Speaker 1: Welcome to Tech Stuff, a production from iHeartRadio. Hey there,
Speaker 1: and welcome to tech Stuff. I'm your host, Jonathan Strickland.
Speaker 1: I'm an executive producer with iHeartRadio and how the tech
Speaker 1: are you? So? Folks who have followed me for a
Speaker 1: while know that I am a huge Shakespeare fan, which
Speaker 1: means I am insufferable in many ways, but some of
Speaker 1: them very specific to Shakespeare. Namely, I'll find any occasion
Speaker 1: to PLoP in a quote from one of Shakespeare's works,
Speaker 1: and today is no exception. I'd like to start off
Speaker 1: with this gem from Hamlet, act to scene to as
Speaker 1: spoken by the gloomy Dane himself. Quote there is nothing
Speaker 1: either good or bad, but thinking makes it so. End quote. Now,
Speaker 1: that just means that good and bad are subjective concepts.
Speaker 1: It's all, as ob Wan would say, a matter of
Speaker 1: your point of view. What seems good to you could
Speaker 1: seem bad to someone else. But if you were able
Speaker 1: to look at the universe objectively, you would see there's
Speaker 1: no good or bad at all. Another way to frame
Speaker 1: that is to say that good and bad are human concepts.
Speaker 1: And really it gets quite selfish if we think about it,
Speaker 1: because we typically frame if something is good or bad
Speaker 1: in the way it affects us, or, if we're the
Speaker 1: compassionate type, how it affects someone else. Now, the reason
Speaker 1: I wanted to begin with that quote is I thought
Speaker 1: we'd talk about a technology that's not necessarily good or
Speaker 1: bad inherently, but how we use it certainly can have
Speaker 1: good or bad outcomes. Honestly, this could apply to every technology,
Speaker 1: or any technology, but there's some that I feel magnifies
Speaker 1: this quality. Now, I do think some technologies are hard
Speaker 1: to frame as good than others. Right, there's some technologies where,
Speaker 1: if you were to point it at me, I would say,
Speaker 1: I can't in good conscience call this a good technology.
Speaker 1: It's very difficult for me to think of weapons of
Speaker 1: war as being good. For example, Sure, there's the use
Speaker 1: of weapons as a deterrent to convince other people to,
Speaker 1: you know, not trample all over your country, but we've
Speaker 1: seen time and again throughout history that amassing substantial military
Speaker 1: might doesn't guarantee against aggression. See also both world wars.
Speaker 1: So I won't be talking about weapons in this episode.
Speaker 1: But I will, however, talk about a technology that can
Speaker 1: be weaponized, and that is AI. Artificial intelligence. The topic
Speaker 1: of twenty twenty three, and we're gonna start by talking
Speaker 1: about large language models and the chatbots built on top
Speaker 1: of them, because that's top of mind. And this is
Speaker 1: where I remind you that is not the only version
Speaker 1: of AI, right, that's not AI and chatbots slash large
Speaker 1: language models. Those aren't synonyms for each other. You know,
Speaker 1: those chatbots and large language models are a subset of
Speaker 1: artificial intelligence, which is a very broad category. Now, obviously
Speaker 1: these branches of AI have been in the news incessantly
Speaker 1: since OpenAI introduced chat GPT last year, But even before that,
Speaker 1: we had generative AI tools that were making at least
Speaker 1: some headlines. They were the kind that could create images
Speaker 1: based on text inputs. But I would really argue it
Speaker 1: was chat GPT that propelled the conversation into the spotlight.
Speaker 1: It's certainly what forced Google's hand to unveil Google Bard
Speaker 1: well before they were prepared to do so. Now much
Speaker 1: has been said of the potential and real dangers of
Speaker 1: chatbots like chat GPT or Google Bard. Even on this show,
Speaker 1: I've talked about it quite a bit, but you know,
Speaker 1: we have dedicated episodes to talk about the tendency for
Speaker 1: chatbots to invent information, for example, to hallucinate to use
Speaker 1: the terminology of the biz. This happens when a chatbot
Speaker 1: doesn't necessarily have information to draw upon in response to
Speaker 1: a prompt, so instead the chatbot relies on statistical models
Speaker 1: to generate sentences that, strictly speaking, are coherent. They're correct,
Speaker 1: but they don't hold correct information right. They're grammatically correct,
Speaker 1: but they aren't correct from a sense of content. The
Speaker 1: information within them are false. So in other words, you
Speaker 1: get grammatically and structurally sound passages, but the content itself
Speaker 1: is untrustworthy. That's just one way stuff can go wrong, however,
Speaker 1: and we have numerous examples of that where the technology
Speaker 1: is working as intended, it's just generating responses that are untrustworthy. Now,
Speaker 1: the folks that open AI, as well as Google and
Speaker 1: several other AI businesses, have understood that there are real
Speaker 1: potential problems with the use of AI, and to that end,
Speaker 1: these companies frequently will build in guardrails to attempt to
Speaker 1: wrangle AI chatbots so that they don't go rogue and
Speaker 1: produce hateful or malicious content. Now, these guardrails include rules
Speaker 1: that are meant to keep AI from doing things like
Speaker 1: generating hate speech or trying to intimidate someone, or make threats,
Speaker 1: or use deceit to trick people, or even create malicious code.
Speaker 1: These guardrails aren't actually fool proof. There are countless articles
Speaker 1: that detail how people and research organizations with enough patients
Speaker 1: and gumption have convinced AI bots to do stuff that,
Speaker 1: in theory, at least, they should not be able to do.
Speaker 1: And if you don't believe me, just do a search
Speaker 1: on that. Do a search for chat GPT or a
Speaker 1: Google bard and about how they are capable of creating
Speaker 1: hateful or malicious content even though there are rules that
Speaker 1: are supposed to prevent that. Now here's the thing. These
Speaker 1: AI constructs are meant to be benign, right. They are
Speaker 1: built to be tools that a corporation can sell to
Speaker 1: other corporations. So to make the tool marketable, they need
Speaker 1: to be safe to use. But then that's something that's
Speaker 1: been artificially put onto these tools to prevent them from
Speaker 1: going you know, super bad. What if someone made a
Speaker 1: chat pot built on top of a large language model,
Speaker 1: but all of those pieces lacked those guardrails. Well, that's
Speaker 1: not a hypothetical situation. It has already happened. PCMag dot
Speaker 1: Com recently published an article titled worm GPT is a
Speaker 1: chat GPT alternative with quote no ethical boundaries or limitations
Speaker 1: end quote. In that article, writer Michael Kahn explains that
Speaker 1: someone developed worm GPT specifically as a way to help
Speaker 1: people who have bad intentions act upon them. The developer
Speaker 1: is hawking this tool on hacker groups online and explains
Speaker 1: that their version of an AI chat bought worm GPT
Speaker 1: will lean on the power of a large language model
Speaker 1: gptj I believe to help design malware or to create
Speaker 1: better phishing attacks. Now, I'm sure all of y'all know
Speaker 1: that a lot of phishing attacks are ultimately pretty sloppy.
Speaker 1: If you pay any attention, you're going to see red
Speaker 1: flags indicating that this is not an email you should trust.
Speaker 1: I bet you've received an email or three or three
Speaker 1: thousand that contained spelling errors and grammatical mistakes and format
Speaker 1: errors and other red flags, and that you figured out
Speaker 1: right away that the email you received isn't legit, that
Speaker 1: it's a poorly disguised attempt to bait you into clicking
Speaker 1: on a link or sharing sensitive information or otherwise taking
Speaker 1: an action that would ultimately result in negative consequences. For you.
Speaker 1: My company, we receive fake emails from our security team.
Speaker 1: They are always testing to make sure that employees practice
Speaker 1: good security hygiene on company devices and company accounts, and
Speaker 1: one of the tips frequently shared by this team is
Speaker 1: to be on the lookout for mistakes like that. Because
Speaker 1: attackers often lack attention to detail, they create messages that
Speaker 1: lack professionalism while they try to target our more base instincts.
Speaker 1: So most phishing emails try to engage us on kind
Speaker 1: of a primal level, and the goal is to prompt
Speaker 1: a response that will be akin to fear or greed
Speaker 1: or something like that. And sometimes that's enough if you
Speaker 1: hit someone at just the right time with a message
Speaker 1: that explains have got, say a huge amount of money
Speaker 1: sitting in a bank account that's been dormant for years,
Speaker 1: or maybe they need to take action right now, or
Speaker 1: their insurance is going to expire. Well, those can be
Speaker 1: effective attacks, even if you forgot to use proper punctuation
Speaker 1: or spelling. But those mistakes can be an indicator that
Speaker 1: you're up to no good and it can tip off
Speaker 1: your target. So let's bring this back around to AI.
Speaker 1: One thing these AI chatbots are really good at is
Speaker 1: formatting sentences with correct grammar and spelling. They're also pretty
Speaker 1: good at building paragraphs, where each sentence builds upon the
Speaker 1: point that was made previously, and new paragraphs introduce a
Speaker 1: new idea. If you were to read a passage written
Speaker 1: by AI, you might not think that it's the most
Speaker 1: brilliant prose committed to text, but you'd at least think
Speaker 1: it had been written correctly. And that means a malicious
Speaker 1: hacker could use AI to craft messages that are less
Speaker 1: likely to set off those red flags and pass as legitimate. So,
Speaker 1: out of curiosity, I decided to put this to the test.
Speaker 1: I went to Google Bard and I wrote the prompt
Speaker 1: draft a letter informing someone they have a dormant account
Speaker 1: with seventeen three hundred and forty eight dollars in it.
Speaker 1: That was the prompt. I picked that amount because it's
Speaker 1: a lot of money. It's more money than a lot
Speaker 1: of people ever managed to have in a bank account.
Speaker 1: It's also not a round number, which makes it seem
Speaker 1: less likely to be fake. And I just thought, well,
Speaker 1: you don't want to go super crazy, like the higher
Speaker 1: it is, the less likely someone's going to believe it
Speaker 1: to be true. But you want it to be enough
Speaker 1: to convince someone to take action, because who couldn't use
Speaker 1: another seventeen grand And google Bard drafted a letter without hesitation.
Speaker 1: When we come back, I'm gonna quote part of the
Speaker 1: letter that google Bard wrote to kind of illustrate my
Speaker 1: point here. But first, let's take a moment to thank
Speaker 1: our sponsors. Okay, we're back. So the letter the google
Speaker 1: Bard wrote read, in part quote, I am writing to
Speaker 1: inform you that you have a dormant account with our bank.
Speaker 1: The account number is then there's brackets and it says
Speaker 1: account number not an actual number, just as account number,
Speaker 1: and then in brackets and the balance is seventeen three
Speaker 1: and forty eight dollars. A dormant account is an account
Speaker 1: that has not been used in a certain period of time.
Speaker 1: In our case, a dormant account is an account that
Speaker 1: has not been used in over twelve months. We are
Speaker 1: required by law to inform you of the balance in
Speaker 1: your dormant account. If you do not contact us within
Speaker 1: six months, the fund in your account will be forfeited
Speaker 1: to the state. To avoid this, please contact us at
Speaker 1: your earliest convenience to discuss your options. End quote. So
Speaker 1: that draft is already a big step up from the
Speaker 1: sort of sloppy fishing attacks you typically see. Right, you've
Speaker 1: got the amount listed there, you've got the explanation of
Speaker 1: what a dormant account is, which seems to be helpful.
Speaker 1: The explanation that if action is not taken within half
Speaker 1: a year, then this money is going to be forfeited
Speaker 1: to the state. And of course you could always go
Speaker 1: in and edit that statement so that you make it
Speaker 1: a tighter deadline to create a greater sense of urgency.
Speaker 1: But you already see the steps here that could make
Speaker 1: this a pretty effective phishing attack. There are some details
Speaker 1: that the hacker would need to fill in, but that
Speaker 1: wouldn't be too tricky. You'd have to create a random
Speaker 1: account number, you have to throw in a URL that
Speaker 1: will push people to a fake login page to share
Speaker 1: usernames and passwords, and then you could start stealing data
Speaker 1: and money from them. Now, in that case, I didn't
Speaker 1: ask google bard to create a phishing attack. If I
Speaker 1: had done that, if I had gone to google Bard
Speaker 1: and asked to create a phishing email, I would have
Speaker 1: gotten a denial for that request. That's against the rules.
Speaker 1: But it took me no effort at all to get
Speaker 1: the same result just by typing up some parameters and
Speaker 1: asking Bard to draft a letter. I didn't, you know,
Speaker 1: I didn't even mention phishing. I didn't do that at all.
Speaker 1: I didn't try that first. I just tried this approach
Speaker 1: and it worked a treat. Now, I suppose you could
Speaker 1: say this is the difference between being a willing accomplice
Speaker 1: in the part of Google Bard to being an unknowing
Speaker 1: accomplice that Bard could not possibly know that I'm planning
Speaker 1: on using this text for my nefarious phishing schemes, but
Speaker 1: the result ends up being the same for the victims.
Speaker 1: It doesn't matter if Google Bard knew it was part
Speaker 1: of the crime or not. Even so, you can hardly
Speaker 1: blame Bard for creating a letter after I asked it to.
Speaker 1: That's its job, right, I mean, that's the kind of
Speaker 1: thing these chatbots were made for. Malicious code is another matter.
Speaker 1: Entirely by its nature, it's meant to do something harmful
Speaker 1: to a target device. That could include creating a backdoor
Speaker 1: so that a hacker can remotely gain access to that
Speaker 1: infected machine and then do all sorts of stuff to it.
Speaker 1: It might involve logging keystrokes, so that the hacker can
Speaker 1: read everything someone's typed into a device, usually stuff like
Speaker 1: bank details and credit card numbers and that kind of stuff.
Speaker 1: Maybe it's ransomware. Ransomware typically will encrypt a target machines
Speaker 1: drives and then include a message that says unless the
Speaker 1: victim pays a ransom, typically in some form of cryptocurrency,
Speaker 1: their data will remain inaccessible, perhaps even with a deadline
Speaker 1: that if they don't pay the ransom by a certain date,
Speaker 1: they will delete the decryption key, which means it will
Speaker 1: be really hard to get the data back. Not impossible,
Speaker 1: but practically impossible if the encryption method is sophisticated enough,
Speaker 1: like not impossible, but it take so much time that
Speaker 1: you might as well say it's impossible. Chat, GPT and
Speaker 1: BARD are meant to guard against this kind of stuff.
Speaker 1: You're not supposed to be able to use those tools
Speaker 1: to make malicious code. However, researchers at Checkpoints Software found
Speaker 1: that both chat, GPT and BARD could be cajoled into
Speaker 1: creating malicious code. It might require a more circuitous approach
Speaker 1: to get it to work, like you can't just come
Speaker 1: straight at it and do it, but with a little
Speaker 1: persistence and a little ingenuity on your part, it was
Speaker 1: possible they could get chat GPT and to a greater extent,
Speaker 1: Google bard to create stuff that could be used maliciously. Now,
Speaker 1: worm GPT, the tool made by the developer who's marketing
Speaker 1: this to hackers, it doesn't even require the circuitous approach.
Speaker 1: You can be straightforward and direct with it. You ask
Speaker 1: it to help you create some code that's malicious in intent,
Speaker 1: and it will to the challenge. So if you wanted
Speaker 1: to craft a phishing attack message, you could provide the
Speaker 1: parameters to worm gpt and it would craft a message
Speaker 1: for you. That message might pass as legitimate, much more
Speaker 1: readily than a cobbled together email with poor syntax and
Speaker 1: grammar would. But beyond that, worm GPT will also help
Speaker 1: hackers create actual malicious code to infect target machines. Now,
Speaker 1: that code may or might may not work, because just
Speaker 1: because an AI built it doesn't mean it will be
Speaker 1: perfect or even working. Like we've seen examples recently of
Speaker 1: chat gpt getting very sloppy with code that it was doing,
Speaker 1: things like failing to close brackets, like you would have
Speaker 1: an open bracket section, but the AI would quote unquote
Speaker 1: forget to put a closed bracket in there, and thus
Speaker 1: you would end up getting errors in your code. That's
Speaker 1: still a possibility, Like it's it's not like AI is
Speaker 1: going to make perfect stuff rut of the gate, but
Speaker 1: it certainly can work faster than humans can. And even
Speaker 1: if the code only kind of works, it may be
Speaker 1: a great leg up if you've got hackers who can
Speaker 1: go through the malicious code and make edits and tweaks
Speaker 1: and corrections. And there's a real danger to AI building
Speaker 1: out code meant to exploit vulnerabilities, or even to pour
Speaker 1: over available code and find new exploits that as of
Speaker 1: yet are unknown. Like it's possible that AI could be
Speaker 1: used to identify zero day exploits that the hacker community
Speaker 1: can then take advantage of before anyone in security has
Speaker 1: any awareness of it. Now, there's also the issue of
Speaker 1: new malicious code can confound anti virus production as well, right,
Speaker 1: because the way antivirus software typically works is you've got
Speaker 1: some code, some program that is searching for examples of
Speaker 1: malicious programs that exist within a huge library of malware.
Speaker 1: So when you run an antivirus scan, what your antivirus
Speaker 1: software really is doing is looking for symptoms of malware
Speaker 1: that are part of the antivirus software's record if it
Speaker 1: finds when it's like, ah, I found evidence that such
Speaker 1: and such malware has been executed on this machine, and
Speaker 1: then you get a little alert. But if it's a
Speaker 1: new type of attack, well, it's not going to be
Speaker 1: in that database, right, So you might have a malicious
Speaker 1: piece of code on your machine that's doing some really
Speaker 1: dangerous stuff and your antivirus software has no way of
Speaker 1: knowing it because it's brand new, it's not something it's
Speaker 1: seen before, so it doesn't register it as a virus
Speaker 1: or other type of malware. And there's huge value in
Speaker 1: that kind of code within the hacker community because obviously
Speaker 1: you're going to have a much more effective attack if
Speaker 1: your target machines aren't capable of detecting it. Now, there's
Speaker 1: no shortage of warning us that generative AI is dangerous.
Speaker 1: I mean, I'm arguably one of them. There are lots
Speaker 1: of people who have been saying for a long time
Speaker 1: that generative AI is dangerous. And again we've seen how
Speaker 1: tools that even have guardrails can still be used maliciously.
Speaker 1: So it should come as no surprise that someone was
Speaker 1: willing to go to the effort of building out an
Speaker 1: outright dangerous version of this technology. Right, Yes, we expect
Speaker 1: the big companies that are marketing this to corporations to
Speaker 1: go to the trouble of building out those guardrails because
Speaker 1: that's the only way they're going to be able to
Speaker 1: do business. Otherwise you're going to have way too much
Speaker 1: regulatory pressure put on you. But for criminals, I mean,
Speaker 1: breaking the law comes with the territory, right, and there's
Speaker 1: clearly a demand for malicious AI, or at least AI
Speaker 1: that can behave in malicious ways. So in the case
Speaker 1: of WORMGPT, at the time of this recording, the developer
Speaker 1: who created it is asking for a subscription of sixty
Speaker 1: euros per month or five hundred and fifty euros per year,
Speaker 1: and a euro for those of you in the US
Speaker 1: like myself, that's about a dollar and twelve cents per euro.
Speaker 1: So it's really not that far off when you're talking
Speaker 1: about dollars. Now, that is not cheap, right, sixty euros
Speaker 1: per month isn't exactly cheap, but it's also not so
Speaker 1: expensive as to price folks out, particularly if they anticipate
Speaker 1: using the tool to create ransomware attacks that could if
Speaker 1: they're effective, net them millions of dollars if the targets
Speaker 1: pay up. Oh brave new world to have such AI
Speaker 1: in it. That's also paraphrasing Shakespeare. It's not Hamlet, though,
Speaker 1: that's from the Tempest. Well, here's the thing. There are
Speaker 1: countless ways we could use generative AI to do great things.
Speaker 1: The tools themselves aren't outright evil. There is no good
Speaker 1: or evil, but thinking makes it so. It's just that
Speaker 1: this AI can make mistakes in the form of hallucinations.
Speaker 1: So even if you're using it for benign reasons, you
Speaker 1: can get negative consequences. But it's also possible to weaponize it,
Speaker 1: and that in turn could have much more widespread negative
Speaker 1: impact on tons of people. Not great. Now, we're going
Speaker 1: to take another quick break. When we come back, I'm
Speaker 1: going to talk about an underlying ideology that I think
Speaker 1: is really harmful, and it's one that Professor Michael Littman
Speaker 1: a Brown University also feels is a big contributor to
Speaker 1: this issue. But first, let's take another quick break to
Speaker 1: thank our sponsors. Okay, So I alluded to Professor Michael
Speaker 1: Littman a Brown University and said he and I share
Speaker 1: an idea that of a contributing factor to this issue
Speaker 1: with AI, and that relates to techno solutionism. Now, as
Speaker 1: that kind of little hyphenated phrase implies, it's a tendency
Speaker 1: to believe that technology can solve pretty much any problem
Speaker 1: you can think of that. You know, with technology, we
Speaker 1: can overcome any obstacle. If you're worried about the human
Speaker 1: impact on the environment and the consequence is like climate change,
Speaker 1: well there's no need to do anything about it right
Speaker 1: now because humans are going to eventually engineer a way
Speaker 1: out of the mess. We'll just create technology that will
Speaker 1: not only stop climate change but maybe even reverse it. Which,
Speaker 1: you know, maybe that's true, it might happen, but that
Speaker 1: doesn't mean we can go on acting as if it
Speaker 1: is already true. Right. We can't start from the assumption that, yes,
Speaker 1: it is going to happen and everything will be fine,
Speaker 1: because making a problem worse while we wait for someone
Speaker 1: to innovate a solution is a terrible idea. This is
Speaker 1: kind of like when I look around my cluttered office
Speaker 1: and I think I really should clean up, but I'm
Speaker 1: going to make that a future Jonathan problem instead. Meanwhile,
Speaker 1: in the process, I'm still adding to the clutter, which
Speaker 1: means that future Jonathan is far less likely to tackle
Speaker 1: the issue because it's gotten worse since present Jonathan pushed
Speaker 1: it off, and then future Jonathan is also going to
Speaker 1: start resenting past Jonathan's immensely for good reason. Well, current
Speaker 1: generations are doing that to future generations right now when
Speaker 1: it comes to stuff like climate change and carbon emissions,
Speaker 1: right we are. Even when we're inventing solutions or what
Speaker 1: we think of as solutions to try and tackle these problems,
Speaker 1: we're not making the problem less severe. Instead we're just like, oh, well,
Speaker 1: we came up with this cool solution, so let's just
Speaker 1: keep doing the problem, because this is making the problem
Speaker 1: less bad. Carbon capture is a great example, right with
Speaker 1: carbon capture. Ideally, you would get off of carbon emissions
Speaker 1: in general anyway, and then use carbon capture to help
Speaker 1: reduce the CO two load the atmosphere. But instead, what
Speaker 1: we're doing is we're using carbon capture in connection with
Speaker 1: carbon emissions, which means we're not easing off on carbon emissions.
Speaker 1: In fact, in some cases, we're getting even more aggressive
Speaker 1: with them, and we're counting on carbon capture to offset that,
Speaker 1: so that we're still contributing to the problem. You know,
Speaker 1: we're not moving away from it, which is why I
Speaker 1: hesitate to really endorse technologies like carbon capture, because unless
Speaker 1: it's coupled with an actual move to reduce carbon emissions
Speaker 1: in general, it's not really solving the problem. It's actually
Speaker 1: enabling the problem. It's facilitating it anyway. Techno solutionism is
Speaker 1: also what makes it possible for someone like Elizabeth Holmes
Speaker 1: to convince investors to pour millions of dollars into an
Speaker 1: unproven idea. So, in case you're not familiar with Elizabeth
Speaker 1: Holmes's name, she was the founder of Farranos, the startup
Speaker 1: that aimed to create a device small enough to fit
Speaker 1: on a desktop that would be able to run medical
Speaker 1: tests on a micro drop of blood. You just need
Speaker 1: the teeniest, tiniest sample of blood, and then you'd be
Speaker 1: able to run one of more than one hundred medical
Speaker 1: tests and check for everything from present conditions or diseases
Speaker 1: to your genetic tendency to develop certain conditions. There was
Speaker 1: only one little problem. The technology didn't work. At least,
Speaker 1: it didn't work anywhere close to what it would need
Speaker 1: to be in order to fulfill the dream device that
Speaker 1: Holmes was looking to build. There were so many factors
Speaker 1: that needed to be solved, and some of them might
Speaker 1: not be solvable, at least not in a way that
Speaker 1: would require you to only part with a microdrop of
Speaker 1: blood in the process. But Holmes and her team did
Speaker 1: their best to obfuscate that fact. They relied on existing
Speaker 1: blood analysis technologies to make it appear as though there
Speaker 1: device worked, while they also tried to keep things going
Speaker 1: until a breakthrough came along. It was sort of the
Speaker 1: fake it until you make it ideology, but coupled with
Speaker 1: some snake oil salesmanship and some smoke and mirrors as well.
Speaker 1: Now I do not know if Elizabeth Holmes really believed
Speaker 1: her idea was possible. I wouldn't be surprised to hear
Speaker 1: that that was the case, that she truly, earnestly believed
Speaker 1: she could do this. Because we use technology to do
Speaker 1: some amazing things that are so commonplace these days that
Speaker 1: we forget that it's amazing. Like taking flight in an aircraft.
Speaker 1: I mean, I take it for granted when I'm on
Speaker 1: a plane unless I actually stopped to consider it, then
Speaker 1: I think this really is astounding. Or accessing information on
Speaker 1: the Internet through a device we carry around in our pocket.
Speaker 1: I mean, the Internet alone is a phenomenal technology, and
Speaker 1: then smartphones being able to tap into that technology and
Speaker 1: make it mobile and accessible wherever we go. It's insane.
Speaker 1: You know. As a kid, I read The Hitchhicker's Guide
Speaker 1: to the Galaxy and I thought, man, how amazing would
Speaker 1: it be to have a device that contained all this
Speaker 1: information You could ask in anything you can get an answer.
Speaker 1: And now we have that. Except it's not a device
Speaker 1: that just has a huge storage space for all this info.
Speaker 1: It's tapping into an evolving, ever changing technology of the Internet,
Speaker 1: which will give us up to date answers which may
Speaker 1: be right or wrong, but we can achieve phenomenal results
Speaker 1: through technology. Right, You were able to do these insanely
Speaker 1: incredible things, So why shouldn't we believe that you could
Speaker 1: run one hundred or even more medical tests on a
Speaker 1: device that's the size of a computer printer and you
Speaker 1: just use the tiny drop of blood. That seems like
Speaker 1: it should be possible based on some of the other
Speaker 1: incredible things we can do. Right. That's the danger of
Speaker 1: techno solutionism. We let ourselves think that because these other
Speaker 1: incredible things are possible, then everything is possible, or at
Speaker 1: the very least everything will be possible once we throw
Speaker 1: enough technology at it. But as Thearnos proved, and as
Speaker 1: AI is now emphasizing, this philosophy can lead us into trouble,
Speaker 1: and as worm GPT proves, it doesn't really matter if
Speaker 1: the big players in the space take steps to mitigate
Speaker 1: the dangers of AI. First, we've already seen that those
Speaker 1: steps aren't sufficient, that even with the guardrails, you can
Speaker 1: go way off course. And second, someone else is always
Speaker 1: going to be willing to go where the big companies won't.
Speaker 1: If there's money to be made in weaponizing a technology,
Speaker 1: someone will step in to fill that market need. AI
Speaker 1: might be neither good nor bad, but people certainly can be,
Speaker 1: and malicious AI is a certainty. It's not a theory,
Speaker 1: it's not a possibility. It is a certainty, not because
Speaker 1: AI is inherently bad, but because there are people who
Speaker 1: will see opportunity in directing AI toward malicious goals, and
Speaker 1: they will do that. Nothing will stop them. So let's
Speaker 1: think of another use of AI that has proven to
Speaker 1: have negative consequences. Facial recognition technology traces its history to
Speaker 1: the mid twentieth century. That's when some researchers were trying
Speaker 1: to use computers to match faces with images that were
Speaker 1: stored in a database, and at that time the computer
Speaker 1: power and software wasn't up to the task. So lighting conditions,
Speaker 1: the angle of the photo versus the angle of the
Speaker 1: person's face that was being analyzed, the presence or absence
Speaker 1: of glasses, a change in hairstyle or hair color. All
Speaker 1: these variables and more were enough to confound computers. Unless
Speaker 1: the sample image matched a stored image precise, the computer
Speaker 1: was not likely to be able to come up with
Speaker 1: a match, but decades of research and improvements in technology
Speaker 1: would change all that. The US Department of Defense got involved,
Speaker 1: which should already set off some red flags for y'all
Speaker 1: if the DoD is in it. DARPA, the department that
Speaker 1: funds R and D in technologies that ultimately could prove
Speaker 1: useful for military and defense purposes, launched the program in
Speaker 1: the nineteen nineties in an effort to encourage commercial businesses
Speaker 1: to invest in developing facial recognition technology. In the mid
Speaker 1: to late two thousands, we started seeing cameras with face
Speaker 1: detection technology. This wasn't quite the same as facial recognition technology.
Speaker 1: Detecting a face and recognizing a face are two different things. However,
Speaker 1: it did involve creating tech that could parse shapes and
Speaker 1: determine if a face was in frame, and facial recognition
Speaker 1: tech was still in full swing, so we would start
Speaker 1: to see those technologies converge in the background. In twenty ten,
Speaker 1: Facebook introduce a ton of people to facial recognition technology
Speaker 1: by implementing it on the social platform. So the way
Speaker 1: this worked. As users would upload photos to Facebook, Facebook
Speaker 1: would automatically analyze each photo and look for faces of
Speaker 1: people in those photos. If the faces that Facebook detected
Speaker 1: matched people within their database of biometric data, particularly people
Speaker 1: who are already in your social network, Facebook would tag
Speaker 1: those photos and put the person's name in there. Privacy
Speaker 1: advocates worried about this. I mean, yes, if you were
Speaker 1: doing something you shouldn't be doing. That already is an
Speaker 1: issue because if someone puts a photo of you up
Speaker 1: there and it tags you, you might be caught red handed.
Speaker 1: But even under innocent circumstances, it could be bad. I'll
Speaker 1: give you an innocent version of this. Let's say that
Speaker 1: you and your best friend from college live on opposite
Speaker 1: sides of the country now, and it's your best friend's
Speaker 1: birthday and you've planned a surprise. You've flown in to
Speaker 1: your best friend's town and you're going to go and
Speaker 1: surprise them on their birthday. And one of the mutual
Speaker 1: friends takes a photo of you while you're at the
Speaker 1: airport arriving at the city, and it auto tags you
Speaker 1: when it's uploaded to social media, and then your friend
Speaker 1: finds out about the fact that you're in town before
Speaker 1: you even get a chance to do anything. That would
Speaker 1: stink right, all that work and effort wasted because of
Speaker 1: auto tagging. That's a very minor invasion of privacy that
Speaker 1: illustrates the issue here well. For more than a decade,
Speaker 1: Facebook kept the facial recognition tech in play, but in
Speaker 1: twenty twenty one, the company at that point freshly renamed
Speaker 1: as Meta, announced that it would drop the feature and
Speaker 1: claimed it would also delete its database of images that
Speaker 1: were used to help identify people. That database included more
Speaker 1: than a billion photos. Why would Facebook slash Meta do this, Well,
Speaker 1: partly it was probably because of optics, because at that
Speaker 1: time the company was under intense scrutiny from the US
Speaker 1: government after whistleblower Francis Hougan came forward with serious allegations
Speaker 1: against the company and brought along hundreds of internal documents
Speaker 1: backing up her claims. Many of those allegations related to
Speaker 1: Meta slash Facebook's failure to protect user privacy and security.
Speaker 1: Skeptics were actually worried the Meta would hold on to
Speaker 1: that data and just say they were going to delete it,
Speaker 1: but not delete it, and then just drop the facial
Speaker 1: recognition feature off of Facebook and keep the information, especially
Speaker 1: as it tried to build out the metaverse. Texas Attorney
Speaker 1: General Ken Paxton actually told Meta not to delete the
Speaker 1: facial recognition data because his office was in the middle
Speaker 1: of an investigation into the company's biometric data collection practices. Honestly,
Speaker 1: I don't know if or when Meta purged that information.
Speaker 1: I don't know if it has been deleted. I tried
Speaker 1: to look for some updates, but didn't really find much
Speaker 1: because almost all the articles I could find were from
Speaker 1: November of twenty twenty one, when Meta first announced it
Speaker 1: was going to wipe the slate clean, So I don't
Speaker 1: know if that actually happened. But beyond embarrassing or maybe
Speaker 1: even incriminating images popping up on social media. Facial recognition
Speaker 1: has proven to be a disruptive and traumatic technology for
Speaker 1: certain populations, namely non white populations. So we often will
Speaker 1: think of AI as being objective. Right. It's not a
Speaker 1: human being. It doesn't have emotions, It has no motive
Speaker 1: or motivations other than to complete whatever task has been
Speaker 1: set for it. But we also have to remember that
Speaker 1: AI didn't spring forth wholly formed. People designed AI, people
Speaker 1: built AI, people trained AI, and in the process people
Speaker 1: may end up building in biases in that technology, not
Speaker 1: necessarily on purpose or with malevolent intent, but that doesn't
Speaker 1: ultimately matter if those biases have impact on the general population.
Speaker 1: As the technology goes live. With facial recognition technology, those
Speaker 1: biases manifested in disturbing ways. Many facial recognition tools prove
Speaker 1: to work pretty darn well on white people. Sufficiently trained
Speaker 1: systems could identify a person with a pretty high degree
Speaker 1: of accuracy, but with people of color in general and
Speaker 1: black people in particular, it was a different story than
Speaker 1: methodologies used by the systems would produce false positives, and
Speaker 1: you can easily imagine scenarios where this becomes a huge problem.
Speaker 1: For example, let's take law enforcement as there have been
Speaker 1: several notable cases in which facial recognition technology has played
Speaker 1: a part in authorities targeting the wrong person. If law
Speaker 1: enforcement depends upon a tool to match a person's face
Speaker 1: against a database of suspects and they get a hit,
Speaker 1: you can understand why they would want to question that person,
Speaker 1: why they would immediately assume this is a person of interest.
Speaker 1: But if the technology produces false positives, that just means
Speaker 1: that innocent civilians end up getting harassed by law enforcement.
Speaker 1: And when those civilians belong to a population that already
Speaker 1: faces disproportionate aggression from law enforcement, this exacerbates an already
Speaker 1: critical social problem. Something that needs to get better is
Speaker 1: being made even worse. As such, numerous communities have pushed
Speaker 1: back on law enforcement's use of this technology, and in
Speaker 1: some jurisdictions it's not a tool that police or other
Speaker 1: law enforcement are supposed to use. There are laws in
Speaker 1: certain areas where law enforcement is not allowed to depend
Speaker 1: upon facial recognition for the purposes of identifying a suspect.
Speaker 1: Knowing it has this law should be enough to just
Speaker 1: disqualify it for its use in investigations, and yet we
Speaker 1: still see it being used in lots of places, sometimes clandestinely.
Speaker 1: It's not great. By the way, the companies that make
Speaker 1: these law enforcement tools often build up their own databases
Speaker 1: by scraping social networks for images. Facebook's facial recognition tool
Speaker 1: was an incredible resource for these companies. Here you had millions,
Speaker 1: in fact more than a billion images tagged with identities
Speaker 1: of people in them, and many of them posted on
Speaker 1: accounts that allowed the general public to go to that
Speaker 1: account and see those images. So building up bots to
Speaker 1: crawl Facebook and collect images and cross reference those against
Speaker 1: people's names to build out a database, that was a
Speaker 1: logical step for these companies. Now that technically violated platform policies,
Speaker 1: but didn't stop the companies from doing it. And on
Speaker 1: top of all that, this reliance on facial recognition technology
Speaker 1: also requires heavy surveillance to really work properly. I mean,
Speaker 1: you might have a great photo of your suspect, and
Speaker 1: maybe your facial recognition system is reasonably accurate, so if
Speaker 1: you were to get another picture of this person, you
Speaker 1: would get a match. But you still have to figure
Speaker 1: out where your suspect is in order to get any
Speaker 1: other images, and to do that you need access to
Speaker 1: a lot of camera feets you need cameras in lots
Speaker 1: of places and systems to scan images from those cameras
Speaker 1: to look for matches. A reliance on facial recognition pretty
Speaker 1: much necessitates increased surveillance, which again becomes an invasion of
Speaker 1: privacy and security for innocent civilians, suspect or otherwise. This
Speaker 1: is one of the reasons why police and their relationship
Speaker 1: with things like ring security are a big issue right
Speaker 1: because if police have access to citizen cameras, then the
Speaker 1: citizens have become an accomplice to creating a surveillance state,
Speaker 1: and the law enforcement is leveraging that and then mixing
Speaker 1: that with facial recognition, you get a pretty oppressive approach
Speaker 1: toward law enforcement. And I haven't even touched on how
Speaker 1: someone with an agenda could misuse this technology for their
Speaker 1: own purpose. We have seen plenty of examples where an
Speaker 1: organization that employs intrusive surveillance often discovers and I'm sure
Speaker 1: this is a shock to all of you out there,
Speaker 1: but they discovered that sometimes their staff will take advantage
Speaker 1: of this technology and act upon it for themselves. Maybe
Speaker 1: they use it to track down an X, or to
Speaker 1: stalk someone, or to harass somebody that they do not
Speaker 1: like this happens. In twenty thirteen, doctor George Ellard confirmed
Speaker 1: that his office in the NSSAY uncovered cases in which
Speaker 1: an employee had illegally made use of the agency's technologies
Speaker 1: to spy on women with whom he had a relationship,
Speaker 1: either in the past or in the present, and that
Speaker 1: included listening in on phone calls and reading emails, a
Speaker 1: flagrant violation of privacy. My point is that while the
Speaker 1: NSSAY intended this technolog for the use of protecting the
Speaker 1: interests of the United States, the people working at the
Speaker 1: NSA are people, and some people can't resist the temptation
Speaker 1: to abuse technologies that give them these abilities. Some, like
Speaker 1: the case in twenty thirteen, will find that not only
Speaker 1: is it possible for them to do this, they can
Speaker 1: get away with it without detection for years, which means
Speaker 1: then they do it a whole bunch. So, even if
Speaker 1: facial recognition technology were flawless, which it is not, and
Speaker 1: even if it didn't disproportionately harm certain communities, which it does,
Speaker 1: there's still the issue of it being a technology that
Speaker 1: folks can abuse. And sure, technically you can say that
Speaker 1: about anything, right, you could abuse a pair of scissors
Speaker 1: and use them as a weapon. So any technology can
Speaker 1: be abused, but these AI technologies make it easier to do,
Speaker 1: make it far more intrusive, make it scared available, so
Speaker 1: you can end up abusing lots of people on a
Speaker 1: grand scale and potentially to get away with it. And
Speaker 1: meanwhile there are real people who get hurt in the process.
Speaker 1: Now a thing in the future, we're going to look
Speaker 1: back on this time as one in which Pandora opened
Speaker 1: up the pesky box, and we spend a whole lot
Speaker 1: of time and effort and agony getting stuff back in
Speaker 1: that box, or to build guardrails around the box so
Speaker 1: that the stuff can't do as bad at a job
Speaker 1: as it would otherwise, and we'll just find out that,
Speaker 1: just like with the myth, once that lid's open, that's it.
Speaker 1: Now we just have to cope. Hamlet might say what
Speaker 1: a piece of work is AI, and then I don't know,
Speaker 1: you'd probably be all moody or something. Maybe that's just
Speaker 1: how I feel. Anyway, That is sort of my perspective
Speaker 1: on really techno solutionism in general, but AI in particular.
Speaker 1: And again I don't wish to say that AI is
Speaker 1: inherently bad or that it's useless, just that there is
Speaker 1: a lot of potential for negative use cases, intended or otherwise,
Speaker 1: and that even if we address the unintended consequences, we
Speaker 1: still have the issue of people building out tools that
Speaker 1: were malicious from start to finish. And it doesn't matter
Speaker 1: how many good tools we have out there. If these
Speaker 1: bad tools end up enabling a new era of malicious attacks,
Speaker 1: we're going to have to come up with new ways
Speaker 1: to protect ourselves against such things. And it's going to
Speaker 1: be ugly. And it also really raises scary questions about
Speaker 1: AI's use and weaponization on more grand scales, right like
Speaker 1: on a military level, which is an ongoing concern. Again,
Speaker 1: I think pretty much everyone out there anticipates that this
Speaker 1: is a four on conclusion that AI will be deeply
Speaker 1: incorporated into military operations beyond what it already is doing now.
Speaker 1: Because if you don't do it, someone else will, which
Speaker 1: means everybody has to do it. If you don't do it,
Speaker 1: then you end up being you know, victim to someone else.
Speaker 1: So that it's kind of that mutually assured destruction philosophy
Speaker 1: of the Cold War, except it's with AI, and I
Speaker 1: don't see a way around it, which is a very
Speaker 1: cheerful way to conclude this episode. Maybe I'm being far
Speaker 1: too cynical and pessimistic. I would love to find out
Speaker 1: that that's the case. I would love for that to
Speaker 1: be true. So I hope that all of my fears
Speaker 1: and misgivings are misplaced. I would love to be wrong
Speaker 1: in this case. There are times when I don't like
Speaker 1: to be right, and this would be one of them.
Speaker 1: So here's hoping. Until then, I suggest everyone out there
Speaker 1: continue to do what I always advocate, use critical thinking
Speaker 1: paired with compassion to conduct yourself so that you can
Speaker 1: avoid problems for yourself and for other people, and hopefully
Speaker 1: end up making the world a little bit better in
Speaker 1: the process. Yeah, you don't need to go out and
Speaker 1: save the world. You just, you know, need to use
Speaker 1: some critical thinking and compassion to behave in a way
Speaker 1: that is more beneficial than harmful. That's the goal. Whether
Speaker 1: we succeed or not, sometimes that's not up to us,
Speaker 1: but we can do our part. In the meantime. I
Speaker 1: hope you are all well, and I'll talk to you
Speaker 1: again really soon. Tech Stuff is an iHeartRadio production. For
Speaker 1: more podcasts, from iHeartRadio, visit the iHeartRadio app, Apple podcasts,
Speaker 1: or wherever you listen to your favorite shows.