BS Summary: This article contains 10 faulty reasoning types, including Slippery Slope, Confirmation Bias, and Negativity Bias, with Optimism Bias as the most egregious example at 31.2% saturation with 149 hits. Analysis detected 470 faulty-reasoning hits from 478 analyzed words, generating a BS Score of 43.8% and a BS Rank of 53% (14,571 of 30,584 articles). This article is worse (more manipulative) than 52.40% of the article peer group.
Google’s best AI models are six months behind the state of the art on coding capability and talent is leaving.
It may never take the LLM lead again, but that likely doesn’t matter.
The company is far from doomed even after Demis Hassabis stepped down as CEO of DeepMind Wednesday and Chief Scientist Jeff Dean left to found a startup.
Whoever stands atop the AI race podium isn’t the main point anymore.
The contest is now about using AI to accomplish real world tasks.
And for that, the benchmark is simple: Does it work and what did it cost?
When users click on Google’s little Gemini diamond, they can do things like use natural language to search email and YouTube , or get AI feedback on writing in Google docs.
Eventually, Google wants the little blue diamond to do more tasks, tying together Google’s many products (and products outside of Google) and pulling data from its billions of users.
Gemini Spark is the early iteration of that concept.
As AI infrastructure gets built and capabilities advance, Google’s suite of offerings — the most popular mobile operating system, internet browser, email, maps and more — will transform into more powerful systems touching every piece of technology in its users’ lives, including physical machines like your stove or your car and eventually robotics.
The tools will start to feel a little bit magical, especially for people who haven’t been experimenting with coding agents.
But the underlying models powering those features will not be state of the art, and none of the people using the features will care about that, because they will work.
Google won’t be using the top models for all of that because using the most powerful AI would be astronomically expensive, and there probably isn’t enough compute power in the world to handle it.
Some people get excited about the idea of building product scaffolding at unfathomable scale.
But for people like Hassabis and Dean, who have accomplished so much and have benefited financially from those contributions, it’s understandable that solving other problems is more appealing.
The most important product person at the company now may be cofounder Sergey Brin, who has no official role but wields a massive amount of power through his supervoting shares and status, according to several Google employees I spoke with.
There is nobody else at his level who has a more vested interest in Google succeeding.
Google now needs smart, technical people to step up and solve the challenges it faces in model development, architecture and efficiency — which shouldn’t be an issue.
The company has always had plenty of people, even if it’s lost some singular talent recently.
Aside from Hassabis, several other chief AI researchers at Google are leaving the tech giant to launch an AI startup , The New York Times reported.
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Analysis
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