Robots and Other Artificial Intelligence Applications – II

This issue, the second of three straight weeks of this series (the jobs report, which drives the monthly AJSN, will be released a week later than usual), is about AI-related articles from January, some of which point up its largest concerns.

 “An A.I. Pioneer on What We Should Really Fear,” by David Marchese in the January 1^st New York Times, an interview of prominent front-line researcher Yejin Choi, dealt with the problem of consciousness.  A Google engineer was fired last year for claiming that some of its products could think, but that view actually cannot be refuted, as we do not know from where sentience arises.  Choi said that “when you work so close to A.I., you see a lot of limitations”; we know that such software, and that’s what it is, can be superhuman or even unbeatable in settings where the rules are well defined, such as playing chess or checkers, but in more general situations it may not match a small child’s abilities, as, per Choi, “A.I. struggles with basic common sense,” as “what’s easy for machines can be hard for humans and vice versa.”  Choi in effect considered the decades-old autonomous goal-seeking problem, in which devices do not have constraints people would consider obvious, the main AI issue, a highly reasonable view.

Continuing in another section of the same newspaper was “Consciousness in Robots Was Once Taboo.  Now It’s the Last Word,” by Oliver Whang on January 6^th.  The author discussed the views of prominent AI engineer Hod Lipson, who was unsure technology could not be sentient, and said “there is no consensus around what it actually refers to.”  Per Lipson, as far as we can tell, “the fundamental difference among types of consciousness – human consciousness and octopus consciousness and rat consciousness, for example – is how far into the future an entity is able to imagine itself.”  That’s a viable theoretical start, but who can determine what a rat comprehends?  The engineer claimed that, while it may improve, currently “we’re doing the cockroach version.”  This piece has much more, which also clarified that we have a long way to go in understanding what silicon and nonhuman living things think, or in the former case if they do at all.  Hard material, which may defy insight indefinitely.

Swerving to a most practical AI application, we have “White Castle hiring robots to ‘give the right tools’ for serving more ‘hot and tasty food’: VP,” by Kristen Altus in Fox Business on January 7^th.  We’ve already seen Flippy, a Miso Robotics device expert at preparing hamburgers and French fries, but not with roll-outs at 100 locations, as has happened here.  Now we can consider restaurant automata a natural response to higher wages, with apparently at least one robust, production-ready product on offer.

A generic term for the internals of chatbots similar to ChatGPT, able to create “text, images, sounds and other media in response to short prompts,” is “generative artificial intelligence,” and was the subject of “A New Area of A.I. Booms, Even Amid the Tech Gloom” (Erin Griffith and Cade Metz, The New York Times, January 7^th).  New companies, such as Stability AI, Jasper, and ChatGPT’s OpenAI, have had little recent problem attracting venture capital, with $1.37 billion reaching the sub-sector in 2022 alone.  Although generative AI has been in progress “for years,” only since early last, “when OpenAI unveiled a system called DALL-E that let people generate photo-realistic images simply by describing what they wanted to see,” did it reach the funding forefront.  And there will be much more.

Also in the Times, Cade Metz himself hit the second major implementation issue, in “AI Is Becoming More Conversant, But Will It Get More Honest?” (January 10^th).  Problems described here take several different forms.  The first is simple factual errors, as explained by a founder of startup Character.AI, who said “these systems are not designed for truth – they are designed for plausible conversation.”  Second is the effect of extreme and easily refutable views, such as denying the Holocaust, picked up along with valid Internet statements.  A third situation arises when chatbots relay reasonable but still debatable views as facts, and beyond that we have the hardest problem of all – when AI products analyze data and reach conclusions factually defensible but offensive to modern sensibilities.  This article did not get into these.

On January 20^th, also by Metz in the New York Times, we saw “How Smart Are the Robots Getting?”  A knotty problem simplified by strict definitions, of which we have possibilities, including passing the 72-year-old Turing Test, “a subjective measure” accomplished when people questioning automata “feel convinced that they are talking to another person.”  Metz ticked off specific AI accomplishments, but the battleground is in less specific settings.  The real current issue “is that when a bot mimics conversation, it can seem smarter than it really is.  When we see a flash of humanlike behavior in a pet or a machine, we tend to assume it behaves like us in other ways, too – even when it does not.”  That is similar to how skilled stage magicians use human shortcomings, such as inability to accurately determine directions of sounds, to bolster and even make their illusions.  There are other intelligence tests described here, and assessing them is not easy.

Next week I continue, maybe with artificial intelligence updates happening after this post’s publication date, and with overall conclusions.