Google’s DeepMind AI breaks records

Plus: Google introduced SQuId in 42 languages. Meta plans to put AI everywhere on its platforms.

Hello Engineering Leaders and AI Enthusiasts!

Welcome to the 38th edition of The AI Edge newsletter. This edition brings you Google’s DeepMind AI breaks records with a 70% faster sorting algorithm.

And a special thank you goes out to our amazing readers. Your ongoing support fuels our passion for delivering quality content. 😊

In today’s edition:

🔥 Google’s DeepMind reveals AlphaDev which discovers faster algorithms
🔊 Google introduced SQuId in 42 languages
🌍 Meta plans to put AI everywhere on its platforms
💡 Knowledge Nugget: A quick guide to implementing Google’s Secure AI Framework

Let’s go!

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Google’s DeepMind AI breaks records with a 70% faster sorting algorithm

Google DeepMind has introduced AlphaDev, an AI system that utilizes reinforcement learning to uncover improved computer science algorithms. Alphadev’s ability to sort algorithms in C++ that surpasses the current best algorithm by 70% and revolutionizes the concept of computational efficiency.

AlphaDev discovered faster algorithms by taking a different approach than traditional methods, focusing on the computer's assembly instructions rather than refining existing algorithms. It ventured into unexplored territory, going beyond the usual path to achieve its breakthroughs.

Why does this matter?

This marks the first time an algorithm developed through reinforcement learning has been added to the sorting library, representing a crucial step toward using AI to optimize global code. 

By optimizing and launching improved sorting used by developers worldwide, AlphaDev has demonstrated its ability to generalize and discover new algorithms with real-world impact. This can be a step towards developing general-purpose AI tools that could help optimize the entire computing ecosystem and solve other problems that will benefit society.

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Google introduces SQuId (Speech Quality Identification) in 42 languages

Google introduces SQuId (Speech Quality Identification), a regression model with 600M parameters. It is trained on over a million quality ratings across 42 languages and tested in 65 languages. It serves as a complement to human ratings, providing a more efficient and standardized evaluation method. 

  

It can be used to complement human ratings for evaluating many languages and is the largest published effort of this type to date.

Why does this matter?

The introduction of SQuId represents a significant effort toward improving the evaluation of speech synthesis systems across multiple languages, offering a promising alternative to solely relying on human evaluation. By complementing human evaluation, SQuId helps overcome the limitations of traditional evaluation methods, facilitates faster iteration, and promotes collaboration in speech synthesis technologies.

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Meta plans to put AI everywhere on its platforms

Meta has announced plans to integrate generative AI into its platforms, including Facebook, Instagram, WhatsApp, and Messenger. The company shared a sneak peek of AI tools it was building, including ChatGPT-like chatbots planned for Messenger and WhatsApp that could converse using different personas. It will also leverage its image generation model to let users modify images and create stickers via text prompts.

Why does this matter?

Meta has been at the forefront of AI research but has been slow to put those technologies into its social media products compared to competitors like Snapchat. However, these plans signal its commitment to catching up and remaining competitive in the AI space.

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Knowledge Nugget: A quick guide to implementing Google’s Secure AI Framework

Google has introduced the Secure AI Framework (SAIF), a conceptual framework for secure AI systems. It is inspired by the security best practices — like reviewing, testing, and controlling the supply chain — applied to software development while incorporating the understanding of security mega-trends and risks specific to AI systems.

This quick guide is intended to provide high-level practical considerations on how organizations could build the SAIF approach into their existing or new adoptions of AI. It will also delve deeper into the topics that need to be addressed under each of the six core elements of SAIF.

Why does this matter?

SAIF is designed to help mitigate risks specific to AI systems, like stealing the model, data poisoning of the training data, injecting malicious inputs through prompt injection, and extracting confidential information in the training data. And as AI capabilities become increasingly integrated into products worldwide, adhering to a bold and responsible framework becomes even more critical.

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What Else Is Happening

🖌️Now fix unsatisfying photos with Photoshop’s Generative Fill! (Link)

💼LinkedIn introduced its own AI-powered tool for ad copies (Link)

💬ChatGPT comes to iPad, adds support for Siri and Shortcuts (Link)

🏛️Microsoft has announced Azure OpenAI Service for government customers (Link)

🔥Adobe announced a new generative AI offering to bring Firefly to enterprises (Link)

🔍Microsoft announced AI Customer Commitments for responsible AI journeys (Link)

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