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#432193 Are ‘You’ Just Inside Your Skin or ...

In November 2017, a gunman entered a church in Sutherland Springs in Texas, where he killed 26 people and wounded 20 others. He escaped in his car, with police and residents in hot pursuit, before losing control of the vehicle and flipping it into a ditch. When the police got to the car, he was dead. The episode is horrifying enough without its unsettling epilogue. In the course of their investigations, the FBI reportedly pressed the gunman’s finger to the fingerprint-recognition feature on his iPhone in an attempt to unlock it. Regardless of who’s affected, it’s disquieting to think of the police using a corpse to break into someone’s digital afterlife.

Most democratic constitutions shield us from unwanted intrusions into our brains and bodies. They also enshrine our entitlement to freedom of thought and mental privacy. That’s why neurochemical drugs that interfere with cognitive functioning can’t be administered against a person’s will unless there’s a clear medical justification. Similarly, according to scholarly opinion, law-enforcement officials can’t compel someone to take a lie-detector test, because that would be an invasion of privacy and a violation of the right to remain silent.

But in the present era of ubiquitous technology, philosophers are beginning to ask whether biological anatomy really captures the entirety of who we are. Given the role they play in our lives, do our devices deserve the same protections as our brains and bodies?

After all, your smartphone is much more than just a phone. It can tell a more intimate story about you than your best friend. No other piece of hardware in history, not even your brain, contains the quality or quantity of information held on your phone: it ‘knows’ whom you speak to, when you speak to them, what you said, where you have been, your purchases, photos, biometric data, even your notes to yourself—and all this dating back years.

In 2014, the United States Supreme Court used this observation to justify the decision that police must obtain a warrant before rummaging through our smartphones. These devices “are now such a pervasive and insistent part of daily life that the proverbial visitor from Mars might conclude they were an important feature of human anatomy,” as Chief Justice John Roberts observed in his written opinion.

The Chief Justice probably wasn’t making a metaphysical point—but the philosophers Andy Clark and David Chalmers were when they argued in “The Extended Mind” (1998) that technology is actually part of us. According to traditional cognitive science, “thinking” is a process of symbol manipulation or neural computation, which gets carried out by the brain. Clark and Chalmers broadly accept this computational theory of mind, but claim that tools can become seamlessly integrated into how we think. Objects such as smartphones or notepads are often just as functionally essential to our cognition as the synapses firing in our heads. They augment and extend our minds by increasing our cognitive power and freeing up internal resources.

If accepted, the extended mind thesis threatens widespread cultural assumptions about the inviolate nature of thought, which sits at the heart of most legal and social norms. As the US Supreme Court declared in 1942: “freedom to think is absolute of its own nature; the most tyrannical government is powerless to control the inward workings of the mind.” This view has its origins in thinkers such as John Locke and René Descartes, who argued that the human soul is locked in a physical body, but that our thoughts exist in an immaterial world, inaccessible to other people. One’s inner life thus needs protecting only when it is externalized, such as through speech. Many researchers in cognitive science still cling to this Cartesian conception—only, now, the private realm of thought coincides with activity in the brain.

But today’s legal institutions are straining against this narrow concept of the mind. They are trying to come to grips with how technology is changing what it means to be human, and to devise new normative boundaries to cope with this reality. Justice Roberts might not have known about the idea of the extended mind, but it supports his wry observation that smartphones have become part of our body. If our minds now encompass our phones, we are essentially cyborgs: part-biology, part-technology. Given how our smartphones have taken over what were once functions of our brains—remembering dates, phone numbers, addresses—perhaps the data they contain should be treated on a par with the information we hold in our heads. So if the law aims to protect mental privacy, its boundaries would need to be pushed outwards to give our cyborg anatomy the same protections as our brains.

This line of reasoning leads to some potentially radical conclusions. Some philosophers have argued that when we die, our digital devices should be handled as remains: if your smartphone is a part of who you are, then perhaps it should be treated more like your corpse than your couch. Similarly, one might argue that trashing someone’s smartphone should be seen as a form of “extended” assault, equivalent to a blow to the head, rather than just destruction of property. If your memories are erased because someone attacks you with a club, a court would have no trouble characterizing the episode as a violent incident. So if someone breaks your smartphone and wipes its contents, perhaps the perpetrator should be punished as they would be if they had caused a head trauma.

The extended mind thesis also challenges the law’s role in protecting both the content and the means of thought—that is, shielding what and how we think from undue influence. Regulation bars non-consensual interference in our neurochemistry (for example, through drugs), because that meddles with the contents of our mind. But if cognition encompasses devices, then arguably they should be subject to the same prohibitions. Perhaps some of the techniques that advertisers use to hijack our attention online, to nudge our decision-making or manipulate search results, should count as intrusions on our cognitive process. Similarly, in areas where the law protects the means of thought, it might need to guarantee access to tools such as smartphones—in the same way that freedom of expression protects people’s right not only to write or speak, but also to use computers and disseminate speech over the internet.

The courts are still some way from arriving at such decisions. Besides the headline-making cases of mass shooters, there are thousands of instances each year in which police authorities try to get access to encrypted devices. Although the Fifth Amendment to the US Constitution protects individuals’ right to remain silent (and therefore not give up a passcode), judges in several states have ruled that police can forcibly use fingerprints to unlock a user’s phone. (With the new facial-recognition feature on the iPhone X, police might only need to get an unwitting user to look at her phone.) These decisions reflect the traditional concept that the rights and freedoms of an individual end at the skin.

But the concept of personal rights and freedoms that guides our legal institutions is outdated. It is built on a model of a free individual who enjoys an untouchable inner life. Now, though, our thoughts can be invaded before they have even been developed—and in a way, perhaps this is nothing new. The Nobel Prize-winning physicist Richard Feynman used to say that he thought with his notebook. Without a pen and pencil, a great deal of complex reflection and analysis would never have been possible. If the extended mind view is right, then even simple technologies such as these would merit recognition and protection as a part of the essential toolkit of the mind.This article was originally published at Aeon and has been republished under Creative Commons.

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#432190 In the Future, There Will Be No Limit to ...

New planets found in distant corners of the galaxy. Climate models that may improve our understanding of sea level rise. The emergence of new antimalarial drugs. These scientific advances and discoveries have been in the news in recent months.

While representing wildly divergent disciplines, from astronomy to biotechnology, they all have one thing in common: Artificial intelligence played a key role in their scientific discovery.

One of the more recent and famous examples came out of NASA at the end of 2017. The US space agency had announced an eighth planet discovered in the Kepler-90 system. Scientists had trained a neural network—a computer with a “brain” modeled on the human mind—to re-examine data from Kepler, a space-borne telescope with a four-year mission to seek out new life and new civilizations. Or, more precisely, to find habitable planets where life might just exist.

The researchers trained the artificial neural network on a set of 15,000 previously vetted signals until it could identify true planets and false positives 96 percent of the time. It then went to work on weaker signals from nearly 700 star systems with known planets.

The machine detected Kepler 90i—a hot, rocky planet that orbits its sun about every two Earth weeks—through a nearly imperceptible change in brightness captured when a planet passes a star. It also found a sixth Earth-sized planet in the Kepler-80 system.

AI Handles Big Data
The application of AI to science is being driven by three great advances in technology, according to Ross King from the Manchester Institute of Biotechnology at the University of Manchester, leader of a team that developed an artificially intelligent “scientist” called Eve.

Those three advances include much faster computers, big datasets, and improved AI methods, King said. “These advances increasingly give AI superhuman reasoning abilities,” he told Singularity Hub by email.

AI systems can flawlessly remember vast numbers of facts and extract information effortlessly from millions of scientific papers, not to mention exhibit flawless logical reasoning and near-optimal probabilistic reasoning, King says.

AI systems also beat humans when it comes to dealing with huge, diverse amounts of data.

That’s partly what attracted a team of glaciologists to turn to machine learning to untangle the factors involved in how heat from Earth’s interior might influence the ice sheet that blankets Greenland.

Algorithms juggled 22 geologic variables—such as bedrock topography, crustal thickness, magnetic anomalies, rock types, and proximity to features like trenches, ridges, young rifts, and volcanoes—to predict geothermal heat flux under the ice sheet throughout Greenland.

The machine learning model, for example, predicts elevated heat flux upstream of Jakobshavn Glacier, the fastest-moving glacier in the world.

“The major advantage is that we can incorporate so many different types of data,” explains Leigh Stearns, associate professor of geology at Kansas University, whose research takes her to the polar regions to understand how and why Earth’s great ice sheets are changing, questions directly related to future sea level rise.

“All of the other models just rely on one parameter to determine heat flux, but the [machine learning] approach incorporates all of them,” Stearns told Singularity Hub in an email. “Interestingly, we found that there is not just one parameter…that determines the heat flux, but a combination of many factors.”

The research was published last month in Geophysical Research Letters.

Stearns says her team hopes to apply high-powered machine learning to characterize glacier behavior over both short and long-term timescales, thanks to the large amounts of data that she and others have collected over the last 20 years.

Emergence of Robot Scientists
While Stearns sees machine learning as another tool to augment her research, King believes artificial intelligence can play a much bigger role in scientific discoveries in the future.

“I am interested in developing AI systems that autonomously do science—robot scientists,” he said. Such systems, King explained, would automatically originate hypotheses to explain observations, devise experiments to test those hypotheses, physically run the experiments using laboratory robotics, and even interpret the results. The conclusions would then influence the next cycle of hypotheses and experiments.

His AI scientist Eve recently helped researchers discover that triclosan, an ingredient commonly found in toothpaste, could be used as an antimalarial drug against certain strains that have developed a resistance to other common drug therapies. The research was published in the journal Scientific Reports.

Automation using artificial intelligence for drug discovery has become a growing area of research, as the machines can work orders of magnitude faster than any human. AI is also being applied in related areas, such as synthetic biology for the rapid design and manufacture of microorganisms for industrial uses.

King argues that machines are better suited to unravel the complexities of biological systems, with even the most “simple” organisms are host to thousands of genes, proteins, and small molecules that interact in complicated ways.

“Robot scientists and semi-automated AI tools are essential for the future of biology, as there are simply not enough human biologists to do the necessary work,” he said.

Creating Shockwaves in Science
The use of machine learning, neural networks, and other AI methods can often get better results in a fraction of the time it would normally take to crunch data.

For instance, scientists at the National Center for Supercomputing Applications, located at the University of Illinois at Urbana-Champaign, have a deep learning system for the rapid detection and characterization of gravitational waves. Gravitational waves are disturbances in spacetime, emanating from big, high-energy cosmic events, such as the massive explosion of a star known as a supernova. The “Holy Grail” of this type of research is to detect gravitational waves from the Big Bang.

Dubbed Deep Filtering, the method allows real-time processing of data from LIGO, a gravitational wave observatory comprised of two enormous laser interferometers located thousands of miles apart in California and Louisiana. The research was published in Physics Letters B. You can watch a trippy visualization of the results below.

In a more down-to-earth example, scientists published a paper last month in Science Advances on the development of a neural network called ConvNetQuake to detect and locate minor earthquakes from ground motion measurements called seismograms.

ConvNetQuake uncovered 17 times more earthquakes than traditional methods. Scientists say the new method is particularly useful in monitoring small-scale seismic activity, which has become more frequent, possibly due to fracking activities that involve injecting wastewater deep underground. You can learn more about ConvNetQuake in this video:

King says he believes that in the long term there will be no limit to what AI can accomplish in science. He and his team, including Eve, are currently working on developing cancer therapies under a grant from DARPA.

“Robot scientists are getting smarter and smarter; human scientists are not,” he says. “Indeed, there is arguably a case that human scientists are less good. I don’t see any scientist alive today of the stature of a Newton or Einstein—despite the vast number of living scientists. The Physics Nobel [laureate] Frank Wilczek is on record as saying (10 years ago) that in 100 years’ time the best physicist will be a machine. I agree.”

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#432181 Putting AI in Your Pocket: MIT Chip Cuts ...

Neural networks are powerful things, but they need a lot of juice. Engineers at MIT have now developed a new chip that cuts neural nets’ power consumption by up to 95 percent, potentially allowing them to run on battery-powered mobile devices.

Smartphones these days are getting truly smart, with ever more AI-powered services like digital assistants and real-time translation. But typically the neural nets crunching the data for these services are in the cloud, with data from smartphones ferried back and forth.

That’s not ideal, as it requires a lot of communication bandwidth and means potentially sensitive data is being transmitted and stored on servers outside the user’s control. But the huge amounts of energy needed to power the GPUs neural networks run on make it impractical to implement them in devices that run on limited battery power.

Engineers at MIT have now designed a chip that cuts that power consumption by up to 95 percent by dramatically reducing the need to shuttle data back and forth between a chip’s memory and processors.

Neural nets consist of thousands of interconnected artificial neurons arranged in layers. Each neuron receives input from multiple neurons in the layer below it, and if the combined input passes a certain threshold it then transmits an output to multiple neurons above it. The strength of the connection between neurons is governed by a weight, which is set during training.

This means that for every neuron, the chip has to retrieve the input data for a particular connection and the connection weight from memory, multiply them, store the result, and then repeat the process for every input. That requires a lot of data to be moved around, expending a lot of energy.

The new MIT chip does away with that, instead computing all the inputs in parallel within the memory using analog circuits. That significantly reduces the amount of data that needs to be shoved around and results in major energy savings.

The approach requires the weights of the connections to be binary rather than a range of values, but previous theoretical work had suggested this wouldn’t dramatically impact accuracy, and the researchers found the chip’s results were generally within two to three percent of the conventional non-binary neural net running on a standard computer.

This isn’t the first time researchers have created chips that carry out processing in memory to reduce the power consumption of neural nets, but it’s the first time the approach has been used to run powerful convolutional neural networks popular for image-based AI applications.

“The results show impressive specifications for the energy-efficient implementation of convolution operations with memory arrays,” Dario Gil, vice president of artificial intelligence at IBM, said in a statement.

“It certainly will open the possibility to employ more complex convolutional neural networks for image and video classifications in IoT [the internet of things] in the future.”

It’s not just research groups working on this, though. The desire to get AI smarts into devices like smartphones, household appliances, and all kinds of IoT devices is driving the who’s who of Silicon Valley to pile into low-power AI chips.

Apple has already integrated its Neural Engine into the iPhone X to power things like its facial recognition technology, and Amazon is rumored to be developing its own custom AI chips for the next generation of its Echo digital assistant.

The big chip companies are also increasingly pivoting towards supporting advanced capabilities like machine learning, which has forced them to make their devices ever more energy-efficient. Earlier this year ARM unveiled two new chips: the Arm Machine Learning processor, aimed at general AI tasks from translation to facial recognition, and the Arm Object Detection processor for detecting things like faces in images.

Qualcomm’s latest mobile chip, the Snapdragon 845, features a GPU and is heavily focused on AI. The company has also released the Snapdragon 820E, which is aimed at drones, robots, and industrial devices.

Going a step further, IBM and Intel are developing neuromorphic chips whose architectures are inspired by the human brain and its incredible energy efficiency. That could theoretically allow IBM’s TrueNorth and Intel’s Loihi to run powerful machine learning on a fraction of the power of conventional chips, though they are both still highly experimental at this stage.

Getting these chips to run neural nets as powerful as those found in cloud services without burning through batteries too quickly will be a big challenge. But at the current pace of innovation, it doesn’t look like it will be too long before you’ll be packing some serious AI power in your pocket.

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#432165 Silicon Valley Is Winning the Race to ...

Henry Ford didn’t invent the motor car. The late 1800s saw a flurry of innovation by hundreds of companies battling to deliver on the promise of fast, efficient and reasonably-priced mechanical transportation. Ford later came to dominate the industry thanks to the development of the moving assembly line.

Today, the sector is poised for another breakthrough with the advent of cars that drive themselves. But unlike the original wave of automobile innovation, the race for supremacy in autonomous vehicles is concentrated among a few corporate giants. So who is set to dominate this time?

I’ve analyzed six companies we think are leading the race to build the first truly driverless car. Three of these—General Motors, Ford, and Volkswagen—come from the existing car industry and need to integrate self-driving technology into their existing fleet of mass-produced vehicles. The other three—Tesla, Uber, and Waymo (owned by the same company as Google)—are newcomers from the digital technology world of Silicon Valley and have to build a mass manufacturing capability.

While it’s impossible to know all the developments at any given time, we have tracked investments, strategic partnerships, and official press releases to learn more about what’s happening behind the scenes. The car industry typically rates self-driving technology on a scale from Level 0 (no automation) to Level 5 (full automation). We’ve assessed where each company is now and estimated how far they are from reaching the top level. Here’s how we think each player is performing.

Volkswagen
Volkswagen has invested in taxi-hailing app Gett and partnered with chip-maker Nvidia to develop an artificial intelligence co-pilot for its cars. In 2018, the VW Group is set to release the Audi A8, the first production vehicle that reaches Level 3 on the scale, “conditional driving automation.” This means the car’s computer will handle all driving functions, but a human has to be ready to take over if necessary.

Ford
Ford already sells cars with a Level 2 autopilot, “partial driving automation.” This means one or more aspects of driving are controlled by a computer based on information about the environment, for example combined cruise control and lane centering. Alongside other investments, the company has put $1 billion into Argo AI, an artificial intelligence company for self-driving vehicles. Following a trial to test pizza delivery using autonomous vehicles, Ford is now testing Level 4 cars on public roads. These feature “high automation,” where the car can drive entirely on its own but not in certain conditions such as when the road surface is poor or the weather is bad.

General Motors
GM also sells vehicles with Level 2 automation but, after buying Silicon Valley startup Cruise Automation in 2016, now plans to launch the first mass-production-ready Level 5 autonomy vehicle that drives completely on its own by 2019. The Cruise AV will have no steering wheel or pedals to allow a human to take over and be part of a large fleet of driverless taxis the company plans to operate in big cities. But crucially the company hasn’t yet secured permission to test the car on public roads.

Waymo (Google)

Waymo Level 5 testing. Image Credit: Waymo

Founded as a special project in 2009, Waymo separated from Google (though they’re both owned by the same parent firm, Alphabet) in 2016. Though it has never made, sold, or operated a car on a commercial basis, Waymo has created test vehicles that have clocked more than 4 million miles without human drivers as of November 2017. Waymo tested its Level 5 car, “Firefly,” between 2015 and 2017 but then decided to focus on hardware that could be installed in other manufacturers’ vehicles, starting with the Chrysler Pacifica.

Uber
The taxi-hailing app maker Uber has been testing autonomous cars on the streets of Pittsburgh since 2016, always with an employee behind the wheel ready to take over in case of a malfunction. After buying the self-driving truck company Otto in 2016 for a reported $680 million, Uber is now expanding its AI capabilities and plans to test NVIDIA’s latest chips in Otto’s vehicles. It has also partnered with Volvo to create a self-driving fleet of cars and with Toyota to co-create a ride-sharing autonomous vehicle.

Tesla
The first major car manufacturer to come from Silicon Valley, Tesla was also the first to introduce Level 2 autopilot back in 2015. The following year, it announced that all new Teslas would have the hardware for full autonomy, meaning once the software is finished it can be deployed on existing cars with an instant upgrade. Some experts have challenged this approach, arguing that the company has merely added surround cameras to its production cars that aren’t as capable as the laser-based sensing systems that most other carmakers are using.

But the company has collected data from hundreds of thousands of cars, driving millions of miles across all terrains. So, we shouldn’t dismiss the firm’s founder, Elon Musk, when he claims a Level 4 Tesla will drive from LA to New York without any human interference within the first half of 2018.

Winners

Who’s leading the race? Image Credit: IMD

At the moment, the disruptors like Tesla, Waymo, and Uber seem to have the upper hand. While the traditional automakers are focusing on bringing Level 3 and 4 partial automation to market, the new companies are leapfrogging them by moving more directly towards Level 5 full automation. Waymo may have the least experience of dealing with consumers in this sector, but it has already clocked up a huge amount of time testing some of the most advanced technology on public roads.

The incumbent carmakers are also focused on the difficult process of integrating new technology and business models into their existing manufacturing operations by buying up small companies. The challengers, on the other hand, are easily partnering with other big players including manufacturers to get the scale and expertise they need more quickly.

Tesla is building its own manufacturing capability but also collecting vast amounts of critical data that will enable it to more easily upgrade its cars when ready for full automation. In particular, Waymo’s experience, technology capability, and ability to secure solid partnerships puts it at the head of the pack.

This article was originally published on The Conversation. Read the original article.

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#432152 Why Elon Musk Is Stepping Down from AI ...

The move could have implications for artificial intelligence development at Tesla. Continue reading

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