Applied AI Weekly News

Justin Grammens

Woot! Welcome Applied AI Weekly Readers to Issue 177. I'm thrilled to once again share with you the most interesting articles I've found this past week on Artificial Intelligence. Before we go there, here are a few things to note:...

I'll be doing a virtual Workshop Wednesday on March 6th entitled Let's Learn LangChain!. Regardless of your background or technical level, if you've always wanted to learn more about LangChain and how it is revolutionizing the way applications work with Large Language Models, I hope you can join us!
I'm continuing to offer free consultation with many business leaders on how AI is changing the landscape of how you will run your business today and into the future. This can be everything from how I'm working on building chatbots, looking at open source LLMs, and how GPTs are changing how products are being developed from product management to software engineering. Connect today and book a meeting with me.
If YouTube and videos are more your jam, I now have AppliedAIWeekly on YouTube. In addition to publishing here, I will be recording each issue as a video.
The latest Conversation on Applied AI Podcast with Will Preble has dropped where we discussed among many things, creating heart-centered technology.
Finally, be sure to visit the AppliedAI.MN and Applied AI Spring Conference 2024 to register for all the upcoming events.

Now that we have that covered, please enjoy the articles that I have spent time finding and curating for you this past week. Reach out if there's anything you feel I might have missed. Enjoy!

News

Gemini, ChatGPT, and the Other AI Bots Are out of Control

When you ask an AI bot for an image of the Founding Fathers or a group of German soldiers from 1943, you expect... something. You probably don’t expect what Google Gemini has been delivering, which is a set of images that goes heavy on diversity and light on historical accuracy. And when you ask ChatGPT a question, you probably don’t expect total gibberish in response. It’s been a very strange couple of days in AI land, but it makes us wonder: what do we actually want from AI?

Why Techies Are Excited About AI Agents That Do Errands for You

Last year, ChatGPT took the world by storm. This year, AI agents that do errands for you are all the rage. NPR's Bobby Allyn looked into why techies are so excited about it.

'Embarrassing and Wrong': Google Admits It Lost Control of Image-Generating AI

Google has apologized (or come very close to apologizing) for another embarrassing AI blunder this week, an image-generating model that injected diversity into pictures with a farcical disregard for historical context. While the underlying issue is perfectly understandable, Google blames the model for “becoming” oversensitive. But the model didn’t make itself, guys.

CEO Says Later This Year, Your AI Will Be Able to Attend Meetings For You

As Otter CEO Sam Liang — whose AI startup offers transcription and automated note-taking services — told Business Insider, he believes a "working prototype" of an AI-powered work avatar able to attend meetings in place of its human counterpart will be ready by "later this year." According to Liang, these workplace AI doppelgängers would ideally be capable of not only attending a meeting and taking notes, but also participating, answering questions, and even asking their own.

AI Needs So Much Power That Old Coal Plants Are Sticking Around

In a 30-square-mile patch of northern Virginia that’s been dubbed “data center alley,” the boom in artificial intelligence is turbocharging electricity use. Struggling to keep up, the power company that serves the area temporarily paused new data center connections at one point in 2022.

Sponsor

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Business

2024 Is a Year of Reckoning for AI

The AI marketing hype, arguably kicked off by OpenAI’s ChatGPT, has reached a fever pitch: investors and executives have stratospheric expectations for the technology. But the higher the expectations, the easier it is to disappoint. The stage is set for 2024 to be a year of reckoning for AI, as business leaders home in on what AI can actually do right now.

Reddit in AI Content Licensing Deal With Google

Social media platform Reddit has struck a deal with Google, opens a new tab to make its content available for training the search engine giant's artificial intelligence models, three people familiar with the matter said. The contract with Alphabet-owned Google is worth about $60 million per year, according to one of the sources.

The deal underscores how Reddit, which is preparing for a high-profile stock market launch, is seeking to generate new revenue amid fierce competition for advertising dollars from the likes of TikTok and Meta Platform's (META.O), opens new tab Facebook.

Development

Gemma: Google introduces new state-of-the-art open models

Gemma is a family of lightweight, state-of-the-art open models built from the same research and technology used to create the Gemini models. Developed by Google DeepMind and other teams across Google, Gemma is inspired by Gemini, and the name reflects the Latin gemma, meaning “precious stone.” Accompanying our model weights, we’re also releasing tools to support developer innovation, foster collaboration, and guide responsible use of Gemma models.

Google Deepmind Proposes ‘Self-Discover’ Framework for LLMs, Improves GPT-4 Performance

In a bid to enhance the reasoning capabilities of large language models (LLMs), researchers from Google Deepmind and the University of Southern California have proposed a new ‘self-discover’ prompting framework.

Published on arXiV and Hugging Face this morning, the approach goes beyond existing prompting techniques used by LLMs and has been found capable of improving the performance of known models out there, including OpenAI’s GPT-4 and Google’s PaLM 2.

Lumiere: A Space-Time Diffusion Model for Video Generation

We introduce Lumiere -- a text-to-video diffusion model designed for synthesizing videos that portray realistic, diverse and coherent motion -- a pivotal challenge in video synthesis. To this end, we introduce a Space-Time U-Net architecture that generates the entire temporal duration of the video at once, through a single pass in the model. This is in contrast to existing video models which synthesize distant keyframes followed by temporal super-resolution -- an approach that inherently makes global temporal consistency difficult to achieve.