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Is anyone else feeling dizzy? Just when the AI community was wrapping its head around the astounding progress of text-to-image systems, we’re already moving on to the next frontier: text-to-video.
Late last week, Meta unveiled Make-A-Video, an AI that generates five-second videos from text prompts.
Built on open-source data sets, Make-A-Video lets you type in a string of words, like “A dog wearing a superhero outfit with a red cape flying through the sky,” and then generates a clip that, while pretty accurate, has the aesthetics of a trippy old home video.
The development is a breakthrough in generative AI that also raises some tough ethical questions. Creating videos from text prompts is a lot more challenging and expensive than generating images, and it’s impressive that Meta has come up with a way to do it so quickly. But as the technology develops, there are fears it could be harnessed as a powerful tool to create and disseminate misinformation. You can read my story about it here.
Just days since it was announced, though, Meta’s system is already starting to look kinda basic. It’s one of a number of text-to-video models submitted in papers to one of the leading AI conferences, the International Conference on Learning Representations.
It can generate video from a still image and a prompt rather than a text prompt alone. It can also make far longer clips: users can create videos multiple minutes long based on several different prompts that form the script for the video. (For example: “A photorealistic teddy bear is swimming in the ocean at San Francisco. The teddy bear goes underwater. The teddy bear keeps swimming under the water with colorful fishes. A panda bear is swimming underwater.”)
Video generated by Phenaki.
A technology like this could revolutionize filmmaking and animation. It’s frankly amazing how quickly this happened. DALL-E was launched just last year. It’s both extremely exciting and slightly horrifying to think where we’ll be this time next year.
Researchers from Google also submitted a paper to the conference about their new model called DreamFusion, which generates 3D images based on text prompts. The 3D models can be viewed from any angle, the lighting can be changed, and the model can be plonked into any 3D environment.
Don’t expect that you’ll get to play with these models anytime soon. Meta isn’t releasing Make-A-Video to the public yet. That’s a good thing. Meta’s model is trained using the same open-source image-data set that was behind Stable Diffusion. The company says it filtered out toxic language and NSFW images, but that’s no guarantee that they will have caught all the nuances of human unpleasantness when data sets consist of millions and millions of samples. And the company doesn’t exactly have a stellar track record when it comes to curbing the harm caused by the systems it builds, to put it lightly.
The creators of Pheraki write in their paper that while the videos their model produces are not yet indistinguishable in quality from real ones, it “is within the realm of possibility, even today.” The models’ creators say that before releasing their model, they want to get a better understanding of data, prompts, and filtering outputs and measure biases in order to mitigate harms.
It’s only going to become harder and harder to know what’s real online, and video AI opens up a slew of unique dangers that audio and images don’t, such as the prospect of turbo-charged deepfakes. Platforms like TikTok and Instagram are already warping our sense of reality through augmented facial filters. AI-generated video could be a powerful tool for misinformation, because people have a greater tendency to believe and share fake videos than fake audio and text versions of the same content, according to researchers at Penn State University.
In conclusion, we haven’t come even close to figuring out what to do about the toxic elements of language models. We’ve only just started examining the harms around text-to-image AI systems. Video? Good luck with that.
Deeper Learning
The EU wants to put companies on the hook for harmful AI
The EU is creating new rules to make it easier to sue AI companies for harm. A new bill published last week, which is likely to become law in a couple of years, is part of a push from Europe to force AI developers not to release dangerous systems.
The bill, called the AI Liability Directive, will add teeth to the EU’s AI Act, which is set to become law around a similar time. The AI Act would require extra checks for “high risk” uses of AI that have the most potential to harm people. This could include AI systems used for policing, recruitment, or health care.
The liability law would kick in once harm has already happened. It would give people and companies the right to sue for damages when they have been harmed by an AI system—for example, if they can prove that discriminatory AI has been used to disadvantage them as part of a hiring process.
But there’s a catch: Consumers will have to prove that the company’s AI harmed them, which could be a huge undertaking. You can read my story about it here.
Bits and Bytes
How robots and AI are helping develop better batteries Researchers at Carnegie Mellon used an automated system and machine-learning software to generate electrolytes that could enable lithium-ion batteries to charge faster, addressing one of the major obstacles to the widespread adoption of electric vehicles. (MIT Technology Review)
Can smartphones help predict suicide? Researchers at Harvard University are using data collected from smartphones and wearable biosensors, such as Fitbit watches, to create an algorithm that might help predict when patients are at risk of suicide and help clinicians intervene. (The New York Times)
OpenAI has made its text-to-image AI DALL-E available to all. AI-generated images are going to be everywhere. You can try the software here.
Someone has made an AI that creates Pokémon lookalikes of famous people. The only image-generation AI that matters. (The Washington Post)
The first step to finding out is to catalogue what microbes we might have lost. To get as close to ancient microbiomes as possible, microbiologists have begun studying multiple Indigenous groups. Two have received the most attention: the Yanomami of the Amazon rainforest and the Hadza, in northern Tanzania.
Researchers have made some startling discoveries already. A study by Sonnenburg and his colleagues, published in July, found that the gut microbiomes of the Hadza appear to include bugs that aren’t seen elsewhere—around 20% of the microbe genomes identified had not been recorded in a global catalogue of over 200,000 such genomes. The researchers found 8.4 million protein families in the guts of the 167 Hadza people they studied. Over half of them had not previously been identified in the human gut.
Plenty of other studies published in the last decade or so have helped build a picture of how the diets and lifestyles of hunter-gatherer societies influence the microbiome, and scientists have speculated on what this means for those living in more industrialized societies. But these revelations have come at a price.
A changing way of life
The Hadza people hunt wild animals and forage for fruit and honey. “We still live the ancient way of life, with arrows and old knives,” says Mangola, who works with the Olanakwe Community Fund to support education and economic projects for the Hadza. Hunters seek out food in the bush, which might include baboons, vervet monkeys, guinea fowl, kudu, porcupines, or dik-dik. Gatherers collect fruits, vegetables, and honey.
Mangola, who has met with multiple scientists over the years and participated in many research projects, has witnessed firsthand the impact of such research on his community. Much of it has been positive. But not all researchers act thoughtfully and ethically, he says, and some have exploited or harmed the community.
One enduring problem, says Mangola, is that scientists have tended to come and study the Hadza without properly explaining their research or their results. They arrive from Europe or the US, accompanied by guides, and collect feces, blood, hair, and other biological samples. Often, the people giving up these samples don’t know what they will be used for, says Mangola. Scientists get their results and publish them without returning to share them. “You tell the world [what you’ve discovered]—why can’t you come back to Tanzania to tell the Hadza?” asks Mangola. “It would bring meaning and excitement to the community,” he says.
Some scientists have talked about the Hadza as if they were living fossils, says Alyssa Crittenden, a nutritional anthropologist and biologist at the University of Nevada in Las Vegas, who has been studying and working with the Hadza for the last two decades.
The Hadza have been described as being “locked in time,” she adds, but characterizations like that don’t reflect reality. She has made many trips to Tanzania and seen for herself how life has changed. Tourists flock to the region. Roads have been built. Charities have helped the Hadza secure land rights. Mangola went abroad for his education: he has a law degree and a master’s from the Indigenous Peoples Law and Policy program at the University of Arizona.
Over the last couple of decades, scientists have come to realize just how important the microbes that crawl all over us are to our health. But some believe our microbiomes are in crisis—casualties of an increasingly sanitized way of life. Disturbances in the collections of microbes we host have been associated with a whole host of diseases, ranging from arthritis to Alzheimer’s.
Some might not be completely gone, though. Scientists believe many might still be hiding inside the intestines of people who don’t live in the polluted, processed environment that most of the rest of us share. They’ve been studying the feces of people like the Yanomami, an Indigenous group in the Amazon, who appear to still have some of the microbes that other people have lost.
But there is a major catch: we don’t know whether those in hunter-gatherer societies really do have “healthier” microbiomes—and if they do, whether the benefits could be shared with others. At the same time, members of the communities being studied are concerned about the risk of what’s called biopiracy—taking natural resources from poorer countries for the benefit of wealthier ones. Read the full story.
—Jessica Hamzelou
Eric Schmidt has a 6-point plan for fighting election misinformation
—by Eric Schmidt, formerly the CEO of Google, and current cofounder of philanthropic initiative Schmidt Futures
The coming year will be one of seismic political shifts. Over 4 billion people will head to the polls in countries including the United States, Taiwan, India, and Indonesia, making 2024 the biggest election year in history.
Generative AI’s ability to harness customer data in a highly sophisticated manner means enterprises are accelerating plans to invest in and leverage the technology’s capabilities. In a study titled “The Future of Enterprise Data & AI,” Corinium Intelligence and WNS Triange surveyed 100 global C-suite leaders and decision-makers specializing in AI, analytics, and data. Seventy-six percent of the respondents said that their organizations are already using or planning to use generative AI.
According to McKinsey, while generative AI will affect most business functions, “four of them will likely account for 75% of the total annual value it can deliver.” Among these are marketing and sales and customer operations. Yet, despite the technology’s benefits, many leaders are unsure about the right approach to take and mindful of the risks associated with large investments.
Mapping out a generative AI pathway
One of the first challenges organizations need to overcome is senior leadership alignment. “You need the necessary strategy; you need the ability to have the necessary buy-in of people,” says Ayer. “You need to make sure that you’ve got the right use case and business case for each one of them.” In other words, a clearly defined roadmap and precise business objectives are as crucial as understanding whether a process is amenable to the use of generative AI.
The implementation of a generative AI strategy can take time. According to Ayer, business leaders should maintain a realistic perspective on the duration required for formulating a strategy, conduct necessary training across various teams and functions, and identify the areas of value addition. And for any generative AI deployment to work seamlessly, the right data ecosystems must be in place.
Ayer cites WNS Triange’s collaboration with an insurer to create a claims process by leveraging generative AI. Thanks to the new technology, the insurer can immediately assess the severity of a vehicle’s damage from an accident and make a claims recommendation based on the unstructured data provided by the client. “Because this can be immediately assessed by a surveyor and they can reach a recommendation quickly, this instantly improves the insurer’s ability to satisfy their policyholders and reduce the claims processing time,” Ayer explains.
All that, however, would not be possible without data on past claims history, repair costs, transaction data, and other necessary data sets to extract clear value from generative AI analysis. “Be very clear about data sufficiency. Don’t jump into a program where eventually you realize you don’t have the necessary data,” Ayer says.
The benefits of third-party experience
Enterprises are increasingly aware that they must embrace generative AI, but knowing where to begin is another thing. “You start off wanting to make sure you don’t repeat mistakes other people have made,” says Ayer. An external provider can help organizations avoid those mistakes and leverage best practices and frameworks for testing and defining explainability and benchmarks for return on investment (ROI).
Using pre-built solutions by external partners can expedite time to market and increase a generative AI program’s value. These solutions can harness pre-built industry-specific generative AI platforms to accelerate deployment. “Generative AI programs can be extremely complicated,” Ayer points out. “There are a lot of infrastructure requirements, touch points with customers, and internal regulations. Organizations will also have to consider using pre-built solutions to accelerate speed to value. Third-party service providers bring the expertise of having an integrated approach to all these elements.”
