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AI system reaches human level on test for ‘general intelligence’
Here’s what that means.
By Michael Timothy Bennet, Elija Perrier on Jan 23 2025
11:11 AM
Is AI capable of "general intelligence"?
A new artificial intelligence (AI) model has just achieved
human-level results on a test designed to measure “general intelligence”.
On 20 December, OpenAI’s o3 system scored 85 per cent on the
ARC-AGI benchmark, well above the
previous AI best score of 55 per cent and on par with the average human score.
It also scored well on a very difficult mathematics test.
Creating artificial general intelligence, or AGI, is the
stated goal of all the major AI research labs.
At first glance, OpenAI appears to have at least made a
significant step towards this goal.
While scepticism remains, many AI researchers and developers
feel something just changed.
For many, the prospect of AGI now seems more real, urgent
and closer than anticipated.
Are they right?
Generalisation and intelligence
To understand what the o3 result means, you need to
understand what the ARC-AGI test is all about. In technical terms, it’s a test
of an AI system’s “sample efficiency” in adapting to something new – how many
examples of a novel situation the system needs to see to figure out how it
works.
An AI system like ChatGPT (GPT-4) is not very sample
efficient.
It was “trained” on millions of examples of human text,
constructing probabilistic “rules” about which combinations of words are most
likely.
The result is pretty good at common tasks.
It is bad at uncommon tasks, because it has less data (fewer
samples) about those tasks.
Until AI systems can learn from small numbers of examples
and adapt with more sample efficiency, they will only be used for very
repetitive jobs and ones where the occasional failure is tolerable.
The ability to accurately solve previously unknown or novel
problems from limited samples of data is known as the capacity to generalise.
It is widely considered a necessary, even fundamental,
element of intelligence.
Grids and patterns
The ARC-AGI benchmark tests for sample efficient adaptation
using little grid square problems like the one below.
The AI needs to figure out the pattern that turns the grid
on the left into the grid on the right.
Each question gives three examples to learn from.
The AI system then needs to figure out the rules that
“generalise” from the three examples to the fourth.
These are a lot like the IQ tests sometimes you might
remember from school.
Weak rules and adaptation
We don’t know exactly how OpenAI has done it, but the
results suggest the o3 model is highly adaptable.
From just a few examples, it finds rules that can be
generalised.
To figure out a pattern, we shouldn’t make any unnecessary
assumptions or be more specific than we really have to be.
In theory,
if you can identify the “weakest” rules that do what you want, then you have
maximised your ability to adapt to new situations.
What do we mean by the weakest rules?
The technical definition is complicated, but weaker rules
are usually ones that can be described
in simpler statements.
In the example above, a plain English expression of the rule
might be something like: “Any shape with a protruding line will move to the end
of that line and ‘cover up’ any other shapes it overlaps with.”
Searching chains of thought?
While we don’t know how OpenAI achieved this result just
yet, it seems unlikely they deliberately optimised the o3 system to find weak
rules.
However, to succeed at the ARC-AGI tasks it must be finding
them.
We do know that OpenAI started with a general-purpose
version of the o3 model (which differs from most other models, because it can
spend more time “thinking” about difficult questions) and then trained it
specifically for the ARC-AGI test.
French AI researcher Francois Chollet, who designed the
benchmark, believes
o3 searches through different “chains of thought” describing steps to solve the
task.
It would then choose the “best” according to some loosely
defined rule, or “heuristic”.
This would be “not dissimilar” to how Google’s AlphaGo
system searched through different possible sequences of moves to beat the world
Go champion.
You can think of these chains of thought like programs that
fit the examples.
Of course, if it is like the Go-playing AI, then it needs a
heuristic, or loose rule, to decide which program is best.
There could be thousands of different seemingly equally
valid programs generated.
That heuristic could be “choose the weakest” or “choose the
simplest”.
However, if it is like AlphaGo then they simply had an AI
create a heuristic.
This was the process for AlphaGo.
Google trained a model to rate different sequences of moves
as better or worse than others.
What we still don’t know
The question then is, is this really closer to AGI? If that
is how o3 works, then the underlying model might not be much better than
previous models.
The concepts the model learns from language might not be any
more suitable for generalisation than before.
Instead, we may just be seeing a more generalisable “chain
of thought” found through the extra steps of training a heuristic specialised
to this test. The proof, as always, will be in the pudding.
Almost everything about o3 remains unknown. OpenAI has
limited disclosure to a few media presentations and early testing to a handful
of researchers, laboratories and AI safety institutions.
Truly understanding the potential of o3 will require
extensive work, including evaluations, an understanding of the distribution of
its capacities, how often it fails and how often it succeeds.
When o3 is finally released, we’ll have a much better idea
of whether it is approximately as adaptable as an average human.
If so, it could have a huge, revolutionary, economic impact,
ushering in a new era of self-improving accelerated intelligence. We will
require new benchmarks for AGI itself and serious consideration of how it ought
to be governed.
If not, then this will still be an impressive result.
However, everyday life will remain much the same.
This article is republished from The Conversation under a
Creative Commons license. Read the original here.
I suspect
all most of us can do is stand back and watch! I guess it had to happen!
David.
