The Singularity Isn’t Here… Yet

So, GPT-4 is out, and it’s all over for us meatbags. Hype has reached fever pitch, here in the latest and greatest of AI chatbots we finally have something that can surpass us. The singularity has happened, and personally I welcome our new AI overlords.

Hang on a minute though, I smell a rat, and it comes in defining just what intelligence is. In my time I’ve hung out with a lot of very bright people, as well as a lot of not-so-bright people who nonetheless think they’re very clever simply because they have a bunch of qualifications and diplomas. Sadly the experience hasn’t bestowed God-like intelligence on me, but it has given me a handle on the difference between intelligence and knowledge.

My premise is that we humans are conditioned by our education system to equate learning with intelligence, mostly because we have flaky CPUs and worse memory, and that makes learning something a bit of an effort. Thus when we see an AI, a machine that can learn everything because it has a decent CPU and memory, we’re conditioned to think of it as intelligent because that’s what our schools train us to do. In fact it seems intelligent to us not because it’s thinking of new stuff, but merely through knowing stuff we don’t because we haven’t had the time or capacity to learn it.

Growing up and making my earlier career around a major university I’ve seen this in action so many times, people who master one skill, rote-learning the school textbook or the university tutor’s pet views and theories, and barfing them up all over the exam paper to get their amazing qualifications. On paper they’re the cream of the crop, and while it’s true they’re not thick, they’re rarely the special clever people they think they are. People with truly above-average intelligence exist, but in smaller numbers, and their occurrence is not a 1:1 mapping with holders of advanced university degrees.

Even the examples touted of GPT’s brilliance tend to reinforce this. It can do the bar exam or the SAT test, thus we’re told it’s as intelligent as a school-age kid or a lawyer. Both of those qualifications follow our educational system’s flawed premise that education equates to intelligence, so as a machine that’s learned all the facts it follows my point above about learning by rote. The machine has simply barfed up what it has learned the answers are onto the exam paper. Is that intelligence? Is a search engine intelligent?

This is not to say that tools such as GPT-4 are not amazing creations that have a lot of potential to do good things aside from filling up the internet with superficially readable spam. Everyone should have a play with them and investigate their potential, and from that will no doubt come some very interesting things. Just don’t confuse them with real people, because sometimes meatbags can surprise you.

Exploring Animal Intelligence Hack Chat

Join us on Wednesday, October 21st at noon Pacific for the Exploring Animal Intelligence Hack Chat with Hans Forsberg!

From our lofty perch atop the food chain it’s easy to make the assumption that we humans are the last word in intelligence. A quick glance at social media or a chat with a random stranger at the store should be enough to convince you that human intelligence isn’t all it’s cracked up to be, or at least that it’s not evenly distributed. But regardless, we are pretty smart, thanks to those big, powerful brains stuffed into our skulls.

We’re far from the only smart species on the planet, though. Fellow primates and other mammals clearly have intelligence, and we’ve seen amazingly complex behaviors from animals in just about every taxonomic rank. But it’s the birds who probably stuff the most functionality into their limited neural hardware, with tool use, including the ability to make new tools, being common, along with long-distance navigation, superb binocular vision, and of course the ability to rapidly maneuver in three-dimensions while flying.

Hans Forsberg has taken an interest in avian intelligence lately, and to explore just what’s possible he devised a fiendishly clever system to train his local magpie flock to clean up his yard, which he calls “BirdBox”. We recently wrote up his initial training attempts, which honestly bear a strong resemblance to training a machine learning algorithm, which is probably no small coincidence since his professional background is with neural networks. He has several years of work into his birds, and he’ll stop by the Hack Chat to talk about what goes into leveraging animal intelligence, what we can learn about our systems from it, and where BirdBox goes next.

join-hack-chatOur Hack Chats are live community events in the Hackaday.io Hack Chat group messaging. This week we’ll be sitting down on Wednesday, October 21 at 12:00 PM Pacific time. If time zones baffle you as much as us, we have a handy time zone converter.

Click that speech bubble to the right, and you’ll be taken directly to the Hack Chat group on Hackaday.io. You don’t have to wait until Wednesday; join whenever you want and you can see what the community is talking about.

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I’m A Tricorder, Not A Doctor, Jim!

Machine learning and automated technologies are poised to disrupt employment in many industries — looking at you autonomous vehicles — and medicine is not immune to this encroachment. The Qualcomm Tricorder competition run by the X-Prize foundation has just wrapped, naming [Final Frontier Medical Devices]’s DxtER the closest thing available to Star Trek’s illustrious medical tricorder which is an oft referenced benchmark for diagnostic automation.

The competition’s objective was for teams to develop a handheld, non-invasive device that could diagnose 12 diseases and an all-clear result in 24 hours or less without any assistance. [Dynamical Biomarkers Group] took second place prize worth $1 million, with [Final Frontier Medical devices] — a company run by two brothers and mostly financed by themselves and their siblings — snagging the top prize of $2.5 million. DxtER comes equipped with a suite of sensors to monitor your vitals and body chemistry, and is actually able to diagnose 34 conditions well in advance of the time limit by monitoring vital signs and comparing them to a wealth of medical databases and encyclopediae. The future, as they say, is now.

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