It's worth remembering that AI is more than LLMs. DeepMind is still doing big stuff: https://deepmind.google/discover/blog/millions-of-new-materi...
2023-11-14: GraphCast, word leading weather prediction model, published in Science
2023-11-15: Student of Games: unified learning algorithm, major algorithmic breath-through, published in Science
2023-11-16: Music generation model, seemingly SOTA
2023-11-29: GNoME model for material discovery, published in Nature
2023-12-06: Gemini, the most advanced LLM according to own benchmarks
Where it has fallen down (compared to its relative performance in relevant research) is public generative AI products [0]. It is trying very hard to catch up at that, and its disadvantage isn't technological, but that doesn't mean it isn't real and durable.
[0] I say "generative AI" because AI is a big an amorphous space, and lots of Google's products have some form of AI that is behind important features, so I'm just talking about products where generative AI is the center of what the product offers, which have become a big deal recently and where Google had definitely been delivering far below its general AI research weight class so far.
In such cases, I actually prefer Google over OpenAI. Monetization isn’t everything
For, what, moral kudos? (to be clear, I'm not saying this is a less important thing in some general sense, I'm saying what is preferred is always dependent on what we are talking about preferences for.)
> Monetization isn’t everything
Providing a user product (monetization is a different issue, though for a for-profit company they tend to be closely connected) is ultimately important for people looking for a product to use.
Other interests favor other things, sure.
For the good of society? Performing and releasing bleeding edge research benefits everyone, because anyone can use it. Case in point: transformers.
There is nothing open about OpenAI and they wouldn't exist in their current form without years of research funded by Google.
OK, but that only works if you actually do the part that lets people actually use the research for something socially beneficial. A research paper doesn't have social benefit in itself, the social benefit comes when you do something with that research, as OpenAI has.
> There is nothing open about OpenAI and they wouldn't exist in their current form without years of research funded by Google.
True enough. But the fact remains that they're the ones delivering something we can actually use.
https://charts.ecmwf.int/products/graphcast_medium-mslp-wind...
I personally think of it as open in the sense that they provide an API to allow anyone to use it (if they pay) and take advantage of the training they did. Is in contrast to large companies like Google which have lots of data and historically just use AI for their own products.
Edit:
I define it as having some level of being open beyond 'nothing'. The name doesn't scale well over time based on business considerations and the business environment changing and was named poorly when 'open source' is a common usage of open within tech. They should have used AI products to help them in naming the company and be aware of such potential controversies.
From chatgpt today (which wasn't an option at the time but they maybe could have gotten similar information or just thought about it more):
What are the drawbacks to calling an AI company 'open'?
...
"1. Expectations of Open Source: Using the term "open" might lead people to expect that the company's AI technology or software is open source. If this is not the case, it could create confusion and disappointment among users and developers who anticipate access to source code and the ability to modify and distribute the software freely.
2. Transparency Concerns: If an AI company claims to be "open," there may be heightened expectations regarding the transparency of their algorithms, decision-making processes, and data usage. Failure to meet these expectations could lead to skepticism or distrust among users and the broader public."
...
Open Group was formed through the merger of Open Software Foundation (est. 1988) and X/Open (est. 1984), and they were all pay-to-play.
Compared to a curated video service like HBO Max, Hulu, or Netflix, that's an accurate way to describe the relative differences. We aren't used to using that terminology through, so yes, it comes across as weird (and if the point is to communicate features, is not particularly useful compared to other terminology that could be used).
It makes a bit less sense for search IMO, since that's the prevalent model as far as I'm aware, so there's not an easy and obvious comparison that is "closed" which allows us to view Google search as "open".
Google is locked behind research bubbles, legal reviews and safety checks.
Mean while OpenAI is eating their lunch.
Sharing fundamental work is more impactful than sharing individual models.
Advancing products that use AI and getting a consumer/public conversation started? That’s clearly (to me) in OpenAIs court
They’re both impactful, interlinked, and I’m not sure there’s some real stack ranking methodology.
Gemini does nothing. Even if it were comparable to GPT-4, they’re late to the party.
OpenAI is blazing the path now.
Microsoft? In the sense that OpenAI is "paying" them... through MS's own investment.
Google has lots of people tagging search rankings, which is very similar with RLHF ranking responses from LLMs. It's interesting that using LLMs with RLHF it is possible to de-junk the search results. RLHF is great for this task, as evidenced by its effect on LLMs.
A few reasons partially (if not fully) responsible for it might be:
- Google is a hot target of SEO, not Phind.
- If Google stops indexing certain low quality without a strong justification, there would be lawsuits, or people saying how "Google hasn't indexed my site" or whatever. How would you authoritatively define "low quality"?
- Having to provide search for all spectrum of users in various languages, countries and not just for "tech users".
The Internet is basically a rubbish dump now imo.
Sure 90% of the Internet is crap. That's because 90% of everything is crap.
There's a constant arms race between shitty SEO, walled gardens, low-quality content farms and search engines.
It will be interesting to see how this percolates through the existing systems.
We are just seeing remnants of that battleground.
Another opposite of "natural" is "designed", and another opposite of artificial is "by chance".
"By chance" is also an accurate descriptor of natural intelligence.
Not sure if you were making a point, but your comment backs up the assertion that "natural" was a better choice than "real" in that sentence.
"
1. Natural 2. Authentic 3. Genuine 4. Real 5. Organic 6. Original 7. Unaffected 8. Unpretentious 9. True 10. Sincere
"