421–430 of 504 posts
Google invested $400M into Anthropic https://news.ycombinator.com/item?id=34663438 Investors include: - Eric Schmidt (former Google CEO/Chairman), Series A - Sam Bankman-Fried, lead investor in Series B - Caroline Ellison, Series B https://news.ycombinator.com/item?id=34664963
If Apple would wake up to what's happening with llama.cpp etc then I don't see such a market in paying for remote access to big models via API, though it's currently the only game in town. Currently a Macbook has a Neural Engine that is sitting idle 99% of the time and only suitable for running limited models (poorly documented, opaque rules about what ops can be accelerated, a black box compiler [1] and an apparent…
If I were Apple I'd be thinking about the following issues with that strategy: 1. That RAM isn't empty, it's being used by apps and the OS. Fill up 64GB of RAM with an LLM and there's nothing left for anything else. 2. 64GB probably isn't enough for competitive LLMs anyway. 3. Inferencing is extremely energy intensive, but the MacBook / Apple Silicon brand is partly about long battery life. 4. Weights are expensive t…
Google had already done some very convincing demos in the last few years well before ChatGPT and GPT-4 captured the popular imagination. Microsoft’s OpenAI deal I would assume will lead to a “Cortana 2.0” (obviously rebranded, probably “Bing for Windows”, “Windows Copilot” or something similar). Google Assistant has been far ahead of Siri for many years longer than that, and they have extensive experience with LLMs. Apple surely realises the position their platforms are in and the risk of being left behind.
I’m also not sure the barrier on iPhone is as great as you suggest - it’s obviously constrained in terms of what it can support now but if the RAM on the device doubles a few times over the next few years I can see this being less of an issue. Multiple models (like the Alpaca sets) could be used for devices with different RAM/performance profiles and this could be sold as another metric to upgrade (i.e. iPhone 16 runs Siri-2.0-7b while iPhone 17 runs Siri-2.0-30b - “More than 3x smarter than iPhone 16. The smartest iPhone we’ve ever made.” etc).
"Dario Amodei, the former VP of research at OpenAI, launched Anthropic in 2021 as a public benefit corporation, taking with him a number of OpenAI employees, including OpenAI’s former policy lead Jack Clark. Amodei split from OpenAI after a disagreement over the company’s direction, namely the startup’s increasingly commercial focus." So Anthropic is the Google-supported equivalent of OpenAI? Isn't the founder going…
Curiously, Anthropic.com was launched in 2021, but a small custom software shop in Arizona around since the mid-late 90s had registered and been using Anthropic.ai in 2020 for a couple projects. How does that name collision work?
Google invested $400M into Anthropic https://news.ycombinator.com/item?id=34663438 Investors include: - Eric Schmidt (former Google CEO/Chairman), Series A - Sam Bankman-Fried, lead investor in Series B - Caroline Ellison, Series B https://news.ycombinator.com/item?id=34664963
Would lawsuits targeting SBF and CE put those investments at risk via clawbacks. Kind of like how many Madoffs investors who made money were forced to return that.
Earlier quoted context omitted.
Curiously, Anthropic.com was launched in 2021, but a small custom software shop in Arizona around since the mid-late 90s had registered and been using Anthropic.ai in 2020 for a couple projects. How does that name collision work?
Maybe they bought the domain name for a mutually agreeable price?
Kind of unsure how it all works.
GPT-4 cost less than a billion dollars? What is the claim here? That they're spending more money on compute than OpenAI, or that they have made algorithmic breakthroughs that enable them to make better use of the same amount of compute?
Earlier quoted context omitted.
Would lawsuits targeting SBF and CE put those investments at risk via clawbacks. Kind of like how many Madoffs investors who made money were forced to return that.
I don’t think it’s much like what you describe because the direction is reversed. They say they expect the debtors to sell that investment over the next few years. Potentially they will sell it at a profit.
Earlier quoted context omitted.
The counter argument is that it's a growing market where any early entrants will be lifted with the tide and can probably yield enough profit from spillover hype for investors to make their investments back.
What do they have besides an admittedly very cool name?
A dozen! Why, golly gee, I've got a business plan right here that says I'm going to enter 87 major industries and dominate every one of them.
I've been around tech, reading headlines like this, since roughly 1993 (and others here were of course seeing the same - adjusted for inflation and scale - types of headlines decades before me). This just reads like every other going-to-fail-miserably hilariously-grandiose-ambition we're-going-to-be-the-next-big-shit headline I've read in past decades during manic bubble phases.
Hey, Masayoshi Son has a 100 year plan to dominate the Interwebs, did ya hear? Oh shit, this isn't 1999? Different century, same bullshit headlines. Rinse and repeat. So I guess we're formally knee deep into the latest AI bubble. These types of stories read just like the garbage dotcom rush where companies would proclaim how they were entering a billion verticals and blah blah blah, we're gonna own all of the b2b ecommerce space, blah blah blah.
If Apple would wake up to what's happening with llama.cpp etc then I don't see such a market in paying for remote access to big models via API, though it's currently the only game in town. Currently a Macbook has a Neural Engine that is sitting idle 99% of the time and only suitable for running limited models (poorly documented, opaque rules about what ops can be accelerated, a black box compiler [1] and an apparent…
If I were Apple I'd be thinking about the following issues with that strategy: 1. That RAM isn't empty, it's being used by apps and the OS. Fill up 64GB of RAM with an LLM and there's nothing left for anything else. 2. 64GB probably isn't enough for competitive LLMs anyway. 3. Inferencing is extremely energy intensive, but the MacBook / Apple Silicon brand is partly about long battery life. 4. Weights are expensive t…
Apple is ahead of the game for a change getting their chips in line as the software exits alpha and goes mainstream.