Inception Emerges From Stealth With a New Type of AI Model
- Reference: 0176553963
- News link: https://slashdot.org/story/25/02/26/2257224/inception-emerges-from-stealth-with-a-new-type-of-ai-model
- Source link:
> Ermon hypothesized generating and modifying large blocks of text in parallel was possible with diffusion models. After years of trying, Ermon and a student of his achieved a major breakthrough, which they detailed in a [2]research paper published last year. Recognizing the advancement's potential, Ermon founded Inception last summer, tapping two former students, UCLA professor Aditya Grover and Cornell professor Volodymyr Kuleshov, to co-lead the company. [...]
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> "What we found is that our models can leverage the GPUs much more efficiently," Ermon said, referring to the computer chips commonly used to run models in production. "I think this is a big deal. This is going to change the way people build language models." Inception offers an API as well as on-premises and edge device deployment options, support for model fine-tuning, and a suite of out-of-the-box DLMs for various use cases. The company claims its DLMs can run up to 10x faster than traditional LLMs while costing 10x less. "Our 'small' coding model is as good as [OpenAI's] GPT-4o mini while more than 10 times as fast," a company spokesperson told TechCrunch. "Our 'mini' model outperforms small open-source models like [Meta's] Llama 3.1 8B and achieves more than 1,000 tokens per second."
[1] https://techcrunch.com/2025/02/26/inception-emerges-from-stealth-with-a-new-type-of-ai-model/
[2] https://arxiv.org/pdf/2310.16834
Bah Humbug! (Score:1)
- Slashdot probably
Re: (Score:3)
> - Slashdot probably
Well, sure. Because so far this AI bubble is mostly unreliable hype.
Image generation models are impressive because there's no "right" and "wrong". There's just "close enough" or "not close enough". But LLMs are exactly that: language models. They're impressive language-parsing tools but their use is often applied to tasks that actually require precision, that's not what they're designed for.
The important next step - I think - is some kind of LFM: Large Fact Model. If we could tokenize facts and tru
Re: (Score:2)
Was that dress blue, or black again?
Re: (Score:3)
A generative model is set up to generate a different answer every time you run it. That's the point. You can make non-generative models with language front ends, that's not a problem. The problem with your "fact model" is figuring out what a fact is.
Since both humans and computers are pretty shit at that, I wouldn't hold my breath.
Re: Bah Humbug! (Score:2)
Did you just invent JSON for agent to agent exchange of factoid objects? Data structures?
It's fast, but still limited to basic tasks (Score:3)
I asked it to generate a transformer implementation for DeepSeek R1 and it spits out a whole lot of: // This is a placeholder for the actual implementation
Like other codegen models, it doesn't go much beyond basic common coding tasks. Even for basic tasks in anything but Javascript, the code doesn't compile cleanly.
Negatives aside, it is an interesting thesis, and I like with the direction they're taking. I skimmed the paper, but I think DeepSeek's MoE approach tackles the same weight distribution optimization in a more elegant way. In a nutshell, it's not the CPU or memory that's the limiting factor, it's that attention mechanisms jump around in memory and overloaded the bus IO.
Parallel processing (Score:3)
Somehow this does not seem surprising. Optimizations of some sort were guaranteed to come sooner or later. The anxiety to grab the next thing is palpable lately, this fits right in. Article says they already already have customers lined up...hmmm... its presented as the coming out of a skunkworks, a list of paying customers should be premature but the proof will be in the release.. but you can't help but notice the hype.
10x less?? (Score:3)
"while costing 10x less"
Was it an AI who wrote that? Stuff doesn't cost "10x less", they cost "90 % less" or "the cost is one tenth" of something else. Duh!