that turning out to be a hit. So even if you had a way to automate this bet, it would not have been a good bet.

cj 1 hour ago | parent

I was under the impression that front-running was something that happened in the span of seconds (or milliseconds), not a timeframe compatible with LLM inference time.

ddp26 1 hour ago | parent

I know this is tongue-in-cheek, but I think your idea could actually work, but not in financial markets. (The "keynesian beauty contest" of trying to predict what others think been played out to death there.)

You could train a model to anticipating scientific trends. Or policy trends. Others will definitely use mainline LLMs to make decisions there, so they may be more predictable now!

zippyman55 1 hour ago | parent

Be sure to sound excited when they call you at 3AM for your award. It helps to say : DYNAMITE! As a term of excitement.

varenc 1 hour ago | parent

Relatedly: I suspect LLMs are influencing baby names. If you ask Claude or ChatGPT for its favorite baby names, you'll get baby names that right now are skyrocketing in terms of popularity.

chairmansteve 57 minutes ago | parent

>Please let me know where I should pick up my Nobel prize.

Maybe you could settle for the FIFA Economics Prize.

GLGirty 44 minutes ago | parent

There are surely prize-worthy discoveries to be made about the long term behaviour of any system that can introspect previous discoveries and adjust it's behaviour.

I suspect that economics and psychology are both examples of these systems, and that, long term, these system will alter behaviour to thwart previous observations.

Economics requires observers to hoard discoveries and insights, so they can enrich themselves while the insights hold.

superxpro12 35 minutes ago | parent

at what point do we call this a game instead of economics? whats the benefit to society if the markets are just AI bots trying to out-maneuver one another?

jachee 30 minutes ago | parent

Guess what high-frequency trading is.

hmokiguess 34 minutes ago | parent

I'll give you 100 Robux how's that

rlt 25 minutes ago | parent

So... an even more intelligent LLM.

But it does beg the question, could Anthropic and OpenAI make a ton of money by using their best models to trade before giving them to the public? It would probably be a deeply unpopular move.

Eddy_Viscosity2 5 minutes ago | parent

They make their money by peeking at what everyone else's LLM's are doing and trading off that info.

bigfishrunning 5 minutes ago | parent

I don't think popularity is a goal for Anthropic or OpenAI. They merely want to have a product that they control and you depend on, and don't care about anything else.

Nobody really *likes* their drug dealer.

SoftTalker 18 minutes ago | parent

Looking good, Billy Ray.

WalterBright 18 minutes ago | parent

Any better-than-market prediction system won't work after it becomes public knowledge.

croes 2 hours ago | parent

Given the training data isn’t that more a win for the wisdom of the crowd?

anon48293 1 hour ago | parent

Paywall

bagels 1 hour ago | parent

Aren't forecasters already using 'artificial intelligence' for decades in the form of non-llm machine learning models?

datsci_est_2015 1 hour ago | parent

You don’t even have to limit it to machine learning, the definition of forecasting is isomorphic to the definition of modeling, which, with the dilution of the term AI, is also isomorphic to the definition of AI.

More simply:

  - forecasting = modeling = AI
Edit: I’d even throw statistics into that extended equality, meaning that Bayes, Bernoulli and even the fellow named John Gaunt have a strong case for having invented AI.

doctoboggan 1 hour ago | parent

> forecasting = modeling = AI

I wouldn't go that far. Humans can forecast by modeling with their wetware, nothing "A" about it.

aeon_ai 1 hour ago | parent

forecasting = modeling = intelligence, you mean?

fnordpiglet 1 hour ago | parent

Forecasting = modeling + intelligence

toxik 1 hour ago | parent

How about: forecasting is something you can do by modeling, AI is just modeling with a computer.

paulpauper 1 hour ago | parent

I think also a lot of it is intuition.

bunderbunder 1 hour ago | parent

Yes, and if the things I learned in my university class on the subject still holds, forecasts are incredibly sensitive to modeling decisions such as what independent variables you choose and how you believe they might mathematically relate to the outcome variable. It’s not a zero skill thing, but if anyone’s found a way to consistently mitigate the luck factor then I’d expect them to be wealthier than Elon Musk by now.

And there’s always a huge amount of variation that you simply can’t model, for whatever reason, and is therefore functionally a random factor.

I don’t want to say too much because this isn’t something I went on to actually do after school so I’m way out of my lane here, but I can see room for this to be more akin to “AI wins parcheesi tournament” than it is to “AI wins chess tournament.”

tfehring 1 hour ago | parent

For statistical time series forecasting, yes. This is for judgment-based forecasting, a somewhat different problem. It often involves, e.g. estimating the probabilities of one-off future events, which time series forecasting models aren’t suited for.

bpt3 55 minutes ago | parent

While time-series forecasting models aren't well suited for this, I would argue that humans aren't either.

Obviously the best humans are better than average, but this isn't all that surprising to me?

ddp26 42 minutes ago | parent

Right. What's really surprising is how much better the best are. Human superforecasters, and prediction markets are surprisingly accurate too.

We could live in a world where things are much more chaotic, and the best humans (or AIs) would only be slightly better than chance. Evidently the world we live in is pretty darn predictable.

qsbuilder 1 hour ago | parent

The test is when reflexivity kicks in and the prediction itself changes market behavior. LLMs usually melt there

tolugenius 1 hour ago | parent

Archive Link: http://archive.today/IVreS

gabrielsroka 1 hour ago | parent

Doesn't show the content

paulpauper 1 hour ago | parent

they fixed it . need better paywall bypasses

Stevvo 1 hour ago | parent

Doesn't work.

autoexec 1 hour ago | parent

So I can guess the AI companies can stop with their plans to infest AI with ads and they'll instead fully fund themselves by using their AI to gamble on stocks and the prediction market right? Surely the chatbots will just print money!

qbit42 38 minutes ago | parent

The quant firms are heavy AI investors I believe.

gyanchawdhary 1 hour ago | parent

At the risk of sounding extremely naieve i have a question for the Wall St / quant / HFT folks lurking here ... but how hard would it actually be to brute force the math/algos behind Medallion Fund (or something in that general class) or even some of the average quant funds

I know it’s not just the math but execution, infrastructure, risk management, data, colocation (if ur an HFT) etc ... but LLMs seem like a pretty powerful apparatus for running experiments that .. a few years ago would have required fairly deep multidisplinary skills across coding .. stats .. and math ..

So assuming you have decent intuition for ideas .. how difficult would it actually be to reverseengineer / rediscover some of the underlying stuff?

wpasc 1 hour ago | parent

I'm no quant/hft/wall st person, but iiuc a lot of those trades happen in dark pools or by other means to make the positions they take hard to track. meaning you can't go get the receipts of every trade made by medallion fund nor some competitor

arn3n 1 hour ago | parent

It’s actually really easy to make models that can predict “will the market move up or down in the next X microseconds” that score above 50% accuracy. It’s just that there are so many ways to do it that overfitting is practically guaranteed and most models don’t work when actually trading against the market, which reacts to you. Doing those trades well requires more understanding of the underlying mechanisms, not to mention access to data sources that the public simply doesn’t have.

seanhunter 1 hour ago | parent

This has to be the least surprising development to date given ml is a universal function estimator

senderista 1 hour ago | parent

You mean neural networks?

ddp26 1 hour ago | parent

As someone who started working on AI forecasting 3 years ago, I can confidently say that most people did not expect AI to beat Tetlock's superforecasters, Metaculus pros, or prediction markets as quickly as it did.

kyboren 23 minutes ago | parent

This is probably the most important concept for "normies" to understand about AI, IMO. It's the stochastic brother of the deterministic Church-Turing thesis. Any function that can be computed can be computed on any computer. And that function can be approximated to an arbitrary degree of precision with a DNN.

The real kicker is DNNs are much easier to program than CPUs because they don't require a closed-form description ("a program") of the function to be approximated; you just throw a bunch of input/output pairs at the model, compute loss, backprop and update weights, repeat.

Hence the unslakeable thirst for input/output pairs, i.e. data.

> In the field of machine learning, the universal approximation theorems (UATs) state that

> neural networks with a certain structure can, in principle, approximate any continuous

> function to any desired degree of accuracy. These theorems provide a mathematical

> justification for using neural networks, assuring researchers that a sufficiently large or

> deep network can model the complex, non-linear relationships often found in real-world data.[1][2]

>

> The best-known version of the theorem applies to feedforward networks with a single hidden

> layer. It states that if the layer's activation function is non-polynomial (which is true

> for common choices like the sigmoid function or ReLU), then the network can act as a

> "universal approximator." Universality is achieved by increasing the number of neurons in

> the hidden layer, making the network "wider." Other versions of the theorem show that

> universality can also be achieved by keeping the network's width fixed but increasing its

> number of layers, making it "deeper."

https://en.wikipedia.org/wiki/Universal_approximation_theore...

RandomLensman 4 minutes ago | parent

Not sure that is enough for forecasting as the function to be edtimated could change over time in random ways.

jesse_dot_id 1 hour ago | parent

It will be interesting to see if this changes because presumably AI is using very predictable historical models, but it seems like the climate is shifting into something unseen that we won't have models for?

cman1444 48 minutes ago | parent

Are you referring specifically to climate as in weather? The article is about forecasting a range of future events, not specifically weather.

ddp26 41 minutes ago | parent

Yes, I heard from one first-rate forecaster that he thinks AI forecasters are especially weak in predicting big disruptive changes to the world.

Hard to study this, obviously!

jacknews 32 minutes ago | parent

Of course model predictions will be acted upon, which will invalidate the predictions.

mbil 1 hour ago | parent

See also The AI Superforecasters Are Here https://www.astralcodexten.com/p/the-ai-superforecasters-are... and discussion https://news.ycombinator.com/item?id=48806296

attels33 1 hour ago | parent

So my plan to go from a developer to an economist is scrapped. What now?

neilwilson 40 minutes ago | parent

Well there’s always the priesthood.

That branch of religion has better uniforms anyway.

throwaway5752 1 hour ago | parent

The best human forecasters working with artificial intelligence are going to do even better than either alone, the dichotomy is artificial.

jacknews 22 minutes ago | parent

That's just a guess.

Do you and your cat make better predictions than your friend without a cat?

ratelimitsteve 58 minutes ago | parent

If 10,000 people guess 10,000 fair coin flips each one of them will get more guesses right than any of the others, one of them will get fewer guesses right than any of the others, and the gulf between the two is likely to be over 4 standard deviations wide. I'm certain that I, being an untutored schmuck from Pittsburgh and having thought of this almost immediately after reading about this contest, cannot be the first person to realize this is a potential problem for a forecasting contest. But I can't find anything they've done to mitigate that problem. Can anyone clue me in?

cman1444 45 minutes ago | parent

I don't understand your analogy. Are you just suggesting that luck plays too large a role in this contest? Clearly there is some "skill" or ability factor because AI's have been scoring higher and higher each year. Also, they make reference to superforecaster humans, who are presumably consistently better at forecasting than their peers.

adleyjulian 24 minutes ago | parent

They aren't guessing heads or tails, they give odds for each event. It's more like eyeballing a thousand coins to guess how fair they are, and then flipping each one just once.

Some are weighted to be 99% heads, others are 10% heads etc.

You could have 1,000,000 people guess random percentages for each coin, but suppose 10 of the coins are weighted 100% heads. To guess within 25% of the true value for all 10 of those coins would be roughly 1 in a million.

So a lucky guy guesses within 25% for all 10, he'd have another 990 coins he's being judged on.

johnecheck 29 minutes ago | parent

The markets are a highly complex dynamic system. There are many instances of it exhibiting disastrous behavior, especially in response to changes and shocks.

AI trading and investment advice meaningfully changes the system and its dynamics. It seems highly probable that this will result in it failing in new ways.

phyzix5761 21 minutes ago | parent

Stock analysts have a success rate of 47% or lower for directional predictions. That's worse than a coin flip. All AI has to do is product fair 50/50 results and it can beat analysts. But you can do it too for the price of a quarter.