Editor’s Pick

On Monday, Adobe announced Firefly Video Model, a new AI-powered text-to-video generation tool that can create novel videos from written prompts. It joins similar offerings from OpenAI, Runway, Google, and Meta in an increasingly crowded field. Unlike the competition, Adobe claims that Firefly Video Model is trained exclusively on licensed content, potentially sidestepping ethical and copyright issues that have plagued other generative AI tools.

Because of its licensed training data roots, Adobe calls Firefly Video Model “the first publicly available video model designed to be commercially safe.” However, the San Jose, California-based software firm hasn’t announced a general release date, and during a beta test period, it’s only granting access to people on a waiting list.

In the works since at least April 2023, the new model builds off of techniques Adobe developed for its Firefly image synthesis models. Like its text-to-image generator, which the company later integrated into Photoshop, Adobe hopes to aim Firefly Video Model at media professionals, such as video creators and editors. The company claims its model can produce footage that blends seamlessly with traditionally created video content.

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The Internet Archive has brought its Wayback Machine back online “in a provisional, read-only manner” as it continues to recover from attacks that took the site down last week, founder Brewster Kahle said in a post last night. The archive.org home page points users to the now-functional Wayback Machine but notes that other Internet Archive services are temporarily offline.

Kahle said it was “safe to resume” the Wayback Machine’s operations, but that it “might need further maintenance, in which case it will be suspended again.” The Wayback Machine’s “Save Page Now” feature that lets users capture a webpage manually is currently unavailable. The related openlibrary.org book-preservation website was still offline today.

Founded in 1996, the nonprofit Internet Archive crawls the web to preserve pages that are publicly available and has captured 916 billion web pages so far. It has a staff of 150 people and also provides free access to many videos, audio files, and books (though it was recently forced to delete 500,000 books after losing a copyright case).

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What if there was a way to sneak malicious instructions into Claude, Copilot, or other top-name AI chatbots and get confidential data out of them by using characters large language models can recognize and their human users can’t? As it turns out, there was—and in some cases still is.

The invisible characters, the result of a quirk in the Unicode text encoding standard, create an ideal covert channel that can make it easier for attackers to conceal malicious payloads fed into an LLM. The hidden text can similarly obfuscate the exfiltration of passwords, financial information, or other secrets out of the same AI-powered bots. Because the hidden text can be combined with normal text, users can unwittingly paste it into prompts. The secret content can also be appended to visible text in chatbot output.

The result is a steganographic framework built into the most widely used text encoding channel.

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Two years ago, Tesla’s Optimus prototype was an underwhelming mess of exposed wires that could only operate in a carefully controlled stage presentation. Last night, Tesla’s “We, Robot” event featured much more advanced Optimus prototypes that could walk around without tethers and interact directly with partygoers.

It was an impressive demonstration of the advancement of a technology Tesla’s Elon Musk said he thinks “will be the biggest product ever of any kind” (way to set reasonable expectations, there). But the live demos have also set off a firestorm of discussion over just how autonomous these Optimus robots currently are.

A robot in every garage

Before the human/robot party could get started, Musk introduced the humanoid Optimus robots as a logical extension of some of the technology that Tesla uses in its cars, from batteries and motors to software. “It’s just a robot with arms and legs instead of a robot with wheels,” Musk said breezily, easily underselling the huge differences between human-like movements and a car’s much more limited input options.

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On Thursday, AMD announced its new MI325X AI accelerator chip, which is set to roll out to datacenter customers in the fourth quarter of this year. At an event hosted in San Francisco, the company claimed the new chip offers “industry-leading” performance compared to Nvidia’s current H200 GPUs, which are widely used in datacenters to power AI applications such as ChatGPT.

With its new chip, AMD hopes to narrow the performance gap with Nvidia in the AI processor market. The Santa Clara-based company also revealed plans for its next-generation MI350 chip, which is positioned as a head-to-head competitor of Nvidia’s new Blackwell system, with an expected shipping date in the second half of 2025.

In an interview with the Financial Times, AMD CEO Lisa Su expressed her ambition for AMD to become the “end-to-end” AI leader over the next decade. “This is the beginning, not the end of the AI race,” she told the publication.

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After a great event last month in San Jose, Ars is switching coasts for October and descending in force on our nation’s capital. If you’re on the East Coast and want to come hang out with Ars EIC Ken Fisher and me while we talk to some neat speakers and learn some stuff, then read on!

Continuing our partnership with IBM, Ars presents “AI in DC: Privacy, Compliance, and Making Infrastructure Smarter.” Our tone this time around will be a little more policy-oriented than our San Jose event. We intend to have three panel discussions, with the overall topics looking like this:

The key to compliance with emerging technologies
Data security in the age of AI-assisted cyber-espionage
The best infrastructure solution for your AI/ML strategy

Specifically, here are our panels and the panelists we’ve confirmed:

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Archive.org, possibly one of the only entities to preserve the entire history of the Internet, was recently compromised in a hack that revealed data of roughly 31 million users.

A little after 2 PM California time, social media blew up with screenshots showing what the archive.org homepage displayed.

It read:

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If 2022 was the year AI image generators went mainstream, 2024 has arguably been the year that AI video synthesis models exploded in capability. These models, while not yet perfect, can generate new videos from text descriptions called prompts, still images, or existing videos. After OpenAI made waves with Sora in February, two major AI models emerged from China: Kuaishou Technology’s Kling and Minimax’s video-01.

Both Chinese models have already powered numerous viral AI-generated video projects, accelerating meme culture in weird new ways, including a recent shot-for-shot translation of the Princess Mononoke trailer using Kling that inspired death threats and a series of videos created with Minimax’s platform. The videos show a synthesized version of TV chef Gordon Ramsay doing ridiculous things.

Kling first emerged in June, and it can generate two minutes of 1080p HD video at 30 frames per second with a level of detail and coherency that some think surpasses Sora. It’s currently only available to people with a Chinese telephone number, and we have not yet used it ourselves.

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Researchers have unearthed two sophisticated toolsets that a nation-state hacking group—possibly from Russia—used to steal sensitive data stored on air-gapped devices, meaning those that are deliberately isolated from the Internet or other networks to safeguard them from malware.

One of the custom tool collections was used starting in 2019 against a South Asian embassy in Belarus. A largely different toolset created by the same threat group infected a European Union government organization three years later. Researchers from ESET, the security firm that discovered the toolkits, said some of the components in both were identical to those fellow security firm Kaspersky described in research published last year and attributed to an unknown group, tracked as GoldenJackal, working for a nation-state. Based on the overlap, ESET has concluded that the same group is behind all the attacks observed by both firms.

Quite unusual

The practice of air gapping is typically reserved for the most sensitive networks or devices connected to them, such as those used in systems for voting, industrial control, manufacturing, and power generation. A host of malware used in espionage hacking over the past 15 years (for instance, here and here) demonstrate that air gapping isn’t a foolproof protection. It nonetheless forces threat groups to expend significant resources that are likely obtainable only by nation-states with superior technical acumen and unlimited budgets. ESET’s discovery puts GoldenJackal in a highly exclusive collection of threat groups.

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On Tuesday, the Royal Swedish Academy of Sciences awarded the 2024 Nobel Prize in Physics to John J. Hopfield of Princeton University and Geoffrey E. Hinton of the University of Toronto for their foundational work in machine learning with artificial neural networks. Hinton notably captured headlines in 2023 for warning about the threat that AI superintelligence may pose to humanity. The win came as a surprise to many, including Hinton himself.

“I’m flabbergasted. I had no idea this would happen. I’m very surprised,” said Hinton in a telephone call with members of the Royal Swedish Academy of Sciences during a live announcement press conference streamed to YouTube that took place this morning.

Hopfield and Hinton’s research, which dates back to the early 1980s, applied principles from physics to develop methods that underpin modern machine-learning techniques. Their work has enabled computers to perform tasks such as image recognition and pattern completion, capabilities that are now ubiquitous in everyday technology.

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