Speaker 1: What happens when an artificially intelligent network figures out how
Speaker 1: to beat your anti AI test. Time to come up
Speaker 1: with a new test. I'm Jonathan Strickland, and this is
Speaker 1: Tech Stuff Daily. If you've ever filled out any sort
Speaker 1: of online form, you've probably encountered a caption test. The
Speaker 1: word stands for a completely automated public touring test to
Speaker 1: tell computers and humans apart. It's a bit of a mouthful,
Speaker 1: so I'm glad we can stick with CAPTA. Perhaps the
Speaker 1: most interesting part of that name is the Turing test.
Speaker 1: Named after a computer scientist and codebreaker Alan Turing, the
Speaker 1: Turing test has evolved into an interesting concept. The Turing
Speaker 1: test of today is a scenario in which a human
Speaker 1: interrogator uses some sort of interface, often a computer terminal,
Speaker 1: to communicate with someone else. That someone else might be
Speaker 1: a human being, or it might be a computer program.
Speaker 1: If enough human interrogators are fooled into thinking the computer
Speaker 1: program is actually a person, that program is said to
Speaker 1: have passed the Turing test. The program has appeared to
Speaker 1: be indistinguishable from a human being to a significant percentage
Speaker 1: of interrogators. In the case of captures, the Turing test
Speaker 1: is a sort of shorthand for a gateway that is
Speaker 1: meant to allow humans to pass through while stopping artificially
Speaker 1: intelligent constructs at the border. The general idea behind the
Speaker 1: captured test is that it should be something relatively simple
Speaker 1: for a human to complete, but very challenging for a
Speaker 1: program to do. This is important because you don't want
Speaker 1: to capture to be challenging for humans to complete, since
Speaker 1: that would discourage people from filling out the form in
Speaker 1: the first place. It doesn't do you any good if
Speaker 1: you prevent anyone from going through the gate. There are
Speaker 1: some things that robots and software can do much better
Speaker 1: than humans, such as drive a car under normal conditions
Speaker 1: or play a game of chess. But there are some
Speaker 1: things that we humans can do pretty easily that AI
Speaker 1: has a really tough time handling. At least that's how
Speaker 1: things stand now. AI is getting better all the time,
Speaker 1: and we humans have seemed to stalled out all the
Speaker 1: little bit. Evolutionarily speaking, a simple capture example that you've
Speaker 1: likely seen would be a series of letters and numbers
Speaker 1: that are deformed or semi obscured by other shapes. As humans,
Speaker 1: we can recognize which shapes represent letters or numbers and
Speaker 1: which are just distractions, But computers have a lot harder
Speaker 1: time separating the signal from the noise. Enter the AI
Speaker 1: research firm Vicarious, a company that has received funding from
Speaker 1: such tech luminaries as Mark Zuckerberg and Jeff Bezos. Vicarious
Speaker 1: created an artificial neural network that the firm claims can
Speaker 1: solve capture puzzles. That's pretty impressive considering some of the
Speaker 1: trickier puzzles can stomp human beings. Google's Recapture has an
Speaker 1: eighty seven percent solution rate among humans. The neural network
Speaker 1: can suss out the shape of letters and numbers while
Speaker 1: ignoring all that distracting noise. A neural network mimics the
Speaker 1: way our brains work. Instead of neurons the nerve cells
Speaker 1: and our brains, the network uses network computer systems. The
Speaker 1: artificial neurons attack problems in layers, with each neuron and
Speaker 1: effectively tackling a different part of the problem, and then
Speaker 1: combining all of those solutions together to come up with
Speaker 1: an overall solution to the caption. These networks aren't just
Speaker 1: automatically good at completing tasks. You have to actually teach
Speaker 1: them how to do it, just as you would a
Speaker 1: human being. Back in two thousand twelve, folks across the
Speaker 1: Internet reacted in glee at the news that a network
Speaker 1: of sixteen thousand computers had learned how to recognize cats
Speaker 1: by examining ten million images culled from YouTube videos. What
Speaker 1: could be more representative of the modern online experience, But
Speaker 1: that example illustrates how complicated this process is. It took
Speaker 1: thousands of computers working together to learn how to identify
Speaker 1: a cat based off a sample size of ten million images.
Speaker 1: So while it is possible to teach computers to do
Speaker 1: things like recognize cats or captures, it's not easy. Humans
Speaker 1: are still better at this than machines are at learning. Unfortunately,
Speaker 1: captured tests aren't really about learning, or else we would
Speaker 1: still have a leg up on the robotic competition. As
Speaker 1: AI has become better at recognizing captures, designers have created
Speaker 1: more difficult puzzles that obviously impacts both humans and non humans,
Speaker 1: according to researchers with Vicarious. Since Vicarious researchers sounds weird,
Speaker 1: even with new and improved captures, their software can solve
Speaker 1: the puzzles sixty six point six percent of the time.
Speaker 1: With Google's recapture system fifty seven point four percent of
Speaker 1: the time for Yahoo's captures and fifty seven point one
Speaker 1: percent at a time with Paypals captures. According to security experts,
Speaker 1: this means we can expect to see hackers make use
Speaker 1: of those same techniques within a few months. That will
Speaker 1: mean the capture will cease to be an effective gatekeeper
Speaker 1: to keep bots out. We may see more creative approaches.
Speaker 1: For example, some sites use a short written passage followed
Speaker 1: by a simple question about the reading material to test
Speaker 1: for human nests. Eventually, AI and neural networks may be
Speaker 1: good enough to navigate those systems that we humans are
Speaker 1: used to without any trouble at all. At that point,
Speaker 1: we may need to re examine how we protect sites
Speaker 1: and services from abuse. Or maybe by then we'll have
Speaker 1: merged with the machines and entered the singularity. To learn
Speaker 1: more about artificial intelligence and how machines and human intelligence
Speaker 1: may one day merge into one. Subscribe to the tech
Speaker 1: Stuff podcast. We also talk about other texts like catapults,
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Speaker 1: The show publishes on Wednesdays and Fridays, and it's a
Speaker 1: deep dive into all things tell I'll see you again soon.