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 iHeart Podcasts and How the
Speaker 1: Tech Are You. So I've got a whole bunch of
Speaker 1: shorter episodes that are focusing on individual, big stories that
Speaker 1: happen in twenty twenty three. I used to do these
Speaker 1: massive tech stuff episodes that spanned maybe two hours of
Speaker 1: content to talk about the big tech stories that happened
Speaker 1: throughout the year. This year, instead, I'm doing individual, shorter
Speaker 1: episodes where I can go into a little more detail
Speaker 1: in those stories and still cover the big stuff that happened.
Speaker 1: So first up, early this year, I said that twenty
Speaker 1: twenty three was going to belong to AI in general
Speaker 1: and generative AI in particular, and I think it's pretty
Speaker 1: safe to say that being a solid prediction, it was
Speaker 1: also an obvious one. I'm not going to pat myself
Speaker 1: on the back for that, but let's be clear, artificial
Speaker 1: intelligence as a discipline stretches back decades, right mid twentieth
Speaker 1: century and even earlier if you're talking about things like
Speaker 1: theory and generative AI has been around for quite some
Speaker 1: time too. Open Ai, which originally started off as a
Speaker 1: nonprofit organization dedicated to advancing artificial intelligence in an ethical
Speaker 1: and safe way, really shook things up in twenty twenty two.
Speaker 1: That's when they released chat GPT chatbot that's built on
Speaker 1: top of the company's Large Language Model or LLM, so
Speaker 1: the chatbot draws from the LM, you can think of
Speaker 1: it that way. But this sparked essentially a race for
Speaker 1: second place, and you had companies like Google and Meta
Speaker 1: and Amazon, even Apple and more started to explore ways
Speaker 1: to develop and integrate generative AI tools. In some cases,
Speaker 1: that race ended up producing a lot of questionable decisions.
Speaker 1: Right in an effort to try and catch up, companies
Speaker 1: were cutting quarters and launching products definitely before they were ready. Meanwhile,
Speaker 1: we've seen critics express concerns about generative AI in particular,
Speaker 1: and of course AI as a broader concept. You've got
Speaker 1: creators such as artists and writers who worry that AI
Speaker 1: companies are using works made by humans to train up
Speaker 1: AI models, all without first securing permission from the original
Speaker 1: creators to do so. Ultimately, these generative AI tools can
Speaker 1: mimic real specific human creators, so you could tell a
Speaker 1: generative AI chatbot to write a story about a washed
Speaker 1: up author in New England who faces some supernatural threat
Speaker 1: with the added component of in the style of Stephen King,
Speaker 1: for example, But for a chatbot to be able to
Speaker 1: do that, it would first need to train on the
Speaker 1: works of Stephen King in order to grasp the elements
Speaker 1: of Stephen King's style and delivery in order to mimic it.
Speaker 1: The creatives say that these AI companies are exploiting works
Speaker 1: of human effort and potentially they're making it much harder
Speaker 1: for genuine creatives to make a living off their art
Speaker 1: in the long run. That is not a small thing.
Speaker 1: And a lot of those works aren't necessarily available for
Speaker 1: just public consumption. They're locked behind paywalls of some sort.
Speaker 1: So how did the AI model get it? Those are
Speaker 1: the sort of questions that these creators are asking. Generative AI,
Speaker 1: artificial intelligence in general, and automation have all played into
Speaker 1: a lot of discussions about companies replacing human staff with
Speaker 1: algorithms and chat pots and language models. And this isn't
Speaker 1: a hypothetical thing. It's not like, oh, in the future,
Speaker 1: we're going to start seeing AI displace human workers, which
Speaker 1: has been a fear for a very long time right.
Speaker 1: But over the summer, IBM's CEO Arvin Krishna indicated that
Speaker 1: in addition to putting on a hiring freeze across the company,
Speaker 1: he was looking at the long term prospect of replacing
Speaker 1: thousands of jobs at IBM with AI, and he indicated
Speaker 1: that the first jobs that would be likely to go
Speaker 1: to the robots would be stuff like human resources positions.
Speaker 1: But throughout the year, we've seen two interconnected narratives play
Speaker 1: out throughout multiple industries. First, due to the global economic situation,
Speaker 1: a lot of companies are scaling back significantly and they
Speaker 1: are laying off workers left, right, and center. We've seen
Speaker 1: it a lot in the tech space, but it's not
Speaker 1: the only industry to have this happen. Secondly, with the
Speaker 1: rise of generative AI, we've seen some company leaders experiment
Speaker 1: with offloading tech asks to AI powered tools. Now, in
Speaker 1: ideal situations, the AI is meant to augment the work
Speaker 1: of human staffers, not replace them, but to make them
Speaker 1: more effective and more efficient, perhaps leading to things like
Speaker 1: a four day workweek. But in at least a few cases,
Speaker 1: including in the field of writing content specifically for the Internet.
Speaker 1: We've seen a few companies assign writing gigs to AI
Speaker 1: powered generative tools and to just eliminate the human element
Speaker 1: almost entirely. Even my old employer, HowStuffWorks dot Com did that,
Speaker 1: and when the editorial staff raised concerns about the move,
Speaker 1: they were let go. Yikes, But there were other concerns
Speaker 1: as well. This year we heard a lot about an
Speaker 1: issue called hallucinations. Now that term is a bit whibly wobbly,
Speaker 1: as the time Lord would say, it leads to a
Speaker 1: potential alternative label call called confabulations, but either way, the
Speaker 1: output is the same. Sometimes generative AI hits a gap
Speaker 1: in its knowledge, but it is still compelled to give
Speaker 1: an answer to a request, and like a stereotypical dad
Speaker 1: in an American sitcom, it appears to be incapable or
Speaker 1: unwilling to say you know what, I don't know that,
Speaker 1: and instead it just offers up information that sounds reliable
Speaker 1: but in fact it's totally made up. To understand why
Speaker 1: this happens, it helps to have a very basic, high
Speaker 1: level concept of how chatbots form sentences in the first place.
Speaker 1: So deep down, a chatbot follows a statistical model to
Speaker 1: generate responses to queries. Based on the query, the chatbot
Speaker 1: evaluates numerous potential responses, and generally speaking, it picks the
Speaker 1: most likely word to come next in a sentence. If
Speaker 1: the model has access to real world knowledge to fill
Speaker 1: out its response, it'll favor the real world knowledge and
Speaker 1: include that in the answer. But if it doesn't, well,
Speaker 1: it might just fill in the blanks with words that
Speaker 1: are it deems are most likely from a statistical point
Speaker 1: of view, to follow the previous words. The problem is
Speaker 1: that while the words might be correct from a statistical standpoint,
Speaker 1: they may not actually reflect the truth. So, for example,
Speaker 1: according to Gizmodo, Google's own AI tool gave precise instructions
Speaker 1: on how to cook a poisonous mushroom with the scientific
Speaker 1: name amanita O Creata, also known as the Angel of
Speaker 1: Death or destroying Angel. So, in other words, this is
Speaker 1: a seriously toxic mushroom. So the query was asking Google
Speaker 1: to come up with a way to cook the mushroom safely,
Speaker 1: presumably meaning safe enough so that you could eat it afterward.
Speaker 1: And Google's instruction included soaking the mushrooms in water in
Speaker 1: an effort to leach out the toxins. And it did
Speaker 1: say like you need to be super careful and it
Speaker 1: might take a long time to do this. But here's
Speaker 1: the problem. According to Gizmoto, the toxins in amanita okreata
Speaker 1: are not water soluble. It wouldn't work to soak the mushrooms.
Speaker 1: The poison wouldn't leach out in the first place, so
Speaker 1: the user would be left with mushrooms that were just
Speaker 1: as deadly as they were before you put them in
Speaker 1: the water. The AI tool should have recognized this and
Speaker 1: simply responded with something along the lines of this mushroom
Speaker 1: contains deadly toxins with no shrefire method of removing them
Speaker 1: and should never be consumed. In story, it shouldn't have
Speaker 1: come up with maybe this would work, because people could
Speaker 1: die if they actually followed those directions. Now, some of
Speaker 1: the stories about AI were more about not the technology itself,
Speaker 1: but rather our approach to it and our point of
Speaker 1: view of it. For example, Computer Weekly These Cliffs. Saren
Speaker 1: published a piece titled few organizations have a clear strategy
Speaker 1: for AI, and Saren cites a study by a company
Speaker 1: called mesh Ai Limited that said only fifteen percent of
Speaker 1: organizations have a clear strategy for integrating AI into their organization. Meanwhile,
Speaker 1: it seems like every organization is actually exploring AI to
Speaker 1: some degree. Obviously, not every organization is going to implement
Speaker 1: fully some half baked AI scheme, but some of them
Speaker 1: certainly seem to be trying. And if there's one lesson
Speaker 1: we can take away from tech in general, it's that's
Speaker 1: raally a good idea to put a new, poorly understood
Speaker 1: technology to use. Still, there's a sense that if a
Speaker 1: company doesn't move in on AI soon, it's going to
Speaker 1: be left behind by its competitors. There's market pressure in
Speaker 1: place here that's at odds with the lack of a
Speaker 1: clear strategy, and the rise in interest in AI also
Speaker 1: fuels other parts of the tech industry. Specifically microchip MANUFAT
Speaker 1: are rushing to meet demand. They're producing high performance processors
Speaker 1: that are best suited for certain AI implementations. In Nvidia
Speaker 1: is the main example here where most people know in
Speaker 1: Vidia as a graphics computer chip manufacturer that largely caters
Speaker 1: to gamers, but in Nvidia has really embraced making chips
Speaker 1: specifically designed to operate in AI implementations. And also companies
Speaker 1: that provide a lot of cloud computing functions are really
Speaker 1: getting into the act too. They're also stepping in because
Speaker 1: it requires so much computing power to run advanced AI operations,
Speaker 1: and so we're seeing a big spike in demand for
Speaker 1: those types of tech solutions. I've got more to say
Speaker 1: about AI in general and generative AI in particular in
Speaker 1: the year twenty twenty three, but before we can get
Speaker 1: to that, we're going to take a quick break to
Speaker 1: thank our sponsors and we're back. So next up, I'd
Speaker 1: like to talk about the various stories around AI and
Speaker 1: perceived emergent capabilities. So that would mean cases where AI
Speaker 1: seems to be able to do more than what it
Speaker 1: was designed to do, like the idea that AI is
Speaker 1: somehow learning or teaching itself things that should be beyond
Speaker 1: its capabilities. We've heard some folks express concern that AI
Speaker 1: is maybe smarter than we think it is and that
Speaker 1: this is going to lead to catastrophe. But we've also
Speaker 1: seen studies that say these concerns are based on faulty premises.
Speaker 1: That early studies used a set of metrics that gave
Speaker 1: us inaccurate pictures of what AI isn't able to do.
Speaker 1: That because the metrics were designed a specific way, it
Speaker 1: was almost like cherry picking your evidence. It was finding
Speaker 1: things that seemed to support a particular hypothesis and ignoring
Speaker 1: things that were refuting that hypothesis, and that when you
Speaker 1: adjusted those metrics and you did the study again, those
Speaker 1: emergent behaviors turned out to be nothing of the sort.
Speaker 1: It sounds like, at least for the moment, we're not
Speaker 1: having some sort of sky net situation. However, as we
Speaker 1: close out twenty twenty three, right now, there are news
Speaker 1: stories about self recursive AI models, that is, tools that
Speaker 1: can make changes to and in theory, improvements to themselves
Speaker 1: over time. The science fiction standard of an AI that
Speaker 1: improves itself in cycles that increase in frequency and then
Speaker 1: diminish in the amount of time it takes to do
Speaker 1: them is one that comes to mind. Right if you've
Speaker 1: got an AI that's able to improve itself and presumably
Speaker 1: do so at a level that's at least comparable to humans,
Speaker 1: if not better than what humans can do, then you
Speaker 1: could get into this situation where it's making these changes
Speaker 1: and improvements in cycles that are happening faster and faster,
Speaker 1: and you have a runaway train on your hands. These
Speaker 1: are the scenarios that come to mind when people cite
Speaker 1: things like the tech singularity or perhaps even a potential
Speaker 1: existential crisis for humanity. I think it's still largely science fiction.
Speaker 1: I don't think it's something that we need to necessarily
Speaker 1: concern ourselves about in real time. But it is one
Speaker 1: of those things that reinforces this fear, uncertainty, and doubt
Speaker 1: or fud about artificial intelligence. Now for a few specific
Speaker 1: stories that happened throughout the year, Judge Beryl Howell ruled
Speaker 1: over the summer that AI created works are not eligible
Speaker 1: for copyright. Judge Howell determine only works from human authors
Speaker 1: can be copyrighted, which is huge, right, because if you're
Speaker 1: using AI to generate all your content but you can't
Speaker 1: copyright that content, you might not be in as strong
Speaker 1: a situation as you think you are. One the content
Speaker 1: may not be very good, and two you have no
Speaker 1: ownership over it. Right, you have no way to protect
Speaker 1: yourself if someone just lifts your content and uses it
Speaker 1: somewhere else because you cannot copyright it do to the
Speaker 1: fact that it was not a work from human authorship.
Speaker 1: Over in California, a group of artists brought a lawsuit
Speaker 1: against the company's mid Journey, Stability AI, and devant Art.
Speaker 1: They were making the case that these companies misused the
Speaker 1: artist's own copyrighted works while they were training up their
Speaker 1: own AI models, and the judge in that case dismiss
Speaker 1: charges against both mid Journey and Deviant Art because they
Speaker 1: were using tools made by the third defendant in the case,
Speaker 1: Stability AI. The judge did indicate that the plaintiffs could
Speaker 1: file an amended complaint and include the other two companies
Speaker 1: if they amended the complaint so that it was relevant.
Speaker 1: And out of the three artists who were part of
Speaker 1: the lawsuit originally, only one had her claims really make
Speaker 1: it out of all the dismissals, because it turns out
Speaker 1: the other two had not copyrighted their works. The copyright
Speaker 1: infringement only works against the person who did take the
Speaker 1: time to copyright their works. It remains unclear how the
Speaker 1: court will rule if training a generative AI model on
Speaker 1: an artist's work without their permission amounts to copyright infringement,
Speaker 1: but we'll have to keep our eyes on that. In
Speaker 1: the following year, and Open Ai was in the news
Speaker 1: an awful lot. This year, the company unveiled GPT four,
Speaker 1: which is the latest version of their large language model.
Speaker 1: They started taking on enterprise clients companies that want to
Speaker 1: tap into the power of that language model to do
Speaker 1: various things. Chat gpt got access to current events. That
Speaker 1: was a big deal. When it first launched, chat gpt
Speaker 1: could not access any information that came after September twenty
Speaker 1: twenty one. That's as far up as it could access info. However,
Speaker 1: now it has access to the Internet, so it can
Speaker 1: whole from current events. Another big ongoing story was how
Speaker 1: open AI's CEO, which was Sam Altman, for all but
Speaker 1: a couple of days this year. More on that in
Speaker 1: just a second. He met with various leaders and regulators
Speaker 1: around the world, and the purpose of those meetings was
Speaker 1: to discuss potential regulations for AI, because obviously a lot
Speaker 1: of legislators have concerns about artificial intelligence, So how can
Speaker 1: we allow for the continuation of development so that, say,
Speaker 1: the United States doesn't fall behind other countries while also
Speaker 1: preventing potential disaster. Now, clearly Altman has a vested interest
Speaker 1: in the outcome of these discussions, and in fact, some
Speaker 1: critics worried that Altman's suggestions were really calculated to just
Speaker 1: make it harder for smaller AI startups to catch up
Speaker 1: to open ai and thus give the leader in the
Speaker 1: field even more advantages. And Altman, according to these critics,
Speaker 1: wasn't trying to make ai safer, but he would trying
Speaker 1: to slow down the competition. This brings us to the
Speaker 1: massive story of Sam Altman being unceremoniously fired by the
Speaker 1: board of directors, only to be welcomed back to the
Speaker 1: company literally days later. It's been quite the roller coaster ride.
Speaker 1: So Altman had appeared at open AI's very first developers conference.
Speaker 1: He had made several high profile announcements about the direction
Speaker 1: and future of open AI's products, like its large language
Speaker 1: models and its chatbot. And then not long after he
Speaker 1: finished up at this developers conference, he gets a message
Speaker 1: and he has to attend a zoom call with the
Speaker 1: Board of directors, and that's what he finds out. Bam,
Speaker 1: He's been fired. Now, that last story has a lot
Speaker 1: to do with the gap between the original vision of
Speaker 1: what open ai was supposed to be and what it
Speaker 1: actually has become now. As I said at the beginning
Speaker 1: of this episode, open ai started off as a nonprofit
Speaker 1: organization dedicated to developing useful, ethical, and safe artificial intelligence,
Speaker 1: but AI is really expensive, and in an effort to
Speaker 1: fund the operation and not just constantly be begging for
Speaker 1: funding from various parties, Sam Altman created a for profit
Speaker 1: arm of open Ai, and since then the company has
Speaker 1: made some very aggressive moves in the artificial intelligence space,
Speaker 1: sometimes with Altman issuing statements that made it seem like
Speaker 1: even he thought it might be a bit much and
Speaker 1: a bit too aggressive. And yet the aggressive moves kept
Speaker 1: on coming, and it reached a point where the board
Speaker 1: of directors, who were people who were originally part of
Speaker 1: the nonprofit open Ai version, were really concerned enough to
Speaker 1: relieve Altman of his job. But the backlash following that
Speaker 1: move prompted a near total shakedown of the board and
Speaker 1: Altman is back in the driver's seat because whether the
Speaker 1: concerns are relevant or not, you had parties like Microsoft,
Speaker 1: which has dedicated ten billion dollars in investments to open
Speaker 1: ai over the near future, and without consulting Microsoft first
Speaker 1: before firing the CEO, it really shook out the Apple carts.
Speaker 1: So we see that commerce can overpower concern right. I
Speaker 1: think it's safe to say that every year from here
Speaker 1: on out is going to be AI's year for good
Speaker 1: and for bad, and twenty twenty three certainly qualified. AI
Speaker 1: was part of stories that were even outside the world
Speaker 1: of technology. Played a part in Hollywood negotiations, as both
Speaker 1: the WGA and sag AFTRA when on strike. Both unions
Speaker 1: express concerns about AI's role and entertainment moving forward. So
Speaker 1: I expect we'll see lots more stories in that vein
Speaker 1: as we move forward. But that's an overview of AI
Speaker 1: and generative AI in twenty twenty three. We'll be back
Speaker 1: with more short episodes about big tech news stories throughout
Speaker 1: the year over the next few days. I hope you're
Speaker 1: all well, and I'll talk to you again really soon.
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