TLDR Index
TLDR AI

TLDR AI 2024-07-10

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01GENERAL

Sponsored

Selling AI to enterprise means getting serious about security and compliance — usually involving features like single sign-on (SSO), Directory Sync (SCIM), RBAC, and Audit Logs.

workos.comSponsor

02HEADLINES & LAUNCHES

03RESEARCH & INNOVATION

DISTILLING SYSTEM 2 INTO SYSTEM 1

System 2 models are those that use something like Chain of Thought to spend more test time compute and improve reasoning. It turns out that you can distill this behavior into a System 1 model that runs faster with similar accuracy.

arxiv.org23 min read

LEARNING AT TEST TIME

A new RNN variant that outperforms Mamba on a number of tasks. Importantly, the update function is itself an ML model - which enables long contexts and in-context learning.

arxiv.org25 min read

04ENGINEERING & RESOURCES

Sponsored

WHY YOU SHOULD USE A UNIVERSAL SEMANTIC LAYER FOR YOUR SNOWFLAKE DATA

A universal semantic layer makes it much easier to run both BI and AI workloads [12] on your data warehouse. AtScale [12], now available as a Snowflake Native App in the Snowflake Marketplace, allows you to define and consume a universal semantic layer directly within your Snowflake account.

bit.lySponsor

GRAPHRAG

GraphRAG is a way to explore unstructured text using the power of language models. The quick start runs on Azure, making it easy to spin up.

github.comGitHub repository

MOBILELLM

MobileLLM optimizes sub-billion parameter language models for on-device use cases.

github.comGitHub repository

ENHANCING TRAFFIC SIGNAL CONTROL WITH AI

Researchers have developed a new framework that combines a large language model with reinforcement learning to improve traffic signal control systems.

github.comGitHub repository

05MISCELLANEOUS

TINYGRAD NOTES

Tinygrad is a toolkit for training deep learning models. This set of notes is a great introduction for AI compilers and prepares one for a deep dive into Tinygrad internals.

mesozoic-egg.github.io18 min read

POP CULTURE

Goldman Sachs released a critical 31-page report titled "Gen AI: Too Much Spend, Too Little Benefit?", arguing that generative AI's productivity benefits and returns are significantly limited and that its power demands will drastically increase utility spending. The report highlights doubts about AI's ability to transform industries, pointing out high costs, power grid challenges, and lack of clear productivity gains or significant revenue generation. It suggests a potentially bleak future for the technology without major breakthroughs.

wheresyoured.at18 min read

THE AI SUMMER

The rapid adoption of ChatGPT, which reached 100 million users in two months, stands in contrast to historical tech developments like the iPhone and e-commerce, which took years to gain traction. Despite the initial hype, many users have not found long-term utility with ChatGPT and enterprise deployment of large language models remains limited, indicating a need for further development to achieve meaningful product-market fit and sustained value.

ben-evans.com10 min read