Humans can still beat AI at video games
AI may have teams of engineers on its side, but humans have lived experience and better learning skills.
https://www.popsci.com/technology/humans-beat-ai-at-video-games/?fbclid=IwdGRjcARCDpJjbGNrBEIOf2V4dG4DYWVtAjExAHNydGMGYXBwX2lkDDM1MDY4NTUzMTcyOAABHkj0Qvu3c8HhI5UYpAcG-bdXhJcr73A9P2cswCC2G7_g61xRJPfwcLLBIuWU_aem_2S34PeYE85wdEPDemLgjJQ

Ask someone to chart the progression of artificial intelligence (AI) models over the past few decades and you’ll likely hear some reference to how good they are at playing games. IBM shocked the world in 1997 when its Deep Blue model vanquished chess grandmaster Garry Kasparov at his own domain. Nearly two decades later, Google’s AlphaGo model trounced a human champion of the game Go, a feat some thought impossible at the time.

Since then, increasingly data rich AI models have graduated from board games to video games. Various models have used a training method called reinforcement learning—a technique that also plays a key role in training AI chatbots like ChatGPT—to teach machines how to learn and outperform humans at a range of Atari games.More recently, reinforcement learning has taught machines how to master incredibly complex strategy games including Dota 2 and Starcraft II.

But there’s one area of gaming remaining—at least for now—where computers still can’t hold a candle to flesh and bone humans. They are still not great at learning different kinds of more open-ended games quickly. When it comes to picking up a random title from a game store that they haven’t seen before and getting the gist, human gamers still learn the ropes much quicker than even the most advanced AI models.

CEOs are resigning due to various factors related to artificial intelligence (AI). Some of the key reasons include ¹:
- Need for Fresh Leadership: CEOs like Doug McMillon (Walmart) and James Quincey (Coca-Cola) are stepping down, citing the need for new leadership to navigate the company’s AI transformation and drive growth. They believe that fresh perspectives and expertise are required to effectively leverage AI and lead the company forward.
- AI-Driven Business Landscape: The rapid evolution of AI technology is reshaping corporate structures, and CEOs are confronting questions about their roles in an AI-driven future. Some CEOs, like Sam Altman of OpenAI, even suggest that AI systems might eventually replace human CEOs due to their efficiency and ability to make data-driven decisions.
- Investor Pressure: Investors are putting pressure on companies to deliver returns on AI investments, leading to leadership turnover. CEOs who fail to demonstrate a clear vision for AI-driven growth may face scrutiny from investors, contributing to their decision to step down.
- Adapting to AI-Powered Workforce: CEOs are also struggling to adapt to the changing workforce dynamics driven by AI. While AI may replace some jobs, it also creates new opportunities for growth and innovation. CEOs need to navigate these changes and find ways to harness AI’s potential to drive business success.

Some notable CEO resignations attributed to AI-related factors include ¹:
- Doug McMillon (Walmart): Stepped down in January 2026, citing the need for fresh leadership to navigate AI transformation.
- James Quincey (Coca-Cola): Announced his decision to step down, influenced by the evolving business landscape and AI’s role in it.
- Shantanu Narayen (Adobe): Cited the need for new leadership under AI growth as the reason for his departure.

These resignations reflect the complex and rapidly changing business landscape driven by AI, and the challenges CEOs face in adapting to these changes.

Exit Row
AI Now Causing CEOs to Resign in Fear
“I could start this next big set of transformations with AI, but I couldn’t finish.”
https://futurism.com/artificial-intelligence/ai-ceos-resign?fbclid=IwdGRjcARBt2tjbGNrBEG3XWV4dG4DYWVtAjExAHNydGMGYXBwX2lkDDM1MDY4NTUzMTcyOAABHrK-jRlfp0qQFcNVGZHsRH4l0ick78BfmwtZyHAaFL2frmmNuqQJuRfU-zRe_aem___OyiWOG9FUmBmlyay8C0A

One Company’s Effort to Make an AI-Ready Catalog of Everything We Buy
In an Arkansas ‘capture factory,’ hand models and food stylists are preparing for the future of shopping
https://www.wsj.com/business/retail/one-companys-effort-to-make-an-ai-ready-catalog-of-everything-we-buy-c33ee2c0?st=aWTHpb&reflink=desktopwebshare_permalink

BENTONVILLE, Ark.—In a warehouse here bigger than two football fields, digital cameras rotate around vitamin bottles, strollers and washing-machine pods as manicurists, hand models and former theater directors work to build what they hope is a digital catalog for retail’s AI future.

Eko, the Brooklyn firm that operates the facility, calls it a “capture factory.”

The goal: to improve the accuracy of online listings for millions of products at Walmart WMT 0.79%increase; green up pointing triangle

, Best Buy and other retailers and make them easily digestible by artificial intelligence. It is a decidedly manual process. Hundreds of Eko employees work to shoot products from every angle on movie-studio-style stages, shifting lighting or buffing out fingerprints on metal surfaces as needed.

“The output of AI is only as good as the input, and there was no good input,” said Ben Kaufman, president of Eko, which counts Bentonville-based Walmart as an investor, as well as venture firms such as Sequoia Capital and Intel Capital.

At the heart of this effort is a fast-moving battle over how consumers will find and buy everything from toilet paper to furniture through AI platforms such as OpenAI’s ChatGPT or Google’s Gemini. Both have cut deals with retailers such as Walmart and Target to offer shopping via their chatbots, while Amazon is building its own AI shopping tools.

With AI-driven shopping, “a lot of the specific features of that inventory become more important,” said Nathan Feather, an internet analyst at Morgan Stanley. A shoe’s width or a precise description of how it could be used becomes more necessary because AI chatbots tend to home in on those sorts of details to suggest products to shoppers, he said.

Retailers have long struggled to make traditional product listings accurate, in part because they largely outsource the task to their suppliers. And while AI-based shopping is still a niche practice, some retailers are giving their inventory descriptions another look to make sure their products are AI-ready if and when more shoppers arrive.

It is still unclear what AI shopping will look like, and there is inherent tension between retailers and AI companies as they figure that out. It hasn’t all gone smoothly. OpenAI recently backed away from an early version of its Instant Checkout feature in favor of directing users to the retailers to complete the purchase. That allows retailers to preserve their direct relationship with shoppers.

Several companies—including PayPal, Salsify, and retail AI upstart Verneek—are touting tools that aim to make a company’s product lists more accurate and AI-ready.

Eko—the company that runs the warehouse in Bentonville—started as an interactive video company but eventually zeroed in on interactive digital product pages and AI-ready product files, said Yoni Bloch, the company’s founder.

Walmart has invested more than $300 million in Eko since 2018, in large part through a joint venture created that year, according to people familiar with the situation. Eko’s revenue is tens of millions of dollars a year and growing, said a spokeswoman

Recently, a pillow seller complained that Eko’s images didn’t make its pillow look fluffy enough, said Bloch. “We were like, ‘But the pillow isn’t fluffy,’” he said. So they didn’t change the image. Realistic images can reduce a retailer’s return rates, and if an item is what a shopper wants, it can boost sales, he said.

The factory process of creating an Eko file takes 10 minutes for something simple like a bottle of vitamins but half a day for a large refrigerator with lots of features, said Amy Long, who has worked at the Eko factory since August.

To get action shots, the refrigerator is hooked up to water and electricity, said Long, who has worked as a professional photographer for nearly two decades. It is a constant battle to remove fingerprints and glare from lights before each shot, she said. Live models display the features. “It can be hard to adjust a refrigerator shelf in a beautiful, natural way,” said Long.

In its Brooklyn headquarters, employees develop shot lists for each product, balancing how long it takes to capture video of every feature versus efficiency.

On a recent day, the team debated how to show a new Tide detergent tile’s ability to quickly dissipate in water. “We know that’s a pain point for similar competitor products—that it doesn’t dissolve well,” said Talia Halperin, head of merchandising for Eko. “We want to just show honestly how it happens.”

Spain’s Xoople raises $130 million Series B to map the Earth for AI
https://techcrunch.com/2026/04/06/spains-xoople-raises-130-million-series-b-to-map-the-earth-for-ai/

Space data companies have argued for years that the private sector needs their products, but the real uptake has been from government buyers. Now, with artificial intelligence top of mind for business, one Spanish startup is trying to become the go-to source of ground truth for enterprise.

Xoople (said like “zoople’) is developing a satellite constellation to collect precise data aimed at deep learning models. The startup was founded in 2019 and has spent the last seven years developing its tech stack around data collected by government spacecraft, and integrating with cloud providers.

CEO and co-founder Fabrizio Pirondini told TechCrunch that the company has closed a $130 million Series B led by Nazca Capital. Other investors include MCH Private Equity, CDTI (a tech development fund backed by the Spanish government), Buenavista Equity Partners, and Endeavor Catalyst.

The startup also announced Monday a deal with U.S. space and defense contractor L3Harris Technologies to begin building sensors for Xoople’s spacecraft, which are designed to collect “a stream of data that is going to be two orders of magnitude better than existing monitoring systems,” Pirondini told TechCrunch.

The Earth data infrastructure layer built for AI
Enabling AI to understand, and predict, daily physical changes on the world’s surface.
https://www.xoople.com/

The Ridiculously Nerdy Intel Bet That Could Rake in Billions
Advanced chip packaging is suddenly at the center of the AI boom. Intel is going all in.
https://www.wired.com/story/why-chip-packaging-could-decide-the-next-phase-of-the-ai-boom/

Sixteen miles north of Albuquerque, in Rio Rancho, New Mexico, an Intel chip plant sits on more than 200 acres of land. The site was established in the 1980s, part of it built on top of a sod farm. In 2007, as Intel’s business faltered, operations in one of the key fabs, Fab 9, came to a halt. Employees say families of raccoons and a badger took up residence in the space.

The Big Bang: A.I. Has Created a Code Overload
https://www.nytimes.com/2026/04/06/technology/ai-code-overload.html

Scoop: Meta to open source versions of its next AI models
https://www.axios.com/2026/04/06/meta-open-source-ai-models

Top record labels and start-up Suno hit impasse in talks over AI-generated music
One executive says there is ‘no path’ towards licensing deal under current proposal
https://www.ft.com/content/b066a226-4871-4669-97a8-f9617cdbf48b?syn-25a6b1a6=1

Talks over licensing deals between the world’s largest record companies and start-up Suno have reached a stalemate, exposing deep divisions over how the industry should respond to the rise of AI-generated music.

There had been little substantive progress in discussions between Universal Music and Suno in recent months, according to people familiar with the matter, and no agreement had been reached with Sony, as the music behemoths reject the start-up’s model for distributing AI-generated music.

“We have ongoing engagement, but there is no path forward with the current proposal,” said a person involved in the negotiations.

People familiar with the talks said there had been little progress since Warner Music, the third-largest label, struck a deal with Suno in November.

Suno, one of the most prominent AI music companies, allows users to create songs in seconds using simple text prompts. It was valued at $2.45bn in a funding round last year and said it had 2mn paying subscribers.

The impasse goes to the heart of a broader fight over the future of music as companies across the media landscape have wrestled with how to protect their copyrights against AI.