What is available now, Gemini Pro, is perhaps better than GPT-3.5. Gemini Ultra is not available yet, and won’t be widely available until sometime next year. Ultra is slightly better than GPT-4 on most benchmarks. Not confirmed but it looks like you’ll need to pay to access Geminin Ultra through some kind of Bard Advanced interface, probably much like ChatGPT Plus. So in terms of just foundational model quality, Gemini gets Google at a level where they are competing against OpenAI on something like an even playing field.
What is interesting though is this is going to bring more advanced AI to a lot more people. Not a lot of people use ChatGPT regularly, much less who pay for ChatGPT Plus. But tons of people use Google Workspace for their jobs, and Bard with Gemini Pro is built into those applications.
Also Gemini Nano, capable of running locally on android phones, could be interesting.
It will be interesting to see where things go from here. Does Gemini Ultra come out before GPT-4s one year anniversary? Does Google release further Gemini versions next year to try to get and stay ahead of OpenAI? Does OpenAI, being dethroned from their place of having the world’s best model plus all the turmoil internally, respond by pushing out GPT-5 to reassert dominance? Do developers move from OpenAI APIs to Gemini, especially given OpenAIs recent instability? Does Anthropic stick with its strategy of offering the most boring and easily offended AI system in the world? Will Google Assistant be useful for anything other than telling me the weather and setting alarms? Many questions to answer in 2024!
So we are actually at the time now when multiple AI are activated within a short window.
Although this is not the strong AI of most science fiction, ChatGPT and similarly modelled systems can behave eerily close to it.
It shall be interesting to see the effect of it becoming widely used. Although it will surely not be like the movies, I’m guessing it still has potential for unexpected results.
Disclaimer: I have no real qualification on this. But it seems like this whole technology is pretty sensitive to the specific model being used and the specific details of the pixels; the whole thing is written like there’s some silver-bullet image alteration that can fool “machine vision” in general, but what it demonstrates is nothing like that.
I asked Midjourney to identify the altered images that machines are supposed to identify as a sheep or a cat or whatever, and it said:
A bouquet of flowers sitting on the table in a brown vase
Some bright colored flowers in a circular vase
An omelette and sandwiches on the table
An omelet with hash browns
… which is what they are.
The last two images were actually a little more interesting – they’re distorted to the point that it’s visually obvious that they’ve been altered, and Midjourney actually picks up that the image is distorted a little, and includes that in the style part of its description, while mostly-accurately describing what’s in the image. These are its full descriptions:
“a red bridge, traffic lights, and a fencedin section of street, in the style of digital mixed media, thermal camera, american realism, found object sculpture, stipple, ricoh r1, xbox 360 graphics”
“a pole with a traffic light and a van, in the style of distorted, fragmented images, manapunk, found objects, webcam photography, suburban ennui capturer, hyper-realistic bird studies, 19th century american art”
Hehe, namedropping "AGI" in the very first paragraph and then going ahead with an AI that is super tailored to a narrow task like formal math proofs and geometry...
Sure. But none of this is about that. And I somehow doubt that'll be the path towards AGI anyways. Does any combination of narrow abilities become general at some point? Is the sum more than it's parts? I think so. Especially with intelligence.
And MoE comes with the issue that it can't really apply knowledge from one domain to another. At least if you separate the subjects. Whereas I as an general intelligent being can apply my math skills to engineering, coding, doing a plethora of every day tasks. So I'm not sure if MoE help with that.
Artificial general intelligence (AGI) with advanced mathematical reasoning has the potential to unlock new frontiers in science and technology.
The first sentence is completely irrelevant grifting. Red flag.
First, the problems were manually translated into formal mathematical language for our systems to understand… Our systems solved one problem within minutes and took up to three days to solve the others.
LMAO. If people translate the question into symbols, then ofc a computer can solve the problem in a few minutes.
If you translate your budget into a spreadsheet, then a computer can calculate your surplus or deficit in microseconds. But the actually hard part is making the spreadsheet.
AlphaProof solved two algebra problems and one number theory problem… two combinatorics problems remained unsolved.
So they got a 60%? That’s pretty good for a human but not so much for a purported “AI”.
I imagine it might not be difficult to develop classes of problems that are easy or hard/impossible for automated proof. Probably already exists but the grift don’t want to talk about limitations.
Did you actually look at the problems or even furher down the page before making these sweeping statements? Simply transforming it into formal mathematical language does not make the problems trivial. These aren’t arithmetic problems.
Despite failing the two problems, it did better than the majority of the contestants, who are some of the most talented math students in the world.
The only major catch was it did not finish in the alloted time, since it went on for days. But once the method has been established, that’s a performance problem.
Deepmind is one of the most respected labs in the AI space, far before the modern generative ai trend. They’re not some random grifters.
deepmind.google
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