Tag Archives: recent

#435046 The Challenge of Abundance: Boredom, ...

Posted on May 6, 2019 by Android

As technology continues to progress, the possibility of an abundant future seems more likely. Artificial intelligence is expected to drive down the cost of labor, infrastructure, and transport. Alternative energy systems are reducing the cost of a wide variety of goods. Poverty rates are falling around the world as more people are able to make a living, and resources that were once inaccessible to millions are becoming widely available.

But such a life presents fuel for the most common complaint against abundance: if robots take all the jobs, basic income provides us livable welfare for doing nothing, and healthcare is a guarantee free of charge, then what is the point of our lives? What would motivate us to work and excel if there are no real risks or rewards? If everything is simply given to us, how would we feel like we’ve ever earned anything?

Time has proven that humans inherently yearn to overcome challenges—in fact, this very desire likely exists as the root of most technological innovation. And the idea that struggling makes us stronger isn’t just anecdotal, it’s scientifically validated.

For instance, kids who use anti-bacterial soaps and sanitizers too often tend to develop weak immune systems, causing them to get sick more frequently and more severely. People who work out purposely suffer through torn muscles so that after a few days of healing their muscles are stronger. And when patients visit a psychologist to handle a fear that is derailing their lives, one of the most common treatments is exposure therapy: a slow increase of exposure to the suffering so that the patient gets stronger and braver each time, able to take on an incrementally more potent manifestation of their fears.

Different Kinds of Struggle
It’s not hard to understand why people might fear an abundant future as a terribly mundane one. But there is one crucial mistake made in this assumption, and it was well summarized by Indian mystic and author Sadhguru, who said during a recent talk at Google:

Stomach empty, only one problem. Stomach full—one hundred problems; because what we refer to as human really begins only after survival is taken care of.

This idea is backed up by Maslow’s hierarchy of needs, which was first presented in his 1943 paper “A Theory of Human Motivation.” Maslow shows the steps required to build to higher and higher levels of the human experience. Not surprisingly, the first two levels deal with physiological needs and the need for safety—in other words, with the body. You need to have food, water, and sleep, or you die. After that, you need to be protected from threats, from the elements, from dangerous people, and from disease and pain.

Maslow’s Hierarchy of Needs. Photo by Wikimedia User:Factoryjoe / CC BY-SA 3.0
The beauty of these first two levels is that they’re clear-cut problems with clear-cut solutions: if you’re hungry, then you eat; if you’re thirsty, then you drink; if you’re tired, then you sleep.

But what about the next tiers of the hierarchy? What of love and belonging, of self-esteem and self-actualization? If we’re lonely, can we just summon up an authentic friend or lover? If we feel neglected by society, can we demand it validate us? If we feel discouraged and disappointed in ourselves, can we simply dial up some confidence and self-esteem?

Of course not, and that’s because these psychological needs are nebulous; they don’t contain clear problems with clear solutions. They involve the external world and other people, and are complicated by the infinite flavors of nuance and compromise that are required to navigate human relationships and personal meaning.

These psychological difficulties are where we grow our personalities, outlooks, and beliefs. The truly defining characteristics of a person are dictated not by the physical situations they were forced into—like birth, socioeconomic class, or physical ailment—but instead by the things they choose. So a future of abundance helps to free us from the physical limitations so that we can truly commit to a life of purpose and meaning, rather than just feel like survival is our purpose.

The Greatest Challenge
And that’s the plot twist. This challenge to come to grips with our own individuality and freedom could actually be the greatest challenge our species has ever faced. Can you imagine waking up every day with infinite possibility? Every choice you make says no to the rest of reality, and so every decision carries with it truly life-defining purpose and meaning. That sounds overwhelming. And that’s probably because in our current socio-economic systems, it is.

Studies have shown that people in wealthier nations tend to experience more anxiety and depression. Ron Kessler, professor of health care policy at Harvard and World Health Organization (WHO) researcher, summarized his findings of global mental health by saying, “When you’re literally trying to survive, who has time for depression? Americans, on the other hand, many of whom lead relatively comfortable lives, blow other nations away in the depression factor, leading some to suggest that depression is a ‘luxury disorder.’”

This might explain why America scores in the top rankings for the most depressed and anxious country on the planet. We surpassed our survival needs, and instead became depressed because our jobs and relationships don’t fulfill our expectations for the next three levels of Maslow’s hierarchy (belonging, esteem, and self-actualization).

But a future of abundance would mean we’d have to deal with these levels. This is the challenge for the future; this is what keeps things from being mundane.

As a society, we would be forced to come to grips with our emotional intelligence, to reckon with philosophy rather than simply contemplate it. Nearly every person you meet will be passionately on their own customized life journey, not following a routine simply because of financial limitations. Such a world seems far more vibrant and interesting than one where most wander sleep-deprived and numb while attempting to survive the rat race.

We can already see the forceful hand of this paradigm shift as self-driving cars become ubiquitous. For example, consider the famous psychological and philosophical “trolley problem.” In this thought experiment, a person sees a trolley car heading towards five people on the train tracks; they see a lever that will allow them to switch the trolley car to a track that instead only has one person on it. Do you switch the lever and have a hand in killing one person, or do you let fate continue and kill five people instead?

For the longest time, this was just an interesting quandary to consider. But now, massive corporations have to have an answer, so they can program their self-driving cars with the ability to choose between hitting a kid who runs into the road or swerving into an oncoming car carrying a family of five. When companies need philosophers to make business decisions, it’s a good sign of what’s to come.

Luckily, it’s possible this forceful reckoning with philosophy and our own consciousness may be exactly what humanity needs. Perhaps our great failure as a species has been a result of advanced cognition still trapped in the first two levels of Maslow’s hierarchy due to a long history of scarcity.

As suggested in the opening scenes in 2001: A Space Odyssey, our ape-like proclivity for violence has long stayed the same while the technology we fight with and live amongst has progressed. So while well-off Americans may have comfortable lives, they still know they live in a system where there is no safety net, where a single tragic failure could still mean hunger and homelessness. And because of this, that evolutionarily hard-wired neurotic part of our brain that fears for our survival has never been able to fully relax, and so that anxiety and depression that come with too much freedom but not enough security stays ever present.

Not only might this shift in consciousness help liberate humanity, but it may be vital if we’re to survive our future creations as well. Whatever values we hold dear as a species are the ones we will imbue into the sentient robots we create. If machine learning is going to take its guidance from humanity, we need to level up humanity’s emotional maturity.

While the physical struggles of the future may indeed fall to the wayside amongst abundance, it’s unlikely to become a mundane world; instead, it will become a vibrant culture where each individual is striving against the most important struggle that affects all of us: the challenge to find inner peace, to find fulfillment, to build meaningful relationships, and ultimately, the challenge to find ourselves.

Image Credit: goffkein.pro / Shutterstock.com Continue reading →

Posted in Human Robots

#434837 In Defense of Black Box AI

Posted on April 17, 2019 by Android

Deep learning is powering some amazing new capabilities, but we find it hard to scrutinize the workings of these algorithms. Lack of interpretability in AI is a common concern and many are trying to fix it, but is it really always necessary to know what’s going on inside these “black boxes”?

In a recent perspective piece for Science, Elizabeth Holm, a professor of materials science and engineering at Carnegie Mellon University, argued in defense of the black box algorithm. I caught up with her last week to find out more.

Edd Gent: What’s your experience with black box algorithms?

Elizabeth Holm: I got a dual PhD in materials science and engineering and scientific computing. I came to academia about six years ago and part of what I wanted to do in making this career change was to refresh and revitalize my computer science side.

I realized that computer science had changed completely. It used to be about algorithms and making codes run fast, but now it’s about data and artificial intelligence. There are the interpretable methods like random forest algorithms, where we can tell how the machine is making its decisions. And then there are the black box methods, like convolutional neural networks.

Once in a while we can find some information about their inner workings, but most of the time we have to accept their answers and kind of probe around the edges to figure out the space in which we can use them and how reliable and accurate they are.

EG: What made you feel like you had to mount a defense of these black box algorithms?

EH: When I started talking with my colleagues, I found that the black box nature of many of these algorithms was a real problem for them. I could understand that because we’re scientists, we always want to know why and how.

It got me thinking as a bit of a contrarian, “Are black boxes all bad? Must we reject them?” Surely not, because human thought processes are fairly black box. We often rely on human thought processes that the thinker can’t necessarily explain.

It’s looking like we’re going to be stuck with these methods for a while, because they’re really helpful. They do amazing things. And so there’s a very pragmatic realization that these are the best methods we’ve got to do some really important problems, and we’re not right now seeing alternatives that are interpretable. We’re going to have to use them, so we better figure out how.

EG: In what situations do you think we should be using black box algorithms?

EH: I came up with three rules. The simplest rule is: when the cost of a bad decision is small and the value of a good decision is high, it’s worth it. The example I gave in the paper is targeted advertising. If you send an ad no one wants it doesn’t cost a lot. If you’re the receiver it doesn’t cost a lot to get rid of it.

There are cases where the cost is high, and that’s then we choose the black box if it’s the best option to do the job. Things get a little trickier here because we have to ask “what are the costs of bad decisions, and do we really have them fully characterized?” We also have to be very careful knowing that our systems may have biases, they may have limitations in where you can apply them, they may be breakable.

But at the same time, there are certainly domains where we’re going to test these systems so extensively that we know their performance in virtually every situation. And if their performance is better than the other methods, we need to do it. Self driving vehicles are a significant example—it’s almost certain they’re going to have to use black box methods, and that they’re going to end up being better drivers than humans.

The third rule is the more fun one for me as a scientist, and that’s the case where the black box really enlightens us as to a new way to look at something. We have trained a black box to recognize the fracture energy of breaking a piece of metal from a picture of the broken surface. It did a really good job, and humans can’t do this and we don’t know why.

What the computer seems to be seeing is noise. There’s a signal in that noise, and finding it is very difficult, but if we do we may find something significant to the fracture process, and that would be an awesome scientific discovery.

EG: Do you think there’s been too much emphasis on interpretability?

EH: I think the interpretability problem is a fundamental, fascinating computer science grand challenge and there are significant issues where we need to have an interpretable model. But how I would frame it is not that there’s too much emphasis on interpretability, but rather that there’s too much dismissiveness of uninterpretable models.

I think that some of the current social and political issues surrounding some very bad black box outcomes have convinced people that all machine learning and AI should be interpretable because that will somehow solve those problems.

Asking humans to explain their rationale has not eliminated bias, or stereotyping, or bad decision-making in humans. Relying too much on interpreted ability perhaps puts the responsibility in the wrong place for getting better results. I can make a better black box without knowing exactly in what way the first one was bad.

EG: Looking further into the future, do you think there will be situations where humans will have to rely on black box algorithms to solve problems we can’t get our heads around?

EH: I do think so, and it’s not as much of a stretch as we think it is. For example, humans don’t design the circuit map of computer chips anymore. We haven’t for years. It’s not a black box algorithm that designs those circuit boards, but we’ve long since given up trying to understand a particular computer chip’s design.

With the billions of circuits in every computer chip, the human mind can’t encompass it, either in scope or just the pure time that it would take to trace every circuit. There are going to be cases where we want a system so complex that only the patience that computers have and their ability to work in very high-dimensional spaces is going to be able to do it.

So we can continue to argue about interpretability, but we need to acknowledge that we’re going to need to use black boxes. And this is our opportunity to do our due diligence to understand how to use them responsibly, ethically, and with benefits rather than harm. And that’s going to be a social conversation as well as as a scientific one.

*Responses have been edited for length and style

Image Credit: Chingraph / Shutterstock.com Continue reading →

Posted in Human Robots

#434823 The Tangled Web of Turning Spider Silk ...

Posted on April 11, 2019 by Android

Spider-Man is one of the most popular superheroes of all time. It’s a bit surprising given that one of the more common phobias is arachnophobia—a debilitating fear of spiders.

Perhaps more fantastical is that young Peter Parker, a brainy high school science nerd, seemingly developed overnight the famous web-shooters and the synthetic spider silk that he uses to swing across the cityscape like Tarzan through the jungle.

That’s because scientists have been trying for decades to replicate spider silk, a material that is five times stronger than steel, among its many superpowers. In recent years, researchers have been untangling the protein-based fiber’s structure down to the molecular level, leading to new insights and new potential for eventual commercial uses.

The applications for such a material seem near endless. There’s the more futuristic visions, like enabling robotic “muscles” for human-like movement or ensnaring real-life villains with a Spider-Man-like web. Near-term applications could include the biomedical industry, such as bandages and adhesives, and as a replacement textile for everything from rope to seat belts to parachutes.

Spinning Synthetic Spider Silk
Randy Lewis has been studying the properties of spider silk and developing methods for producing it synthetically for more than three decades. In the 1990s, his research team was behind cloning the first spider silk gene, as well as the first to identify and sequence the proteins that make up the six different silks that web slingers make. Each has different mechanical properties.

“So our thought process was that you could take that information and begin to to understand what made them strong and what makes them stretchy, and why some are are very stretchy and some are not stretchy at all, and some are stronger and some are weaker,” explained Lewis, a biology professor at Utah State University and director of the Synthetic Spider Silk Lab, in an interview with Singularity Hub.

Spiders are naturally territorial and cannibalistic, so any intention to farm silk naturally would likely end in an orgy of arachnid violence. Instead, Lewis and company have genetically modified different organisms to produce spider silk synthetically, including inserting a couple of web-making genes into the genetic code of goats. The goats’ milk contains spider silk proteins.

The lab also produces synthetic spider silk through a fermentation process not entirely dissimilar to brewing beer, but using genetically modified bacteria to make the desired spider silk proteins. A similar technique has been used for years to make a key enzyme in cheese production. More recently, companies are using transgenic bacteria to make meat and milk proteins, entirely bypassing animals in the process.

The same fermentation technology is used by a chic startup called Bolt Threads outside of San Francisco that has raised more than $200 million for fashionable fibers made out of synthetic spider silk it calls Microsilk. (The company is also developing a second leather-like material, Mylo, using the underground root structure of mushrooms known as mycelium.)

Lewis’ lab also uses transgenic silkworms to produce a kind of composite material made up of the domesticated insect’s own silk proteins and those of spider silk. “Those have some fairly impressive properties,” Lewis said.

The researchers are even experimenting with genetically modified alfalfa. One of the big advantages there is that once the spider silk protein has been extracted, the remaining protein could be sold as livestock feed. “That would bring the cost of spider silk protein production down significantly,” Lewis said.

Building a Better Web
Producing synthetic spider silk isn’t the problem, according to Lewis, but the ability to do it at scale commercially remains a sticking point.

Another challenge is “weaving” the synthetic spider silk into usable products that can take advantage of the material’s marvelous properties.

“It is possible to make silk proteins synthetically, but it is very hard to assemble the individual proteins into a fiber or other material forms,” said Markus Buehler, head of the Department of Civil and Environmental Engineering at MIT, in an email to Singularity Hub. “The spider has a complex spinning duct in which silk proteins are exposed to physical forces, chemical gradients, the combination of which generates the assembly of molecules that leads to silk fibers.”

Buehler recently co-authored a paper in the journal Science Advances that found dragline spider silk exhibits different properties in response to changes in humidity that could eventually have applications in robotics.

Specifically, spider silk suddenly contracts and twists above a certain level of relative humidity, exerting enough force to “potentially be competitive with other materials being explored as actuators—devices that move to perform some activity such as controlling a valve,” according to a press release.

Studying Spider Silk Up Close
Recent studies at the molecular level are helping scientists learn more about the unique properties of spider silk, which may help researchers develop materials with extraordinary capabilities.

For example, scientists at Arizona State University used magnetic resonance tools and other instruments to image the abdomen of a black widow spider. They produced what they called the first molecular-level model of spider silk protein fiber formation, providing insights on the nanoparticle structure. The research was published last October in Proceedings of the National Academy of Sciences.

A cross section of the abdomen of a black widow (Latrodectus Hesperus) spider used in this study at Arizona State University. Image Credit: Samrat Amin.
Also in 2018, a study presented in Nature Communications described a sort of molecular clamp that binds the silk protein building blocks, which are called spidroins. The researchers observed for the first time that the clamp self-assembles in a two-step process, contributing to the extensibility, or stretchiness, of spider silk.

Another team put the spider silk of a brown recluse under an atomic force microscope, discovering that each strand, already 1,000 times thinner than a human hair, is made up of thousands of nanostrands. That helps explain its extraordinary tensile strength, though technique is also a factor, as the brown recluse uses a special looping method to reinforce its silk strands. The study also appeared last year in the journal ACS Macro Letters.

Making Spider Silk Stick
Buehler said his team is now trying to develop better and faster predictive methods to design silk proteins using artificial intelligence.

“These new methods allow us to generate new protein designs that do not naturally exist and which can be explored to optimize certain desirable properties like torsional actuation, strength, bioactivity—for example, tissue engineering—and others,” he said.

Meanwhile, Lewis’ lab has discovered a method that allows it to solubilize spider silk protein in what is essentially a water-based solution, eschewing acids or other toxic compounds that are normally used in the process.

That enables the researchers to develop materials beyond fiber, including adhesives that “are better than an awful lot of the current commercial adhesives,” Lewis said, as well as coatings that could be used to dampen vibrations, for example.

“We’re making gels for various kinds of of tissue regeneration, as well as drug delivery, and things like that,” he added. “So we’ve expanded the use profile from something beyond fibers to something that is a much more extensive portfolio of possible kinds of materials.”

And, yes, there’s even designs at the Synthetic Spider Silk Lab for developing a Spider-Man web-slinger material. The US Navy is interested in non-destructive ways of disabling an enemy vessel, such as fouling its propeller. The project also includes producing synthetic proteins from the hagfish, an eel-like critter that exudes a gelatinous slime when threatened.

Lewis said that while the potential for spider silk is certainly headline-grabbing, he cautioned that much of the hype is not focused on the unique mechanical properties that could lead to advances in healthcare and other industries.

“We want to see spider silk out there because it’s a unique material, not because it’s got marketing appeal,” he said.

Image Credit: mycteria / Shutterstock.com Continue reading →

Posted in Human Robots

#434818 Watch These Robots Do Tasks You Thought ...

Posted on April 9, 2019 by Android

Robots have been masters of manufacturing at speed and precision for decades, but give them a seemingly simple task like stacking shelves, and they quickly get stuck. That’s changing, though, as engineers build systems that can take on the deceptively tricky tasks most humans can do with their eyes closed.

Boston Dynamics is famous for dramatic reveals of robots performing mind-blowing feats that also leave you scratching your head as to what the market is—think the bipedal Atlas doing backflips or Spot the galloping robot dog.

Last week, the company released a video of a robot called Handle that looks like an ostrich on wheels carrying out the seemingly mundane task of stacking boxes in a warehouse.

It might seem like a step backward, but this is exactly the kind of practical task robots have long struggled with. While the speed and precision of industrial robots has seen them take over many functions in modern factories, they’re generally limited to highly prescribed tasks carried out in meticulously-controlled environments.

That’s because despite their mechanical sophistication, most are still surprisingly dumb. They can carry out precision welding on a car or rapidly assemble electronics, but only by rigidly following a prescribed set of motions. Moving cardboard boxes around a warehouse might seem simple to a human, but it actually involves a variety of tasks machines still find pretty difficult—perceiving your surroundings, navigating, and interacting with objects in a dynamic environment.

But the release of this video suggests Boston Dynamics thinks these kinds of applications are close to prime time. Last week the company doubled down by announcing the acquisition of start-up Kinema Systems, which builds computer vision systems for robots working in warehouses.

It’s not the only company making strides in this area. On the same day the video went live, Google unveiled a robot arm called TossingBot that can pick random objects from a box and quickly toss them into another container beyond its reach, which could prove very useful for sorting items in a warehouse. The machine can train on new objects in just an hour or two, and can pick and toss up to 500 items an hour with better accuracy than any of the humans who tried the task.

And an apple-picking robot built by Abundant Robotics is currently on New Zealand farms navigating between rows of apple trees using LIDAR and computer vision to single out ripe apples before using a vacuum tube to suck them off the tree.

In most cases, advances in machine learning and computer vision brought about by the recent AI boom are the keys to these rapidly improving capabilities. Robots have historically had to be painstakingly programmed by humans to solve each new task, but deep learning is making it possible for them to quickly train themselves on a variety of perception, navigation, and dexterity tasks.

It’s not been simple, though, and the application of deep learning in robotics has lagged behind other areas. A major limitation is that the process typically requires huge amounts of training data. That’s fine when you’re dealing with image classification, but when that data needs to be generated by real-world robots it can make the approach impractical. Simulations offer the possibility to run this training faster than real time, but it’s proved difficult to translate policies learned in virtual environments into the real world.

Recent years have seen significant progress on these fronts, though, and the increasing integration of modern machine learning with robotics. In October, OpenAI imbued a robotic hand with human-level dexterity by training an algorithm in a simulation using reinforcement learning before transferring it to the real-world device. The key to ensuring the translation went smoothly was injecting random noise into the simulation to mimic some of the unpredictability of the real world.

And just a couple of weeks ago, MIT researchers demonstrated a new technique that let a robot arm learn to manipulate new objects with far less training data than is usually required. By getting the algorithm to focus on a few key points on the object necessary for picking it up, the system could learn to pick up a previously unseen object after seeing only a few dozen examples (rather than the hundreds or thousands typically required).

How quickly these innovations will trickle down to practical applications remains to be seen, but a number of startups as well as logistics behemoth Amazon are developing robots designed to flexibly pick and place the wide variety of items found in your average warehouse.

Whether the economics of using robots to replace humans at these kinds of menial tasks makes sense yet is still unclear. The collapse of collaborative robotics pioneer Rethink Robotics last year suggests there are still plenty of challenges.

But at the same time, the number of robotic warehouses is expected to leap from 4,000 today to 50,000 by 2025. It may not be long until robots are muscling in on tasks we’ve long assumed only humans could do.

Image Credit: Visual Generation / Shutterstock.com Continue reading →

Posted in Human Robots

#434781 What Would It Mean for AI to Become ...

Posted on March 26, 2019 by Android

As artificial intelligence systems take on more tasks and solve more problems, it’s hard to say which is rising faster: our interest in them or our fear of them. Futurist Ray Kurzweil famously predicted that “By 2029, computers will have emotional intelligence and be convincing as people.”

We don’t know how accurate this prediction will turn out to be. Even if it takes more than 10 years, though, is it really possible for machines to become conscious? If the machines Kurzweil describes say they’re conscious, does that mean they actually are?

Perhaps a more relevant question at this juncture is: what is consciousness, and how do we replicate it if we don’t understand it?

In a panel discussion at South By Southwest titled “How AI Will Design the Human Future,” experts from academia and industry discussed these questions and more.

Wait, What Is AI?
Most of AI’s recent feats—diagnosing illnesses, participating in debate, writing realistic text—involve machine learning, which uses statistics to find patterns in large datasets then uses those patterns to make predictions. However, “AI” has been used to refer to everything from basic software automation and algorithms to advanced machine learning and deep learning.

“The term ‘artificial intelligence’ is thrown around constantly and often incorrectly,” said Jennifer Strong, a reporter at the Wall Street Journal and host of the podcast “The Future of Everything.” Indeed, one study found that 40 percent of European companies that claim to be working on or using AI don’t actually use it at all.

Dr. Peter Stone, associate chair of computer science at UT Austin, was the study panel chair on the 2016 One Hundred Year Study on Artificial Intelligence (or AI100) report. Based out of Stanford University, AI100 is studying and anticipating how AI will impact our work, our cities, and our lives.

“One of the first things we had to do was define AI,” Stone said. They defined it as a collection of different technologies inspired by the human brain to be able to perceive their surrounding environment and figure out what actions to take given these inputs.

Modeling on the Unknown
Here’s the crazy thing about that definition (and about AI itself): we’re essentially trying to re-create the abilities of the human brain without having anything close to a thorough understanding of how the human brain works.

“We’re starting to pair our brains with computers, but brains don’t understand computers and computers don’t understand brains,” Stone said. Dr. Heather Berlin, cognitive neuroscientist and professor of psychiatry at the Icahn School of Medicine at Mount Sinai, agreed. “It’s still one of the greatest mysteries how this three-pound piece of matter can give us all our subjective experiences, thoughts, and emotions,” she said.

This isn’t to say we’re not making progress; there have been significant neuroscience breakthroughs in recent years. “This has been the stuff of science fiction for a long time, but now there’s active work being done in this area,” said Amir Husain, CEO and founder of Austin-based AI company Spark Cognition.

Advances in brain-machine interfaces show just how much more we understand the brain now than we did even a few years ago. Neural implants are being used to restore communication or movement capabilities in people who’ve been impaired by injury or illness. Scientists have been able to transfer signals from the brain to prosthetic limbs and stimulate specific circuits in the brain to treat conditions like Parkinson’s, PTSD, and depression.

But much of the brain’s inner workings remain a deep, dark mystery—one that will have to be further solved if we’re ever to get from narrow AI, which refers to systems that can perform specific tasks and is where the technology stands today, to artificial general intelligence, or systems that possess the same intelligence level and learning capabilities as humans.

The biggest question that arises here, and one that’s become a popular theme across stories and films, is if machines achieve human-level general intelligence, does that also mean they’d be conscious?

Wait, What Is Consciousness?
As valuable as the knowledge we’ve accumulated about the brain is, it seems like nothing more than a collection of disparate facts when we try to put it all together to understand consciousness.

“If you can replace one neuron with a silicon chip that can do the same function, then replace another neuron, and another—at what point are you still you?” Berlin asked. “These systems will be able to pass the Turing test, so we’re going to need another concept of how to measure consciousness.”

Is consciousness a measurable phenomenon, though? Rather than progressing by degrees or moving through some gray area, isn’t it pretty black and white—a being is either conscious or it isn’t?

This may be an outmoded way of thinking, according to Berlin. “It used to be that only philosophers could study consciousness, but now we can study it from a scientific perspective,” she said. “We can measure changes in neural pathways. It’s subjective, but depends on reportability.”

She described three levels of consciousness: pure subjective experience (“Look, the sky is blue”), awareness of one’s own subjective experience (“Oh, it’s me that’s seeing the blue sky”), and relating one subjective experience to another (“The blue sky reminds me of a blue ocean”).

“These subjective states exist all the way down the animal kingdom. As humans we have a sense of self that gives us another depth to that experience, but it’s not necessary for pure sensation,” Berlin said.

Husain took this definition a few steps farther. “It’s this self-awareness, this idea that I exist separate from everything else and that I can model myself,” he said. “Human brains have a wonderful simulator. They can propose a course of action virtually, in their minds, and see how things play out. The ability to include yourself as an actor means you’re running a computation on the idea of yourself.”

Most of the decisions we make involve envisioning different outcomes, thinking about how each outcome would affect us, and choosing which outcome we’d most prefer.

“Complex tasks you want to achieve in the world are tied to your ability to foresee the future, at least based on some mental model,” Husain said. “With that view, I as an AI practitioner don’t see a problem implementing that type of consciousness.”

Moving Forward Cautiously (But Not too Cautiously)
To be clear, we’re nowhere near machines achieving artificial general intelligence or consciousness, and whether a “conscious machine” is possible—not to mention necessary or desirable—is still very much up for debate.

As machine intelligence continues to advance, though, we’ll need to walk the line between progress and risk management carefully.

Improving the transparency and explainability of AI systems is one crucial goal AI developers and researchers are zeroing in on. Especially in applications that could mean the difference between life and death, AI shouldn’t advance without people being able to trace how it’s making decisions and reaching conclusions.

Medicine is a prime example. “There are already advances that could save lives, but they’re not being used because they’re not trusted by doctors and nurses,” said Stone. “We need to make sure there’s transparency.” Demanding too much transparency would also be a mistake, though, because it will hinder the development of systems that could at best save lives and at worst improve efficiency and free up doctors to have more face time with patients.

Similarly, self-driving cars have great potential to reduce deaths from traffic fatalities. But even though humans cause thousands of deadly crashes every day, we’re terrified by the idea of self-driving cars that are anything less than perfect. “If we only accept autonomous cars when there’s zero probability of an accident, then we will never accept them,” Stone said. “Yet we give 16-year-olds the chance to take a road test with no idea what’s going on in their brains.”

This brings us back to the fact that, in building tech modeled after the human brain—which has evolved over millions of years—we’re working towards an end whose means we don’t fully comprehend, be it something as basic as choosing when to brake or accelerate or something as complex as measuring consciousness.

“We shouldn’t charge ahead and do things just because we can,” Stone said. “The technology can be very powerful, which is exciting, but we have to consider its implications.”

Image Credit: agsandrew / Shutterstock.com Continue reading →

Posted in Human Robots