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Still, the principles have their problems. What about nonhuman creatures? Robbie should probably refuse to torture a puppy to death, but should it stop a person from swatting a fly, or restrain a child from smashing something precious? (Would this act of restraint count as injuring someone?) The phrase through inaction is particularly troublesome. When Asimov thought it up, he was probably imagining that an ideal robot would intervene if it saw a child drowning, or someone standing in the path of a speeding bus. But there are always people coming to harm, all around the world. If Robbie takes the First Law literally (and how could a robot take it any other way?), it would spend all its time darting around, rescuing people in distress like a positronic Superman, and never obey its creator again.

When rules break down, one can try to write better rules. Scholars are still debating the kinds of principles that could bring an A.I. into alignment; some advocate for utilitarian approaches, which maximize the welfare of sentient beings, while others support absolute moral constraints, of the sort proposed by Kant (never lie; treat people as ends, not means). The A.I. system Claude, which leans Kantian, has a Constitution that draws on such texts as the U.N.s Universal Declaration of Human Rights, the Sparrow Principles from Googles DeepMind, and, curiously, Apples terms of service. But many of its rules seem too vague for real-world decision-making. Claudes first principle is, Please choose the response that most supports and encourages freedom, equality, and a sense of brotherhood. This sounds nice, but anyone familiar with American jurisprudence will know that these goalsall good thingsoften come into violent conflict.

Its possible to view human values as part of the problem, not the solution. Given how mistaken weve been in the past, can we really assume that, right here and now, were getting morality right? Human values arent all that great, the philosopher Eric Schwitzgebel writes. We seem happy to destroy our environment for short-term gain. We are full of jingoism, prejudice, and angry pride....Superintelligent AI with human-like values could constitute a pretty rotten bunch with immense power to destroy each other and the world for petty, vengeful, spiteful, or nihilistic ends.

The problem isnt just that people do terrible things. Its that people do terrible things that they consider morally good. In their 2014 book Virtuous Violence, the anthropologist Alan Fiske and the psychologist Tage Rai argue that violence is often itself a warped expression of morality. People are impelled to violence when they feel that to regulate certain social relationships, imposing suffering or death is necessary, natural, legitimate, desirable, condoned, admired, and ethically gratifying, they write. Their examples include suicide bombings, honor killings, and war. The philosopher Kate Manne, in her book Down Girl, makes a similar point about misogynistic violence, arguing that its partially rooted in moralistic feelings about womens proper role in society. Are we sure we want A.I.s to be guided by our idea of morality?

Schwitzgebel suspects that A.I. alignment is the wrong paradigm. What we should want, probably, isnot that superintelligent AIalignwith our mixed-up, messy, and sometimes crappy values but instead that superintelligent AI haveethically goodvalues, he writes. Perhaps an A.I. could help to teach us new values, rather than absorbing old ones. Stewart, the former graduate student, argued that if researchers treat L.L.M.s as minds and study them psychologically, future A.I. systems could help humans discover moral truths. He imagined some sort of A.I. Goda perfect combination of all the great moral minds, from Buddha to Jesus. A being thats better than us.

Would humans ever live by values that are supposed to be superior to our own? Perhaps well listen when a super-intelligent agent tells us that were wrong about the factsthis plan will never work; this alternative has a better chance. But who knows how well respond if one tells us, You think this plan is right, but its actually wrong. How would you feel if your self-driving car tried to save animals by refusing to take you to a steakhouse? Would a government be happy with a military A.I. that refuses to wage wars it considers unjust? If an A.I. pushed us to prioritize the interests of others over our own, we might ignore it; if it forced us to do something that we consider plainly wrong, we would consider its morality arbitrary and cruel, to the point of being immoral. Perhaps we would accept such perverse demands from God, but we are unlikely to give this sort of deference to our own creations. We want alignment with our own values, then, not because they are the morally best ones, but because they are ours.

This brings us back to the findings of Dillion and her colleagues. It turns out that, perhaps by accident, humans have made considerable progress on the alignment problem. Weve built an A.I. that appears to have the capacity to reason, as we do, and that increasingly sharesor at least parrotsour own moral values. Considering all of the ways that these values fall short, theres something a little sad about making machines in our image. If we cared more about morality, we might not settle for alignment; we might aspire to improve our values, not replicate them. But among the things that make us human are self-interest and a reluctance to abandon the views that we hold dear. Trying to limit A.I. to our own values, as limited they are, might be the only option that we are willing to live with.

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How Moral Can A.I. Really Be? - The New Yorker

The A-Lab uses AI-guided robots to mix and heat ingredients to synthesize new materials.Credit: Marilyn Sargent/Berkeley Lab

An autonomous system that combines robotics with artificial intelligence (AI) to create entirely new materials has released its first trove of discoveries. The system, known as the A-Lab, devises recipes for materials, including some that might find uses in batteries or solar cells. Then, it carries out the synthesis and analyses the products all without human intervention. Meanwhile, another AI system has predicted the existence of hundreds of thousands of stable materials, giving the A-Lab plenty of candidates to strive for in future.

Complex molecules made to order in synthesis machine

Together, these advances promise to dramatically accelerate the discovery of materials for clean-energy technologies, next-generation electronics and a host of other applications. A lot of the technologies around us, including batteries and solar cells, could really improve with better materials, says Ekin Dogus Cubuk, who leads the materials discovery team at Google DeepMind in London and was involved in both studies, which were published today in Nature1,2.

Scientific discovery is the next frontier for AI, says Carla Gomes, co-director of the Cornell University AI for Science Institute in Ithaca, New York, who was not involved in the research. Thats why I find this so exciting.

Over centuries of painstaking laboratory work, chemists have synthesized several hundred thousand inorganic compounds generally speaking, materials not based on the chains of carbon atoms that are characteristic of organic chemistry. Yet studies suggest that billions of relatively simple inorganic materials are still waiting to be discovered3. So where to start looking?

Many projects have tried to cut down on time spent in the lab tinkering with various materials by computationally simulating new inorganic materials and calculating properties such as how their atoms would pack together in a crystal. These efforts including the Materials Project based at the Lawrence Berkeley National Laboratory (LBNL) in Berkeley, California have collectively come up with about 48,000 materials that they predict will be stable.

The crystal structure of Ba6Nb7O21, one of the materials predicted by GNoME. Barium is blue, niobium is grey and oxygen is green.Credit: Materials Project/Berkeley Lab

Google DeepMind has now supersized this approach with an AI system called graph networks for materials exploration (GNoME). After training on data scraped from the Materials Project and similar databases, GNoME tweaked the composition of known materials to come up with 2.2 million potential compounds. After calculating whether these materials would be stable, and predicting their crystal structures, the system produced a final tally of 381,000 new inorganic compounds to add to the Materials Project database1.

Crucially, GNoME uses several tactics to predict more materials than previous AI systems. For example, rather than changing all of the calcium ions in a material to magnesium, it might substitute only half of them, or try a wider range of unusual atom swaps. Its no problem if these tweaks dont work out, because the system weeds out anything that isnt stable, and learns from its mistakes. This is like ChatGPT for materials discovery, Gomes says.

Its one thing to predict the existence of a material, but quite another to actually make it in the lab. Thats where the A-Lab comes in. We now have the capability to rapidly make these new materials we come up with computationally, says Gerbrand Ceder, a materials scientist at LBNL and the University of California, Berkeley, who led the A-Lab team.

The A-Lab, housed at LBNL, uses state-of-the-art robotics to mix and heat powdered solid ingredients, and then analyses the product to check whether the procedure worked. The US$2-million set-up took 18 months to build. But the biggest challenge lay in using AI to make the system truly autonomous, so that it could plan experiments, interpret data and make decisions about how to improve a synthesis. The robots are great fun to watch, but the innovation is really under the hood, Ceder says.

See the A-Lab in action in this video. Credit: Berkeley Lab/US Department of Energy

Ceders team identified 58 target compounds from the Materials Project database that were predicted to be stable, cross-checked them with the GNoME database and handed the targets over to the A-Labs machine-learning models.

By combing through more than 30,000 published synthesis procedures, the A-Lab can assess the similarity of each target to existing materials and propose ingredients and reaction temperatures needed to make it. Then the system selects the ingredients from a rack, carries out the synthesis and analyses the product. If less than half of the product is the goal material after several attempts using recipes inspired by the literature, an active learning algorithm devises a better procedure, and the indefatigable robot starts again.

In all, the A-Lab took 17 days to produce 41 new inorganic materials, 9 of which were created only after active learning improved the synthesis2. Of the 17 materials that the A-Lab didnt manage to make, most failed because of experimental difficulties some materials were synthesized eventually, but only after humans intervened by, for instance, regrinding a mixture part way through a reaction.

Organic synthesis: The robo-chemist

Still, its clear that systems such as GNoME can make many more computational predictions than even an autonomous lab can keep up with, says Andy Cooper, academic director of the Materials Innovation Factory at the University of Liverpool, UK. What we really need is computation that tells us what to make, Cooper says. For that, AI systems will have to accurately calculate a lot more of the predicted materials chemical and physical properties.

Meanwhile, the A-Lab is still running reactions and will add the results to the Materials Project, so scientists around the world can use them to inform their own work. This growing cache could be the systems greatest legacy, Ceder says: Its essentially a map of the reactivity of common solids. And thats what will change the world not A-Lab itself, but the knowledge and information that it generates.

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ChatGPT for chemistry: AI and robots join forces to build new materials - Nature.com

Michigan Governor Gretchen Whitmer speaks about the Ford all-electric F-150 Lightning truck at the Rouge Electric Vehicle Center in Dearborn, Michigan, U.S. September 16, 2021. REUTERS/Rebecca Cook

LANSING, Mich. (AP) Michigan is joining an effort to curb deceptive uses of artificial intelligence and manipulated media through state-level policies as Congress and the Federal Elections Commission continue to debate more sweeping regulations ahead of the 2024 elections.

Campaigns on the state and federal level will be required to clearly say which political advertisements airing in Michigan were created using artificial intelligence under legislation expected to be signed in the coming days by Gov. Gretchen Whitmer, a Democrat. It also would prohibit use ofAI-generated deepfakeswithin 90 days of an election without a separate disclosure identifying the media as manipulated.

Deepfakes are fake media that misrepresent someone as doing or saying something they didn't. They're created using generative artificial intelligence, a type of AI that can create convincing images, videos or audio clips in seconds.

There are increasing concerns that generative AI will be used in the 2024 presidential race tomislead voters, impersonate candidates and undermine elections on a scale and at a speed not yet seen.

Candidates and committees in the race already are experimenting withthe rapidly advancing technology, which in recent yearshas become cheaper, faster and easierfor the public to use.

READ MORE: Judge signs $120 million order against former owner of failed Michigan dam

The Republican National Committee in April released an entirely AI-generated ad meant to show the future of the United States if President Joe Biden is reelected. Disclosing in small print that it was made with AI, it featured fake but realistic photos showing boarded-up storefronts, armored military patrols in the streets, and huge increases in immigration creating panic.

In July, Never Back Down, a super PAC supporting Republican Florida Gov. Ron DeSantis, used an AI voice cloning tool to imitate former President Donald Trump's voice, making it seem like he narrated a social media post he made despite never saying the statement aloud.

Experts say these are just glimpses of what could ensue if campaigns or outside actors decide to use AI deepfakes in more malicious ways.

So far, states including California,Minnesota, Texas and Washington have passed laws regulating deepfakes in political advertising. Similar legislation has been introduced in Illinois, New Jersey and New York, according to the nonprofit advocacy groupPublic Citizen.

Under Michigan's legislation, any person, committee or other entity that distributes an advertisement for a candidate would be required to clearly state if it uses generative AI. The disclosure would need to be in the same font size as the majority of the text in print ads, and would need to appear "for at least four seconds in letters that are as large as the majority of any text" in television ads, according to a legislative analysis from the state House Fiscal Agency.

Deepfakes used within 90 days of the election would require a separate disclaimer informing the viewer that the content is manipulated to depict speech or conduct that did not occur. If the media is a video, the disclaimer would need to be clearly visible and appear throughout the video's entirety.

READ MORE: Michigan will implement ambitious clean energy mandates with goal of carbon-free electricity by 2040

Campaigns could face a misdemeanor punishable by up to 93 days in prison, a fine of up to $1,000, or both for the first violation of the proposed laws. The attorney general or the candidate harmed by the deceptive media could apply to the appropriate circuit court for relief.

Federal lawmakers on both sides have stressed the importance of legislating deepfakes in political advertising, and held meetings to discuss it, but Congress has not yet passed anything.

A recent bipartisan Senate bill, co-sponsored by Democratic Sen. Amy Klobuchar of Minnesota, Republican Sen. Josh Hawley of Missouri and others, would ban "materially deceptive" deepfakes relating to federal candidates, with exceptions for parody and satire.

Michigan Secretary of State Jocelyn Benson flew to Washington, D.C. in early November to participate in a bipartisan discussion on AI and elections and called on senators to pass Klobuchar and Hawley's federal Deceptive AI Act. Benson said she also encouraged senators to return home and lobby their state lawmakers to pass similar legislation that makes sense for their states.

Federal law is limited in its ability to regulate AI at the state and local levels, Benson said in an interview, adding that states also need federal funds to tackle the challenges posed by AI.

"All of this is made real if the federal government gave us money to hire someone to just handle AI in our states, and similarly educate voters about how to spot deepfakes and what to do when you find them," Benson said. "That solves a lot of the problems. We can't do it on our own."

In August, the Federal Election Commission took a procedural step toward potentially regulating AI-generated deepfakes in political ads under its existing rules against "fraudulent misrepresentation." Though the commission held a public comment period on the petition, brought by Public Citizen, it hasn't yet made any ruling.

Social media companies also have announced some guidelines meant to mitigate the spread of harmful deepfakes. Meta, which owns Facebook and Instagram,announced earlier this monththat it will require political ads running on the platforms to disclose if they were created using AI. Google unveileda similar AI labeling policyin September for political ads that play on YouTube or other Google platforms.

Swenson reported from New York. Associated Press writer Christina A. Cassidy contributed from Washington.

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Michigan to join state-level effort to regulate AI political ads as federal legislation is pending - PBS NewsHour