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AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms
121–130 of 298 posts
Re: AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms
#122I'm surprised I'm not able to find this out - can some one tell me whether AlphaEvolve involves backprop or not? I honestly have no idea how AlphaEvolve works - does it work purely on the text level? Meaning I might be able to come up with something like AlphaEvolve with some EC2's and a Gemini API access?
No, the program and prompt databases use a genetic algorithm.
So with just a server an Gemini access + their code I can achieve the same thing? Nice
Re: AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms
#123I find it quite profound that there is no mention of the generation of corresponding code documentation. Without design diagrams, source and commit comments, etc the resulting code and changes will become incomprehensible unmaintainable. Unless that is somehow the point?
Re: AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms
#124Interestingly, it seems alphaevolve has already been in use for a year, and it is just now being publicly shown. The paper also mentions that it uses Gemini 2.0 (pro and flash), which creates a situation where Gemini 2.0 was used in a way to train Gemini 2.5. I don't know if I would call this the fabled "self improving feedback loop", but it seems to have some degree of it. It also begs the question if Alphaevolve wa…
It is really about autonomy. Can it make changes to itself without human review? If it does, what is the proof such changes won't just stop at some point? All I am seeing here is a coder assist tool, and unsure how helpful inexplicable solutions are in the long run. Could result in an obtuse code base. Is that the point?
Re: AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms
#125For the people awaiting the singularity, lines like this written almost straight from science fiction: > By suggesting modifications in the standard language of chip designers, AlphaEvolve promotes a collaborative approach between AI and hardware engineers to accelerate the design of future specialized chips."
Honestly it's this line that did it for me: > AlphaEvolve enhanced the efficiency of Google's data centers, chip design and AI training processes — *including training the large language models underlying AlphaEvolve itself*. Singularity people have been talking for decades about AI improving itself better than humans could, and how that results in runaway compounding growth of superintelligence, and now it's here.
Most code optimizations end up looking somewhat asymptotic towards a non-zero minimum.
If it takes you a week to find a 1% speedup, and the next 0.7% speedup takes you 2 weeks to find ... well, by using the 1% speedup the next one only takes you 13.86 days. This kind of small optimization doesn't lead to exponential gains.
That doesn't mean it's not worthwhile - it's great to save power & money and reduce iteration time by a small amount. And it combines with other optimizations over time. But this is in no way an example of the kind of thing that the singularity folks envisioned, regardless of the realism of their vision or not.
Re: AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms
#126Interestingly, it seems alphaevolve has already been in use for a year, and it is just now being publicly shown. The paper also mentions that it uses Gemini 2.0 (pro and flash), which creates a situation where Gemini 2.0 was used in a way to train Gemini 2.5. I don't know if I would call this the fabled "self improving feedback loop", but it seems to have some degree of it. It also begs the question if Alphaevolve wa…
If you have the brain power, the compute and control the hardware, what is there to prevent the take off feedback loop? Deepmind is at this point in the timeline uniquely positioned.
Running out of improvements after the first pass would prevent that. Who is to say this Alpha Evolve is not already obsolete, having already served its purpose?
Not to sound metaphysical or anything, but dependency on artificial intelligence seems to be something you would find at the peak of Mount Stupid (where the Darwin Awards are kept).
I am late for a chess game, l8r sk8rs.
Re: AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms
#127Earlier quoted context omitted.
This just means that it operates on the (debug text form of the) intermediate representation of a compiler.
Not necessarily. Theorem provers provide goals that can serve the same function as "debug text." Instead of interpreting the natural language chosen by the dev who wrote the compiler, these goals provide concrete, type-accurate statements that indicate the progress of an ongoing proof.
I'm referring to what the authors actually claim they did in the paper. They operated on XLA-generated textual IR.
Cf. the second paragraph of 3.3.4 of https://storage.googleapis.com/deepmind-media/DeepMind.com/B...
Re: AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms
#128For the people awaiting the singularity, lines like this written almost straight from science fiction: > By suggesting modifications in the standard language of chip designers, AlphaEvolve promotes a collaborative approach between AI and hardware engineers to accelerate the design of future specialized chips."
The singularity has always existed. It is located at the summit of Mount Stupid, where the Darwin Awards are kept. AI is really just psuedo-intelligence; an automated chairlift to peak overconfidence.
Re: AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms
#129Does this remind anyone else of genetic algorithms?
Is this basically a merge of LLM's with genetic algorithm iteration?
Re: AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms
#130Earlier quoted context omitted.
100%. LLMs are extremely useful for doing obvious but repetitive optimizations that a human might miss.
What it essentially does is a debugging/optimization loop where you change one thing, eval, repeat it again and compare results. Previously we needed to have a human in the loop to do the change. Of course we have automated hyperparameter tuning (and similar things), but that only works only in a rigidly defined search space. Will we see LLMs generating new improved LLM architectures, now fully incomprehensible to hu…
If I understood, isn't this software only as useful as the llm powering it is? It sounds like something very useful, but either I'm missing something or it put into a loop and a validator a "please optimize this code". Useful, but maybe not as revolutionary as the underlying llm tech itself
Edit the white paper says this: AlphaEvolve employs an ensemble of large language models. Specifically, we utilize a combination of Gemini 2.0 Flash and Gemini 2.0 Pro. This ensemble approach allows us to balance computational throughput with the quality of generated solutions. Gemini 2.0 Flash, with its lower latency, enables a higher rate of candidate generation, increasing the number of ideas explored per unit of time. Concurrently, Gemini 2.0 Pro, possessing greater capabilities, provides occasional, higher-quality suggestions that can significantly advance the evolutionary search and potentially lead to breakthroughs. This strategic mix optimizes the overall discovery process by maximizing the volume of evaluated ideas while retaining the potential for substantial improvements driven by the more powerful model.
So, I remain of my opinion before. Furthermore, in the paper they don't present it as something extraordinary as some people here say it is, but as an evolution of another existing software, funsearch