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#432568 Tech Optimists See a Golden ...

Posted on April 22, 2018 by Android

Technology evangelists dream about a future where we’re all liberated from the more mundane aspects of our jobs by artificial intelligence. Other futurists go further, imagining AI will enable us to become superhuman, enhancing our intelligence, abandoning our mortal bodies, and uploading ourselves to the cloud.

Paradise is all very well, although your mileage may vary on whether these scenarios are realistic or desirable. The real question is, how do we get there?

Economist John Maynard Keynes notably argued in favor of active intervention when an economic crisis hits, rather than waiting for the markets to settle down to a more healthy equilibrium in the long run. His rebuttal to critics was, “In the long run, we are all dead.” After all, if it takes 50 years of upheaval and economic chaos for things to return to normality, there has been an immense amount of human suffering first.

Similar problems arise with the transition to a world where AI is intimately involved in our lives. In the long term, automation of labor might benefit the human species immensely. But in the short term, it has all kinds of potential pitfalls, especially in exacerbating inequality within societies where AI takes on a larger role. A new report from the Institute for Public Policy Research has deep concerns about the future of work.

Uneven Distribution
While the report doesn’t foresee the same gloom and doom of mass unemployment that other commentators have considered, the concern is that the gains in productivity and economic benefits from AI will be unevenly distributed. In the UK, jobs that account for £290 billion worth of wages in today’s economy could potentially be automated with current technology. But these are disproportionately jobs held by people who are already suffering from social inequality.

Low-wage jobs are five times more likely to be automated than high-wage jobs. A greater proportion of jobs held by women are likely to be automated. The solution that’s often suggested is that people should simply “retrain”; but if no funding or assistance is provided, this burden is too much to bear. You can’t expect people to seamlessly transition from driving taxis to writing self-driving car software without help. As we have already seen, inequality is exacerbated when jobs that don’t require advanced education (even if they require a great deal of technical skill) are the first to go.

No Room for Beginners
Optimists say algorithms won’t replace humans, but will instead liberate us from the dull parts of our jobs. Lawyers used to have to spend hours trawling through case law to find legal precedents; now AI can identify the most relevant documents for them. Doctors no longer need to look through endless scans and perform diagnostic tests; machines can do this, leaving the decision-making to humans. This boosts productivity and provides invaluable tools for workers.

But there are issues with this rosy picture. If humans need to do less work, the economic incentive is for the boss to reduce their hours. Some of these “dull, routine” parts of the job were traditionally how people getting into the field learned the ropes: paralegals used to look through case law, but AI may render them obsolete. Even in the field of journalism, there’s now software that will rewrite press releases for publication, traditionally something close to an entry-level task. If there are no entry-level jobs, or if entry-level now requires years of training, the result is to exacerbate inequality and reduce social mobility.

Automating Our Biases
The adoption of algorithms into employment has already had negative impacts on equality. Cathy O’Neil, mathematics PhD from Harvard, raises these concerns in her excellent book Weapons of Math Destruction. She notes that algorithms designed by humans often encode the biases of that society, whether they’re racial or based on gender and sexuality.

Google’s search engine advertises more executive-level jobs to users it thinks are male. AI programs predict that black offenders are more likely to re-offend than white offenders; they receive correspondingly longer sentences. It needn’t necessarily be that bias has been actively programmed; perhaps the algorithms just learn from historical data, but this means they will perpetuate historical inequalities.

Take candidate-screening software HireVue, used by many major corporations to assess new employees. It analyzes “verbal and non-verbal cues” of candidates, comparing them to employees that historically did well. Either way, according to Cathy O’Neil, they are “using people’s fear and trust of mathematics to prevent them from asking questions.” With no transparency or understanding of how the algorithm generates its results, and no consensus over who’s responsible for the results, discrimination can occur automatically, on a massive scale.

Combine this with other demographic trends. In rich countries, people are living longer. An increasing burden will be placed on a shrinking tax base to support that elderly population. A recent study said that due to the accumulation of wealth in older generations, millennials stand to inherit more than any previous generation, but it won’t happen until they’re in their 60s. Meanwhile, those with savings and capital will benefit as the economy shifts: the stock market and GDP will grow, but wages and equality will fall, a situation that favors people who are already wealthy.

Even in the most dramatic AI scenarios, inequality is exacerbated. If someone develops a general intelligence that’s near-human or super-human, and they manage to control and monopolize it, they instantly become immensely wealthy and powerful. If the glorious technological future that Silicon Valley enthusiasts dream about is only going to serve to make the growing gaps wider and strengthen existing unfair power structures, is it something worth striving for?

What Makes a Utopia?
We urgently need to redefine our notion of progress. Philosophers worry about an AI that is misaligned—the things it seeks to maximize are not the things we want maximized. At the same time, we measure the development of our countries by GDP, not the quality of life of workers or the equality of opportunity in the society. Growing wealth with increased inequality is not progress.

Some people will take the position that there are always winners and losers in society, and that any attempt to redress the inequalities of our society will stifle economic growth and leave everyone worse off. Some will see this as an argument for a new economic model, based around universal basic income. Any moves towards this will need to take care that it’s affordable, sustainable, and doesn’t lead towards an entrenched two-tier society.

Walter Schiedel’s book The Great Leveller is a huge survey of inequality across all of human history, from the 21st century to prehistoric cave-dwellers. He argues that only revolutions, wars, and other catastrophes have historically reduced inequality: a perfect example is the Black Death in Europe, which (by reducing the population and therefore the labor supply that was available) increased wages and reduced inequality. Meanwhile, our solution to the financial crisis of 2007-8 may have only made the problem worse.

But in a world of nuclear weapons, of biowarfare, of cyberwarfare—a world of unprecedented, complex, distributed threats—the consequences of these “safety valves” could be worse than ever before. Inequality increases the risk of global catastrophe, and global catastrophes could scupper any progress towards the techno-utopia that the utopians dream of. And a society with entrenched inequality is no utopia at all.

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#432538 Cloud Computing and Robotics: The ...

Posted on April 18, 2018 by Android

Cloud Robotics is a term that was popularized by James Kuffner after he brought together researchers from different relevant fields (robotics, machine learning, and computer vision) to assist in coming up with the initial Cloud Robotics concept. Cloud robotics, as the name suggests is bringing together cloud computing and robotics. In essence, taking all the …

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#432512 How Will Merging Minds and Machines ...

Posted on April 12, 2018 by Android

One of the most exciting and frightening outcomes of technological advancement is the potential to merge our minds with machines. If achieved, this would profoundly boost our cognitive capabilities. More importantly, however, it could be a revolution in human identity, emotion, spirituality, and self-awareness.

Brain-machine interface technology is already being developed by pioneers and researchers around the globe. It’s still early and today’s tech is fairly rudimentary, but it’s a fast-moving field, and some believe it will advance faster than generally expected. Futurist Ray Kurzweil has predicted that by the 2030s we will be able to connect our brains to the internet via nanobots that will “provide full-immersion virtual reality from within the nervous system, provide direct brain-to-brain communication over the internet, and otherwise greatly expand human intelligence.” Even if the advances are less dramatic, however, they’ll have significant implications.

How might this technology affect human consciousness? What about its implications on our sentience, self-awareness, or subjective experience of our illusion of self?

Consciousness can be hard to define, but a holistic definition often encompasses many of our most fundamental capacities, such as wakefulness, self-awareness, meta-cognition, and sense of agency. Beyond that, consciousness represents a spectrum of awareness, as seen across various species of animals. Even humans experience different levels of existential awareness.

From psychedelics to meditation, there are many tools we already use to alter and heighten our conscious experience, both temporarily and permanently. These tools have been said to contribute to a richer life, with the potential to bring experiences of beauty, love, inner peace, and transcendence. Relatively non-invasive, these tools show us what a seemingly minor imbalance of neurochemistry and conscious internal effort can do to the subjective experience of being human.

Taking this into account, what implications might emerging brain-machine interface technologies have on the “self”?

The Tools for Self-Transcendence
At the basic level, we are currently seeing the rise of “consciousness hackers” using techniques like non-invasive brain stimulation through EEG, nutrition, virtual reality, and ecstatic experiences to create environments for heightened consciousness and self-awareness. In Stealing Fire, Steven Kotler and Jamie Wheal explore this trillion-dollar altered-states economy and how innovators and thought leaders are “harnessing rare and controversial states of consciousness to solve critical challenges and outperform the competition.” Beyond enhanced productivity, these altered states expose our inner potential and give us a glimpse of a greater state of being.

Expanding consciousness through brain augmentation and implants could one day be just as accessible. Researchers are working on an array of neurotechnologies as simple and non-invasive as electrode-based EEGs to invasive implants and techniques like optogenetics, where neurons are genetically reprogrammed to respond to pulses of light. We’ve already connected two brains via the internet, allowing the two to communicate, and future-focused startups are researching the possibilities too. With an eye toward advanced brain-machine interfaces, last year Elon Musk unveiled Neuralink, a company whose ultimate goal is to merge the human mind with AI through a “neural lace.”

Many technologists predict we will one day merge with and, more speculatively, upload our minds onto machines. Neuroscientist Kenneth Hayworth writes in Skeptic magazine, “All of today’s neuroscience models are fundamentally computational by nature, supporting the theoretical possibility of mind-uploading.” This might include connecting with other minds using digital networks or even uploading minds onto quantum computers, which can be in multiple states of computation at a given time.

In their book Evolving Ourselves, Juan Enriquez and Steve Gullans describe a world where evolution is no longer driven by natural processes. Instead, it is driven by human choices, through what they call unnatural selection and non-random mutation. With advancements in genetic engineering, we are indeed seeing evolution become an increasingly conscious process with an accelerated pace. This could one day apply to the evolution of our consciousness as well; we would be using our consciousness to expand our consciousness.

What Will It Feel Like?
We may be able to come up with predictions of the impact of these technologies on society, but we can only wonder what they will feel like subjectively.

It’s hard to imagine, for example, what our stream of consciousness will feel like when we can process thoughts and feelings 1,000 times faster, or how artificially intelligent brain implants will impact our capacity to love and hate. What will the illusion of “I” feel like when our consciousness is directly plugged into the internet? Overall, what impact will the process of merging with technology have on the subjective experience of being human?

The Evolution of Consciousness
In The Future Evolution of Consciousness, Thomas Lombardo points out, “We are a journey rather than a destination—a chapter in the evolutionary saga rather than a culmination. Just as probable, there will also be a diversification of species and types of conscious minds. It is also very likely that new psychological capacities, incomprehensible to us, will emerge as well.”

Humans are notorious for fearing the unknown. For any individual who has never experienced an altered state, be it spiritual or psychedelic-induced, it is difficult to comprehend the subjective experience of that state. It is why many refer to their first altered-state experience as “waking up,” wherein they didn’t even realize they were asleep.

Similarly, exponential neurotechnology represents the potential of a higher state of consciousness and a range of experiences that are unimaginable to our current default state.

Our capacity to think and feel is set by the boundaries of our biological brains. To transform and expand these boundaries is to transform and expand the first-hand experience of consciousness. Emerging neurotechnology may end up providing the awakening our species needs.

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#432311 Everyone Is Talking About AI—But Do ...

Posted on March 15, 2018 by Android

In 2017, artificial intelligence attracted $12 billion of VC investment. We are only beginning to discover the usefulness of AI applications. Amazon recently unveiled a brick-and-mortar grocery store that has successfully supplanted cashiers and checkout lines with computer vision, sensors, and deep learning. Between the investment, the press coverage, and the dramatic innovation, “AI” has become a hot buzzword. But does it even exist yet?

At the World Economic Forum Dr. Kai-Fu Lee, a Taiwanese venture capitalist and the founding president of Google China, remarked, “I think it’s tempting for every entrepreneur to package his or her company as an AI company, and it’s tempting for every VC to want to say ‘I’m an AI investor.’” He then observed that some of these AI bubbles could burst by the end of 2018, referring specifically to “the startups that made up a story that isn’t fulfillable, and fooled VCs into investing because they don’t know better.”

However, Dr. Lee firmly believes AI will continue to progress and will take many jobs away from workers. So, what is the difference between legitimate AI, with all of its pros and cons, and a made-up story?

If you parse through just a few stories that are allegedly about AI, you’ll quickly discover significant variation in how people define it, with a blurred line between emulated intelligence and machine learning applications.

I spoke to experts in the field of AI to try to find consensus, but the very question opens up more questions. For instance, when is it important to be accurate to a term’s original definition, and when does that commitment to accuracy amount to the splitting of hairs? It isn’t obvious, and hype is oftentimes the enemy of nuance. Additionally, there is now a vested interest in that hype—$12 billion, to be precise.

This conversation is also relevant because world-renowned thought leaders have been publicly debating the dangers posed by AI. Facebook CEO Mark Zuckerberg suggested that naysayers who attempt to “drum up these doomsday scenarios” are being negative and irresponsible. On Twitter, business magnate and OpenAI co-founder Elon Musk countered that Zuckerberg’s understanding of the subject is limited. In February, Elon Musk engaged again in a similar exchange with Harvard professor Steven Pinker. Musk tweeted that Pinker doesn’t understand the difference between functional/narrow AI and general AI.

Given the fears surrounding this technology, it’s important for the public to clearly understand the distinctions between different levels of AI so that they can realistically assess the potential threats and benefits.

As Smart As a Human?
Erik Cambria, an expert in the field of natural language processing, told me, “Nobody is doing AI today and everybody is saying that they do AI because it’s a cool and sexy buzzword. It was the same with ‘big data’ a few years ago.”

Cambria mentioned that AI, as a term, originally referenced the emulation of human intelligence. “And there is nothing today that is even barely as intelligent as the most stupid human being on Earth. So, in a strict sense, no one is doing AI yet, for the simple fact that we don’t know how the human brain works,” he said.

He added that the term “AI” is often used in reference to powerful tools for data classification. These tools are impressive, but they’re on a totally different spectrum than human cognition. Additionally, Cambria has noticed people claiming that neural networks are part of the new wave of AI. This is bizarre to him because that technology already existed fifty years ago.

However, technologists no longer need to perform the feature extraction by themselves. They also have access to greater computing power. All of these advancements are welcomed, but it is perhaps dishonest to suggest that machines have emulated the intricacies of our cognitive processes.

“Companies are just looking at tricks to create a behavior that looks like intelligence but that is not real intelligence, it’s just a mirror of intelligence. These are expert systems that are maybe very good in a specific domain, but very stupid in other domains,” he said.

This mimicry of intelligence has inspired the public imagination. Domain-specific systems have delivered value in a wide range of industries. But those benefits have not lifted the cloud of confusion.

Assisted, Augmented, or Autonomous
When it comes to matters of scientific integrity, the issue of accurate definitions isn’t a peripheral matter. In a 1974 commencement address at the California Institute of Technology, Richard Feynman famously said, “The first principle is that you must not fool yourself—and you are the easiest person to fool.” In that same speech, Feynman also said, “You should not fool the layman when you’re talking as a scientist.” He opined that scientists should bend over backwards to show how they could be wrong. “If you’re representing yourself as a scientist, then you should explain to the layman what you’re doing—and if they don’t want to support you under those circumstances, then that’s their decision.”

In the case of AI, this might mean that professional scientists have an obligation to clearly state that they are developing extremely powerful, controversial, profitable, and even dangerous tools, which do not constitute intelligence in any familiar or comprehensive sense.

The term “AI” may have become overhyped and confused, but there are already some efforts underway to provide clarity. A recent PwC report drew a distinction between “assisted intelligence,” “augmented intelligence,” and “autonomous intelligence.” Assisted intelligence is demonstrated by the GPS navigation programs prevalent in cars today. Augmented intelligence “enables people and organizations to do things they couldn’t otherwise do.” And autonomous intelligence “establishes machines that act on their own,” such as autonomous vehicles.

Roman Yampolskiy is an AI safety researcher who wrote the book “Artificial Superintelligence: A Futuristic Approach.” I asked him whether the broad and differing meanings might present difficulties for legislators attempting to regulate AI.

Yampolskiy explained, “Intelligence (artificial or natural) comes on a continuum and so do potential problems with such technology. We typically refer to AI which one day will have the full spectrum of human capabilities as artificial general intelligence (AGI) to avoid some confusion. Beyond that point it becomes superintelligence. What we have today and what is frequently used in business is narrow AI. Regulating anything is hard, technology is no exception. The problem is not with terminology but with complexity of such systems even at the current level.”

When asked if people should fear AI systems, Dr. Yampolskiy commented, “Since capability comes on a continuum, so do problems associated with each level of capability.” He mentioned that accidents are already reported with AI-enabled products, and as the technology advances further, the impact could spread beyond privacy concerns or technological unemployment. These concerns about the real-world effects of AI will likely take precedence over dictionary-minded quibbles. However, the issue is also about honesty versus deception.

Is This Buzzword All Buzzed Out?
Finally, I directed my questions towards a company that is actively marketing an “AI Virtual Assistant.” Carl Landers, the CMO at Conversica, acknowledged that there are a multitude of explanations for what AI is and isn’t.

He said, “My definition of AI is technology innovation that helps solve a business problem. I’m really not interested in talking about the theoretical ‘can we get machines to think like humans?’ It’s a nice conversation, but I’m trying to solve a practical business problem.”

I asked him if AI is a buzzword that inspires publicity and attracts clients. According to Landers, this was certainly true three years ago, but those effects have already started to wane. Many companies now claim to have AI in their products, so it’s less of a differentiator. However, there is still a specific intention behind the word. Landers hopes to convey that previously impossible things are now possible. “There’s something new here that you haven’t seen before, that you haven’t heard of before,” he said.

According to Brian Decker, founder of Encom Lab, machine learning algorithms only work to satisfy their preexisting programming, not out of an interior drive for better understanding. Therefore, he views AI as an entirely semantic argument.

Decker stated, “A marketing exec will claim a photodiode controlled porch light has AI because it ‘knows when it is dark outside,’ while a good hardware engineer will point out that not one bit in a register in the entire history of computing has ever changed unless directed to do so according to the logic of preexisting programming.”

Although it’s important for everyone to be on the same page regarding specifics and underlying meaning, AI-powered products are already powering past these debates by creating immediate value for humans. And ultimately, humans care more about value than they do about semantic distinctions. In an interview with Quartz, Kai-Fu Lee revealed that algorithmic trading systems have already given him an 8X return over his private banking investments. “I don’t trade with humans anymore,” he said.

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#432051 What Roboticists Are Learning From Early ...

Posted on February 18, 2018 by Android

You might not have heard of Hanson Robotics, but if you’re reading this, you’ve probably seen their work. They were the company behind Sophia, the lifelike humanoid avatar that’s made dozens of high-profile media appearances. Before that, they were the company behind that strange-looking robot that seemed a bit like Asimo with Albert Einstein’s head—or maybe you saw BINA48, who was interviewed for the New York Times in 2010 and featured in Jon Ronson’s books. For the sci-fi aficionados amongst you, they even made a replica of legendary author Philip K. Dick, best remembered for having books with titles like Do Androids Dream of Electric Sheep? turned into films with titles like Blade Runner.

Hanson Robotics, in other words, with their proprietary brand of life-like humanoid robots, have been playing the same game for a while. Sometimes it can be a frustrating game to watch. Anyone who gives the robot the slightest bit of thought will realize that this is essentially a chat-bot, with all the limitations this implies. Indeed, even in that New York Times interview with BINA48, author Amy Harmon describes it as a frustrating experience—with “rare (but invariably thrilling) moments of coherence.” This sensation will be familiar to anyone who’s conversed with a chatbot that has a few clever responses.

The glossy surface belies the lack of real intelligence underneath; it seems, at first glance, like a much more advanced machine than it is. Peeling back that surface layer—at least for a Hanson robot—means you’re peeling back Frubber. This proprietary substance—short for “Flesh Rubber,” which is slightly nightmarish—is surprisingly complicated. Up to thirty motors are required just to control the face; they manipulate liquid cells in order to make the skin soft, malleable, and capable of a range of different emotional expressions.

A quick combinatorial glance at the 30+ motors suggests that there are millions of possible combinations; researchers identify 62 that they consider “human-like” in Sophia, although not everyone agrees with this assessment. Arguably, the technical expertise that went into reconstructing the range of human facial expressions far exceeds the more simplistic chat engine the robots use, although it’s the second one that allows it to inflate the punters’ expectations with a few pre-programmed questions in an interview.

Hanson Robotics’ belief is that, ultimately, a lot of how humans will eventually relate to robots is going to depend on their faces and voices, as well as on what they’re saying. “The perception of identity is so intimately bound up with the perception of the human form,” says David Hanson, company founder.

Yet anyone attempting to design a robot that won’t terrify people has to contend with the uncanny valley—that strange blend of concern and revulsion people react with when things appear to be creepily human. Between cartoonish humanoids and genuine humans lies what has often been a no-go zone in robotic aesthetics.

The uncanny valley concept originated with roboticist Masahiro Mori, who argued that roboticists should avoid trying to replicate humans exactly. Since anything that wasn’t perfect, but merely very good, would elicit an eerie feeling in humans, shirking the challenge entirely was the only way to avoid the uncanny valley. It’s probably a task made more difficult by endless streams of articles about AI taking over the world that inexplicably conflate AI with killer humanoid Terminators—which aren’t particularly likely to exist (although maybe it’s best not to push robots around too much).

The idea behind this realm of psychological horror is fairly simple, cognitively speaking.

We know how to categorize things that are unambiguously human or non-human. This is true even if they’re designed to interact with us. Consider the popularity of Aibo, Jibo, or even some robots that don’t try to resemble humans. Something that resembles a human, but isn’t quite right, is bound to evoke a fear response in the same way slightly distorted music or slightly rearranged furniture in your home will. The creature simply doesn’t fit.

You may well reject the idea of the uncanny valley entirely. David Hanson, naturally, is not a fan. In the paper Upending the Uncanny Valley, he argues that great art forms have often resembled humans, but the ultimate goal for humanoid roboticists is probably to create robots we can relate to as something closer to humans than works of art.

Meanwhile, Hanson and other scientists produce competing experiments to either demonstrate that the uncanny valley is overhyped, or to confirm it exists and probe its edges.

The classic experiment involves gradually morphing a cartoon face into a human face, via some robotic-seeming intermediaries—yet it’s in movement that the real horror of the almost-human often lies. Hanson has argued that incorporating cartoonish features may help—and, sometimes, that the uncanny valley is a generational thing which will melt away when new generations grow used to the quirks of robots. Although Hanson might dispute the severity of this effect, it’s clearly what he’s trying to avoid with each new iteration.

Hiroshi Ishiguro is the latest of the roboticists to have dived headlong into the valley.

Building on the work of pioneers like Hanson, those who study human-robot interaction are pushing at the boundaries of robotics—but also of social science. It’s usually difficult to simulate what you don’t understand, and there’s still an awful lot we don’t understand about how we interpret the constant streams of non-verbal information that flow when you interact with people in the flesh.

Ishiguro took this imitation of human forms to extreme levels. Not only did he monitor and log the physical movements people made on videotapes, but some of his robots are based on replicas of people; the Repliee series began with a ‘replicant’ of his daughter. This involved making a rubber replica—a silicone cast—of her entire body. Future experiments were focused on creating Geminoid, a replica of Ishiguro himself.

As Ishiguro aged, he realized that it would be more effective to resemble his replica through cosmetic surgery rather than by continually creating new casts of his face, each with more lines than the last. “I decided not to get old anymore,” Ishiguro said.

We love to throw around abstract concepts and ideas: humans being replaced by machines, cared for by machines, getting intimate with machines, or even merging themselves with machines. You can take an idea like that, hold it in your hand, and examine it—dispassionately, if not without interest. But there’s a gulf between thinking about it and living in a world where human-robot interaction is not a field of academic research, but a day-to-day reality.

As the scientists studying human-robot interaction develop their robots, their replicas, and their experiments, they are making some of the first forays into that world. We might all be living there someday. Understanding ourselves—decrypting the origins of empathy and love—may be the greatest challenge to face. That is, if you want to avoid the valley.

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