Google Chrome Gets An Exciting AI Makeover
Plus: Google Research presents Lumiere, Binoculars detect 90% of ChatGPT-generated text.
Hello Engineering Leaders and AI Enthusiasts!
Welcome to the 195th edition of The AI Edge newsletter. This edition brings you Google’s new features for Chrome and Ads.
And a huge shoutout to our amazing readers. We appreciate you😊
In today’s edition:
🆕Google Chrome and Ads are getting new AI features
🎥 Google Research presents Lumiere for SoTA video generation
🔍 Binoculars can detect over 90% of ChatGPT-generated text
📚 Knowledge Nugget: Optimizing Distributed Training on Frontier for Large Language Models by
Let’s go!
Google Chrome and Ads are getting new AI features
Google Chrome is getting 3 new experimental generative AI features:
Smartly organize your tabs: With Tab Organizer, Chrome will automatically suggest and create tab groups based on your open tabs.
Create your own themes with AI: You’ll be able to quickly generate custom themes based on a subject, mood, visual style and color that you choose– no need to become an AI prompt expert!
Get help drafting things on the web: A new feature will help you write with more confidence on the web– whether you want to leave a well-written review for a restaurant, craft a friendly RSVP for a party, or make a formal inquiry about an apartment rental.
(Source)
In addition, Gemini will now power the conversational experience within the Google Ads platform. With this new update, it will be easier for advertisers to quickly build and scale Search ad campaigns.
(Source)
Why does this matter?
Over the last few years, Google has brought the latest ML and AI technologies into Chrome to make searching the web easier, safer, and more accessible. These releases make browsing even easier and more efficient, all while keeping your experience personalized to you.
Google is bringing more AI and ML into Chrome this year, including integrating Gemini to help you browse even easier and faster.
Google Research presents Lumiere for SoTA video generation
Lumiere is a text-to-video (T2V) diffusion model designed for synthesizing videos that portray realistic, diverse, and coherent motion– a pivotal challenge in video synthesis. It demonstrates state-of-the-art T2V generation results and shows that the design easily facilitates a wide range of content creation tasks and video editing applications.
The approach introduces a new T2V diffusion framework that generates the full temporal duration of the video at once. This is achieved by using a Space-Time U-Net (STUNet) architecture that learns to downsample the signal in both space and time, and performs the majority of its computation in a compact space-time representation.
Why does this matter?
Despite tremendous progress, training large-scale T2V foundation models remains an open challenge due to the added complexities that motion introduces. Existing T2V models often use cascaded designs but face limitations in generating globally coherent motion. This new approach aims to overcome the limitations associated with cascaded training regimens and improve the overall quality of motion synthesis.
Binoculars can detect over 90% of ChatGPT-generated text
Researchers have introduced a novel LLM detector that only requires simple calculations using a pair of pre-trained LLMs. The method, called Binoculars, achieves state-of-the-art accuracy without any training data.
It is capable of spotting machine text from a range of modern LLMs without any model-specific modifications. Researchers comprehensively evaluated Binoculars on a number of text sources and in varied situations. Over a wide range of document types, Binoculars detects over 90% of generated samples from ChatGPT (and other LLMs) at a false positive rate of 0.01%, despite not being trained on any ChatGPT data.
Why does this matter?
A common first step in harm reduction for generative AI is detection. Binoculars excel in zero-shot settings where no data from the model being detected is available. This is particularly advantageous as the number of LLMs grows rapidly. Binoculars' ability to detect multiple LLMs using a single detector proves valuable in practical applications, such as platform moderation.
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Knowledge Nugget: Optimizing Distributed Training on Frontier for Large Language Models
provides an overview of this interesting paper which discusses how large LLMs were trained on the Frontier supercomputer, ranked number 1 in the TOP500 list.Frontier is unique in that it is built on AMD CPUs and GPUs, EPYC and MI250X, respectively. The second supercomputer in the TOP500, Aurora, is entirely built on Intel (both CPUs and GPUs). Only the third, Eagle, uses NVIDIA H100 and Intel Xeon.
The most interesting part of this story is exactly how they trained these models on these cards, as for a long time using anything other than Nvidia and CUDA was not very feasible.
Why does this matter?
Training LLMs with billions of parameters poses significant challenges and requires considerable computational resources. This research explores efficient distributed training strategies to extract this computation from Frontier, the world's first exascale supercomputer dedicated to open science.
What Else Is Happening❗
🧠Microsoft forms a team to make generative AI cheaper.
Microsoft has formed a new team to develop conversational AI that requires less computing power compared to the software it is using from OpenAI. It has moved several top AI developers from its research group to the new GenAI team. (Link)
⚽Sevilla FC transforms the player recruitment process with IBM WatsonX.
Sevilla FC introduced Scout Advisor, an innovative generative AI tool that it will use to provide its scouting team with a comprehensive, data-driven identification and evaluation of potential recruits. Built on watsonx, Sevilla FC's Scout Advisor will integrate with their existing suite of self-developed data-intensive applications. (Link)
🔄SAP will restructure 8,000 roles in a push towards AI.
SAP unveiled a $2.2 billion restructuring program for 2024 that will affect 8,000 roles, as it seeks to better focus on growth in AI-driven business areas. It would be implemented primarily through voluntary leave programs and internal re-skilling measures. SAP expects to exit 2024 with a headcount "similar to the current levels". (Link)
🛡️Kin.art launches a free tool to prevent GenAI models from training on artwork.
Kin.art uses image segmentation (i.e., concealing parts of artwork) and tag randomization (swapping an art piece’s image metatags) to interfere with the model training process. While the tool is free, artists have to upload their artwork to Kin.art’s portfolio platform in order to use it. (Link)
🚫Google cancels contract with an AI data firm that’s helped train Bard.
Google ended its contract with Appen, an Australian data company involved in training its LLM AI tools used in Bard, Search, and other products. The decision was made as part of its ongoing effort to evaluate and adjust many supplier partnerships across Alphabet to ensure vendor operations are as efficient as possible. (Link)
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