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It was shockingly simple to create a convincing Kamala Harris audio deepfake on Election Day. It price me $5 and took lower than two minutes, illustrating how low cost, ubiquitous generative AI has opened the floodgates to disinformation.
Making a Harris deepfake wasn’t my unique intent. I used to be taking part in round with Cartesia’s Voice Changer, a mannequin that transforms your voice into a unique voice whereas preserving the unique’s prosody. That second voice could be a “clone” of one other individual’s — Cartesia will create a digital voice double from any 10-second recording.
So, I questioned, would Voice Changer remodel my voice into Harris’? I paid $5 to unlock Cartesia’s voice cloning function, created a clone of Harris’ voice utilizing latest marketing campaign speeches, and chosen that clone because the output in Voice Changer.
It labored like a appeal:
I’m assured that Cartesia didn’t precisely intend for its instruments for use on this method. To allow voice cloning, Cartesia requires that you simply examine a field indicating that you simply gained’t generate something dangerous or unlawful and that you simply consent to your speech recordings being cloned.
However that’s simply an honor system. Absent any actual safeguards, there’s nothing stopping an individual from creating as many “harmful or illegal” deepfakes as they need.
That’s an issue, it goes with out saying. So what’s the answer? Is there one? Cartesia can implement voice verification, as another platforms have finished. However by the point it does, likelihood is a brand new, unfettered voice cloning instrument can have emerged.
I spoke about this very situation with specialists at TC’s Disrupt convention final week. Some had been supportive of the concept of invisible watermarks in order that it’s simpler to inform whether or not content material has been AI-generated. Others pointed to content material moderation legal guidelines such because the On-line Security Act within the U.Ok., which they argued may assist stem the tide of disinformation.
Name me a pessimist, however I feel these ships have sailed. We’re , as CEO of the Middle for Countering Digital Hate Imran Ahmed put it, a “perpetual bulls— machine.”
Disinformation is spreading at an alarming fee. Some high-profile examples from the previous yr embody a bot community on X concentrating on U.S. federal elections and a voicemail deepfake of President Joe Biden discouraging New Hampshire residents from voting. However U.S. voters and tech-savvy individuals aren’t the targets of most of this content material, in response to True Media.org’s evaluation, so we are inclined to underestimate its presence elsewhere.
The quantity of AI-generated deepfakes grew 900% between 2019 and 2020, in response to information from the World Financial Discussion board.
In the meantime, there’s comparatively few deepfake-targeting legal guidelines on the books. And deepfake detection is poised to change into a unending arms race. Some instruments inevitably gained’t choose to make use of security measures resembling watermarking, or might be deployed with expressly malicious functions in thoughts.
In need of a sea change, I feel the most effective we will do is be intensely skeptical of what’s on the market — significantly viral content material. It’s not as simple because it as soon as was to inform fact from fiction on-line. However we’re nonetheless in charge of what we share versus what we don’t. And that’s way more impactful than it may appear.
Information
ChatGPT Search assessment: My colleague Max took OpenAI’s new search integration for ChatGPT, ChatGPT Search, for a spin. He discovered it to be spectacular in some methods, however unreliable for brief queries containing only a few phrases.
Amazon drones in Phoenix: A number of months after ending its drone-based supply program, Prime Air, in California, Amazon says that it’s begun making deliveries to pick prospects through drone in Phoenix, Arizona.
Ex-Meta AR lead joins OpenAI: The previous head of Meta’s AR glasses efforts, together with Orion, introduced on Monday she’s becoming a member of OpenAI to steer robotics and shopper {hardware}. The information comes after OpenAI employed the co-founder of X (previously Twitter) challenger Pebble.
Held again by compute: In a Reddit AMA, OpenAI CEO Sam Altman admitted {that a} lack of compute capability is one main issue stopping the corporate from transport merchandise as typically because it’d like.
AI-generated recaps: Amazon has launched “X-Ray Recaps,” a generative AI-powered function that creates concise summaries of complete TV seasons, particular person episodes, and even elements of episodes.
Anthropic hikes Haiku costs: Anthropic’s latest AI mannequin has arrived: Claude 3.5 Haiku. But it surely’s pricier than the final technology, and in contrast to Anthropic’s different fashions, it will probably’t analyze photos, graphs, or diagrams simply but.
Apple acquires Pixelmator: AI-powered picture editor Pixelmator introduced on Friday that it’s being acquired by Apple. The deal comes as Apple has grown extra aggressive about integrating AI into its imaging apps.
An ‘agentic’ Alexa: Amazon CEO Andy Jassy final week hinted at an improved “agentic” model of the corporate’s Alexa assistant — one that would take actions on a consumer’s behalf. The revamped Alexa has reportedly confronted delays and technical setbacks, and may not launch till someday in 2025.
Analysis paper of the week
Pop-ups on the internet can idiot AI, too — not simply grandparents.
In a brand new paper, researchers from Georgia Tech, the College of Hong Kong, and Stanford present that AI “agents” — AI fashions that may full duties — could be hijacked by “adversarial pop-ups” that instruct the fashions to do issues like obtain malicious file extensions.
A few of these pop-ups are fairly clearly traps to the human eye — however AI isn’t as discerning. The researchers say that the image- and text-analyzing fashions they examined did not ignore pop-ups 86% of the time, and — because of this — had been 47% much less prone to full duties.
Fundamental defenses, like instructing the fashions to disregard the pop-ups, weren’t efficient. “Deploying computer-use agents still suffers from significant risks,” the co-authors of the research wrote, “and more robust agent systems are needed to ensure safe agent workflow.”
Mannequin of the week
Meta introduced yesterday that it’s working with companions to make its Llama “open” AI fashions out there for protection functions. Right this moment, a type of companions, Scale AI, introduced Protection Llama, a mannequin constructed on high of Meta’s Llama 3 that’s “customized and fine-tuned to support American national security missions.”
Protection Llama, which is offered in Scale’s Donavan chatbot platform for U.S. authorities prospects, was optimized for planning navy and intelligence operations, Scale says. Protection Llama can reply defense-related questions, for instance like how an adversary may plan an assault in opposition to a U.S. navy base.
So what makes Protection Llama totally different from inventory Llama? Properly, Scale says it was fine-tuned on content material that may be related to navy operations, like navy doctrine and worldwide humanitarian legislation, in addition to the capabilities of varied weapons and protection techniques. It additionally isn’t restricted from answering questions on warfare, like a civilian chatbot may be:
It’s not clear who may be inclined use it, although.
The U.S. navy has been gradual to undertake generative AI — and skeptical of its ROI. Thus far, the U.S. Military is the solely department of the U.S. armed forces with a generative AI deployment. Army officers have expressed considerations about safety vulnerabilities in industrial fashions, in addition to authorized challenges related to intelligence information sharing and fashions’ unpredictability when confronted with edge circumstances.
Seize bag
Spawning AI, a startup creating instruments to allow creators to choose out of generative AI coaching, has launched a picture dataset for coaching AI fashions that it claims is totally public area.
Most generative AI fashions are educated on public internet information, a few of which can be copyrighted or below a restrictive license. OpenAI and plenty of different AI distributors argue that fair-use doctrine shields them from copyright claims. However that hasn’t stopped information house owners from submitting lawsuits.
Spawning AI says its coaching dataset of 12.4 million image-caption pairs consists of solely content material with “known provenance” and “labeled with clear, unambiguous rights” for AI coaching. In contrast to another datasets, it’s additionally out there for obtain from a devoted host, eliminating the necessity to web-scrape.
“Significantly, the public-domain status of the dataset is integral to these larger goals,” Spawning writes in a weblog publish. “Datasets that include copyrighted images will continue to rely on web-scraping because hosting the images would violate copyright.”
Spawning’s dataset, PD12M, and a model curated for “aesthetically pleasing” photos, PD3M, could be discovered at this hyperlink.
