All posts by Bruce Schneier

The AI Dividend

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/07/the-ai-dividend.html

For four decades, Alaskans have opened their mailboxes to find checks waiting for them, their cut of the black gold beneath their feet. This is Alaska’s Permanent Fund, funded by the state’s oil revenues and paid to every Alaskan each year. We’re now in a different sort of resource rush, with companies peddling bits instead of oil: generative AI.

Everyone is talking about these new AI technologies—like ChatGPT—and AI companies are touting their awesome power. But they aren’t talking about how that power comes from all of us. Without all of our writings and photos that AI companies are using to train their models, they would have nothing to sell. Big Tech companies are currently taking the work of the American people, without our knowledge and consent, without licensing it, and are pocketing the proceeds.

You are owed profits for your data that powers today’s AI, and we have a way to make that happen. We call it the AI Dividend.

Our proposal is simple, and harkens back to the Alaskan plan. When Big Tech companies produce output from generative AI that was trained on public data, they would pay a tiny licensing fee, by the word or pixel or relevant unit of data. Those fees would go into the AI Dividend fund. Every few months, the Commerce Department would send out the entirety of the fund, split equally, to every resident nationwide. That’s it.

There’s no reason to complicate it further. Generative AI needs a wide variety of data, which means all of us are valuable—not just those of us who write professionally, or prolifically, or well. Figuring out who contributed to which words the AIs output would be both challenging and invasive, given that even the companies themselves don’t quite know how their models work. Paying the dividend to people in proportion to the words or images they create would just incentivize them to create endless drivel, or worse, use AI to create that drivel. The bottom line for Big Tech is that if their AI model was created using public data, they have to pay into the fund. If you’re an American, you get paid from the fund.

Under this plan, hobbyists and American small businesses would be exempt from fees. Only Big Tech companies—those with substantial revenue—would be required to pay into the fund. And they would pay at the point of generative AI output, such as from ChatGPT, Bing, Bard, or their embedded use in third-party services via Application Programming Interfaces.

Our proposal also includes a compulsory licensing plan. By agreeing to pay into this fund, AI companies will receive a license that allows them to use public data when training their AI. This won’t supersede normal copyright law, of course. If a model starts producing copyright material beyond fair use, that’s a separate issue.

Using today’s numbers, here’s what it would look like. The licensing fee could be small, starting at $0.001 per word generated by AI. A similar type of fee would be applied to other categories of generative AI outputs, such as images. That’s not a lot, but it adds up. Since most of Big Tech has started integrating generative AI into products, these fees would mean an annual dividend payment of a couple hundred dollars per person.

The idea of paying you for your data isn’t new, and some companies have tried to do it themselves for users who opted in. And the idea of the public being repaid for use of their resources goes back to well before Alaska’s oil fund. But generative AI is different: It uses data from all of us whether we like it or not, it’s ubiquitous, and it’s potentially immensely valuable. It would cost Big Tech companies a fortune to create a synthetic equivalent to our data from scratch, and synthetic data would almost certainly result in worse output. They can’t create good AI without us.

Our plan would apply to generative AI used in the US. It also only issues a dividend to Americans. Other countries can create their own versions, applying a similar fee to AI used within their borders. Just like an American company collects VAT for services sold in Europe, but not here, each country can independently manage their AI policy.

Don’t get us wrong; this isn’t an attempt to strangle this nascent technology. Generative AI has interesting, valuable, and possibly transformative uses, and this policy is aligned with that future. Even with the fees of the AI Dividend, generative AI will be cheap and will only get cheaper as technology improves. There are also risks—both every day and esoteric—posed by AI, and the government may need to develop policies to remedy any harms that arise.

Our plan can’t make sure there are no downsides to the development of AI, but it would ensure that all Americans will share in the upsides—particularly since this new technology isn’t possible without our contribution.

This essay was written with Barath Raghavan, and previously appeared on Politico.com.

Belgian Tax Hack

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/07/belgian-tax-hack.html

Here’s a fascinating tax hack from Belgium (listen to the details here, episode #484 of “No Such Thing as a Fish,” at 28:00).

Basically, it’s about a music festival on the border between Belgium and Holland. The stage was in Holland, but the crowd was in Belgium. When the copyright collector came around, they argued that they didn’t have to pay any tax because the audience was in a different country. Supposedly it worked.

Class-Action Lawsuit for Scraping Data without Permission

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/07/class-action-lawsuit-for-scraping-data-without-permission.html

I have mixed feelings about this class-action lawsuit against OpenAI and Microsoft, claiming that it “scraped 300 billion words from the internet” without either registering as a data broker or obtaining consent. On the one hand, I want this to be a protected fair use of public data. On the other hand, I want us all to be compensated for our uniquely human ability to generate language.

There’s an interesting wrinkle on this. A recent paper showed that using AI generated text to train another AI invariably “causes irreversible defects.” From a summary:

The tails of the original content distribution disappear. Within a few generations, text becomes garbage, as Gaussian distributions converge and may even become delta functions. We call this effect model collapse.

Just as we’ve strewn the oceans with plastic trash and filled the atmosphere with carbon dioxide, so we’re about to fill the Internet with blah. This will make it harder to train newer models by scraping the web, giving an advantage to firms which already did that, or which control access to human interfaces at scale. Indeed, we already see AI startups hammering the Internet Archive for training data.

This is the same idea that Ted Chiang wrote about: that ChatGPT is a “blurry JPEG of all the text on the Web.” But the paper includes the math that proves the claim.

What this means is that text from before last year—text that is known human-generated—will become increasingly valuable.

Self-Driving Cars Are Surveillance Cameras on Wheels

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/07/self-driving-cars-are-surveillance-cameras-on-wheels.html

Police are already using self-driving car footage as video evidence:

While security cameras are commonplace in American cities, self-driving cars represent a new level of access for law enforcement ­ and a new method for encroachment on privacy, advocates say. Crisscrossing the city on their routes, self-driving cars capture a wider swath of footage. And it’s easier for law enforcement to turn to one company with a large repository of videos and a dedicated response team than to reach out to all the businesses in a neighborhood with security systems.

“We’ve known for a long time that they are essentially surveillance cameras on wheels,” said Chris Gilliard, a fellow at the Social Science Research Council. “We’re supposed to be able to go about our business in our day-to-day lives without being surveilled unless we are suspected of a crime, and each little bit of this technology strips away that ability.”

[…]

While self-driving services like Waymo and Cruise have yet to achieve the same level of market penetration as Ring, the wide range of video they capture while completing their routes presents other opportunities. In addition to the San Francisco homicide, Bloomberg’s review of court documents shows police have sought footage from Waymo and Cruise to help solve hit-and-runs, burglaries, aggravated assaults, a fatal collision and an attempted kidnapping.

In all cases reviewed by Bloomberg, court records show that police collected footage from Cruise and Waymo shortly after obtaining a warrant. In several cases, Bloomberg could not determine whether the recordings had been used in the resulting prosecutions; in a few of the cases, law enforcement and attorneys said the footage had not played a part, or was only a formality. However, video evidence has become a lynchpin of criminal cases, meaning it’s likely only a matter of time.

The US Is Spying on the UN Secretary General

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/06/the-us-is-spying-on-the-un-secretary-general.html

The Washington Post is reporting that the US is spying on the UN Secretary General.

The reports on Guterres appear to contain the secretary general’s personal conversations with aides regarding diplomatic encounters. They indicate that the United States relied on spying powers granted under the Foreign Intelligence Surveillance Act (FISA) to gather the intercepts.

Lots of details about different conversations in the article, which are based on classified documents leaked on Discord by Jack Teixeira.

There will probably a lot of faux outrage at this, but spying on foreign leaders is a perfectly legitimate use of the NSA’s capabilities and authorities. (If the NSA didn’t spy on the UN Secretary General, we should fire it and replace it with a more competent NSA.) It’s the bulk surveillance of whole populations that should outrage us.

Redacting Documents with a Black Sharpie Doesn’t Work

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/06/redacting-documents-with-a-black-sharpie-doesnt-work.html

We have learned this lesson again:

As part of the FTC v. Microsoft hearing, Sony supplied a document from PlayStation chief Jim Ryan that includes redacted details on the margins Sony shares with publishers, its Call of Duty revenues, and even the cost of developing some of its games.

It looks like someone redacted the documents with a black Sharpie ­ but when you scan them in, it’s easy to see some of the redactions. Oops.

I don’t particularly care about the redacted information, but it’s there in the article.

Stalkerware Vendor Hacked

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/06/stalkerware-vendor-hacked.html

The stalkerware company LetMeSpy has been hacked:

TechCrunch reviewed the leaked data, which included years of victims’ call logs and text messages dating back to 2013.

The database we reviewed contained current records on at least 13,000 compromised devices, though some of the devices shared little to no data with LetMeSpy. (LetMeSpy claims to delete data after two months of account inactivity.)

[…]

The database also contained over 13,400 location data points for several thousand victims. Most of the location data points are centered over population hotspots, suggesting the majority of victims are located in the United States, India and Western Africa.

The data also contained the spyware’s master database, including information about 26,000 customers who used the spyware for free and the email addresses of customers who bought paying subscriptions.

The leaked data contains no identifying information, which means people whose data was leaked can’t be notified. (This is actually much more complicated than it might seem, because alerting the victims often means alerting the stalker—which can put the victims into unsafe situations.)

Typing Incriminating Evidence in the Memo Field

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/06/typing-incriminating-evidence-in-the-memo-field.html

Don’t do it:

Recently, the manager of the Harvard Med School morgue was accused of stealing and selling human body parts. Cedric Lodge and his wife Denise were among a half-dozen people arrested for some pretty grotesque crimes. This part is also at least a little bit funny though:

Over a three-year period, Taylor appeared to pay Denise Lodge more than $37,000 for human remains. One payment, for $1,000 included the memo “head number 7.” Another, for $200, read “braiiiiiins.”

It’s so easy to think that you won’t get caught.

Excel Data Forensics

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/06/excel-data-forensics.html

In this detailed article about academic plagiarism are some interesting details about how to do data forensics on Excel files. It really needs the graphics to understand, so see the description at the link.

(And, yes, an author of a paper on dishonesty is being accused of dishonesty. There’s more evidence.)

AI as Sensemaking for Public Comments

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/06/ai-as-sensemaking-for-public-comments.html

It’s become fashionable to think of artificial intelligence as an inherently dehumanizing technology, a ruthless force of automation that has unleashed legions of virtual skilled laborers in faceless form. But what if AI turns out to be the one tool able to identify what makes your ideas special, recognizing your unique perspective and potential on the issues where it matters most?

You’d be forgiven if you’re distraught about society’s ability to grapple with this new technology. So far, there’s no lack of prognostications about the democratic doom that AI may wreak on the US system of government. There are legitimate reasons to be concerned that AI could spread misinformation, break public comment processes on regulations, inundate legislators with artificial constituent outreach, help to automate corporate lobbying, or even generate laws in a way tailored to benefit narrow interests.

But there are reasons to feel more sanguine as well. Many groups have started demonstrating the potential beneficial uses of AI for governance. A key constructive-use case for AI in democratic processes is to serve as discussion moderator and consensus builder.

To help democracy scale better in the face of growing, increasingly interconnected populations—as well as the wide availability of AI language tools that can generate reams of text at the click of a button—the US will need to leverage AI’s capability to rapidly digest, interpret and summarize this content.

There are two different ways to approach the use of generative AI to improve civic participation and governance. Each is likely to lead to drastically different experience for public policy advocates and other people trying to have their voice heard in a future system where AI chatbots are both the dominant readers and writers of public comment.

For example, consider individual letters to a representative, or comments as part of a regulatory rulemaking process. In both cases, we the people are telling the government what we think and want.

For more than half a century, agencies have been using human power to read through all the comments received, and to generate summaries and responses of their major themes. To be sure, digital technology has helped.

In 2021, the Council of Federal Chief Data Officers recommended modernizing the comment review process by implementing natural language processing tools for removing duplicates and clustering similar comments in processes governmentwide. These tools are simplistic by the standards of 2023 AI. They work by assessing the semantic similarity of comments based on metrics like word frequency (How often did you say “personhood”?) and clustering similar comments and giving reviewers a sense of what topic they relate to.

Think of this approach as collapsing public opinion. They take a big, hairy mass of comments from thousands of people and condense them into a tidy set of essential reading that generally suffices to represent the broad themes of community feedback. This is far easier for a small agency staff or legislative office to handle than it would be for staffers to actually read through that many individual perspectives.

But what’s lost in this collapsing is individuality, personality, and relationships. The reviewer of the condensed comments may miss the personal circumstances that led so many commenters to write in with a common point of view, and may overlook the arguments and anecdotes that might be the most persuasive content of the testimony.

Most importantly, the reviewers may miss out on the opportunity to recognize committed and knowledgeable advocates, whether interest groups or individuals, who could have long-term, productive relationships with the agency.

These drawbacks have real ramifications for the potential efficacy of those thousands of individual messages, undermining what all those people were doing it for. Still, practicality tips the balance toward of some kind of summarization approach. A passionate letter of advocacy doesn’t hold any value if regulators or legislators simply don’t have time to read it.

There is another approach. In addition to collapsing testimony through summarization, government staff can use modern AI techniques to explode it. They can automatically recover and recognize a distinctive argument from one piece of testimony that does not exist in the thousands of other testimonies received. They can discover the kinds of constituent stories and experiences that legislators love to repeat at hearings, town halls and campaign events. This approach can sustain the potential impact of individual public comment to shape legislation even as the volumes of testimony may rise exponentially.

In computing, there is a rich history of that type of automation task in what is called outlier detection. Traditional methods generally involve finding a simple model that explains most of the data in question, like a set of topics that well describe the vast majority of submitted comments. But then they go a step further by isolating those data points that fall outside the mold—comments that don’t use arguments that fit into the neat little clusters.

State-of-the-art AI language models aren’t necessary for identifying outliers in text document data sets, but using them could bring a greater degree of sophistication and flexibility to this procedure. AI language models can be tasked to identify novel perspectives within a large body of text through prompting alone. You simply need to tell the AI to find them.

In the absence of that ability to extract distinctive comments, lawmakers and regulators have no choice but to prioritize on other factors. If there is nothing better, “who donated the most to our campaign” or “which company employs the most of my former staffers” become reasonable metrics for prioritizing public comments. AI can help elected representatives do much better.

If Americans want AI to help revitalize the country’s ailing democracy, they need to think about how to align the incentives of elected leaders with those of individuals. Right now, as much as 90% of constituent communications are mass emails organized by advocacy groups, and they go largely ignored by staffers. People are channeling their passions into a vast digital warehouses where algorithms box up their expressions so they don’t have to be read. As a result, the incentive for citizens and advocacy groups is to fill that box up to the brim, so someone will notice it’s overflowing.

A talented, knowledgeable, engaged citizen should be able to articulate their ideas and share their personal experiences and distinctive points of view in a way that they can be both included with everyone else’s comments where they contribute to summarization and recognized individually among the other comments. An effective comment summarization process would extricate those unique points of view from the pile and put them into lawmakers’ hands.

This essay was written with Nathan Sanders, and previously appeared in the Conversation.

Ethical Problems in Computer Security

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/06/ethical-problems-in-computer-security.html

Tadayoshi Kohno, Yasemin Acar, and Wulf Loh wrote excellent paper on ethical thinking within the computer security community: “Ethical Frameworks and Computer Security Trolley Problems: Foundations for Conversation“:

Abstract: The computer security research community regularly tackles ethical questions. The field of ethics / moral philosophy has for centuries considered what it means to be “morally good” or at least “morally allowed / acceptable.” Among philosophy’s contributions are (1) frameworks for evaluating the morality of actions—including the well-established consequentialist and deontological frameworks—and (2) scenarios (like trolley problems) featuring moral dilemmas that can facilitate discussion about and intellectual inquiry into different perspectives on moral reasoning and decision-making. In a classic trolley problem, consequentialist and deontological analyses may render different opinions. In this research, we explicitly make and explore connections between moral questions in computer security research and ethics / moral philosophy through the creation and analysis of trolley problem-like computer security-themed moral dilemmas and, in doing so, we seek to contribute to conversations among security researchers about the morality of security research-related decisions. We explicitly do not seek to define what is morally right or wrong, nor do we argue for one framework over another. Indeed, the consequentialist and deontological frameworks that we center, in addition to coming to different conclusions for our scenarios, have significant limitations. Instead, by offering our scenarios and by comparing two different approaches to ethics, we strive to contribute to how the computer security research field considers and converses about ethical questions, especially when there are different perspectives on what is morally right or acceptable. Our vision is for this work to be broadly useful to the computer security community, including to researchers as they embark on (or choose not to embark on), conduct, and write about their research, to program committees as they evaluate submissions, and to educators as they teach about computer security and ethics.

The paper will be presented at USENIX Security.

Power LED Side-Channel Attack

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/06/power-led-side-channel-attack.html

This is a clever new side-channel attack:

The first attack uses an Internet-connected surveillance camera to take a high-speed video of the power LED on a smart card reader­—or of an attached peripheral device—­during cryptographic operations. This technique allowed the researchers to pull a 256-bit ECDSA key off the same government-approved smart card used in Minerva. The other allowed the researchers to recover the private SIKE key of a Samsung Galaxy S8 phone by training the camera of an iPhone 13 on the power LED of a USB speaker connected to the handset, in a similar way to how Hertzbleed pulled SIKE keys off Intel and AMD CPUs.

There are lots of limitations:

When the camera is 60 feet away, the room lights must be turned off, but they can be turned on if the surveillance camera is at a distance of about 6 feet. (An attacker can also use an iPhone to record the smart card reader power LED.) The video must be captured for 65 minutes, during which the reader must constantly perform the operation.

[…]

The attack assumes there is an existing side channel that leaks power consumption, timing, or other physical manifestations of the device as it performs a cryptographic operation.

So don’t expect this attack to be recovering keys in the real world anytime soon. But, still, really nice work.

More details from the researchers.

Friday Squid Blogging: Squid Can Edit Their RNA

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/06/friday-squid-blogging-squid-can-edit-their-rna.html

This is just crazy:

Scientists don’t yet know for sure why octopuses, and other shell-less cephalopods including squid and cuttlefish, are such prolific editors. Researchers are debating whether this form of genetic editing gave cephalopods an evolutionary leg (or tentacle) up or whether the editing is just a sometimes useful accident. Scientists are also probing what consequences the RNA alterations may have under various conditions.

I sometimes think that cephalopods are aliens that crash-landed on this planet eons ago.

Another article.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Read my blog posting guidelines here.

Security and Human Behavior (SHB) 2023

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/06/security-and-human-behavior-shb-2023.html

I’m just back from the sixteenth Workshop on Security and Human Behavior, hosted by Alessandro Acquisti at Carnegie Mellon University in Pittsburgh.

SHB is a small, annual, invitational workshop of people studying various aspects of the human side of security, organized each year by Alessandro Acquisti, Ross Anderson, and myself. The fifty or so attendees include psychologists, economists, computer security researchers, criminologists, sociologists, political scientists, designers, lawyers, philosophers, anthropologists, geographers, neuroscientists, business school professors, and a smattering of others. It’s not just an interdisciplinary event; most of the people here are individually interdisciplinary.

Our goal is always to maximize discussion and interaction. We do that by putting everyone on panels, and limiting talks to six to eight minutes, with the rest of the time for open discussion. Short talks limit presenters’ ability to get into the boring details of their work, and the interdisciplinary audience discourages jargon.

For the past decade and a half, this workshop has been the most intellectually stimulating two days of my professional year. It influences my thinking in different and sometimes surprising ways­ 00 and has resulted in some unexpected collaborations.

And that’s what’s valuable. One of the most important outcomes of the event is new collaborations. Over the years, we have seen new interdisciplinary research between people who met at the workshop, and ideas and methodologies move from one field into another based on connections made at the workshop. This is why some of us have been coming back every year for over a decade.

This year’s schedule is here. This page lists the participants and includes links to some of their work. As he does every year, Ross Anderson is live blogging the talks. We are back 100% in person after two years of fully remote and one year of hybrid.

Here are my posts on the first, second, third, fourth, fifth, sixth, seventh, eighth, ninth, tenth, eleventh, twelfth, thirteenth, fourteenth, and fifteenth SHB workshops. Follow those links to find summaries, papers, and occasionally audio/video recordings of the sessions. Ross also maintains a good webpage of psychology and security resources.

It’s actually hard to believe that the workshop has been going on for this long, and that it’s still vibrant. We rotate between organizers, so next year is my turn in Cambridge (the Massachusetts one).

On the Need for an AI Public Option

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/06/on-the-need-for-an-ai-public-option.html

Artificial intelligence will bring great benefits to all of humanity. But do we really want to entrust this revolutionary technology solely to a small group of US tech companies?

Silicon Valley has produced no small number of moral disappointments. Google retired its “don’t be evil” pledge before firing its star ethicist. Self-proclaimed “free speech absolutist” Elon Musk bought Twitter in order to censor political speech, retaliate against journalists, and ease access to the platform for Russian and Chinese propagandists. Facebook lied about how it enabled Russian interference in the 2016 US presidential election and paid a public relations firm to blame Google and George Soros instead.

These and countless other ethical lapses should prompt us to consider whether we want to give technology companies further abilities to learn our personal details and influence our day-to-day decisions. Tech companies can already access our daily whereabouts and search queries. Digital devices monitor more and more aspects of our lives: We have cameras in our homes and heartbeat sensors on our wrists sending what they detect to Silicon Valley.

Now, tech giants are developing ever more powerful AI systems that don’t merely monitor you; they actually interact with you—and with others on your behalf. If searching on Google in the 2010s was like being watched on a security camera, then using AI in the late 2020s will be like having a butler. You will willingly include them in every conversation you have, everything you write, every item you shop for, every want, every fear, everything. It will never forget. And, despite your reliance on it, it will be surreptitiously working to further the interests of one of these for-profit corporations.

There’s a reason Google, Microsoft, Facebook, and other large tech companies are leading the AI revolution: Building a competitive large language model (LLM) like the one powering ChatGPT is incredibly expensive. It requires upward of $100 million in computational costs for a single model training run, in addition to access to large amounts of data. It also requires technical expertise, which, while increasingly open and available, remains heavily concentrated in a small handful of companies. Efforts to disrupt the AI oligopoly by funding start-ups are self-defeating as Big Tech profits from the cloud computing services and AI models powering those start-ups—and often ends up acquiring the start-ups themselves.

Yet corporations aren’t the only entities large enough to absorb the cost of large-scale model training. Governments can do it, too. It’s time to start taking AI development out of the exclusive hands of private companies and bringing it into the public sector. The United States needs a government-funded-and-directed AI program to develop widely reusable models in the public interest, guided by technical expertise housed in federal agencies.

So far, the AI regulation debate in Washington has focused on the governance of private-sector activity—which the US Congress is in no hurry to advance. Congress should not only hurry up and push AI regulation forward but also go one step further and develop its own programs for AI. Legislators should reframe the AI debate from one about public regulation to one about public development.

The AI development program could be responsive to public input and subject to political oversight. It could be directed to respond to critical issues such as privacy protection, underpaid tech workers, AI’s horrendous carbon emissions, and the exploitation of unlicensed data. Compared to keeping AI in the hands of morally dubious tech companies, the public alternative is better both ethically and economically. And the switch should take place soon: By the time AI becomes critical infrastructure, essential to large swaths of economic activity and daily life, it will be too late to get started.

Other countries are already there. China has heavily prioritized public investment in AI research and development by betting on a handpicked set of giant companies that are ostensibly private but widely understood to be an extension of the state. The government has tasked Alibaba, Huawei, and others with creating products that support the larger ecosystem of state surveillance and authoritarianism.

The European Union is also aggressively pushing AI development. The European Commission already invests 1 billion euros per year in AI, with a plan to increase that figure to 20 billion euros annually by 2030. The money goes to a continent-wide network of public research labs, universities, and private companies jointly working on various parts of AI. The Europeans’ focus is on knowledge transfer, developing the technology sector, use of AI in public administration, mitigating safety risks, and preserving fundamental rights. The EU also continues to be at the cutting edge of aggressively regulating both data and AI.

Neither the Chinese nor the European model is necessarily right for the United States. State control of private enterprise remains anathema in American political culture and would struggle to gain mainstream traction. The tech companies—and their supporters in both US political parties—are opposed to robust public governance of AI. But Washington can take inspiration from China and Europe’;s long-range planning and leadership on regulation and public investment. With boosters pointing to hundreds of trillions of dollars of global economic value associated with AI, the stakes of international competition are compelling. As in energy and medical research, which have their own federal agencies in the Department of Energy and the National Institutes of Health, respectively, there is a place for AI research and development inside government.

Beside the moral argument against letting private companies develop AI, there’s a strong economic argument in favor of a public option as well. A publicly funded LLM could serve as an open platform for innovation, helping any small business, nonprofit, or individual entrepreneur to build AI-assisted applications.

There’s also a practical argument. Building AI is within public reach because governments don’t need to own and operate the entire AI supply chain. Chip and computer production, cloud data centers, and various value-added applications—such as those that integrate AI with consumer electronics devices or entertainment software—do not need to be publicly controlled or funded.

One reason to be skeptical of public funding for AI is that it might result in a lower quality and slower innovation, given greater ethical scrutiny, political constraints, and fewer incentives due to a lack of market competition. But even if that is the case, it would be worth broader access to the most important technology of the 21st century. And it is by no means certain that public AI has to be at a disadvantage. The open-source community is proof that it’s not always private companies that are the most innovative.

Those who worry about the quality trade-off might suggest a public buyer model, whereby Washington licenses or buys private language models from Big Tech instead of developing them itself. But that doesn’t go far enough to ensure that the tools are aligned with public priorities and responsive to public needs. It would not give the public detailed insight into or control of the inner workings and training procedures for these models, and it would still require strict and complex regulation.

There is political will to take action to develop AI via public, rather than private, funds—but this does not yet equate to the will to create a fully public AI development agency. A task force created by Congress recommended in January a $2.6 billion federal investment in computing and data resources to prime the AI research ecosystem in the United States. But this investment would largely serve to advance the interests of Big Tech, leaving the opportunity for public ownership and oversight unaddressed.

Nonprofit and academic organizations have already created open-access LLMs. While these should be celebrated, they are not a substitute for a public option. Nonprofit projects are still beholden to private interests, even if they are benevolent ones. These private interests can change without public input, as when OpenAI effectively abandoned its nonprofit origins, and we can’t be sure that their founding intentions or operations will survive market pressures, fickle donors, and changes in leadership.

The US government is by no means a perfect beacon of transparency, a secure and responsible store of our data, or a genuine reflection of the public’s interests. But the risks of placing AI development entirely in the hands of demonstrably untrustworthy Silicon Valley companies are too high. AI will impact the public like few other technologies, so it should also be developed by the public.

This essay was written with Nathan Sanders, and appeared in Foreign Policy.

Identifying the Idaho Killer

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/06/identifying-the-idaho-killer.html

The New York Times has a long article on the investigative techniques used to identify the person who stabbed and killed four University of Idaho students.

Pay attention to the techniques:

The case has shown the degree to which law enforcement investigators have come to rely on the digital footprints that ordinary Americans leave in nearly every facet of their lives. Online shopping, car sales, carrying a cellphone, drives along city streets and amateur genealogy all played roles in an investigation that was solved, in the end, as much through technology as traditional sleuthing.

[…]

At that point, investigators decided to try genetic genealogy, a method that until now has been used primarily to solve cold cases, not active murder investigations. Among the growing number of genealogy websites that help people trace their ancestors and relatives via their own DNA, some allow users to select an option that permits law enforcement to compare crime scene DNA samples against the websites’ data.

A distant cousin who has opted into the system can help investigators building a family tree from crime scene DNA to triangulate and identify a potential perpetrator of a crime.

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On Dec. 23, investigators sought and received Mr. Kohberger’s cellphone records. The results added more to their suspicions: His phone was moving around in the early morning hours of Nov. 13, but was disconnected from cell networks ­- perhaps turned off—in the two hours around when the killings occurred.