Neeva went for an AI-powered experience earlier this year, but that wasn’t the issue—people were already duct taped to google and bing, and the cognitive switching cost created an enormous barrier to growth, even if Neeva was able to get to AI powered results faster.
AI
-
Neeva Acquired by Snowflake for $150M After $77.5M Funding
By
–
Neeva had raised around $77.5m from investors including Sequoia and Greylock, led by former Google ads boss Sridhar Ramaswamy. It sold to Snowflake for $150m, per what I’ve heard, and snowflake was one of multiple companies evaluating it.
-
Snowflake Acquires Neeva: AI Search Engine Acquihire Trend
By
–
Earlier this week Snowflake acquired Neeva, a startup trying to make an ad-free search engine powered by generative AI. But it didn’t acquire a search engine—and stories like Neeva’s acquihire are ones that we’ll see a lot in the coming years.
-
Robotics Progress: From Leg Lab to Boston Dynamics Achievements
By
–
I was a PhD student in the Leg Lab. That was 40 years ago. Marc and @BostonDynamics have made remarkable progress but this compilation left out the bloopers and it misses @ylecun’s point that progress in LLMs does not necc. imply progress in robotics. https://t.co/dhvvCst1sd
— Ken Goldberg (@Ken_Goldberg) 26 mai 2023I was a PhD student in the Leg Lab. That was 40 years ago. Marc and @BostonDynamics have made remarkable progress but this compilation left out the bloopers and it misses @ylecun
’s point that progress in LLMs does not necc. imply progress in robotics. -
Barkour: Robotic Agility Benchmark for Quadruped Locomotion
By
–
Presenting Barkour, a robotic agility benchmark that tests the implementation of low-level locomotion skills useful for movement through real-world environments, along with an ML-based example generalist locomotion policy for quadruped robots. Learn more →… pic.twitter.com/DlXQiJx1Ne
— Google AI (@GoogleAI) 26 mai 2023Presenting Barkour, a robotic agility benchmark that tests the implementation of low-level locomotion skills useful for movement through real-world environments, along with an ML-based example generalist locomotion policy for quadruped robots. Learn more →
-
Single Universal LLM vs Multiple Specialized Models Strategy
By
–
Do you think there is going to be one LLM for all use cases or we will need to deploy multiple special (fine tuned) LLMs? @aesadde
-
Retrieval and Context Management: Premature Optimization or Essential?
By
–
It seems that retrieval and context management are all the rage now. Is that premature optimization? Do you think there is an easy buy vs build decision there? @sidravi_
-
Building Software Infrastructure for LLM Interoperability
By
–
With projects like babyAGI and Auto-GPT, what other software pieces need to be built to actually achieve effective communication within different LLM’s? @tanishabassan
-
Training LLMs with Code Execution Traces Improves Reasoning
By
–
People say that adding code to the LLM training corpus improves the model's "reasoning" ability. If so, would including a training set of short programs followed by an execution trace improve its reasoning further as it would have to predict the executed code path from the src?
-
Small Organizations Training Custom LLMs: Infrastructure Requirements
By
–
How difficult is it for small organizations to tune/build their own LLMs? Will every company eventually have its own little datacenter to train these LLMs? @husseinmuhaisen @GabiImmelman