From Picasso’s “The Young Ladies of Avignon” to Munch’s “The Scream,” what was it about some paintings that arrested people’s attention upon viewing them, that cemented them in the canon of art history as iconic works?
In many cases, it’s because the artist incorporated a technique, form or style that had never been used before. They exhibited a creative and innovative flair that would go on to be mimicked by artists for years to come.
Throughout human history, experts have often highlighted these artistic innovations, using them to judge a painting’s relative worth. But can a painting’s level of creativity be quantified by Artificial Intelligence (AI)?
At Rutgers’ Art and Artificial Intelligence Laboratory, my colleagues and I proposed a novel algorithm that assessed the creativity of any given painting, while taking into account the painting’s context within the scope of art history.
In the end, we found that, when introduced with a large collection of works, the algorithm can successfully highlight paintings that art historians consider masterpieces of the medium.
The results show that humans are no longer the only judges of creativity. Computers can perform the same task – and may even be more objective.
We make a huge number of decisions every day. When it comes to eating, for example, we make 200 more decisions than we’re consciously aware of every day. How is this possible? Because, as Daniel Kahneman has explained, while we’d like to think our decisions are rational, in fact many are driven by gut feel and intuition. The ability to reach a decision based on what we know and what we expect is an inherently human characteristic.
The problem we face now is that we have too many decisions to make every day, leading to decision fatigue – we find the act of making our own decisions exhausting. Even more so than simply deliberate different options or being told by others what to do.
Why not allow technology to ease the burden of decision-making? The latest smart technologies are designed to monitor and learn from our behavior, physical performance, work productivity levels and energy use. This is what has been called Era Three of Automation – when machine intelligence becomes faster and more reliable than humans at making decisions.
Technology enhanced with artificial intelligence is all around us. You might have a robot vacuum cleaner ready to leap into action to clean up your kitchen floor. Maybe you asked Siri or Google—two apps using decent examples of artificial intelligence technology—for some help already today. The continual enhancement of AI and its increased presence in our world speak to achievements in science and engineering that have tremendous potential to improve our lives.
Or destroy us.
At least, that’s the central theme in the new Avengers: Age of Ultron movie with headliner Ultron serving as exemplar for AI gone bad. It’s a timely theme, given some high-profile AI concerns lately. But is it something we should be worried about?
It’s difficult to deny that humans began as Homo sapiens, an evolutionary offshoot of the primates. Nevertheless, for most of what is properly called “human history” (that is, the history starting with the invention of writing), most of Homo sapiens have not qualified as “human”—and not simply because they were too young or too disabled.
In sociology, we routinely invoke a trinity of shame—race, class, and gender—to characterize the gap that remains between the normal existence of Homo sapiens and the normative ideal of full humanity. Much of the history of social science can be understood as either directly or indirectly aimed at extending the attribution of humanity to as much of Homo sapiens as possible. It’s for this reason that the welfare state is reasonably touted as social science’s great contribution to politics in the modern era. But perhaps membership in Homo sapiens is neither sufficient nor even necessary to qualify a being as “human.” What happens then?
This article was originally published on The Conversation.
After years of trying, it looks like a chatbot has finally passed the Turing Test. Eugene Goostman, a computer program posing as a 13-year old Ukrainian boy, managed to convince 33% of judges that he was a human after having a series of brief conversations with them. (Try the program yourself here.)
Most people misunderstand the Turing test, though. When Alan Turing wrote his famous paper on computing intelligence, the idea that machines could think in any way was totally alien to most people. Thinking – and hence intelligence – could only occur in human minds.
Turing’s point was that we do not need to think about what is inside a system to judge whether it behaves intelligently. In his paper he explores how broadly a clever interlocutor can test the mind on the other side of a conversation by talking about anything from maths to chess, politics to puns, Shakespeare’s poetry or childhood memories. In order to reliably imitate a human, the machine needs to be flexible and knowledgeable: for all practical purposes, intelligent.
The problem is that many people see the test as a measurement of a machine’s ability to think. They miss that Turing was treating the test as a thought experiment: actually doing it might not reveal very useful information, while philosophizing about it does tell us interesting things about intelligence and the way we see machines.
Andrea Kuszewski is a behavior therapist and consultant, science writer, and robopsychologist at Syntience in San Francisco. She is interested in creativity, intelligence, and learning, in both humans and machines. Find her on Twitter at @AndreaKuszewski.
Before you read this post, please see “I, Robopsychologist, Part 1: Why Robots Need Psychologists.”
A current trend in AI research involves attempts to replicate a human learning system at the neuronal level—beginning with a single functioning synapse, then an entire neuron, the ultimate goal being a complete replication of the human brain. This is basically the traditional reductionist perspective: break the problem down into small pieces and analyze them, and then build a model of the whole as a combination of many small pieces. There are neuroscientists working on these AI problems—replicating and studying one neuron under one condition—and that is useful for some things. But to replicate a single neuron and its function at one snapshot in time is not helping us understand or replicate human learning on a broad scale for use in the natural environment.
We are quite some ways off from reaching the goal of building something structurally similar to the human brain, and even further from having one that actually thinks like one. Which leads me to the obvious question: What’s the purpose of pouring all that effort into replicating a human-like brain in a machine, if it doesn’t ultimately function like a real brain?
If we’re trying to create AI that mimics humans, both in behavior and learning, then we need to consider how humans actually learn—specifically, how they learn best—when teaching them. Therefore, it would make sense that you’d want people on your team who are experts in human behavior and learning. So in this way, the field of psychology is pretty important to the successful development of strong AI, or AGI (artificial general intelligence): intelligence systems that think and act the way humans do. (I will be using the term AI, but I am generally referring to strong AI.)
Basing an AI system on the function of a single neuron is like designing an entire highway system based on the function of a car engine, rather than the behavior of a population of cars and their drivers in the context of a city. Psychologists are experts at the context. They study how the brain works in practice—in multiple environments, over variable conditions, and how it develops and changes over a lifespan.
The brain is actually not like a computer; it doesn’t always follow the rules. Sometimes not following the rules is the best course of action, given a specific context. The brain can act in unpredictable, yet ultimately serendipitous ways. Sometimes the brain develops “mental shortcuts,” or automated patterns of behavior, or makes intuitive leaps of reason. Human brain processes often involve error, which also happens to be a very necessary element of creativity, innovation, and human learning in general. Take away the errors, remove serendipitous learning, discount intuition, and you remove any chance of any true creative cognition. In essence, when it gets too rule-driven and perfect, it ceases to function like a real human brain.
To get a computer that thinks like a person, we have to consider some of the key strengths of human thinking and use psychology to figure out how to foster similar thinking in computers.
Andrea Kuszewski is a behavior therapist and consultant, science writer, and robopsychologist at Syntience in San Francisco. She is interested in creativity, intelligence, and learning, in both humans and machines. Find her on Twitter a @AndreaKuszewski.
“My brain is not like a computer.”
The day those words were spoken to me marked a significant milestone for both me and the 6-year-old who uttered them. The words themselves may not seem that profound (and some may actually disagree), but that simple sentence represented months of therapy, hours upon hours of teaching, all for the hope that someday, a phrase like that would be spoken at precisely the right time. When he said that to me, he was showing me that the light had been turned on, the fire ignited. And he was letting me know that he realized this fact himself. Why was this a big deal?
I began my career as a behavior therapist, treating children on the autism spectrum. My specialty was Asperger syndrome, or high-functioning autism. This 6-year-old boy, whom I’ll call David, was a client of mine that I’d been treating for about a year at that time. His mom had read a book that had recently come out, The Curious Incident of the Dog in the Night-Time, and told me how much David resembled the main character in the book (who had autism), in regards to his thinking and processing style. The main character said, “My brain is like a computer.”
David heard his mom telling me this, and that quickly became one of his favorite memes. He would say things like “I need input” or “Answer not in the database” or simply “You have reached an error,” when he didn’t know the answer to a question. He truly did think like a computer at that point in time—he memorized questions, formulas, and the subsequent list of acceptable responses. He had developed some extensive social algorithms for human interactions, and when they failed, he went into a complete emotional meltdown.
My job was to change this. To make him less like a computer, to break him out of that rigid mindset. He operated purely on an input-output framework, and if a situation presented itself that wasn’t in the database of his brain, it was rejected, returning a 404 error.
I can feel it in the air, so thick I can taste it. Can you? It’s the we’re-going-to-build-an-artificial-brain-at-any-moment feeling. It’s exuded into the atmosphere from news media plumes (“IBM Aims to Build Artificial Human Brain Within 10 Years”) and science-fiction movie fountains…and also from science research itself, including projects like Blue Brain and IBM’s SyNAPSE. For example, here’s a recent press release about the latter:
Today, IBM (NYSE: IBM) researchers unveiled a new generation of experimental computer chips designed to emulate the brain’s abilities for perception, action and cognition.
Now, I’m as romantic as the next scientist (as evidence, see my earlier post on science monk Carl Sagan), but even I carry around a jug of cold water for cases like this. Here are four flavors of chilled water to help clear the palate.
The Worm in the Pass
In the story about the Spartans at the Battle of Thermopylae, 300 soldiers prevent a million-man army from making their way through a narrow mountain pass. In neuroscience it is the 300 neurons of the roundworm C. elegans that stand in the way of our understanding the huge collections of neurons found in our or any mammal’s brain.