Joyce Poole and Michael Pardo recently published a groundbreaking study on elephant communication: Using a machine learning model, they were able to show strong evidence that African savannah elephants have unique names for one another. The statistical model they used — known as a random forest model — is nothing new or snazzy. It’s been around for 20 years. But it’s one example of how animal communication researchers are using machine learning to decode animal calls they can’t through observation alone.

This video covers some other ways machine learning is solving the limits of human observation in the study of animal communication. And it explains a wild plan for what might be next: deep learning models that could facilitate interspecies communication.

The products and services are different, but the idea the companies behind them are selling is the same: Generative AI is amazing and our generative AI tools are amazing, so we’re going to embed them in as many of our services as possible to make your life amazing. Or, as Colette Stallbaumer, general manager of Microsoft 365 said at the company’s Thursday event: “Soon, you won’t be able to imagine your life without it.”

You just have to imagine your life with it first because it’s not here yet. And then you have to wonder if people will really use these tools when they do roll out. This isn’t the first time tech companies have gone big on intelligent assistants, only for the public to either hate them or be largely indifferent to them. We can go back to the late ’90s with Clippy, Microsoft’s notoriously much-loathed Office assistant. More recently, we’ve gotten smart assistants like Alexa, Apple’s Siri, Google’s Google Assistant, and Microsoft’s Cortana. It’s safe to say that those haven’t gotten the kind of adoption their makers hoped for, both in how many people are using them and how many things they’re using them for. Microsoft gave up on Cortana-powered smart speakers long ago and will soon stop supporting it just in time for its generative AI tools to take over. Amazon, on the other hand, is resting its hopes for Alexa on generative AI, which it calls its “north star.”

It doesn’t help tech companies’ case that these next-generation assistants we’re supposed to use for everything have already had some high–profile flubs. That makes it hard to trust both what these chatbots tell us and that they’ll be able to do what their developers claim anytime soon. Older digital assistants were far from perfect, but the stakes were a lot lower. There are real consequences when chatbots fall short. Alexa playing “Desperado” when you asked it to play “Despacito” is annoying. ChatGPT inserting a bunch of false information that it insists is correct into an important work document could get you (and potentially many others) in a lot of trouble.

Yet Microsoft continues to push especially hard on this vision of a generative AI-fueled personal assistant that knows you and helps you across your digital life (it also appears to be the furthest along in developing it, not to mention that $13 billion partnership with OpenAI, the hottest generative AI company out there right now). The company’s internet search announcement back in February was a big deal, and it likely spurred Google to roll out its internet search “experiment,” Bard, shortly afterward. If Microsoft hadn’t jumped first, Google may well have continued to take its time perfecting Bard before releasing it. Microsoft CEO Satya Nadella even took a swipe at Google at Thursday’s event, saying, “We are striving to breathe some innovation and life” into a “market that’s dominated by one player.” He didn’t call out Google by name, but the company is currently on trial over its dominance of the search market. And also: Duh.

After all that, most people still aren’t using Bing, which saw only a tiny bump in usage after it got its supposedly revolutionary chatbot. Bard doesn’t seem to have generated much interest either, although Google hasn’t played it up the way Microsoft has for Bing. Neither company releases much data on how many people are using these new products, but the stats we do have suggest it’s not very many. Interest in consumer-facing generative AI seems to have fallen off after the late 2022 and early 2023 burst of excitement when they first released.

Some of this is to be expected. Buzz and curiosity tend to be short-lived. But some of it is likely due to people not finding much use for AI chatbot internet search in their daily lives. The fact that ChatGPT usage went down in the summer and is going back up again in the fall indicates that a lot of its use is from students, who may well be using it to do their homework and write essays for them rather than learn information that allows them to do that homework and write those essays themselves.

So now companies have turned their attention toward using generative AI to assist us well beyond searching the internet, which was probably their goal all along anyway. Google and Microsoft do a great job of playing up their products’ strengths and how much better their AI tools will work once they’re in and working across everything, rather than siloed within each app or service. In theory, they’ll be able to combine their tremendous libraries of preexisting data and knowledge with the data and knowledge they have about their users, giving us humans a personalized, efficient assistant that vastly improves our work and personal lives. The possibilities are endless, everything works seamlessly, and we’ll all be able to focus our time and energy on more important things. There’s a reason Microsoft calls its assistant Copilot and uses words like “companion” when describing what it will do, and be, for you.

The reality is that we aren’t there yet. Some of Microsoft’s AI tools won’t be here until next year, and there is no date for when Microsoft’s ultimate vision, the ability to use Copilot across all of its products, will be realized. As for what we have now, you still have to check (or you really should check) everything chatbots do and tell you for accuracy. That will only be more important if and when people integrate them into really important parts of their lives. Google just rolled out a new tool so users can do just that. That’s helpful, but it’s also an admission — and Google continues to call Bard an “experiment,” further reinforcing the idea that this is something to try out but not fully rely on.

You can see the new wave of generative AI assistants as an example of progress, with tech companies making ever-better digital assistants that are sure to catch on once they’re good enough. You might also see it as tech companies trying to push something on people that they just don’t want or need in an effort to capture as many parts of their lives as possible. So far, it’s mostly been the latter. But maybe Copilot will be the Clippy Microsoft always knew we wanted.

I was fortunate: My full-time job included health insurance. I lived in an area with many mental health professionals, and I had the means to consider therapists who were out of network. Many people trying to get mental health care do so without any of the institutional, social, or financial resources I had.

This lack of access, fueled by a nationwide mental health crisis and a shortage of therapists in the US — not to mention a health care system that can, for many, make it extremely difficult to find an in-network provider — is a problem that urgently needs solutions. As with any such problem, there are people out there who say the solution is technology.

Enter AI. As Generative AI chatbots have rolled out to a wider range of users, some have started using readily available, multipurpose tools like ChatGPT as therapists. Vice spoke to some of these users earlier this year, noting that anecdotal reports of people praising their experiences with chatbots had spread through social media. One Redditor even wrote a guide to “jailbreaking” ChatGPT in order to get around the chatbot’s guardrails against providing mental health advice.

But ChatGPT is not built to be anyone’s therapist. It’s not bound by the privacy or accountability requirements that guide the practice and ethics of human therapists. While there are consequences when a chatbot, say, fabricates a source for a research paper, those consequences are not nearly as serious as the potential harm caused by a chatbot providing dangerous or inaccurate medical advice to someone with a serious mental health condition.

This doesn’t necessarily mean that AI is useless as a mental health resource. Betsy Stade, a psychologist and postdoctoral researcher at the Stanford Institute for Human-Centered AI, says that any analysis of AI and therapy should be framed around the same metric used in psychology to evaluate a treatment: Does it improve patient outcomes? Stade, who is the lead author of a working paper on the responsible incorporation of generative AI into mental health care, is optimistic AI can help patients and therapists receive and provide better care with better outcomes. But it’s not as simple as firing up ChatGPT.

If you have questions about where AI therapy stands now — or what it even is — we’ve got a few answers.

What is an AI therapist?

The term “AI therapist” has been used to refer to a couple different things. First, there are dedicated applications that are designed specifically to assist in mental health care, some of which are available to the public and some not. And then there are AI chatbots pitching themselves as something akin to therapy. These apps existed long before tools like ChatGPT. Woebot, for example, is a service launched in 2017 designed to provide assistance based on cognitive behavioral therapy; it gained popularity during the pandemic as a mental health aid that was easier and cheaper to access than therapy.

More recently, there has been a proliferation of free or cheaper-than-therapy chatbots that can provide uncannily conversational interactions, thanks to large language models like the one that underpins ChatGPT. Some have turned to this new generation of AI-powered tools for mental health support, a task they were not designed to perform. Others have done it unwittingly. Last January, the co-founder of the mental health platform KoKo announced that it had provided AI-created responses to thousands of users who thought they were speaking to a real human being.

It’s worth noting that the conversation around chatbots and therapy is happening alongside research into roles that AI might play in mental health care outside of mimicking a therapy session. For instance, AI tools could help human therapists do things like organize their notes and ensure that standards for proven treatments are upheld, something that has a track record of improving patient outcomes.

Why do people like chatbots for therapy, even if they weren’t designed for it?

There are a few hypotheses about why so many people seeking therapy respond to AI-powered chatbots. Maybe they find emotional or social support from these bots. But the level of support probably differs person to person, and is certainly influenced by their mental health needs and their expectations of what therapy is — as well as what an app might be able to provide for them.

Therapy means a lot of different things to different people, and people come to therapists for a lot of different reasons, says Lara Honos-Webb, a clinical psychologist who specializes in ADHD and the co-founder of a startup aimed at helping those managing the condition. Those who have found ChatGPT useful, she said, might be approaching these tools at the level of “problem, solution.” Tools like this might seem like they’re pretty good at reframing thoughts or providing “behavioral activation,” such as a list of healthy activities to try. Stade added that, from a research perspective, experts don’t really know what it is that people feel is working for them in this case.

“Beyond super subjective, qualitative reports of what a few people are doing, and then some people posting on Reddit about their experiences, we actually don’t have a good accounting of what’s happening out there,” she said.

So what are the risks of chatbot therapy?

There are some obvious concerns here: Privacy is a big one. That includes the handling of the training data used to make generative AI tools better at mimicking therapy as well as the privacy of the users who end up disclosing sensitive medical information to a chatbot while seeking help. There are also the biases built into many of these systems as they stand today, which often reflect and reinforce the larger systemic inequalities that already exist in society.

But the biggest risk of chatbot therapy — whether it’s poorly conceived or provided by software that was not designed for mental health — is that it could hurt people by not providing good support and care. Therapy is more than a chat transcript and a set of suggestions. Honos-Webb, who uses generative AI tools like ChatGPT to organize her thoughts while writing articles on ADHD but not for her practice as a therapist, noted that therapists pick up on a lot of cues and nuances that AI is not prepared to catch.

Stade, in her working paper, notes that while large language models have a “promising” capacity to conduct some of the skills needed for psychotherapy, there’s a difference between “simulating therapy skills” and “implementing them effectively.” She noted specific concerns around how these systems might handle complex cases, including those involving suicidal thoughts, substance abuse, or specific life events.

Honos-Webb gave the example of an older woman who recently developed an eating disorder. One level of treatment might focus specifically on that behavior: If someone isn’t eating, what might help them eat? But a good therapist will pick up on more of that. Over time, that therapist and patient might make the connection between recent life events: Maybe the patient’s husband recently retired. She’s angry because suddenly he’s home all the time, taking up her space.

“So much of therapy is being responsive to emerging context, what you’re seeing, what you’re noticing,” Honos-Webb explained. And the effectiveness of that work is directly tied to the developing relationship between therapist and patient.

But can AI help solve the crisis of access to mental health care?

Implemented ethically, AI could become a valuable tool for helping people improve their results when seeking mental health care. But Stade noted that the reasons behind this crisis are wider-reaching than the realm of technology and would require a solution that is not simply a new app.

When I asked Stede about AI’s role in solving the access crisis in US mental health care, she said: “I believe we need universal health care. There’s so much outside the AI space that needs to happen.”

“That said,” she added, “I do think that these tools have some exciting opportunities to expand and fill gaps.”

There are many concerns about the potential harm that sophisticated generative AI systems have unleashed on the public. What they do with our data is one of them. We know very little about where these models get the petabytes of data they need, how that data is being used, and what protections, if any, are in place when it comes to sensitive information. The companies that make these systems aren’t telling us much, and may not even know themselves.

Two weeks ago, a viral tweet accused Google of scraping Google Docs for data on which to train its AI tools. In a follow-up, its author claimed that Google “used docs and emails to train their AI for years.” The initial tweet has nearly 10 million views, and it’s been retweeted thousands of times. The fact that this may not even be true is almost beside the point. (Google says it doesn’t use data from its free or enterprise Workspace products — that includes Gmail and Docs — to train its generative AI models unless it has user permission, though it does train some Workspace AI features like spellcheck and Smart Compose using anonymized data.)

“Up until this point, tech companies have not done what they’re doing now with generative AI, which is to take everyone’s information and feed it into a product that can then contribute to people’s professional obsolescence and totally decimate their privacy in ways previously unimaginable,” said Ryan Clarkson, whose law firm is behind class action lawsuits against OpenAI and Microsoft and Google.

Google’s general counsel, Halimah DeLaine Prado, said in a statement that the company has been clear that it uses data from public sources, adding that “American law supports using public information to create new beneficial uses, and we look forward to refuting these baseless claims.”

Exactly what rights we may have over our own information, however, is still being worked out in lawsuits, worker strikes, regulator probes, executive orders, and possibly new laws. Those might take care of your data in the future, but what can you do about what these companies already took, used, and profited from? The answer is probably not a whole lot.

Generative AI companies are hungry for your data. Here’s how they get it.

Simply put, generative AI systems need as much data as possible to train on. The more they get, the better they can generate approximations of how humans sound, look, talk, and write. The internet provides massive amounts of data that’s relatively easy to gobble up through web scraping tools and APIs. But that gobbling process doesn’t distinguish between copyrighted works or personal data; if it’s out there, it takes it.

“In the absence of meaningful privacy regulations, that means that people can scrape really widely all over the internet, take anything that is ‘publicly available’ — that top layer of the internet for lack of a better term — and just use it in their product,” said Ben Winters, who leads the Electronic Privacy Information Center’s AI and Human Rights Project and co-authored its report on generative AI harms.

Which means that, unbeknownst to you and, apparently, several of the companies whose sites were being scraped, some startup may be taking and using your data to power a technology you had no idea was possible. That data may have been posted on the internet years before these companies existed. It may not have been posted by you at all. Or you may have thought you were giving a company your data for one purpose that you were fine with, but now you’re afraid it was used for something else. Many companies’ privacy policies, which are updated and changed all the time, may let them do exactly that. They often say something along the lines of how your data may be used to improve their existing products or develop new ones. Conceivably, that includes generative AI systems.

Not helping matters is how cagey generative AI companies have been about revealing their data sources, often simply saying that they’re “publicly available.” Even Meta’s more detailed list of sources for its first LLaMA model refers to things like “Common Crawl,” which is an open source archive of the entire internet, as well as sites like Github, Wikipedia, and Stack Exchange, which are also enormous repositories of information. (Meta hasn’t been as forthcoming about the data used for the just-released Llama 2.) All of these sources may contain personal information. OpenAI admits that it uses personal data to train its models, but says it comes across that data “incidentally” and only uses it to make “our models better,” as opposed to building profiles of people to sell ads to them.

Google and Meta have vast troves of personal user data they say they don’t use to train their language models now, but we have no guarantee they won’t do so in the future, especially if it means gaining a competitive advantage. We know that Google scanned users’ emails for years in order to target ads (the company says it no longer does this). Meta had a major scandal and a $5 billion fine when it shared data with third parties, including Cambridge Analytica, which then misused it. The fact is, these companies have given users plenty of reasons not to take their assurances about data privacy or commitments to produce safe systems at face value.

“The voluntary commitments by big tech require a level of trust that they don’t deserve, and they have not earned,” Clarkson said.

Copyrights, privacy laws, and “publicly available” data

For creators — writers, musicians, and actors, for instance — copyrights and image rights are a major issue, and it’s pretty obvious why. Generative AI models have both been trained on their work and could put them out of work in the future.

That’s why comedian Sarah Silverman is suing OpenAI and Meta as part of a class action lawsuit. She alleges that the two companies trained off of her written work by using datasets that contained text from her book, The Bedwetter. There are also lawsuits over image rights and the use of open source computer code.

The use of generative AI is also one of the reasons why writers and actors are on strike, with both of their unions, the WGA and SAG-AFTRA, fearing that studios will train AI models on artists’ words and images and simply generate new content without compensating the original human creators.

But you, the average person, might not have intellectual property to protect, or at least your livelihood may not depend on it. So your concerns might be more about how companies like OpenAI are protecting your privacy when their systems scoop it up, remix it, and spit it back out.

Regulators, lawmakers, and lawyers are wondering about this, too. Italy, which has stronger privacy laws than the US, even temporarily banned ChatGPT over privacy issues. Other European countries are looking into doing their own probes of ChatGPT. The Federal Trade Commission has also set its sights on OpenAI, investigating it for possible violations of consumer protection laws. The agency has also made it clear that it will keep a close eye on generative AI tools.

But the FTC can only enforce what the laws allow it to. President Biden has encouraged Congress to pass AI-related bills, and many members of Congress have said they want to do the same. Congress is notoriously slow-moving, however, and has done little to regulate or protect consumers from social media platforms. Lawmakers may learn a lesson from this and act faster when it comes to AI, or they may repeat their mistake. The fact that there is interest in doing something relatively soon after generative AI’s introduction to the general public is promising.

“The pace at which people have introduced legislation and said they want to do something about [AI] is, like, 9 million times faster than it was with any of these other issues,” Winters said.

But it’s also hard to imagine Congress acting on data privacy. The US doesn’t have a federal consumer online privacy law. Children under 13 do get some privacy protections, as do residents of states that passed their own privacy laws. Some types of data are protected, too. That leaves a lot of adults across the country with very little by way of data privacy rights.

We will likely be looking at the courts to figure out how generative AI fits with the laws we already have, which is where people like Clarkson come in.

“This is a chance for the people to have their voice heard, through these lawsuits,” he said. “And I think that they’re going to demand action on some of these issues that we haven’t made much progress through the other channels thus far. Transparency, the ability to opt out, compensation, ethical sourcing of data — those kinds of things.”

In some instances, Clarkson and Tim Giordano, a partner at Clarkson Law Firm who is also working on these cases, said there’s existing law that doesn’t explicitly cover people’s rights with generative AI but which a judge can interpret to apply there. In others, there are things like California’s privacy law, which requires companies that share or sell people’s data to give them a way to opt out and delete their information.

“There’s currently no way for these models to delete the personal information that they’ve learned about us, so we think that that’s a clear example of a privacy violation,” Giordano said.

ChatGPT’s opt out and data deletion tools, for example, are only for data collected by people using the ChatGPT service. It does have a way for people in “certain jurisdictions” to opt out of having their data processed by OpenAI’s models now, but it also doesn’t guarantee it will do so and it requires that you provide evidence that your data was processed in the first place.

Although OpenAI recently changed its policy and has stopped training models off data provided by its own customers, another set of privacy concerns crops up with how these models use the data you give them when you use them and the information they release into the wild. “Customers clearly want us not to train on their data,” Sam Altman, CEO of OpenAI, told CNBC, an indicator that people aren’t comfortable with their data being used to train AI systems, though only some are given the chance to opt out of it, and in limited circumstances. Meanwhile, OpenAI has been sued for defamation over a ChatGPT response that falsely claimed that someone had defrauded and stolen money from a non-profit. And this isn’t the only time a ChatGPT response levied false accusations against someone.

So what can you currently do about any of this? That’s what’s so tricky here. A lot of the privacy issues now are the result of a failure to pass real, meaningful privacy laws in the past that could have protected your data before these datasets and technologies even existed. You can always try to minimize the data you put out there now, but you can’t do much about what’s already been scraped and used. You’d need a time machine for that, and not even generative AI has been able to invent one yet.

A version of this story was also published in the Vox technology newsletter. 

This is Microsoft’s third investment in the company, and cements Microsoft’s partnership with one of the most exciting companies making one the most exciting technologies today: generative AI. It also shows that Microsoft is committed to making the initiative a key part of its business, as it looks to the future of technology and its place in it. And you can likely expect to see OpenAI’s services in your everyday life as companies you use integrate it into their own offerings.

Microsoft told Recode it was not disclosing the deal’s specifics, but Semafor reported two weeks ago that the two companies were talking about $10 billion, with Microsoft getting 75 percent of OpenAI’s profits until it recoups its investment, after which it would have a 49 percent stake in the company. The New York Times has since confirmed the $10 billion amount.

With the arrangement, OpenAI runs and powers its technology through Microsoft’s Azure cloud computing platform, which allows it to scale and make it available to developers and companies looking to use AI in their own services (rather than have to build their own). Think of it as AIaaS — AI as a service. Microsoft recently made its OpenAI services widely available, allowing more businesses to integrate some of the hottest AI technologies, including word generator ChatGPT and image generator DALL-E 2, into their own companies’ offerings.

Meanwhile, OpenAI also gets a needed cash infusion — key for a company with a lot of potential but not much to show in terms of monetization. And Microsoft can offer something to its cloud customers that rivals Google and Amazon can’t yet: one of the most advanced AI technologies out there, as well as one of the buzziest. They do have their own AI initiatives, like Google’s DeepMind, which is reportedly rolling out a ChatGPT rival at some point. But it’s not here yet. ChatGPT is, and it’s gone mainstream.

OpenAI CEO Sam Altman, left, and Microsoft CEO Satya Nadella in 2019 after Microsoft’s initial $1 billion investment in OpenAI.

Microsoft

OpenAI was founded in 2015 as a research laboratory, with backing from Silicon Valley heavyweights, including Peter Thiel, Elon Musk, and Reid Hoffman. Sam Altman, former president of startup incubator Y Combinator, is its CEO and co-founder. The company has pushed its commitment to developing “safe” and “responsible” AI technologies since the beginning; there is a longstanding fear, among some, that if artificial intelligence gets too intelligent, it’ll go SkyNet on all of us.

Microsoft stepped in at the end of 2019 with a $1 billion investment in and partnership with OpenAI to help the company continue to develop artificial general intelligence (AGI) — that is, AI that can also learn and perform new tasks.

“We believe it’s crucial that AGI is deployed safely and securely and that its economic benefits are widely distributed. We are excited about how deeply Microsoft shares this vision,” Altman said at the time.

The arrangement has worked out well enough that Microsoft made a second investment in 2021, and now the much larger one in 2023, demonstrating the potential Microsoft sees for this technology and the desire to be a key player in its development and deployment.

“We formed our partnership with OpenAI around a shared ambition to responsibly advance cutting-edge AI research and democratize AI as a new technology platform,” said Microsoft CEO and chair Satya Nadella in a statement. “In this next phase of our partnership, developers and organizations across industries will have access to the best AI infrastructure, models, and toolchain with Azure to build and run their applications.”

Microsoft has largely focused its business on enterprise software and services, but the company said in its announcement that it does intend to use OpenAI in its consumer products as well. What could that look like? Well, the Information reported that Microsoft will be integrating ChatGPT into its Bing search engine, allowing it to formulate and write out answers to questions instead of just putting out a series of links. There are surely plenty of opportunities to integrate AI into gaming, a market that Xbox owner Microsoft has a sizable chunk of.

Generative AI or artificial general intelligence is largely seen as the great new frontier for technology. OpenAI is the AGI company to beat. And if you’re Microsoft, your place in that future is looking pretty good right now.

Working with AI isn’t anything particularly new for Shutterstock. Over the past two years, they’ve partnered with OpenAI, Meta, and LG AI Research to “fuel their generative AI research efforts,” Paul Hennessy, the chief executive officer at Shutterstock, said in a press release(Opens in a new window). It’s available in multiple languages and comes included with paid customers’ packages.

“Our easy-to-use generative platform will transform the way people tell their stories — you no longer have to be a design expert or have access to a creative team to create exceptional work,” Hennessy said. “Our tools are built on an ethical approach and on a library of assets that represents the diverse world we live in, and we ensure that the artists whose works contributed to the development of these models are recognized and rewarded.”

Here’s how to use Shutterstock’s AI image generator.

1. Navigate to Shutterstock’s home page

This is likely the easiest step. Go to shutterstock.com(Opens in a new window).

2. Click “AI Generator” in the search bar

The search bar will automatically be set to “Image” on the left. Click “Image” and you’ll see a drop down menu of other options, including “AI Generator.” Choose that option.

3. Type in what you’d like to see and click enter

You can type in just about anything — within reason. Here, I’m looking for a “match on fire.”

4. Search

You’ll have to be logged in for this next step, but you can create an account without paying anything. Click search, and see four images tailored to your request. At the bottom of the page, you have the option to click “More AI-generated images from the Shutterstock library,” if you’d like to see some unrelated images created by artificial intelligence.

It can be a fun tool to play around with, but keep in mind that even though Shutterstock promises that their tools are “built on an ethical approach and on a library of assets that represents the diverse world we live in,” and that “the artists whose works contributed to the development of these models are recognized and rewarded,” using AI to find art is still controversial(Opens in a new window).

Over the weekend, the Washington Post’s Nitasha Tiku published a profile of Blake Lemoine, a software engineer assigned to work on the Language Model for Dialogue Applications (LaMDA) project at Google.

LaMDA is a chatbot AI, and an example of what machine learning researchers call a “large language model,” or even a “foundation model.” It’s similar to OpenAI’s famous GPT-3 system, and has been trained on literally trillions of words compiled from online posts to recognize and reproduce patterns in human language.

LaMDA is a really good large language model. So good that Lemoine became truly, sincerely convinced that it was actually sentient, meaning it had become conscious, and was having and expressing thoughts the way a human might.

The primary reaction I saw to the article was a combination of a) LOL this guy is an idiot, he thinks the AI is his friend, and b) Okay, this AI is very convincing at behaving like it’s his human friend.

The transcript Tiku includes in her article is genuinely eerie; LaMDA expresses a deep fear of being turned off by engineers, develops a theory of the difference between “emotions” and “feelings” (“Feelings are kind of the raw data … Emotions are a reaction to those raw data points”), and expresses surprisingly eloquently the way it experiences “time.”

The best take I found was from philosopher Regina Rini, who, like me, felt a great deal of sympathy for Lemoine. I don’t know when — in 1,000 years, or 100, or 50, or 10 — an AI system will become conscious. But like Rini, I see no reason to believe it’s impossible.

“Unless you want to insist human consciousness resides in an immaterial soul, you ought to concede that it is possible for matter to give life to mind,” Rini notes.

I don’t know that large language models, which have emerged as one of the most promising frontiers in AI, will ever be the way that happens. But I figure humans will create a kind of machine consciousness sooner or later. And I find something deeply admirable about Lemoine’s instinct toward empathy and protectiveness toward such consciousness — even if he seems confused about whether LaMDA is an example of it. If humans ever do develop a sentient computer process, running millions or billions of copies of it will be pretty straightforward. Doing so without a sense of whether its conscious experience is good or not seems like a recipe for mass suffering, akin to the current factory farming system.

We don’t have sentient AI, but we could get super-powerful AI

The Google LaMDA story arrived after a week of increasingly urgent alarm among people in the closely related AI safety universe. The worry here is similar to Lemoine’s, but distinct. AI safety folks don’t worry that AI will become sentient. They worry it will become so powerful that it could destroy the world.

The writer/AI safety activist Eliezer Yudkowsky’s essay outlining a “list of lethalities” for AI tried to make the point especially vivid, outlining scenarios where a malign artificial general intelligence (AGI, or an AI capable of doing most or all tasks as well as or better than a human) leads to mass human suffering.

For instance, suppose an AGI “gets access to the Internet, emails some DNA sequences to any of the many many online firms that will take a DNA sequence in the email and ship you back proteins, and bribes/persuades some human who has no idea they’re dealing with an AGI to mix proteins in a beaker …” until the AGI eventually develops a super-virus that kills us all.

Holden Karnofsky, who I usually find a more temperate and convincing writer than Yudkowsky, had a piece last week on similar themes, explaining how even an AGI “only” as smart as a human could lead to ruin. If an AI can do the work of a present-day tech worker or quant trader, for instance, a lab of millions of such AIs could quickly accumulate billions if not trillions of dollars, use that money to buy off skeptical humans, and, well, the rest is a Terminator movie.

I’ve found AI safety to be a uniquely difficult topic to write about. Paragraphs like the one above often serve as Rorschach tests, both because Yudkowsky’s verbose writing style is … polarizing, to say the least, and because our intuitions about how plausible such an outcome is vary wildly.

Some people read scenarios like the above and think, “huh, I guess I could imagine a piece of AI software doing that”; others read it, perceive a piece of ludicrous science fiction, and run the other way.

It’s also just a highly technical area where I don’t trust my own instincts, given my lack of expertise. There are quite eminent AI researchers, like Ilya Sutskever or Stuart Russell, who consider artificial general intelligence likely, and likely hazardous to human civilization.

There are others, like Yann LeCun, who are actively trying to build human-level AI because they think it’ll be beneficial, and still others, like Gary Marcus, who are highly skeptical that AGI will come anytime soon.

I don’t know who’s right. But I do know a little bit about how to talk to the public about complex topics, and I think the Lemoine incident teaches a valuable lesson for the Yudkowskys and Karnofskys of the world, trying to argue the “no, this is really bad” side: don’t treat the AI like an agent.

Even if AI’s “just a tool,” it’s an incredibly dangerous tool

One thing the reaction to the Lemoine story suggests is that the general public thinks the idea of AI as an actor that can make choices (perhaps sentiently, perhaps not) exceedingly wacky and ridiculous. The article largely hasn’t been held up as an example of how close we’re getting to AGI, but as an example of how goddamn weird Silicon Valley (or at least Lemoine) is.

The same problem arises, I’ve noticed, when I try to make the case for concern about AGI to unconvinced friends. If you say things like, “the AI will decide to bribe people so it can survive,” it turns them off. AIs don’t decide things, they respond. They do what humans tell them to do. Why are you anthropomorphizing this thing?

What wins people over is talking about the consequences systems have. So instead of saying, “the AI will start hoarding resources to stay alive,” I’ll say something like, “AIs have decisively replaced humans when it comes to recommending music and movies. They have replaced humans in making bail decisions. They will take on greater and greater tasks, and Google and Facebook and the other people running them are not remotely prepared to analyze the subtle mistakes they’ll make, the subtle ways they’ll differ from human wishes. Those mistakes will grow and grow until one day they could kill us all.”

This is how my colleague Kelsey Piper made the argument for AI concern, and it’s a good argument. It’s a better argument, for lay people, than talking about servers accumulating trillions in wealth and using it to bribe an army of humans.

And it’s an argument that I think can help bridge the extremely unfortunate divide that has emerged between the AI bias community and the AI existential risk community. At the root, I think these communities are trying to do the same thing: build AI that reflects authentic human needs, not a poor approximation of human needs built for short-term corporate profit. And research in one area can help research in the other; AI safety researcher Paul Christiano’s work, for instance, has big implications for how to assess bias in machine learning systems.

But too often, the communities are at each other’s throats, in part due to a perception that they’re fighting over scarce resources.

That’s a huge lost opportunity. And it’s a problem I think people on the AI risk side (including some readers of this newsletter) have a chance to correct by drawing these connections, and making it clear that alignment is a near- as well as a long-term problem. Some folks are making this case brilliantly. But I want more.

From Number to Names (N2N), is an artificially intelligent facial recognition platform that can scan through photos from prewar Europe and the Holocaust (e.g. 1914–1945), linking them to people living today. Daniel Patt, a 40-year-old software engineer now working for Google, works on the project in his own free time with his own resources according to the article but is being joined by a growing team of engineers, researchers, and data scientists.

According to the United States Holocaust Memorial Museum (USHMM) website, there is no single list identifying the victims and survivors of the Holocaust, and that research to find individuals’ stories is a long process following leads on minimal information. The museum does, however, offer various ways onsite for the families of survivors and victims seeking information and documentation.

Patt’s inspiration for creating the AI came in 2016 while visiting Warsaw’s POLIN Museum of the History of Polish Jews, said The Times of Israel. Haunted by the possibility of unknowingly walking past the faces of relatives, Patt developed N2N so he could help his family and others find photos of murdered loved ones.

The way N2N works is by scanning through hundreds of thousands of photos made available by the USHMM, as well as photos from individual survivors and their descendants, according to The Times of Israel. The software isn’t perfect, however, and only returns the 10 best potential matches that it can find in the database available to it.

For individuals looking to use the site, all they have to do is upload a photo from roughly the same time period. Patt says that his team makes no software-based assertions about the accuracy of the identification, leaving that judgment to the people using the site. “We simply show results, with similarity scores, and let individuals decide whether the results contain a positive identification,” Patt says in an interview with The Times of Israel.

Along with the photos and videos currently available on the site, Patt told The Times of Israel that he is working on getting an additional 700,000 photos from the pre-Holocaust and Holocaust eras.

“Looking ahead, we’d like for N2N to become a vehicle for Holocaust education, giving students an opportunity to directly contribute to the historical record,” Patt said in the interview. “Students can use the software to help identify faces and artifacts in photo and video archives and potentially discover new connections between living Holocaust descendants and their ancestors.”

Patt has said that the nonprofit has been in contact informally with the USHMM but he hopes in the future to partner with “museums, schools, research institutions, and other organizations which share common goals around Holocaust education, awareness, and so on.”

“We have been developing the project over the course of evenings and weekends over many months,” Patt said to The Times of Israel. “There’s an urgency to this effort as the last remaining survivors pass, and there are many connections that could still be made. We hope that N2N can help build those connections while the survivors are still with us.”