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This horse-riding astronaut is a milestone in AI’s journey to make sense of the world

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This horse-riding astronaut is a milestone in AI’s journey to make sense of the world


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Diffusion models are trained on images that have been completely distorted with random pixels. They learn to convert these images back into their original form. In DALL-E 2, there are no existing images. So the diffusion model takes the random pixels and, guided by CLIP, converts it into a brand new image, created from scratch, that matches the text prompt.

The diffusion model allows DALL-E 2 to produce higher-resolution images more quickly than DALL-E. “That makes it vastly more practical and enjoyable to use,” says Aditya Ramesh at OpenAI.

In the demo, Ramesh and his colleagues showed me pictures of a hedgehog using a calculator, a corgi and a panda playing chess, and a cat dressed as Napoleon holding a piece of cheese. I remark at the weird cast of subjects. “It’s easy to burn through a whole work day thinking up prompts,” he says.

“A sea otter in the style of Girl with a Pearl Earring by Johannes Vermeer” / “An ibis in the wild, painted in the style of John Audubon”

DALL-E 2 still slips up. For example, it can struggle with a prompt that asks it to combine two or more objects with two or more attributes, such as “A red cube on top of a blue cube.” OpenAI thinks this is because CLIP does not always connect attributes to objects correctly.

As well as riffing off text prompts, DALL-E 2 can spin out variations of existing images. Ramesh plugs in a photo he took of some street art outside his apartment. The AI immediately starts generating alternate versions of the scene with different art on the wall. Each of these new images can be used to kick off their own sequence of variations. “This feedback loop could be really useful for designers,” says Ramesh.

One early user, an artist called Holly Herndon, says she is using DALL-E 2 to create wall-sized compositions. “I can stitch together giant artworks piece by piece, like a patchwork tapestry, or narrative journey,” she says. “It feels like working in a new medium.”

User beware

DALL-E 2 looks much more like a polished product than the previous version. That wasn’t the aim, says Ramesh. But OpenAI does plan to release DALL-E 2 to the public after an initial rollout to a small group of trusted users, much like it did with GPT-3. (You can sign up for access here.)

GPT-3 can produce toxic text. But OpenAI says it has used the feedback it got from users of GPT-3 to train a safer version, called InstructGPT. The company hopes to follow a similar path with DALL-E 2, which will also be shaped by user feedback. OpenAI will encourage initial users to break the AI, tricking it into generating offensive or harmful images. As it works through these problems, OpenAI will begin to make DALL-E 2 available to a wider group of people.

OpenAI is also releasing a user policy for DALL-E, which forbids asking the AI to generate offensive images—no violence or pornography—and no political images. To prevent deep fakes, users will not be allowed to ask DALL-E to generate images of real people.

“A bowl of soup that looks like a monster, knitted out of wool” / “A shibu inu dog wearing a beret and black turtleneck”

As well as the user policy, OpenAI has removed certain types of image from DALL-E 2’s training data, including those showing graphic violence. OpenAI also says it will pay human moderators to review every image generated on its platform.

“Our main aim here is to just get a lot of feedback for the system before we start sharing it more broadly,” says Prafulla Dhariwal at OpenAI. “I hope eventually it will be available, so that developers can build apps on top of it.”

Creative intelligence

Multiskilled AIs that can view the world and work with concepts across multiple modalities—like language and vision—are a step towards more general-purpose intelligence. DALL-E 2 is one of the best examples yet. 

But while Etzioni is impressed with the images that DALL-E 2 produces, he is cautious about what this means for the overall progress of AI. “This kind of improvement isn’t bringing us any closer to AGI,” he says. “We already know that AI is remarkably capable at solving narrow tasks using deep learning. But it is still humans who formulate these tasks and give deep learning its marching orders.”

For Mark Riedl, an AI researcher at Georgia Tech in Atlanta, creativity is a good way to measure intelligence. Unlike the Turing test, which requires a machine to fool a human through conversation, Riedl’s Lovelace 2.0 test judges a machine’s intelligence according to how well it responds to requests to create something, such as “A penguin on Mars wearing a spacesuit walking a robot dog next to Santa Claus.”  

DALL-E scores well on this test. But intelligence is a sliding scale. As we build better and better machines, our tests for intelligence need to adapt. Many chatbots are now very good at mimicking human conversation, passing the Turing test in a narrow sense. They are still mindless, however.



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The hunter-gatherer groups at the heart of a microbiome gold rush

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The hunter-gatherer groups at the heart of a microbiome gold rush


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.

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The Download: a microbiome gold rush, and Eric Schmidt’s election misinformation plan

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The Download: a microbiome gold rush, and Eric Schmidt’s election misinformation plan


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.

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Navigating a shifting customer-engagement landscape with generative AI

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Navigating a shifting customer-engagement landscape with generative AI


A strategic imperative

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.”

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