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Making A.I. Systems that See the World as Humans Do

A Northwestern University team developed a new computational model that performs at human levels on a standard intelligence test. This work is an important step toward making artificial intelligence systems that see and understand the world as humans do. 

“The model performs in the 75th percentile for American adults, making it better than average,” said Northwestern Engineering’s Ken Forbus. “The problems that are hard for people are also hard for the model, providing additional evidence that its operation is capturing some important properties of human cognition.”

The new computational model is built on CogSketch, an artificial intelligence platform previously developed in Forbus’ laboratory. The platform has the ability to solve visual problems and understand sketches in order to give immediate, interactive feedback. CogSketch also incorporates a computational model of analogy, based on Northwestern psychology professor Dedre Gentner’s structure-mapping theory. (Gentner received the 2016 David E. Rumelhart Prize for her work on this theory.)

Forbus, Walter P. Murphy Professor of Electrical Engineering and Computer Science at Northwestern’s McCormick School of Engineering, developed the model with Andrew Lovett, a former Northwestern postdoctoral researcher in psychology. Their research was published online this month in the journal Psychological Review.

The ability to solve complex visual problems is one of the hallmarks of human intelligence. Developing artificial intelligence systems that have this ability not only provides new evidence for the importance of symbolic representations and analogy in visual reasoning, but it could potentially shrink the gap between computer and human cognition.

(Image caption: An example question from the Raven’s Progressive Matrices standardized test. The test taker should choose answer D because the relationships between it and the other elements in the bottom row are most similar to the relationships between the elements of the top rows)

While Forbus and Lovett’s system can be used to model general visual problem-solving phenomena, they specifically tested it on Raven’s Progressive Matrices, a nonverbal standardized test that measures abstract reasoning. All of the test’s problems consist of a matrix with one image missing. The test taker is given six to eight choices with which to best complete the matrix. Forbus and Lovett’s computational model performed better than the average American.

“The Raven’s test is the best existing predictor of what psychologists call ‘fluid intelligence, or the general ability to think abstractly, reason, identify patterns, solve problems, and discern relationships,’” said Lovett, now a researcher at the US Naval Research Laboratory. “Our results suggest that the ability to flexibly use relational representations, comparing and reinterpreting them, is important for fluid intelligence.”

The ability to use and understand sophisticated relational representations is a key to higher-order cognition. Relational representations connect entities and ideas such as “the clock is above the door” or “pressure differences cause water to flow.” These types of comparisons are crucial for making and understanding analogies, which humans use to solve problems, weigh moral dilemmas, and describe the world around them.

“Most artificial intelligence research today concerning vision focuses on recognition, or labeling what is in a scene rather than reasoning about it,” Forbus said. “But recognition is only useful if it supports subsequent reasoning. Our research provides an important step toward understanding visual reasoning more broadly.”

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Why do we dream?

In the 3rd millennium BCE, Mesopotamian kings recorded and interpreted their dreams on wax tablets. In the years since, we haven’t paused in our quest to understand why we dream. And while we still don’t have any definitive answers, we have some theories. Here are seven reasons we might dream.

1. In the early 1900’s, Sigmund Freud proposed that while all of our dreams, including our nightmares, are a collection of images from our daily conscious lives, they also have symbolic meanings which relate to the fulfillment of our subconscious wishes.  Freud theorized that everything we remember when we wake up from a dream is a symbolic representation of our unconscious, primitive thoughts, urges and desires. Freud believed that by analyzing those remembered elements, the unconscious content would be revealed to our conscious mind, and psychological issues stemming from its repression could be addressed and resolved.

2. To increase performance on certain mental tasks, sleep is good, but dreaming while sleeping is better.  In 2010, researchers found that subjects were much better at getting through a complex 3D maze if they had napped and dreamed of the maze prior to their second attempt. In fact, they were up to ten times better at it than those who only thought of the maze while awake between attempts, and those who napped but did not dream about the maze. Researchers theorize that certain memory processes can happen only when we are asleep, and our dreams are a signal that these processes are taking place.

3. There are about ten thousand trillion neural connections within the architecture of your brain. They are created by everything you think, and everything you do.  A 1983 neurobiological theory of dreaming, called “reverse learning,” holds that while sleeping, and mainly during REM sleep cycles, your neocortex reviews these neural connections and dumps the unnecessary ones. Without this unlearning process, which results in your dreams, your brain could be overrun by useless connections, and parasitic thoughts could disrupt the necessary thinking you need to do while you’re awake.    

4. The “Continual Activation Theory” proposes that your dreams result from your brain’s need to constantly consolidate and create long term memories in order to function properly. So when external input falls below a certain level, like when you’re asleep, your brain automatically triggers the generation of data from its memory storages, which appear to you in the form of the thoughts and feelings you experience in your dreams. In other words, your dreams might be a random screensaver your brain turns on so it doesn’t completely shut down.   

5. Dreams involving dangerous and threatening situations are very common, and the Primitive Instinct Rehearsal Theory holds that the content of a dream is significant to its purpose.  Whether it’s an anxiety filled night of being chased through the woods by a bear, or fighting off a ninja in a dark alley, these dreams allow you to practice your fight or flight instincts and keep them sharp and dependable, in case you’ll need them in real life. But it doesn’t always have to be unpleasant; for instance, dreams about your attractive neighbor could actually give your reproductive instinct some practice too.

6. Stress neurotransmitters in the brain are much less active during the REM stage of sleep, even during dreams of traumatic experiences, leading some researchers to theorize that one purpose of dreaming is to take the edge off painful experiences to allow for psychological healing. Reviewing traumatic events in your dreams with less mental stress may grant you a clearer perspective and an enhanced ability to process them in psychologically healthy ways. People with certain mood disorders and PTSD often have difficulty sleeping, leading some scientists to believe that lack of dreaming may be a contributing factor to their illnesses.   

7. Unconstrained by reality and the rules of conventional logic, in your dreams your mind can create limitless scenarios to help you grasp problems and formulate solutions that you may not consider while awake. John Steinbeck called it “the Committee of Sleep” and research has demonstrated the effectiveness of dreaming on problem solving. It’s also how renowned chemist August Kekule discovered the structure of the benzene molecule, and it’s the reason that sometimes the best solution for a problem is to “sleep on it”.

And those are just a few of the more prominent theories. As technology increases our capability for understanding the brain, it’s possible that one day we will discover the definitive reason for them; but until that time arrives, we’ll just have to keep on dreaming.

From the TED-Ed Lesson Why do we dream? - Amy Adkins

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In the winter of 1995, scientists pointed the Hubble Telescope at an area of the sky near the Big Dipper, a spot that was dark and out of the way of light pollution from surrounding stars. The location was apparently empty, and the whole endeavor was risky. What, if anything, was going to show up? Over ten consecutive days, the telescope took close to 150 hours of exposure of that same area. And what came back was nothing short of spectacular: an image of over 1,500 distinct galaxies glimmering in a tiny sliver of the universe. 

Now, let’s take a step back to understand the scale of this image. If you were to take a ballpoint pen and hold it at arm’s length in front of the night sky, focusing on its very tip, that is what the Hubble Telescope captured in its first Deep Field image. In other words, those 3,000 galaxies were seen in just a tiny speck of the universe, approximately one two-millionth of the night sky.

So the next time you stand gazing up at the night sky, take a moment to think about the enormity of what is beyond your vision, out in the dark spaces between the stars.

From the TED-Ed Lesson How small are we in the scale of the universe? - Alex Hofeldt

Animation by Yukai Du

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