Keeping up with an industry as fast-moving as AI is a tall order. So until an AI can do it for you, here’s a handy roundup of recent stories in the world of machine learning, along with notable research and experiments we didn’t cover on their own. By the way, TechCrunch plans to launch an AI newsletter […]
© 2024 TechCrunch. All rights reserved. For personal use only.
Keeping up with an industry as fast-moving as AI is a tall order. So until an AI can do it for you, here’s a handy roundup of recent stories in the world of machine learning, along with notable research and experiments we didn’t cover on their own. By the way, TechCrunch plans to launch an AI newsletter […]
© 2024 TechCrunch. All rights reserved. For personal use only.
Keeping up with an industry as fast-moving as AI is a tall order. So until an AI can do it for you, here’s a handy roundup of recent stories in the world of machine learning, along with notable research and experiments we didn’t cover on their own. By the way, TechCrunch plans to launch an AI newsletter […]
© 2024 TechCrunch. All rights reserved. For personal use only.
Welcome, folks, to Week in Review (WiR), TechCrunch’s regular newsletter that recaps the week that was in tech. This edition’s a tad bittersweet for me — it’ll be my last (for a while, anyway). Soon, I’ll be shifting my attention to a new AI-focused newsletter, which I’m super thrilled about. Stay tuned!
Now, on with the news: This week Google laid off staff from its Flutter, Dart and Python teams weeks before its annual I/O developer conference. A total of 200 people were let go across Google’s “Core” teams, which included those working on app platforms and other engineering roles.
Elsewhere, Tesla CEO Elon Musk gutted the company’s team responsible for overseeing its Supercharger network in a new round of layoffs — despite recently winning over major automakers like Ford and General Motors. The cuts are so complete that Musk suggested in an email that they’ll force Tesla to slow the Supercharger network’s expansion.
And UnitedHealthcare’s CEO, Andrew Witty, told a House subcommittee that the ransomware gang that hacked U.S. health tech giant Change Healthcare — UnitedHealthcare’s subsidiary — used a set of stolen credentials to access Change Healthcare systems that weren’t protected by multifactor authentication. Last week, UnitedHealthcare said that the hackers stole health data on a “substantial proportion of people in America.”
Lots else happened. We recap it all in this edition of WiR — but first, a reminder to sign up to receive the WiR newsletter in your inbox every Saturday.
Hallucinations, hallucinations: OpenAI is facing another privacy complaint in the EU. This one — filed by privacy rights nonprofit noyb on behalf of an individual complainant — targets the inability of its AI chatbot ChatGPT to correct misinformation it generates about individuals.
Just walk out … of Sam’s Club: Sam’s Club customers who pay either at a register or through the Scan & Go mobile app can now walk out of the store without having their purchases double-checked. The technology, unveiled at the Consumer Electronics Show in January, has now been deployed at 20% of Sam’s Club locations.
TikTok circumvents Apple rules: TikTok is presenting some users with a link to a website for purchasing the coins used to tip digital creators on the platform. Typically, these coins must be bought via in-app purchase — which requires a 30% commission paid to Apple — suggesting TikTok might be attempting to skirt Apple’s App Store rules.
NIST’s GenAI platform: The National Institute of Standards and Technology (NIST), the U.S. Commerce Department agency that develops and tests tech for the U.S. government, companies and the broader public, has launched NIST GenAI, a new program to assess generative AI technologies, including text- and image-generating AI.
Getir pulls out: Getir, the quick commerce behemoth, has pulled out of the U.S., U.K. and Europe to focus on Turkey, its home country. The company — once valued close to $12 billion — said that the move would impact thousands of gig and full-time workers.
Inside the Techstars “cold war”: Brilliant reporting by Dom peels back the curtains on a year of financial losses and employee cuts at startup accelerator Techstars, whose CEO, Maëlle Gavet, has been a controversial force for change.
AI-powered coding: Yours truly takes a look at Copilot Workspace, somewhat of an evolution of GitHub’s AI-powered coding assistant Copilot into a more general tool — building on recently introduced capabilities like Copilot Chat, which lets developers ask questions about code in natural language.
Keeping up with an industry as fast-moving as AI is a tall order. So until an AI can do it for you, here’s a handy roundup of recent stories in the world of machine learning, along with notable research and experiments we didn’t cover on their own.
By the way — TechCrunch plans to launch an AI newsletter soon. Stay tuned.
This week in AI, eight prominent U.S. newspapers owned by investment giant Alden Global Capital, including the New York Daily News, Chicago Tribune and Orlando Sentinel, sued OpenAI and Microsoft for copyright infringement relating to the companies’ use of generative AI tech. They, like The New York Times in its ongoing lawsuit against OpenAI, accuse OpenAI and Microsoft of scraping their IP without permission or compensation to build and commercialize generative models such as GPT-4.
“We’ve spent billions of dollars gathering information and reporting news at our publications, and we can’t allow OpenAI and Microsoft to expand the big tech playbook of stealing our work to build their own businesses at our expense,” Frank Pine, the executive editor overseeing Alden’s newspapers, said in a statement.
The suit seems likely to end in a settlement and licensing deal, given OpenAI’s existing partnerships with publishers and its reluctance to hinge the whole of its business model on the fair use argument. But what about the rest of the content creators whose works are being swept up in model training without payment?
It seems OpenAI’s thinking about that.
A recently-published research paper co-authored by Boaz Barak, a scientist on OpenAI’s Superalignment team, proposes a framework to compensate copyright owners “proportionally to their contributions to the creation of AI-generated content.” How? Through cooperative game theory.
The framework evaluates to what extent content in a training data set — e.g. text, images or some other data — influences what a model generates, employing a game theory concept known as the Shapley value. Then, based on that evaluation, it determines the content owners’ “rightful share” (i.e. compensation).
Let’s say you have an image-generating model trained using artwork from four artists: John, Jacob, Jack and Jebediah. You ask it to draw a flower in Jack’s style. With the framework, you can determine the influence each artists’ works had on the art the model generates and, thus, the compensation that each should receive.
There is a downside to the framework, however — it’s computationally expensive. The researchers’ workarounds rely on estimates of compensation rather than exact calculations. Would that satisfy content creators? I’m not so sure. If OpenAI someday puts it into practice, we’ll certainly find out.
Here are some other AI stories of note from the past few days:
Image Credits: US Dept of Energy
Sounds like there was quite a party at Argonne National Lab this winter when they brought in a hundred AI and energy sector experts to talk about how the rapidly evolving tech could be helpful to the country’s infrastructure and R&D in that area. The resulting report is more or less what you’d expect from that crowd: a lot of pie in the sky, but informative nonetheless.
Looking at nuclear power, the grid, carbon management, energy storage, and materials, the themes that emerged from this get-together were, first, that researchers need access to high-powered compute tools and resources; second, learning to spot the weak points of the simulations and predictions (including those enabled by the first thing); third, the need for AI tools that can integrate and make accessible data from multiple sources and in many formats. We’ve seen all these things happening across the industry in various ways, so it’s no big surprise, but nothing gets done at the federal level without a few boffins putting out a paper, so it’s good to have it on the record.
Georgia Tech and Meta are working on part of that with a big new database called OpenDAC, a pile of reactions, materials, and calculations intended to help scientists designing carbon capture processes to do so more easily. It focuses on metal-organic frameworks, a promising and popular material type for carbon capture, but one with thousands of variations, which haven’t been exhaustively tested.
The Georgia Tech team got together with Oak Ridge National Lab and Meta’s FAIR to simulate quantum chemistry interactions on these materials, using some 400 million compute hours — way more than a university can easily muster. Hopefully it’s helpful to the climate researchers working in this field. It’s all documented here.
We hear a lot about AI applications in the medical field, though most are in what you might call an advisory role, helping experts notice things they might not otherwise have seen, or spotting patterns that would have taken hours for a tech to find. That’s partly because these machine learning models just find connections between statistics without understanding what caused or led to what. Cambridge and Ludwig-Maximilians-Universität München researchers are working on that, since moving past basic correlative relationships could be hugely helpful in creating treatment plans.
The work, led by Professor Stefan Feuerriegel from LMU, aims to make models that can identify causal mechanisms, not just correlations: “We give the machine rules for recognizing the causal structure and correctly formalizing the problem. Then the machine has to learn to recognize the effects of interventions and understand, so to speak, how real-life consequences are mirrored in the data that has been fed into the computers,” he said. It’s still early days for them, and they’re aware of that, but they believe their work is part of an important decade-scale development period.
Over at University of Pennsylvania, grad student Ro Encarnación is working on a new angle in the “algorithmic justice” field we’ve seen pioneered (primarily by women and people of color) in the last 7-8 years. Her work is more focused on the users than the platforms, documenting what she calls “emergent auditing.”
When Tiktok or Instagram puts out a filter that’s kinda racist, or an image generator that does something eye-popping, what do users do? Complain, sure, but they also continue to use it, and learn how to circumvent or even exacerbate the problems encoded in it. It may not be a “solution” the way we think of it, but it demonstrates the diversity and resilience of the user side of the equation — they’re not as fragile or passive as you might think.
With the number of people using e-mail globally approaching 5 billion, newsletters delivered regularly into people’s inboxes continue to look like a sticky way of getting attention for whatever it is that you’re writing. Now, in a signal of the popularity of the medium, one of the startups building a platform for creating and distributing newsletters is announcing some funding. New York-based startup beehiiv has raised $33 million, funding that it will be using to expand its business as well as the technical capabilities of its platform.
NEA is leading this round with Sapphire Sport and previous backer Lightspeed Venture Partners also participating. The startup is not disclosing its valuation with this round but it has now raised $46.5 million.
The money is coming on the heels of some significant growth. When we covered beehiiv’s $12.5 million Series A in June 2023 (a round led by Lightspeed), the company had 7,500 active newsletters with 35 million unique readers and 350 million monthly impressions. Now, the company is sending out 1 billion emails per month from around 20,000 active newsletters (it didn’t disclose the number of unique readers, although that figure will have undoubtedly also risen). Newsletter users include both individual writers (plus individual “brands”: Arnold Schwarzenegger is among its customers) as well as bigger organizations like Boston Globe Media and Brex.
That sounds like a lot of newsletters, but beehiiv is looking for more. In the last year, the startup also built and launched an advertising network that sits alongside a range of pricing tiers based on different features and functionalities. (The company claims that in an average month its customers collectively make around $1.2 million monthly in revenues on its platform.)
CEO and co-founder Tyler Denk described the ad network it’s introduced as a “holy grail” for advertisers because of how it can link up specific campaigns to niche audiences that might be most attuned to seeing and responding to them. “It also means these newsletters can now monetize with ads like Netflix’s sponsoring them,” he added.
Niche or not, digital advertising business models are based on economies of scale, and so the focus will be on investing in more marketing, and signing deals with larger publishers, in order to bring more inventory into the mix.
“We’re only two years into this and we have a billion emails going out,” Denk said, referring to the period between being founded in October 2021 and now as “going from zero to one.”
“Obviously, that’s the the hardest thing to do. Now that we have scale, are looking for network effects,” he said.
Denk and co-founders Benjamin Hargett and Jacob Hurd all previously worked together at Morning Brew — a publisher that really pushed the envelope on leveraging newsletters — and that background has lent itself to beehiiv focusing mainly on publishers and “content” up to now. However, when asked if and when beehiiv would ever explore building out the other part of the newsletter business — focusing on marketing emails — Denk would not rule that out over time. “Email is email,” he said
The company is not without a huge range of competitors. In addition to Substack — arguably the startup that brought newsletters back into the conversation when it started to blow up a few years ago — there are open source competitors like Ghost, which earlier this month said it would start to support ActivityPub to become more closely integrated with other social platforms using the “fediverse” format and Buttondown, as well as services like HubSpot and MailChimp coming from strong DNA in the area of email marketing, among many others.
Denk noted that one way beehiiv hopes to make a mark is by making it easy for customers to migrate to its platform and, by way of an API, to use it in tandem with whatever CRM or other tools that they prefer to use. Whether that will be enough to differentiate the business in a very crowded pool is one challenge.
The other will be measuring and matching consumers’ tastes. Right now, publishers are wringing their hands as they weigh up the many ways that their jobs are getting harder.
They are contending with the whimsies of Google and its algorithms; the decline of Facebook as a traffic engine; the huge swing away from reading on the internet to the rise of apps like TikTok and Instagram and their highly visual formats; and what all of that means for their advertising and traffic. Some will consider paywalls, some will not. All of it means that right now may well be a prime window of opportunity for newsletters, and for companies like beehiiv to really swarm. But “peak”-anything is a real risk, and these days and can apply as much to tried-and-true mediums as much as it does to viral platforms.
Investors still believe that even with those many what-ifs, there are still big opportunities for beehiiv.
“Email is one of the most enduring digital channels, but there’s immense untapped potential for publishers to grow and monetize newsletter audiences,” said Danielle Lay, Partner at NEA, in a statement. “We believe Tyler and his team are pioneers in this space and true customer-centric builders. We’re thrilled to partner with them to establish beehiiv as the leading email platform for creators and advertisers alike.”
Welcome, folks, to Week in Review (WiR), TechCrunch’s weekly news recap. The weather’s getting hotter — but not quite as hot as the generative AI space, which saw a slew of new models released this week, including Meta’s Llama 3.
In other AI news, Hyundai-owned robotics company Boston Dynamics unveiled an electric-powered humanoid follow-up to its long-running Atlas robot, which it recently retired. As Brian writes, the new robot — also called Atlas — has a kinder, gentler design than both the original Atlas and more contemporary robots like the Figure 01 and Tesla Optimus.
Turning our attention to YouTube for a moment, Dom and Amanda wrote about how Marques Brownlee (MKBHD), the famed gadget reviewer, shouldn’t be blamed for the fate of AI startup Humane AI, whose product, the Ai Pin, Brownlee gave a scathing review of earlier this week. They point out that Humane is a well-funded company with plenty of funds in the bank to burn, and find that critics of Brownlee — who accuse him of being unfairly harsh — have misplaced their rage.
Lots else happened. We recap it all in this edition of WiR — but first, a reminder to sign up to receive the WiR newsletter in your inbox every Saturday.
X charges for posting: X CEO Elon Musk is planning to charge new X users a small fee to enable posting on the social network in an effort to curb what he describes as a “bot problem.”
Change ransomware: An extortion group has published a portion of what it claims are the private and sensitive patient records on millions of Americans stolen during the ransomware attack on Change Healthcare in February.
Tesla adjusts prices: In more Tesla news, the automaker ditched EV inventory price discounts in what CEO Elon Musk characterized as a move to “streamline” sales and delivery. Tesla also dropped the price of its advanced driver assistance package, Full Self-Driving, to $99 per month in the U.S.
Mars free-for-all: Devin reports that space startups are licking their lips over NASA’s decision to convert its $11 billion, 15-year mission to collect and return samples from Mars into essentially a commercial free-for-all.
Waymo problems: Six Waymo robotaxis blocked traffic moving onto an on-ramp in San Francisco on Tuesday. It’s not the first time Waymo vehicles have caused a road blockage, notes Rebecca — but this is the first documented incident involving a freeway.
Google Cloud bets on generative AI: Ron writes about how Google Cloud is investing heavily in generative AI, as evidenced by the string of announcements during Google’s Cloud Next conference earlier in the month.
Generative AI in health: Generative AI is coming for healthcare — but not everyone’s thrilled. Some experts don’t think the tech is ready for prime time.
Airchat, for talking: Anthony breaks down the hype over Airchat, an app launched by former AngelList founder Naval Ravikant and ex-Tinder product exec Brian Norgard that focuses on voice, not text.
Keeping up with an industry as fast-moving as AI is a tall order. So until an AI can do it for you, here’s a handy roundup of recent stories in the world of machine learning, along with notable research and experiments we didn’t cover on their own.
This week, Meta released the latest in its Llama series of generative AI models: Llama 3 8B and Llama 3 70B. Capable of analyzing and writing text, the models are “open sourced,” Meta said — intended to be a “foundational piece” of systems that developers design with their unique goals in mind.
“We believe these are the best open source models of their class, period,” Meta wrote in a blog post. “We are embracing the open source ethos of releasing early and often.”
There’s only one problem: the Llama 3 models aren’t really “open source,” at least not in the strictest definition.
Open source implies that developers can use the models how they choose, unfettered. But in the case of Llama 3 — as with Llama 2 — Meta has imposed certain licensing restrictions. For example, Llama models can’t be used to train other models. And app developers with over 700 million monthly users must request a special license from Meta.
Debates over the definition of open source aren’t new. But as companies in the AI space play fast and loose with the term, it’s injecting fuel into long-running philosophical arguments.
Last August, a study co-authored by researchers at Carnegie Mellon, the AI Now Institute and the Signal Foundation found that many AI models branded as “open source” come with big catches — not just Llama. The data required to train the models is kept secret. The compute power needed to run them is beyond the reach of many developers. And the labor to fine-tune them is prohibitively expensive.
So if these models aren’t truly open source, what are they, exactly? That’s a good question; defining open source with respect to AI isn’t an easy task.
One pertinent unresolved question is whether copyright, the foundational IP mechanism open source licensing is based on, can be applied to the various components and pieces of an AI project, in particular a model’s inner scaffolding (e.g. embeddings). Then, there’s the mismatch between the perception of open source and how AI actually functions to overcome: open source was devised in part to ensure that developers could study and modify code without restrictions. With AI, though, which ingredients you need to do the studying and modifying is open to interpretation.
Wading through all the uncertainty, the Carnegie Mellon study does make clear the harm inherent in tech giants like Meta co-opting the phrase “open source.”
Often, “open source” AI projects like Llama end up kicking off news cycles — free marketing — and providing technical and strategic benefits to the projects’ maintainers. The open source community rarely sees these same benefits, and, when they do, they’re marginal compared to the maintainers’.
Instead of democratizing AI, “open source” AI projects — especially those from big tech companies — tend to entrench and expand centralized power, say the study’s co-authors. That’s good to keep in mind the next time a major “open source” model release comes around.
Here are some other AI stories of note from the past few days:
Image Credits: DrAfter123 / Getty Images
Can a chatbot change your mind? Swiss researchers found that not only can they, but if they are pre-armed with some personal information about you, they can actually be more persuasive in a debate than a human with that same info.
“This is Cambridge Analytica on steroids,” said project lead Robert West from EPFL. The researchers suspect the model — GPT-4 in this case — drew from its vast stores of arguments and facts online to present a more compelling and confident case. But the outcome kind of speaks for itself. Don’t underestimate the power of LLMs in matters of persuasion, West warned: “In the context of the upcoming US elections, people are concerned because that’s where this kind of technology is always first battle tested. One thing we know for sure is that people will be using the power of large language models to try to swing the election.”
Why are these models so good at language anyway? That’s one area there is a long history of research into, going back to ELIZA. If you’re curious about one of the people who’s been there for a lot of it (and performed no small amount of it himself), check out this profile on Stanford’s Christopher Manning. He was just awarded the John von Neuman Medal; congrats!
In a provocatively titled interview, another long-term AI researcher (who has graced the TechCrunch stage as well), Stuart Russell, and postdoc Michael Cohen speculate on “How to keep AI from killing us all.” Probably a good thing to figure out sooner rather than later! It’s not a superficial discussion, though — these are smart people talking about how we can actually understand the motivations (if that’s the right word) of AI models and how regulations ought to be built around them.
The interview is actually regarding a paper in Science published earlier this month, in which they propose that advanced AIs capable of acting strategically to achieve their goals, which they call “long-term planning agents,” may be impossible to test. Essentially, if a model learns to “understand” the testing it must pass in order to succeed, it may very well learn ways to creatively negate or circumvent that testing. We’ve seen it at a small scale, why not a large one?
Russell proposes restricting the hardware needed to make such agents… but of course, Los Alamos and Sandia National Labs just got their deliveries. LANL just had the ribbon-cutting ceremony for Venado, a new supercomputer intended for AI research, composed of 2,560 Grace Hopper Nvidia chips.
Researchers look into the new neuromorphic computer.
And Sandia just received “an extraordinary brain-based computing system called Hala Point,” with 1.15 billion artificial neurons, built by Intel and believed to be the largest such system in the world. Neuromorphic computing, as it’s called, isn’t intended to replace systems like Venado, but to pursue new methods of computation that are more brain-like than the rather statistics-focused approach we see in modern models.
“With this billion-neuron system, we will have an opportunity to innovate at scale both new AI algorithms that may be more efficient and smarter than existing algorithms, and new brain-like approaches to existing computer algorithms such as optimization and modeling,” said Sandia researcher Brad Aimone. Sounds dandy… just dandy!
Heya, folks, welcome to Week in Review (WiR), TechCrunch’s newsletter recapping the noteworthy happenings in tech over the past several days (and change).
Famed startup accelerator Y Combinator had its Demo Days, and the venture desk took it all in with an appropriately skeptical eye. You can read their day one and day two coverage, along with an AI roundup from yours truly and analysis pieces from the rest of the dogged edit team.
But the world didn’t stop turning for YC. Also this week, Microsoft and Quantinuum, a quantum computing startup, made a scientific breakthrough — or so they claim. The companies say that they were able to run thousands of experiments on a quantum computer without a single error, a feat that’s long eluded the industry.
Elsewhere, Apple could be getting into home robots. Reportedly, the company — fresh off its decision to cancel its long-in-the-works autonomous EV — has put Apple Home and AI execs on some form of robotics project for households, although many of the details have yet to be finalized.
Lots else happened. We recap it all in this edition of WiR — but first, a reminder to sign up to receive the WiR newsletter in your inbox every Saturday.
Canoo paid for its CEO’s jet: Kirsten reports that EV startup Canoo paid the rent for the CEO’s private jet — $1.7 million— in 2023. That’s double the amount of revenue the company generated that year.
AT&T leak: Phone giant AT&T has reset millions of account passcodes after a huge cache of data containing customer records was dumped online earlier this month, Zack reports.
No ChatGPT account required: OpenAI is making its flagship conversational AI, ChatGPT, accessible to everyone — even people who haven’t bothered making an account. But it won’t be quite the same experience. Devin has the story.
Microsoft unbundles: Microsoft has introduced new versions of its Microsoft 365 and Office 365 subscription services that exclude Teams, its business collaboration chat offering, following scrutiny from European Union regulators and complaints from rival Slack.
Ghost ghosts: Ghost Autonomy, a startup working on autonomous driving software for automaker partners, has shut down after raising nearly $220 million.
Alphabet and HubSpot: Reuters reported on Thursday that Google’s parent company, Alphabet, is exploring the possibility of buying Boston-based HubSpot, a CRM and marketing automation company with a market cap of over $33 billion. Ron explains why that’d make for strange bedfellows.
This week on Equity, Alex chatted about BlaBlaCar’s new credit facility (and how it managed to land it), and he discusses how PipeDreams could be onto a clever model of startup construction, GoStudent’s rebound and profitability, Hailo’s chip business and the two new brands that GGV calls home as it divvies up its operations on opposite sides of the Pacific.
And over on Found, Nick Green, the co-founder and CEO of Thrive Market, was the featured guest. Thrive is a membership-based online grocery store that focuses on natural and organic food and household products. Green spoke about how Thrive isn’t just focused on offering healthy options, but also wants to ensure that everyone has access to them — including those with SNAP and EBT benefits.
NSFW on X: The social media company has confirmed that authorized users on the platform can create NSFW communities, ahead of a change that’ll see all NSFW content on X filtered by default.
Heya, folks, welcome to Week in Review (WiR), TechCrunch’s regular newsletter that recaps the past few days in tech.
Google’s annual enterprise-focused dev conference, Google Cloud Next, dominated the headlines — and we had plenty of coverage from the event. But it wasn’t the only thing afoot (see: the spectacular eclipse).
Lorenzo wrote about how hackers stole over ~340,000 Social Security numbers from government consulting firm Greylock McKinnon Associates (GMA). It took GMA nine months to determine the extent of the breach and notify victims; as of yet, it’s unclear why.
Elsewhere, Sarah had the story on Spotify’s personalized AI playlists, which lets users create a playlist based on written prompts.
And Connie reported on the death of entrepreneur Mahbod Moghadam, who rose to fame as the co-founder of Genius, the online music encyclopedia. Moghadam passed away at the age of 41 owing to complications from a recurring brain tumor.
Lots else happened. We recap it all in this edition of WiR — but first, a reminder to sign up to receive the WiR newsletter in your inbox every Saturday.
Tesla price drop: Tesla dropped prices of unsold Model Y SUVs in the U.S. by thousands of dollars in an attempt to clear out an unprecedented inventory backlog.
Snapchat turns off its solar system: Snapchat adjusted a feature in its app that visualizes how “close” you are to your friends after reporting revealed that it was adding to teens’ anxiety.
Noninvasive anxiety treatment: Neurovalens, a startup developing tech to deliver noninvasive electrical stimulation of the brain and nervous system, achieved FDA clearance thanks to a 2019 agency rule change aimed at encouraging innovations targeting insomnia and anxiety.
Llama 3: At an event in London, Meta confirmed that it plans an initial release of Llama 3 — the next generation of its AI model used to power chatbots and other apps — within the month.
Emulators in the store: Apple updated its App Store rules to globally allow emulators for retro console games an option for downloading titles.
AT&T breach: AT&T began notifying U.S. state authorities and regulators of a security incident after confirming that millions of customer records posted online last month were authentic.
Web3 and beauty: Kiki World, a beauty brand that uses web3 for customer co-creation and ownership, has closed a $7 million round led by Andreessen Horowitz.
Magnets in keyboards: Frederic writes about an intriguing development in mechanical keyboard design: magnetic switches, which can quickly change the actuation point — the point during the keypress where the switch registers a downstroke.
WFH, here to stay: Working from home isn’t going away — even if some CEOs wish it would. Ron writes that most workers crave flexibility and work-life balance — who knew?
On Equity’s startup-focused Wednesday show, the crew dug into the Multiverse’s acquisition of Searchlight, the latest Guesty round, the Monad Labs transaction and a new venture capital fund targeting growth rounds in Africa.
Meanwhile, Found featured Ben Christensen, the founder and CEO of Cambium, a startup that’s reimagining the wood supply chain and reallocating previously wasted materials to be used in new building projects.
Microsoft passwords exposed: Security researchers discovered an open and public database hosted on Microsoft’s Azure cloud service that was storing internal information relating to Microsoft’s Bing search engine. Microsoft says that it has resolved the lapse.
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