One day in the future, we’ll look back in wonder at how our physical objects used to be singular, disconnected pieces of matter.
We’ll be in awe of the fact that a car used to be just a piece of metal full of gears and belts that we would drive from one place to another, that a refrigerator was a box that kept our food cold — and a phone was a piece of plastic we used to communicate to one other person at a time.
That’s because the future we’re rapidly moving towards is one where physical items become intelligent and interconnected — and as a fascinating result, their functionality changes.
There is probably no better example of this trend than the cell phone. The mobile phone used to be just that — a mobile phone. Now it’s your flashlight, your bank, your TV, and your funny, yet kind of dumb personal assistant. The cell phone — or really, more accurately, the hand-held computer — has become mostly a gateway to all the mobile services we use on it.
And those services are constantly morphing and improving, changing what our smartphones can do without requiring the physical phone itself to change all that much at all.
Is creativity a uniquely human trait? What about self-awareness or intuition?
Defining the line between human and machine is becoming blurrier by the day as startups, big companies, and research institutions all compete to build the next generation of advanced AI.
This arms race is bringing a new era of AI that won’t prove its power by mastering human games, but by independently exhibiting ingenuity and creativity.
Sophisticated AI is undertaking increasingly complex tasks like stock market predictions, research synthesis, political speech writing—don’t worry, this article was still written by a human—and companies are beginning to pair deep learning with new robotics and digital manufacturing tools to create “smart manufacturing.”
It was hailed as the most significant test of machine intelligence since Deep Blue defeated Garry Kasparov in chess nearly 20 years ago. Google’s AlphaGo has won two of the first three games against grandmaster Lee Sedol in a Go tournament, showing the dramatic extent to which AI has improved over the years. That fateful day when machines finally become smarter than humans has never appeared closer—yet we seem no closer in grasping the implications of this epochal event.
Combining synthetic biology approaches with time-lapse movies, a team led by Caltech biologists has determined how some proteins shape a cell's ability to remember particular states of gene expression.
What if we could program living cells to do what we would like them to do in the body? Having such control—a major goal of synthetic biology—could allow for the development of cell-based therapies that might one day replace traditional drugs for diseases such as cancer. In order to reach this long-term goal, however, scientists must first learn to program many of the key things that cells do, such as communicate with one another, change their fate to become a particular cell type, and remember the chemical signals they have encountered.
Now a team of researchers led by Caltech biologists Michael Elowitz, Lacramioara Bintu, and John Yong (PhD '15) have taken an important step toward being able to program that kind of cellular memory using tools that cells have evolved naturally. By combining synthetic biology approaches with time-lapse movies that track the behaviors of individual cells, they determined how four members of a class of proteins known as chromatin regulators establish and control a cell's ability to maintain a particular state of gene expression—to remember it—even once the signal that established that state is gone.
The researchers reported their findings in the February 12 issue of the journal Science.
Among dance forms, tango holds a unique and potent allure. It showcases two individuals—each with a separate mind, body, and bundle of goals and intentions, moving at times in close embrace, at times stepping away from each other, improvising moves and flourishes while responding to the imaginative overtures of the other—who somehow manage to give the impression of two bodies answering to a single mind. For performers and viewers alike, much of tango’s appeal comes from this apparent psychic fusion into a super-individual unit. Michael Kimmel, a social and cultural anthropologist who has researched the interpersonal dynamics of tango, writes that dancers “speak in awe of the way that individuality dissolves into a meditative unity for the three minutes that the dance lasts. Time and space give way to a unique moment of presence, of flow within and between partners.”
Tango offers more than aesthetic bliss; like all artistic practices that demand great skill, it also presents a seductive scientific puzzle, highlighting the mind’s potential to learn and re-shape itself in dramatic ways. But it’s only very recently that scientists have started building a systematic framework to explain how a person might achieve the sort of fusion that is needed for activities like social dancing, and what the impact of such an interpersonal entanglement might be.
At the heart of the puzzle is the notion of a body schema—a mental representation of the physical self that allows us to navigate through space without smashing into things, to scratch our nose without inadvertently smacking it, and to know how far and how quickly to reach for a cup of coffee without knocking it over. We can do all these things because our brains have learned to identify the edges of our bodies using information from multiple senses and devote exquisite attention to stimuli near our bodily boundaries.
Scientists from Imperial College London have identified two clusters (“gene networks”) of genes that are linked to human intelligence. Called M1 and M3, these gene networks appear to influence cognitive function, which includes memory, attention, processing speed and reasoning.
Importantly, the scientists have discovered that these two networks are likely to be under the control of master regulator switches. The researcher want to identify those switches and see if they can manipulate them, and ultimately find out if this knowledge of gene networks could allow for boosting cognitive function.
“We know that genetics plays a major role in intelligence but until now, haven’t known which genes are relevant,” said Michael Johnson, lead author of the study from the Imperial College London Department of Medicine. Johnson says the genes they have found so far are likely to share a common regulation, which means it may be possible to manipulate a whole set of genes linked to human intelligence.
Combining data from brain samples, genomic information, and IQ tests
In the study, published in the journal Nature Neuroscience, the international team of researchers looked at samples of human brain from patients who had undergone neurosurgery for epilepsy. The investigators analyzed thousands of genes expressed in the human brain, and then combined these results with genetic information from healthy people who had undergone IQ tests and from people with neurological disorders such as autism spectrum disorder and intellectual disability.
Then they conducted various computational analyses and comparisons to identify the gene networks influencing healthy human cognitive abilities. Remarkably, they found that some of the same genes that influence human intelligence in healthy people cause impaired cognitive ability and epilepsy when mutated. And they found that genes that make new memories or sensible decisions when faced with lots of complex information also overlap with those that cause severe childhood onset epilepsy or intellectual disability.
Bostrom and Yudkowsky view intelligent systems through the lens of reinforcement learning – they view them as “reward-maximizers” and worry about what happens when a very powerful and intelligent reward-maximizer is paired with a goal system that gives rewards for achieving foolish goals like tiling the universe with paperclips. Weinbaum and Veitas’s recent paper “Open-Ended Intelligence” presents a starkly alternative perspective on intelligence, viewing it as centered not on reward maximization, but rather on complex self-organization and self-transcending development that occurs in close coupling with a complex environment that is also ongoingly self-organizing, in only partially knowable ways.
Engineers from the universities of Sheffield and Sussex are planning on scanning the brains of bees and uploading them into autonomous flying robots that will then fly and act like the real thing.
Bionic bees -- or perhaps that should be "beeonic" -- could, it is hoped, be used for a range of situations where tiny thinking flying machines should be more useful than current technology, which might mean seeking out gas or chemical leaks, or people who are trapped in small spaces. They might even help pollinate plants in places where natural bee populations have fallen due to the still-mysterious Colony Collapse Disorder.
It's important to note that this won't be an entirely comprehensive model of a bee's brain -- it's only going to be the parts associated with its sense of smell and vision. These modules will be melded with other software to form what the team call a "
Green Brain", one that can react to new situations and improvise rapidly just like a "real" animal or insect brain.
Oxford philosopher Nick Bostrom, in his recent and celebrated book Superintelligence: Paths, Dangers, Strategies, argues that advanced AI poses a potentially major existential risk to humanity, and that advanced AI development should be heavily regulated and perhaps even restricted to a small set of government-approved researchers.
Bostrom’s ideas and arguments are reviewed and explored in detail, and compared with the thinking of three other current thinkers on the nature and implications of AI: Eliezer Yudkowsky of the Machine Intelligence Research Institute (formerly Singularity Institute for AI), and David Weinbaum (Weaver) and Viktoras Veitas of the Global Brain Institute. Relevant portions of Yudkowsky’s book Rationality: From AI to Zombies are briefly reviewed, and it is found that nearly all the core ideas of Bostrom’s work appeared previously or concurrently in Yudkowsky’s thinking.
However, Yudkowsky often presents these shared ideas in a more plain-spoken and extreme form, making clearer the essence of what is being claimed. For instance, the elitist strain of thinking that one sees in the background in Bostrom is plainly and openly articulated in Yudkowsky, with many of the same practical conclusions (e.g., that it may well be best if advanced AI is developed in secret by a small elite group).
Bostrom and Yudkowsky view intelligent systems through the lens of reinforcement learning — they view them as “reward-maximizers” and worry about what happens when a very powerful and intelligent reward-maximizer is paired with a goal system that gives rewards for achieving foolish goals, like tiling the universe with paperclips. Weinbaum and Veitas’s recent paper “Open-Ended Intelligence” presents a starkly alternative perspective on intelligence, viewing it as centered not on reward maximization, but rather on complex self-organization and self-transcending development that occurs in close coupling with a complex environment that is also ongoingly self-organizing, in only partially knowable ways.
It is concluded that Bostrom and Yudkowsky’s arguments for existential risk have some logical foundation, but are often presented in an exaggerated way. For instance, formal arguments whose implication is that the “worst case scenarios” for advanced AI development are extremely dire are often informally discussed as if they demonstrated the likelihood, rather than just the possibility, of highly negative outcomes. And potential dangers of reward-maximizing AI are taken as problems with AI in general, rather than just as problems of the reward-maximization paradigm as an approach to building superintelligence.
If one views past, current, and future intelligence as “open-ended,” in the vernacular of Weaver and Veitas, the potential dangers no longer appear to loom so large, and one sees a future that is wide-open, complex and uncertain, just as it has always been.
WHEN I WAS a wee Catholic lad growing up in the New York City suburbs of the late 1950s and early 1960s, I learned that good people go to heaven after they die. This was consoling. But it made me wonder precisely which part of me would go to heaven: my body, my mind, or my soul. Thanks to dead hamsters and such, I understood that bodies die, decay, and disperse. There was talk in school and at church of the resurrection of the body on Judgment Day, but that event, I reckoned, might not happen for several million years, and surely I’d be well ensconced in heaven by then. My mother tentatively explained that the part of me that loved peanut butter and jelly sandwiches and chocolate ice cream sodas would most likely not go to heaven, or, if it did, would not need or want peanut butter and jelly sandwiches and chocolate ice cream sodas anymore — possibly, I speculated, because, in the heavenly state, I’d be able mentally to conjure those great pleasures without there being actual physical manifestations of me or them. I surmised that those perfectly good human desires would either be gone (because my body would be gone), or somehow be eternally satisfied.
So, which was it, my mind or my soul that would go to heaven? Or both? And how did they differ? I didn’t want to go to heaven without my personality and memories. I wanted to be in heaven with my brothers and sisters, parents and grandparents, if not bodily then at least mentally. But personality and memories were, in my little boy ontology, associated with mind, and there was talk that the part of me that would go to heaven was something more ethereal than my mind. It was my eternal soul. But my soul, unlike my mind, seemed a bit too vague and general to be “me.” I wanted to be in heaven with me as me myself. Such were the vicissitudes of boyhood. I was troubled by three-ism. I was not, and am not, alone.
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