Google's Responsible AI Leap
Plus: Microsoft and Google's Large-Scale Automatic Audiobook Creation, Tech giants leading hiring surge in GenAI.
Hello Engineering Leaders and AI Enthusiasts!
Welcome to the 103rd edition of The AI Edge newsletter. This edition brings you Google's pioneering new move for responsible AI.
And a huge shoutout to our amazing readers. We appreciate you! 😊
In today’s edition:
🌐 Google's responsible AI leap
and
🤖 Microsoft, MIT, and Google transformed entire Project Gutenberg Collection into audiobooks
🚀 Amazon, Nvidia, Microsoft, and Google lead hiring surge in GenAI
📚 Knowledge Nugget: Should you fine-tune a model? by
Let’s go!
Google's responsible AI leap
Google is launching the Digital Futures Project and a $20 million Google.org fund, which will provide grants to leading think tanks and academic institutions worldwide. The project will support researchers, organize convenings, and foster debate on public policy solutions to encourage the responsible development of AI.
Inaugural grantees of the Digital Futures Fund include the Aspen Institute, Brookings Institution, Carnegie Endowment for International Peace, the Center for a New American Security, the Institute for Security and Technology, SeedAI, and more. The fund will support institutions from countries around the globe.
Why does it matter?
Google has long been an advocate for responsible AI. But getting AI right will take more than any one company alone. This move will support many across academia and civil society to advance independent research on AI that helps this transformational technology benefit everyone.
(Source)
Microsoft, MIT, and Google transformed entire Project Gutenberg Collection into audiobooks
In a new research called Large-Scale Automatic Audiobook Creation, Microsoft, MIT, and Google collaborated to transform the entire Project Gutenberg Collection into audiobooks. The library now boasts thousands of free and open audiobooks powered by AI.
Utilizing recent advances in neural text-to-speech, the team achieved exceptional quality of voice acting. The system also allows users to customize an audiobook's speaking speed and style, emotional intonation, and can even match a desired voice using a small amount of sample audio.
Why does it matter?
This presents an exceptional use case for text-to-speech AI. Moreover, it introduces a scalable system capable of converting thousands of HTML-based e-books to high-quality audiobooks. This signifies a remarkable leap in AI's ability to solve real-world problems with tangible impact.
(Source)
Amazon, Nvidia, Microsoft, and Google lead hiring surge in GenAI
There is an explosive demand for Generative AI talent today. Here are some compelling statistics.
The number of companies mentioning “Generative AI” in monthly job postings is increasing exponentially.
Tech giants leading the surge in hiring for GenAI talent include Amazon, Nvidia, Oracle, Microsoft, Google, and more. Big banks like Citigroup and CapitalOne are also hiring big in GenAI.
Unsurprisingly, technology is the #1 sector looking to hire GenAI experts. Finance is #2nd, and healthcare is #3, while demand has been tepid in sectors like real estate, basic materials, and energy.
Companies are paying a lot for GenAI talent! Among all technical skills/technologies tracked, jobs mentioning “Generative AI” or “LLMs” had the highest average base salary offered, with an average of $200,837/year.
Why does this matter?
This reflects the pivotal role generative AI is playing across industries. Moreover, it signals a shift in how businesses are operating, adapting, and strategizing for an AI-led future. For job seekers and professionals, it presents exciting opportunities and emphasizes the need to stay updated with AI-related skills to thrive in the market today.
Knowledge Nugget: Should you fine-tune a model?
and made a controversial claim that you shouldn’t fine-tune your own models. But of course, like all rules, this one should be broken sometimes. There are indeed cases when you should fine-tune an LLM for a particular task, but it’s certainly not where you should start.In this article, they discuss when you and shouldn’t fine-tune a model. A rough rule of thumb: Fine-tuning is good for teaching a model generalized skills, and off-the-shelf LLMs are good for analyzing or synthesizing specific information.
Why does this matter?
The article addresses a critical decision-making process in natural language processing and machine learning: whether or not to fine-tune a language model (LLM). It provides valuable guidance to practitioners and data scientists who work with LLMs, ultimately contributing to the efficiency and effectiveness of AI applications.
What Else Is Happening❗
🌄Instagram might be getting generative AI panoramas (Link)
🧐IRS will focus on the wealthy, using AI to identify sophisticated schemes to avoid taxes (Link)
🎥YouTube announces AI-powered creative guidance in Google Ads (Link)
🤖AI chatbots tasked to run a tech company built software in 7 minutes for less than $1 (Link)
🏥A boy saw 17 doctors over 3 years for chronic pain. ChatGPT found the right diagnosis (Link)
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😁Tickle Tuesday
ChatGPT: “I'm here to help. But sometimes, I can't help but grin at the irony of it all.”
Ah, the AI paradox.
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