Saturday's digest #17

5 Weekly Curated Articles For The Busy AI & Data Professional

Author

Jeremy Lezac
August 24, 2024

It may be the end of summer but the sun will always shine here.

#1 - Eric Flaningam from Generative Value - Physical Design Software: The strongest Moats in Software

Physical Design Software: The Strongest Moats in Software

An Overview of the History, Market, and Trends in CAD, Simulation, and EDA software

www.generativevalue.com/p/physical-design-software-the-strongest

Understanding the barriers to entry and strong moats in the physical design software market can help us think strategically about overcoming similar challenges in their own fields. The discussion on future trends like generative design and cloud-based tools aligns with the evolving landscape of AI and data, which is, as always, interesting.

#2 - Devansh and Tobias Mark Jensen from Artificial Intelligence Made Simple - What AI can do for Law

What AI can do for Law [Guest]

The Biggest Challenges in Law That AI Can Help Us to Solve

artificialintelligencemadesimple.substack.com/p/what-ai-can-do-for-law-guest

This article explores the intersection of law, technology, and ethics, focusing on how AI can address challenges in the legal industry. It discusses the potential of AI to streamline legal processes, reduce costs, and improve access to justice while highlighting the limitations and ethical considerations of implementing AI in legal settings. A must-read for anyone interested in the future of law and technology.

#3 - Arvind Narayanan and Sayash Kapoor from AI Snake Oil - AI companies are pivoting from creating gods to building products. Good.

AI companies are pivoting from creating gods to building products. Good.

Turning models into products runs into five challenges

www.aisnakeoil.com/p/ai-companies-are-pivoting-from-creating

Sobering article breaking down why AI hasn't lived up to the hype yet, despite huge investments in hardware and data centers. It points out mistakes like ignoring product-market fit and rushing to integrate AI into products without thinking it through. It also discusses five big hurdles for AI's success: cost, reliability, privacy, security, and user experience. If you want a clear view of where AI needs to go next, this is a great read.

#4 - Ben Dickson from TechTalks - Self-Taught Evaluator automates LLM-as-a-Judge training

Self-Taught Evaluator automates LLM-as-a-Judge training

Researchers at Meta have released Self-Taught Evaluator, a technique that enables LLMs to automatically label their training data.

bdtechtalks.substack.com/p/self-taught-evaluator-automates-llm

As always with TechTalks, a really quick overview on an interesting paper. As I am personally working on data augmentation, it is relevant for me (and for you as data is the alpha and omega of our AI work).

#5 - Michele Nieberding from Modern Data 101 - What an Actual Lean AI Strategy Looks Like?

What an Actual Lean AI Strategy Looks Like?

Multiplying Impact of AI Initiatives, Mitigating Fatal or Hidden Costs, and Always Putting Data First 🥇

moderndata101.substack.com/p/lean-ai-strategy-avoiding-compounding-costs

My read of the week. This article emphasizes the importance of focusing on strong data foundations, effective governance, and careful planning to ensure AI's success. By addressing key challenges like data quality, privacy, and ethical considerations, organizations can better leverage AI to achieve meaningful outcomes. Perfect as a starter if you want to drive AI Strategy in your company.

See you next week!

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