Entries Tagged "artificial intelligence"

Page 1 of 8

Detecting AI-Generated Text

There are no reliable ways to distinguish text written by a human from text written by an large language model. OpenAI writes:

Do AI detectors work?

  • In short, no. While some (including OpenAI) have released tools that purport to detect AI-generated content, none of these have proven to reliably distinguish between AI-generated and human-generated content.
  • Additionally, ChatGPT has no “knowledge” of what content could be AI-generated. It will sometimes make up responses to questions like “did you write this [essay]?” or “could this have been written by AI?” These responses are random and have no basis in fact.
  • To elaborate on our research into the shortcomings of detectors, one of our key findings was that these tools sometimes suggest that human-written content was generated by AI.
    • When we at OpenAI tried to train an AI-generated content detector, we found that it labeled human-written text like Shakespeare and the Declaration of Independence as AI-generated.
    • There were also indications that it could disproportionately impact students who had learned or were learning English as a second language and students whose writing was particularly formulaic or concise.
  • Even if these tools could accurately identify AI-generated content (which they cannot yet), students can make small edits to evade detection.

There is some good research in watermarking LLM-generated text, but the watermarks are not generally robust.

I don’t think the detectors are going to win this arms race.

Posted on September 19, 2023 at 7:08 AM • View Comments

LLM Summary of My Book Beyond Fear

Claude (Anthropic’s LLM) was given this prompt: Please summarize the themes and arguments of Bruce Schneier’s book Beyond Fear. I’m particularly interested in a taxonomy of his ethical arguments—please expand on that. Then lay out the most salient criticisms of the book.

Claude’s reply:

Here’s a brief summary of the key themes and arguments made in security expert Bruce Schneier’s book Beyond Fear: Thinking Sensibly About Security in an Uncertain World:

Central Themes:

  • Effective security requires realistic risk assessment, not fear-driven responses. We need to rationally determine actual threats versus perceived ones.
  • Security trade-offs need to be carefully balanced against costs to other values like privacy, freedom, and social/economic impacts.
  • Human psychology and biases lead to exaggerated fears that politicians/marketers can exploit, resulting in wasteful and theatrical “security theater.”
  • Security is a process that requires ongoing flexibility, not total prevention. We can’t anticipate every threat but can build resilience.

Key Arguments:

    Analyze risks empirically using evidence, statistics, and probability rather than intuition. Avoid underestimating everyday risks while overestimating novel or sensational ones.

  • Reject the temptation to implement oppressive security restrictions after tragic but unlikely events. Focus only on proportional responses.
  • Where possible, favor openness and transparency over aggressive data collection or restrictions which erode civil liberties.
  • Seek security policies and technologies that enhance society broadly rather than benefit special interests.

Schneier argues for rational, ethical approaches to security aimed at genuine protection while avoiding violations of human rights in the name of safety.

Here are some of the key ethical arguments Bruce Schneier makes in Beyond Fear regarding security policy and practices:

  • Utilitarian Ethics – Security measures should aim to provide the greatest societal benefit and well-being for the most people, not just special interests.
  • Individual Autonomy – Restrictions should be minimized to avoid excessive infringements on civil liberties and individual freedoms. Surveillance creep risks violating autonomy.
  • Privacy Rights – Pervasive monitoring and data collection erode privacy rights and dignity. Citizens are entitled to some degree of opacity.
  • Proportional Responses – Reactive policies should weigh costs versus statistically likely benefits. Draconian rules beyond proportionality are unethical.
  • Justice and Fairness – Security should not create racial, religious or other profiling biases that punish groups rather than conduct.
  • Accountability and Transparency – Ethical security requires checks against excessive power and oversight for responsibility. Secrecy enables abuses.
  • Honest Representation – Officials and pundits have an ethical duty not to stoke disproportionate public fears. Security theater manufactures consent.
  • Integrity and Character – Leaders should have the wisdom and courage to make difficult trade-offs rather than exploit fears for authority and control.

Overall Schneier argues security policies grounded in ethics rather than fear promote human well-being while recognizing inherent trade-offs. Prudence, transparency, and proportionality are essential.

Here are some of the most salient criticisms made against the arguments put forth in Bruce Schneier’s book Beyond Fear:

  • Downplays real threats – Some argue Schneier dangerously downplays serious risks like terrorism and cybercrime by calling fears overblown.
  • Too theoretical – His risk analysis approach may rely too much on statistics versus real-world uncertainties that defy probabilistic reasoning.
  • No easy answers – While citing misguided policies, critics say Schneier provides few actionable solutions or concrete policy guidance.
  • Conflicting values – His balancing of security, privacy and liberty involves inherent trade-offs and tough choices he gives little direction on.
  • Politically naive – Suggesting special interests drive policy may under-appreciate legitimate national security concerns of elected officials.
  • Hindsight bias – Assessing past overreactions easier than predicting future threats that justify caution today.
  • No accounting for psychology – Factoring human irrationality into policy is essential, not dismissed as “security theater.”
  • Technical focus – Focuses narrowly on certain technologies while ignoring broader organizational and sociological dimensions.

In summary, critics argue Schneier’s risk analysis orientation is too clinical, abstract, and devoid of political and human realities to transform concrete policy in the ways he intends. His framework may therefore lack sufficient nuance or guidance.

It’s interesting. The summary is pretty accurate, and so are the criticisms. The book was a product of my thinking twenty years ago, and my own thinking has evolved to address those criticisms.

Of course, this only works with older books that the LLM has ingested, and probably works better with books that have been written about.

Posted on September 15, 2023 at 3:12 PM • View Comments

On Robots Killing People

The robot revolution began long ago, and so did the killing. One day in 1979, a robot at a Ford Motor Company casting plant malfunctioned—human workers determined that it was not going fast enough. And so twenty-five-year-old Robert Williams was asked to climb into a storage rack to help move things along. The one-ton robot continued to work silently, smashing into Williams’s head and instantly killing him. This was reportedly the first incident in which a robot killed a human; many more would follow.

At Kawasaki Heavy Industries in 1981, Kenji Urada died in similar circumstances. A malfunctioning robot he went to inspect killed him when he obstructed its path, according to Gabriel Hallevy in his 2013 book, When Robots Kill: Artificial Intelligence Under Criminal Law. As Hallevy puts it, the robot simply determined that “the most efficient way to eliminate the threat was to push the worker into an adjacent machine.” From 1992 to 2017, workplace robots were responsible for 41 recorded deaths in the United States—and that’s likely an underestimate, especially when you consider knock-on effects from automation, such as job loss. A robotic anti-aircraft cannon killed nine South African soldiers in 2007 when a possible software failure led the machine to swing itself wildly and fire dozens of lethal rounds in less than a second. In a 2018 trial, a medical robot was implicated in killing Stephen Pettitt during a routine operation that had occurred a few years earlier.

You get the picture. Robots—”intelligent” and not—have been killing people for decades. And the development of more advanced artificial intelligence has only increased the potential for machines to cause harm. Self-driving cars are already on American streets, and robotic "dogs" are being used by law enforcement. Computerized systems are being given the capabilities to use tools, allowing them to directly affect the physical world. Why worry about the theoretical emergence of an all-powerful, superintelligent program when more immediate problems are at our doorstep? Regulation must push companies toward safe innovation and innovation in safety. We are not there yet.

Historically, major disasters have needed to occur to spur regulation—the types of disasters we would ideally foresee and avoid in today’s AI paradigm. The 1905 Grover Shoe Factory disaster led to regulations governing the safe operation of steam boilers. At the time, companies claimed that large steam-automation machines were too complex to rush safety regulations. This, of course, led to overlooked safety flaws and escalating disasters. It wasn’t until the American Society of Mechanical Engineers demanded risk analysis and transparency that dangers from these huge tanks of boiling water, once considered mystifying, were made easily understandable. The 1911 Triangle Shirtwaist Factory fire led to regulations on sprinkler systems and emergency exits. And the preventable 1912 sinking of the Titanic resulted in new regulations on lifeboats, safety audits, and on-ship radios.

Perhaps the best analogy is the evolution of the Federal Aviation Administration. Fatalities in the first decades of aviation forced regulation, which required new developments in both law and technology. Starting with the Air Commerce Act of 1926, Congress recognized that the integration of aerospace tech into people’s lives and our economy demanded the highest scrutiny. Today, every airline crash is closely examined, motivating new technologies and procedures.

Any regulation of industrial robots stems from existing industrial regulation, which has been evolving for many decades. The Occupational Safety and Health Act of 1970 established safety standards for machinery, and the Robotic Industries Association, now merged into the Association for Advancing Automation, has been instrumental in developing and updating specific robot-safety standards since its founding in 1974. Those standards, with obscure names such as R15.06 and ISO 10218, emphasize inherent safe design, protective measures, and rigorous risk assessments for industrial robots.

But as technology continues to change, the government needs to more clearly regulate how and when robots can be used in society. Laws need to clarify who is responsible, and what the legal consequences are, when a robot’s actions result in harm. Yes, accidents happen. But the lessons of aviation and workplace safety demonstrate that accidents are preventable when they are openly discussed and subjected to proper expert scrutiny.

AI and robotics companies don’t want this to happen. OpenAI, for example, has reportedly fought to “water down” safety regulations and reduce AI-quality requirements. According to an article in Time, it lobbied European Union officials against classifying models like ChatGPT as “high risk” which would have brought “stringent legal requirements including transparency, traceability, and human oversight.” The reasoning was supposedly that OpenAI did not intend to put its products to high-risk use—a logical twist akin to the Titanic owners lobbying that the ship should not be inspected for lifeboats on the principle that it was a “general purpose” vessel that also could sail in warm waters where there were no icebergs and people could float for days. (OpenAI did not comment when asked about its stance on regulation; previously, it has said that “achieving our mission requires that we work to mitigate both current and longer-term risks,” and that it is working toward that goal by “collaborating with policymakers, researchers and users.”)

Large corporations have a tendency to develop computer technologies to self-servingly shift the burdens of their own shortcomings onto society at large, or to claim that safety regulations protecting society impose an unjust cost on corporations themselves, or that security baselines stifle innovation. We’ve heard it all before, and we should be extremely skeptical of such claims. Today’s AI-related robot deaths are no different from the robot accidents of the past. Those industrial robots malfunctioned, and human operators trying to assist were killed in unexpected ways. Since the first-known death resulting from the feature in January 2016, Tesla’s Autopilot has been implicated in more than 40 deaths according to official report estimates. Malfunctioning Teslas on Autopilot have deviated from their advertised capabilities by misreading road markings, suddenly veering into other cars or trees, crashing into well-marked service vehicles, or ignoring red lights, stop signs, and crosswalks. We’re concerned that AI-controlled robots already are moving beyond accidental killing in the name of efficiency and “deciding” to kill someone in order to achieve opaque and remotely controlled objectives.

As we move into a future where robots are becoming integral to our lives, we can’t forget that safety is a crucial part of innovation. True technological progress comes from applying comprehensive safety standards across technologies, even in the realm of the most futuristic and captivating robotic visions. By learning lessons from past fatalities, we can enhance safety protocols, rectify design flaws, and prevent further unnecessary loss of life.

For example, the UK government already sets out statements that safety matters. Lawmakers must reach further back in history to become more future-focused on what we must demand right now: modeling threats, calculating potential scenarios, enabling technical blueprints, and ensuring responsible engineering for building within parameters that protect society at large. Decades of experience have given us the empirical evidence to guide our actions toward a safer future with robots. Now we need the political will to regulate.

This essay was written with Davi Ottenheimer, and previously appeared on Atlantic.com.

Posted on September 11, 2023 at 7:04 AM • View Comments

LLMs and Tool Use

Last March, just two weeks after GPT-4 was released, researchers at Microsoft quietly announced a plan to compile millions of APIs—tools that can do everything from ordering a pizza to solving physics equations to controlling the TV in your living room—into a compendium that would be made accessible to large language models (LLMs). This was just one milestone in the race across industry and academia to find the best ways to teach LLMs how to manipulate tools, which would supercharge the potential of AI more than any of the impressive advancements we’ve seen to date.

The Microsoft project aims to teach AI how to use any and all digital tools in one fell swoop, a clever and efficient approach. Today, LLMs can do a pretty good job of recommending pizza toppings to you if you describe your dietary preferences and can draft dialog that you could use when you call the restaurant. But most AI tools can’t place the order, not even online. In contrast, Google’s seven-year-old Assistant tool can synthesize a voice on the telephone and fill out an online order form, but it can’t pick a restaurant or guess your order. By combining these capabilities, though, a tool-using AI could do it all. An LLM with access to your past conversations and tools like calorie calculators, a restaurant menu database, and your digital payment wallet could feasibly judge that you are trying to lose weight and want a low-calorie option, find the nearest restaurant with toppings you like, and place the delivery order. If it has access to your payment history, it could even guess at how generously you usually tip. If it has access to the sensors on your smartwatch or fitness tracker, it might be able to sense when your blood sugar is low and order the pie before you even realize you’re hungry.

Perhaps the most compelling potential applications of tool use are those that give AIs the ability to improve themselves. Suppose, for example, you asked a chatbot for help interpreting some facet of ancient Roman law that no one had thought to include examples of in the model’s original training. An LLM empowered to search academic databases and trigger its own training process could fine-tune its understanding of Roman law before answering. Access to specialized tools could even help a model like this better explain itself. While LLMs like GPT-4 already do a fairly good job of explaining their reasoning when asked, these explanations emerge from a “black box” and are vulnerable to errors and hallucinations. But a tool-using LLM could dissect its own internals, offering empirical assessments of its own reasoning and deterministic explanations of why it produced the answer it did.

If given access to tools for soliciting human feedback, a tool-using LLM could even generate specialized knowledge that isn’t yet captured on the web. It could post a question to Reddit or Quora or delegate a task to a human on Amazon’s Mechanical Turk. It could even seek out data about human preferences by doing survey research, either to provide an answer directly to you or to fine-tune its own training to be able to better answer questions in the future. Over time, tool-using AIs might start to look a lot like tool-using humans. An LLM can generate code much faster than any human programmer, so it can manipulate the systems and services of your computer with ease. It could also use your computer’s keyboard and cursor the way a person would, allowing it to use any program you do. And it could improve its own capabilities, using tools to ask questions, conduct research, and write code to incorporate into itself.

It’s easy to see how this kind of tool use comes with tremendous risks. Imagine an LLM being able to find someone’s phone number, call them and surreptitiously record their voice, guess what bank they use based on the largest providers in their area, impersonate them on a phone call with customer service to reset their password, and liquidate their account to make a donation to a political party. Each of these tasks invokes a simple tool—an Internet search, a voice synthesizer, a bank app—and the LLM scripts the sequence of actions using the tools.

We don’t yet know how successful any of these attempts will be. As remarkably fluent as LLMs are, they weren’t built specifically for the purpose of operating tools, and it remains to be seen how their early successes in tool use will translate to future use cases like the ones described here. As such, giving the current generative AI sudden access to millions of APIs—as Microsoft plans to—could be a little like letting a toddler loose in a weapons depot.

Companies like Microsoft should be particularly careful about granting AIs access to certain combinations of tools. Access to tools to look up information, make specialized calculations, and examine real-world sensors all carry a modicum of risk. The ability to transmit messages beyond the immediate user of the tool or to use APIs that manipulate physical objects like locks or machines carries much larger risks. Combining these categories of tools amplifies the risks of each.

The operators of the most advanced LLMs, such as OpenAI, should continue to proceed cautiously as they begin enabling tool use and should restrict uses of their products in sensitive domains such as politics, health care, banking, and defense. But it seems clear that these industry leaders have already largely lost their moat around LLM technology—open source is catching up. Recognizing this trend, Meta has taken an “If you can’t beat ’em, join ’em” approach and partially embraced the role of providing open source LLM platforms.

On the policy front, national—and regional—AI prescriptions seem futile. Europe is the only significant jurisdiction that has made meaningful progress on regulating the responsible use of AI, but it’s not entirely clear how regulators will enforce it. And the US is playing catch-up and seems destined to be much more permissive in allowing even risks deemed “unacceptable” by the EU. Meanwhile, no government has invested in a “public option” AI model that would offer an alternative to Big Tech that is more responsive and accountable to its citizens.

Regulators should consider what AIs are allowed to do autonomously, like whether they can be assigned property ownership or register a business. Perhaps more sensitive transactions should require a verified human in the loop, even at the cost of some added friction. Our legal system may be imperfect, but we largely know how to hold humans accountable for misdeeds; the trick is not to let them shunt their responsibilities to artificial third parties. We should continue pursuing AI-specific regulatory solutions while also recognizing that they are not sufficient on their own.

We must also prepare for the benign ways that tool-using AI might impact society. In the best-case scenario, such an LLM may rapidly accelerate a field like drug discovery, and the patent office and FDA should prepare for a dramatic increase in the number of legitimate drug candidates. We should reshape how we interact with our governments to take advantage of AI tools that give us all dramatically more potential to have our voices heard. And we should make sure that the economic benefits of superintelligent, labor-saving AI are equitably distributed.

We can debate whether LLMs are truly intelligent or conscious, or have agency, but AIs will become increasingly capable tool users either way. Some things are greater than the sum of their parts. An AI with the ability to manipulate and interact with even simple tools will become vastly more powerful than the tools themselves. Let’s be sure we’re ready for them.

This essay was written with Nathan Sanders, and previously appeared on Wired.com.

Posted on September 8, 2023 at 7:05 AM • View Comments

December’s Reimagining Democracy Workshop

Imagine that we’ve all—all of us, all of society—landed on some alien planet, and we have to form a government: clean slate. We don’t have any legacy systems from the US or any other country. We don’t have any special or unique interests to perturb our thinking.

How would we govern ourselves?

It’s unlikely that we would use the systems we have today. The modern representative democracy was the best form of government that mid-eighteenth-century technology could conceive of. The twenty-first century is a different place scientifically, technically and socially.

For example, the mid-eighteenth-century democracies were designed under the assumption that both travel and communications were hard. Does it still make sense for all of us living in the same place to organize every few years and choose one of us to go to a big room far away and create laws in our name?

Representative districts are organized around geography, because that’s the only way that made sense 200-plus years ago. But we don’t have to do it that way. We can organize representation by age: one representative for the thirty-one-year-olds, another for the thirty-two-year-olds, and so on. We can organize representation randomly: by birthday, perhaps. We can organize any way we want.

US citizens currently elect people for terms ranging from two to six years. Is ten years better? Is ten days better? Again, we have more technology and therefor more options.

Indeed, as a technologist who studies complex systems and their security, I believe the very idea of representative government is a hack to get around the technological limitations of the past. Voting at scale is easier now than it was 200 year ago. Certainly we don’t want to all have to vote on every amendment to every bill, but what’s the optimal balance between votes made in our name and ballot measures that we all vote on?

In December 2022, I organized a workshop to discuss these and other questions. I brought together fifty people from around the world: political scientists, economists, law professors, AI experts, activists, government officials, historians, science fiction writers and more. We spent two days talking about these ideas. Several themes emerged from the event.

Misinformation and propaganda were themes, of course—and the inability to engage in rational policy discussions when people can’t agree on the facts.

Another theme was the harms of creating a political system whose primary goals are economic. Given the ability to start over, would anyone create a system of government that optimizes the near-term financial interest of the wealthiest few? Or whose laws benefit corporations at the expense of people?

Another theme was capitalism, and how it is or isn’t intertwined with democracy. And while the modern market economy made a lot of sense in the industrial age, it’s starting to fray in the information age. What comes after capitalism, and how does it affect how we govern ourselves?

Many participants examined the effects of technology, especially artificial intelligence. We looked at whether—and when—we might be comfortable ceding power to an AI. Sometimes it’s easy. I’m happy for an AI to figure out the optimal timing of traffic lights to ensure the smoothest flow of cars through the city. When will we be able to say the same thing about setting interest rates? Or designing tax policies?

How would we feel about an AI device in our pocket that voted in our name, thousands of times per day, based on preferences that it inferred from our actions? If an AI system could determine optimal policy solutions that balanced every voter’s preferences, would it still make sense to have representatives? Maybe we should vote directly for ideas and goals instead, and leave the details to the computers. On the other hand, technological solutionism regularly fails.

Scale was another theme. The size of modern governments reflects the technology at the time of their founding. European countries and the early American states are a particular size because that’s what was governable in the 18th and 19th centuries. Larger governments—the US as a whole, the European Union—reflect a world in which travel and communications are easier. The problems we have today are primarily either local, at the scale of cities and towns, or global—even if they are currently regulated at state, regional or national levels. This mismatch is especially acute when we try to tackle global problems. In the future, do we really have a need for political units the size of France or Virginia? Or is it a mixture of scales that we really need, one that moves effectively between the local and the global?

As to other forms of democracy, we discussed one from history and another made possible by today’s technology.

Sortition is a system of choosing political officials randomly to deliberate on a particular issue. We use it today when we pick juries, but both the ancient Greeks and some cities in Renaissance Italy used it to select major political officials. Today, several countries—largely in Europe—are using sortition for some policy decisions. We might randomly choose a few hundred people, representative of the population, to spend a few weeks being briefed by experts and debating the problem—and then decide on environmental regulations, or a budget, or pretty much anything.

Liquid democracy does away with elections altogether. Everyone has a vote, and they can keep the power to cast it themselves or assign it to another person as a proxy. There are no set elections; anyone can reassign their proxy at any time. And there’s no reason to make this assignment all or nothing. Perhaps proxies could specialize: one set of people focused on economic issues, another group on health and a third bunch on national defense. Then regular people could assign their votes to whichever of the proxies most closely matched their views on each individual matter—or step forward with their own views and begin collecting proxy support from other people.

This all brings up another question: Who gets to participate? And, more generally, whose interests are taken into account? Early democracies were really nothing of the sort: They limited participation by gender, race and land ownership.

We should debate lowering the voting age, but even without voting we recognize that children too young to vote have rights—and, in some cases, so do other species. Should future generations get a “voice,” whatever that means? What about nonhumans or whole ecosystems?

Should everyone get the same voice? Right now in the US, the outsize effect of money in politics gives the wealthy disproportionate influence. Should we encode that explicitly? Maybe younger people should get a more powerful vote than everyone else. Or maybe older people should.

Those questions lead to ones about the limits of democracy. All democracies have boundaries limiting what the majority can decide. We all have rights: the things that cannot be taken away from us. We cannot vote to put someone in jail, for example.

But while we can’t vote a particular publication out of existence, we can to some degree regulate speech. In this hypothetical community, what are our rights as individuals? What are the rights of society that supersede those of individuals?

Personally, I was most interested in how these systems fail. As a security technologist, I study how complex systems are subverted—hacked, in my parlance—for the benefit of a few at the expense of the many. Think tax loopholes, or tricks to avoid government regulation. I want any government system to be resilient in the face of that kind of trickery.

Or, to put it another way, I want the interests of each individual to align with the interests of the group at every level. We’ve never had a system of government with that property before—even equal protection guarantees and First Amendment rights exist in a competitive framework that puts individuals’ interests in opposition to one another. But—in the age of such existential risks as climate and biotechnology and maybe AI—aligning interests is more important than ever.

Our workshop didn’t produce any answers; that wasn’t the point. Our current discourse is filled with suggestions on how to patch our political system. People regularly debate changes to the Electoral College, or the process of creating voting districts, or term limits. But those are incremental changes.

It’s hard to find people who are thinking more radically: looking beyond the horizon for what’s possible eventually. And while true innovation in politics is a lot harder than innovation in technology, especially without a violent revolution forcing change, it’s something that we as a species are going to have to get good at—one way or another.

This essay previously appeared in The Conversation.

Posted on August 23, 2023 at 7:06 AM • View Comments

Applying AI to License Plate Surveillance

License plate scanners aren’t new. Neither is using them for bulk surveillance. What’s new is that AI is being used on the data, identifying “suspicious” vehicle behavior:

Typically, Automatic License Plate Recognition (ALPR) technology is used to search for plates linked to specific crimes. But in this case it was used to examine the driving patterns of anyone passing one of Westchester County’s 480 cameras over a two-year period. Zayas’ lawyer Ben Gold contested the AI-gathered evidence against his client, decrying it as “dragnet surveillance.”

And he had the data to back it up. A FOIA he filed with the Westchester police revealed that the ALPR system was scanning over 16 million license plates a week, across 480 ALPR cameras. Of those systems, 434 were stationary, attached to poles and signs, while the remaining 46 were mobile, attached to police vehicles. The AI was not just looking at license plates either. It had also been taking notes on vehicles’ make, model and color—useful when a plate number for a suspect vehicle isn’t visible or is unknown.

Posted on August 22, 2023 at 7:04 AM • View Comments

White House Announces AI Cybersecurity Challenge

At Black Hat last week, the White House announced an AI Cyber Challenge. Gizmodo reports:

The new AI cyber challenge (which is being abbreviated “AIxCC”) will have a number of different phases. Interested would-be competitors can now submit their proposals to the Small Business Innovation Research program for evaluation and, eventually, selected teams will participate in a 2024 “qualifying event.” During that event, the top 20 teams will be invited to a semifinal competition at that year’s DEF CON, another large cybersecurity conference, where the field will be further whittled down.

[…]

To secure the top spot in DARPA’s new competition, participants will have to develop security solutions that do some seriously novel stuff. “To win first-place, and a top prize of $4 million, finalists must build a system that can rapidly defend critical infrastructure code from attack,” said Perri Adams, program manager for DARPA’s Information Innovation Office, during a Zoom call with reporters Tuesday. In other words: the government wants software that is capable of identifying and mitigating risks by itself.

This is a great idea. I was a big fan of DARPA’s AI capture-the-flag event in 2016, and am happy to see that DARPA is again inciting research in this area. (China has been doing this every year since 2017.)

Posted on August 21, 2023 at 7:10 AM • View Comments

Zoom Can Spy on Your Calls and Use the Conversation to Train AI, But Says That It Won’t

This is why we need regulation:

Zoom updated its Terms of Service in March, spelling out that the company reserves the right to train AI on user data with no mention of a way to opt out. On Monday, the company said in a blog post that there’s no need to worry about that. Zoom execs swear the company won’t actually train its AI on your video calls without permission, even though the Terms of Service still say it can.

Of course, these are Terms of Service. They can change at any time. Zoom can renege on its promise at any time. There are no rules, only the whims of the company as it tries to maximize its profits.

It’s a stupid way to run a technological revolution. We should not have to rely on the benevolence of for-profit corporations to protect our rights. It’s not their job, and it shouldn’t be.

Posted on August 15, 2023 at 7:03 AM • View Comments

Political Milestones for AI

ChatGPT was released just nine months ago, and we are still learning how it will affect our daily lives, our careers, and even our systems of self-governance.

But when it comes to how AI may threaten our democracy, much of the public conversation lacks imagination. People talk about the danger of campaigns that attack opponents with fake images (or fake audio or video) because we already have decades of experience dealing with doctored images. We’re on the lookout for foreign governments that spread misinformation because we were traumatized by the 2016 US presidential election. And we worry that AI-generated opinions will swamp the political preferences of real people because we’ve seen political “astroturfing”—the use of fake online accounts to give the illusion of support for a policy—grow for decades.

Threats of this sort seem urgent and disturbing because they’re salient. We know what to look for, and we can easily imagine their effects.

The truth is, the future will be much more interesting. And even some of the most stupendous potential impacts of AI on politics won’t be all bad. We can draw some fairly straight lines between the current capabilities of AI tools and real-world outcomes that, by the standards of current public understanding, seem truly startling.

With this in mind, we propose six milestones that will herald a new era of democratic politics driven by AI. All feel achievable—perhaps not with today’s technology and levels of AI adoption, but very possibly in the near future.

Good benchmarks should be meaningful, representing significant outcomes that come with real-world consequences. They should be plausible; they must be realistically achievable in the foreseeable future. And they should be observable—we should be able to recognize when they’ve been achieved.

Worries about AI swaying an election will very likely fail the observability test. While the risks of election manipulation through the robotic promotion of a candidate’s or party’s interests is a legitimate threat, elections are massively complex. Just as the debate continues to rage over why and how Donald Trump won the presidency in 2016, we’re unlikely to be able to attribute a surprising electoral outcome to any particular AI intervention.

Thinking further into the future: Could an AI candidate ever be elected to office? In the world of speculative fiction, from The Twilight Zone to Black Mirror, there is growing interest in the possibility of an AI or technologically assisted, otherwise-not-traditionally-eligible candidate winning an election. In an era where deepfaked videos can misrepresent the views and actions of human candidates and human politicians can choose to be represented by AI avatars or even robots, it is certainly possible for an AI candidate to mimic the media presence of a politician. Virtual politicians have received votes in national elections, for example in Russia in 2017. But this doesn’t pass the plausibility test. The voting public and legal establishment are likely to accept more and more automation and assistance supported by AI, but the age of non-human elected officials is far off.

Let’s start with some milestones that are already on the cusp of reality. These are achievements that seem well within the technical scope of existing AI technologies and for which the groundwork has already been laid.

Milestone #1: The acceptance by a legislature or agency of a testimony or comment generated by, and submitted under the name of, an AI.

Arguably, we’ve already seen legislation drafted by AI, albeit under the direction of human users and introduced by human legislators. After some early examples of bills written by AIs were introduced in Massachusetts and the US House of Representatives, many major legislative bodies have had their “first bill written by AI,” “used ChatGPT to generate committee remarks,” or “first floor speech written by AI” events.

Many of these bills and speeches are more stunt than serious, and they have received more criticism than consideration. They are short, have trivial levels of policy substance, or were heavily edited or guided by human legislators (through highly specific prompts to large language model-based AI tools like ChatGPT).

The interesting milestone along these lines will be the acceptance of testimony on legislation, or a comment submitted to an agency, drafted entirely by AI. To be sure, a large fraction of all writing going forward will be assisted by—and will truly benefit from—AI assistive technologies. So to avoid making this milestone trivial, we have to add the second clause: “submitted under the name of the AI.”

What would make this benchmark significant is the submission under the AI’s own name; that is, the acceptance by a governing body of the AI as proffering a legitimate perspective in public debate. Regardless of the public fervor over AI, this one won’t take long. The New York Times has published a letter under the name of ChatGPT (responding to an opinion piece we wrote), and legislators are already turning to AI to write high-profile opening remarks at committee hearings.

Milestone #2: The adoption of the first novel legislative amendment to a bill written by AI.

Moving beyond testimony, there is an immediate pathway for AI-generated policies to become law: microlegislation. This involves making tweaks to existing laws or bills that are tuned to serve some particular interest. It is a natural starting point for AI because it’s tightly scoped, involving small changes guided by a clear directive associated with a well-defined purpose.

By design, microlegislation is often implemented surreptitiously. It may even be filed anonymously within a deluge of other amendments to obscure its intended beneficiary. For that reason, microlegislation can often be bad for society, and it is ripe for exploitation by generative AI that would otherwise be subject to heavy scrutiny from a polity on guard for risks posed by AI.

Milestone #3: AI-generated political messaging outscores campaign consultant recommendations in poll testing.

Some of the most important near-term implications of AI for politics will happen largely behind closed doors. Like everyone else, political campaigners and pollsters will turn to AI to help with their jobs. We’re already seeing campaigners turn to AI-generated images to manufacture social content and pollsters simulate results using AI-generated respondents.

The next step in this evolution is political messaging developed by AI. A mainstay of the campaigner’s toolbox today is the message testing survey, where a few alternate formulations of a position are written down and tested with audiences to see which will generate more attention and a more positive response. Just as an experienced political pollster can anticipate effective messaging strategies pretty well based on observations from past campaigns and their impression of the state of the public debate, so can an AI trained on reams of public discourse, campaign rhetoric, and political reporting.

With these near-term milestones firmly in sight, let’s look further to some truly revolutionary possibilities. While these concepts may have seemed absurd just a year ago, they are increasingly conceivable with either current or near-future technologies.

Milestone #4: AI creates a political party with its own platform, attracting human candidates who win elections.

While an AI is unlikely to be allowed to run for and hold office, it is plausible that one may be able to found a political party. An AI could generate a political platform calculated to attract the interest of some cross-section of the public and, acting independently or through a human intermediary (hired help, like a political consultant or legal firm), could register formally as a political party. It could collect signatures to win a place on ballots and attract human candidates to run for office under its banner.

A big step in this direction has already been taken, via the campaign of the Danish Synthetic Party in 2022. An artist collective in Denmark created an AI chatbot to interact with human members of its community on Discord, exploring political ideology in conversation with them and on the basis of an analysis of historical party platforms in the country. All this happened with earlier generations of general purpose AI, not current systems like ChatGPT. However, the party failed to receive enough signatures to earn a spot on the ballot, and therefore did not win parliamentary representation.

Future AI-led efforts may succeed. One could imagine a generative AI with skills at the level of or beyond today’s leading technologies could formulate a set of policy positions targeted to build support among people of a specific demographic, or even an effective consensus platform capable of attracting broad-based support. Particularly in a European-style multiparty system, we can imagine a new party with a strong news hook—an AI at its core—winning attention and votes.

Milestone #5: AI autonomously generates profit and makes political campaign contributions.

Let’s turn next to the essential capability of modern politics: fundraising. “An entity capable of directing contributions to a campaign fund” might be a realpolitik definition of a political actor, and AI is potentially capable of this.

Like a human, an AI could conceivably generate contributions to a political campaign in a variety of ways. It could take a seed investment from a human controlling the AI and invest it to yield a return. It could start a business that generates revenue. There is growing interest and experimentation in auto-hustling: AI agents that set about autonomously growing businesses or otherwise generating profit. While ChatGPT-generated businesses may not yet have taken the world by storm, this possibility is in the same spirit as the algorithmic agents powering modern high-speed trading and so-called autonomous finance capabilities that are already helping to automate business and financial decisions.

Or, like most political entrepreneurs, AI could generate political messaging to convince humans to spend their own money on a defined campaign or cause. The AI would likely need to have some humans in the loop, and register its activities to the government (in the US context, as officers of a 501(c)(4) or political action committee).

Milestone #6: AI achieves a coordinated policy outcome across multiple jurisdictions.

Lastly, we come to the most meaningful of impacts: achieving outcomes in public policy. Even if AI cannot—now or in the future—be said to have its own desires or preferences, it could be programmed by humans to have a goal, such as lowering taxes or relieving a market regulation.

An AI has many of the same tools humans use to achieve these ends. It may advocate, formulating messaging and promoting ideas through digital channels like social media posts and videos. It may lobby, directing ideas and influence to key policymakers, even writing legislation. It may spend; see milestone #5.

The “multiple jurisdictions” piece is key to this milestone. A single law passed may be reasonably attributed to myriad factors: a charismatic champion, a political movement, a change in circumstances. The influence of any one actor, such as an AI, will be more demonstrable if it is successful simultaneously in many different places. And the digital scalability of AI gives it a special advantage in achieving these kinds of coordinated outcomes.

The greatest challenge to most of these milestones is their observability: will we know it when we see it? The first campaign consultant whose ideas lose out to an AI may not be eager to report that fact. Neither will the campaign. Regarding fundraising, it’s hard enough for us to track down the human actors who are responsible for the “dark money” contributions controlling much of modern political finance; will we know if a future dominant force in fundraising for political action committees is an AI?

We’re likely to observe some of these milestones indirectly. At some point, perhaps politicians’ dollars will start migrating en masse to AI-based campaign consultancies and, eventually, we may realize that political movements sweeping across states or countries have been AI-assisted.

While the progression of technology is often unsettling, we need not fear these milestones. A new political platform that wins public support is itself a neutral proposition; it may lead to good or bad policy outcomes. Likewise, a successful policy program may or may not be beneficial to one group of constituents or another.

We think the six milestones outlined here are among the most viable and meaningful upcoming interactions between AI and democracy, but they are hardly the only scenarios to consider. The point is that our AI-driven political future will involve far more than deepfaked campaign ads and manufactured letter-writing campaigns. We should all be thinking more creatively about what comes next and be vigilant in steering our politics toward the best possible ends, no matter their means.

This essay was written with Nathan Sanders, and previously appeared in MIT Technology Review.

Posted on August 4, 2023 at 7:07 AM • View Comments

1 2 3 … 8

Sidebar photo of Bruce Schneier by Joe MacInnis.