Go is a two-player board game that originated in China more than 2,500 years ago. The rules are simple, but Go is widely considered the most difficult strategy game to master. For artificial intelligence researchers, building an algorithm that could take down a Go world champion represents the holy grail of achievements.
Well, consider the holy grail found. A team of researchers led by Google DeepMind researchers David Silver and Demis Hassabis designed an algorithm, called AlphaGo, which in October 2015 handily defeated back-to-back-to-back European Go champion Fan Hui five games to zero. And as a side note, AlphaGo won 494 out of 495 games played against existing Go computer programs prior to its match with Hui — AlphaGo even spotted inferior programs four free moves.
“It’s fair to say that this is five to 10 years ahead of what people were expecting, even experts in the field,” Hassabis said in a news conference Tuesday. Read More
If you’ve ever tried to hold a conversation with a chatbot like CleverBot, you know how quickly the conversation turns to nonsense, no matter how hard you try to keep it together.
But now, a research team led by Bruno Golosio, assistant professor of applied physics at Università di Sassari in Italy, has taken a significant step toward improving human-to-computer conversation. Golosio and colleagues built an artificial neural network, called ANNABELL, that aims to emulate the large-scale structure of human working memory in the brain — and its ability to hold a conversation is eerily human-like. Read More
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.
Think you’re good at classic arcade games such as Space Invaders, Breakout and Pong? Think again.
In a groundbreaking paper published yesterday in Nature, a team of researchers led by DeepMind co-founder Demis Hassabis reported developing a deep neural network that was able to learn to play such games at an expert level.
What makes this achievement all the more impressive is that the program was not given any background knowledge about the games. It just had access to the score and the pixels on the screen.
It didn’t know about bats, balls, lasers or any of the other things we humans need to know about in order to play the games.
But by playing lots and lots of games many times over, the computer learned first how to play, and then how to play well.
Eye tracking devices sound a lot more like expensive pieces of scientific research equipment than joysticks – yet if the latest announcements about the latest Assassin’s Creed game are anything to go by, eye tracking will become a commonplace feature of how we interact with computers, and particularly games.
Eye trackers provide computers with a user’s gaze position in real time by tracking the position of their pupils. The trackers can either be worn directly on the user’s face, like glasses, or placed in front of them, such as beneath a computer monitor for example.
Eye trackers are usually composed of cameras and infrared lights to illuminate the eyes. Although it’s invisible to the human eye, the cameras can use infrared light to generate a grayscale image in which the pupil is easily recognizable. From the position of the pupil in the image, the eye tracker’s software can work out where the user’s gaze is directed – whether that’s on a computer screen or looking out into the world.
But what’s the use? Well, our eyes can reveal a lot about a person’s intentions, thoughts and actions, as they are good indicators of what we’re interested in. In our interactions with others we often subconsciously pick up on cues that the eyes give away. So it’s possible to gather this unconscious information and use it in order to get a better understanding of what the user is thinking, their interests and habits, or to enhance the interaction between them and the computer they’re using.
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.
Neuroskeptic is a neuroscientist who takes a skeptical look at his own field and beyond at the Neuroskeptic blog.
Why do we sleep? We spend a third of our lives doing so, and all known animals with a nervous system either sleep, or show some kind of related behaviour. But scientists still don’t know what the point of it is.
There are plenty of theories. Some researchers argue that sleep has no specific function, but rather serves as evolution’s way of keeping us inactive, to save energy and keep us safely tucked away at those times of day when there’s not much point being awake. On this view, sleep is like hibernation in bears, or even autumn leaf fall in trees.
But others argue that sleep has a restorative function—something about animal biology means that we need sleep to survive. This seems like common sense. Going without sleep feels bad, after all, and prolonged sleep deprivation is used as a form of torture. We also know that in severe cases it can lead to mental disturbances, hallucinations and, in some laboratory animals, eventually death.
Waking up after a good night’s sleep, you feel restored, and many studies have shown the benefits of sleep for learning, memory, and cognition. Yet if sleep is beneficial, what is the mechanism?
Recently, some neuroscientists have proposed that the function of sleep is to reorganize connections and “prune” synapses—the connections between brain cells. Last year, one group of researchers, led by Gordon Wang of Stanford University reviewed the evidence for this idea in a paper called Synaptic plasticity in sleep: learning, homeostasis and disease.
This illustration, taken from their paper, shows the basic idea:
While we’re awake, your brain is forming memories. Memory formation involves a process called long-term potentiation (LTP), which is essentially the strengthening of synaptic connections between nerve cells. We also know that learning can actually cause neurons to sprout entirely new synapses.
Yet this poses a problem for the brain. If LTP and synapse formation is constantly strengthening our synapses, and we are learning all our lives, might the synapses eventually reach a limit? Couldn’t they “max out,” so that they could never get any stronger?
Worse, most of the synapses that strengthen during memory are based on glutamate. Glutamate is dangerous. It’s the most common neurotransmitter in the brain, and it’s also a popular flavouring: “MSG”, monosodium glutamate. But in the brain, too much of it is toxic.
By now you may have heard about Oxford Nanopore’s new whole-genome sequencing technology, which has the promise of taking the enterprise of sequencing an individual’s genome out of the basic science laboratory, and out to the consumer mass market. From what I gather the hype is not just vaporware; it’s a foretaste of what’s to come. But at the end of the day, this particular device is not the important point in any case. Do you know which firm popularized television? Probably not. When technology goes mainstream, it ceases to be buzzworthy. Rather, it becomes seamlessly integrated into our lives and disappears into the fabric of our daily background humdrum. The banality of what was innovation is a testament to its success. We’re on the cusp of the age when genomics becomes banal, and cutting-edge science becomes everyday utility.
Granted, the short-term impact of mass personal genomics is still going to be exceedingly technical. Scientific genealogy nuts will purchase the latest software, and argue over the esoteric aspects of “coverage,” (the redundancy of the sequence data, which correlates with accuracy) and the necessity of supplementing the genome with the epigenome. Physicians and other health professionals will add genomic information to the arsenal of their diagnostic toolkit, and an alphabet soup of new genome-related terms will wash over you as you visit a doctor’s office. Your genome is not you, but it certainly informs who you are. Your individual genome will become ever more important to your health care.
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.