Tag Archives: modern

#435186 What’s Behind the International Rush ...

There’s no better way of ensuring you win a race than by setting the rules yourself. That may be behind the recent rush by countries, international organizations, and companies to put forward their visions for how the AI race should be governed.

China became the latest to release a set of “ethical standards” for the development of AI last month, which might raise eyebrows given the country’s well-documented AI-powered state surveillance program and suspect approaches to privacy and human rights.

But given the recent flurry of AI guidelines, it may well have been motivated by a desire not to be left out of the conversation. The previous week the OECD, backed by the US, released its own “guiding principles” for the industry, and in April the EU released “ethical guidelines.”

The language of most of these documents is fairly abstract and noticeably similar, with broad appeals to ideals like accountability, responsibility, and transparency. The OECD’s guidelines are the lightest on detail, while the EU’s offer some more concrete suggestions such as ensuring humans always know if they’re interacting with AI and making algorithms auditable. China’s standards have an interesting focus on promoting openness and collaboration as well as expressly acknowledging AIs potential to disrupt employment.

Overall, though, one might be surprised that there aren’t more disagreements between three blocs with very divergent attitudes to technology, regulation, and economics. Most likely these are just the opening salvos in what will prove to be a long-running debate, and the devil will ultimately be in the details.

The EU seems to have stolen a march on the other two blocs, being first to publish its guidelines and having already implemented the world’s most comprehensive regulation of data—the bedrock of modern AI—with last year’s GDPR. But its lack of industry heavyweights is going to make it hard to hold onto that lead.

One organization that seems to be trying to take on the role of impartial adjudicator is the World Economic Forum, which recently hosted an event designed to find common ground between various stakeholders from across the world. What will come of the effort remains to be seen, but China’s release of guidelines broadly similar to those of its Western counterparts is a promising sign.

Perhaps most telling, though, is the ubiquitous presence of industry leaders in both advisory and leadership positions. China’s guidelines are backed by “an AI industrial league” including Baidu, Alibaba, and Tencent, and the co-chairs of the WEF’s AI Council are Microsoft President Brad Smith and prominent Chinese AI investor Kai-Fu Lee.

Shortly after the EU released its proposals one of the authors, philosopher Thomas Metzinger, said the process had been compromised by the influence of the tech industry, leading to the removal of “red lines” opposing the development of autonomous lethal weapons or social credit score systems like China’s.

For a long time big tech argued for self-regulation, but whether they’ve had an epiphany or have simply sensed the shifting winds, they are now coming out in favor of government intervention.

Both Amazon and Facebook have called for regulation of facial recognition, and in February Google went even further, calling for the government to set down rules governing AI. Facebook chief Mark Zuckerberg has also since called for even broader regulation of the tech industry.

But considering the current concern around the anti-competitive clout of the largest technology companies, it’s worth remembering that tough rules are always easier to deal with for companies with well-developed compliance infrastructure and big legal teams. And these companies are also making sure the regulation is on their terms. Wired details Microsoft’s protracted effort to shape Washington state laws governing facial recognition technology and Google’s enormous lobbying effort.

“Industry has mobilized to shape the science, morality and laws of artificial intelligence,” Harvard law professor Yochai Benkler writes in Nature. He highlights how Amazon’s funding of a National Science Foundation (NSF) program for projects on fairness in artificial intelligence undermines the ability of academia to act as an impartial counterweight to industry.

Excluding industry from the process of setting the rules to govern AI in a fair and equitable way is clearly not practical, writes Benkler, because they are the ones with the expertise. But there also needs to be more concerted public investment in research and policymaking, and efforts to limit the influence of big companies when setting the rules that will govern AI.

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#435174 Revolt on the Horizon? How Young People ...

As digital technologies facilitate the growth of both new and incumbent organizations, we have started to see the darker sides of the digital economy unravel. In recent years, many unethical business practices have been exposed, including the capture and use of consumers’ data, anticompetitive activities, and covert social experiments.

But what do young people who grew up with the internet think about this development? Our research with 400 digital natives—19- to 24-year-olds—shows that this generation, dubbed “GenTech,” may be the one to turn the digital revolution on its head. Our findings point to a frustration and disillusionment with the way organizations have accumulated real-time information about consumers without their knowledge and often without their explicit consent.

Many from GenTech now understand that their online lives are of commercial value to an array of organizations that use this insight for the targeting and personalization of products, services, and experiences.

This era of accumulation and commercialization of user data through real-time monitoring has been coined “surveillance capitalism” and signifies a new economic system.

Artificial Intelligence
A central pillar of the modern digital economy is our interaction with artificial intelligence (AI) and machine learning algorithms. We found that 47 percent of GenTech do not want AI technology to monitor their lifestyle, purchases, and financial situation in order to recommend them particular things to buy.

In fact, only 29 percent see this as a positive intervention. Instead, they wish to maintain a sense of autonomy in their decision making and have the opportunity to freely explore new products, services, and experiences.

As individuals living in the digital age, we constantly negotiate with technology to let go of or retain control. This pendulum-like effect reflects the ongoing battle between humans and technology.

My Life, My Data?
Our research also reveals that 54 percent of GenTech are very concerned about the access organizations have to their data, while only 19 percent were not worried. Despite the EU General Data Protection Regulation being introduced in May 2018, this is still a major concern, grounded in a belief that too much of their data is in the possession of a small group of global companies, including Google, Amazon, and Facebook. Some 70 percent felt this way.

In recent weeks, both Facebook and Google have vowed to make privacy a top priority in the way they interact with users. Both companies have faced public outcry for their lack of openness and transparency when it comes to how they collect and store user data. It wasn’t long ago that a hidden microphone was found in one of Google’s home alarm products.

Google now plans to offer auto-deletion of users’ location history data, browsing, and app activity as well as extend its “incognito mode” to Google Maps and search. This will enable users to turn off tracking.

At Facebook, CEO Mark Zuckerberg is keen to reposition the platform as a “privacy focused communications platform” built on principles such as private interactions, encryption, safety, interoperability (communications across Facebook-owned apps and platforms), and secure data storage. This will be a tough turnaround for the company that is fundamentally dependent on turning user data into opportunities for highly individualized advertising.

Privacy and transparency are critically important themes for organizations today, both for those that have “grown up” online as well as the incumbents. While GenTech want organizations to be more transparent and responsible, 64 percent also believe that they cannot do much to keep their data private. Being tracked and monitored online by organizations is seen as part and parcel of being a digital consumer.

Despite these views, there is a growing revolt simmering under the surface. GenTech want to take ownership of their own data. They see this as a valuable commodity, which they should be given the opportunity to trade with organizations. Some 50 percent would willingly share their data with companies if they got something in return, for example a financial incentive.

Rewiring the Power Shift
GenTech are looking to enter into a transactional relationship with organizations. This reflects a significant change in attitudes from perceiving the free access to digital platforms as the “product” in itself (in exchange for user data), to now wishing to use that data to trade for explicit benefits.

This has created an opportunity for companies that seek to empower consumers and give them back control of their data. Several companies now offer consumers the opportunity to sell the data they are comfortable sharing or take part in research that they get paid for. More and more companies are joining this space, including People.io, Killi, and Ocean Protocol.

Sir Tim Berners Lee, the creator of the world wide web, has also been working on a way to shift the power from organizations and institutions back to citizens and consumers. The platform, Solid, offers users the opportunity to be in charge of where they store their data and who can access it. It is a form of re-decentralization.

The Solid POD (Personal Online Data storage) is a secure place on a hosted server or the individual’s own server. Users can grant apps access to their POD as a person’s data is stored centrally and not by an app developer or on an organization’s server. We see this as potentially being a way to let people take back control from technology and other companies.

GenTech have woken up to a reality where a life lived “plugged in” has significant consequences for their individual privacy and are starting to push back, questioning those organizations that have shown limited concern and continue to exercise exploitative practices.

It’s no wonder that we see these signs of revolt. GenTech is the generation with the most to lose. They face a life ahead intertwined with digital technology as part of their personal and private lives. With continued pressure on organizations to become more transparent, the time is now for young people to make their move.

Dr Mike Cooray, Professor of Practice, Hult International Business School and Dr Rikke Duus, Research Associate and Senior Teaching Fellow, UCL

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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#434865 5 AI Breakthroughs We’ll Likely See in ...

Convergence is accelerating disruption… everywhere! Exponential technologies are colliding into each other, reinventing products, services, and industries.

As AI algorithms such as Siri and Alexa can process your voice and output helpful responses, other AIs like Face++ can recognize faces. And yet others create art from scribbles, or even diagnose medical conditions.

Let’s dive into AI and convergence.

Top 5 Predictions for AI Breakthroughs (2019-2024)
My friend Neil Jacobstein is my ‘go-to expert’ in AI, with over 25 years of technical consulting experience in the field. Currently the AI and Robotics chair at Singularity University, Jacobstein is also a Distinguished Visiting Scholar in Stanford’s MediaX Program, a Henry Crown Fellow, an Aspen Institute moderator, and serves on the National Academy of Sciences Earth and Life Studies Committee. Neil predicted five trends he expects to emerge over the next five years, by 2024.

AI gives rise to new non-human pattern recognition and intelligence results

AlphaGo Zero, a machine learning computer program trained to play the complex game of Go, defeated the Go world champion in 2016 by 100 games to zero. But instead of learning from human play, AlphaGo Zero trained by playing against itself—a method known as reinforcement learning.

Building its own knowledge from scratch, AlphaGo Zero demonstrates a novel form of creativity, free of human bias. Even more groundbreaking, this type of AI pattern recognition allows machines to accumulate thousands of years of knowledge in a matter of hours.

While these systems can’t answer the question “What is orange juice?” or compete with the intelligence of a fifth grader, they are growing more and more strategically complex, merging with other forms of narrow artificial intelligence. Within the next five years, who knows what successors of AlphaGo Zero will emerge, augmenting both your business functions and day-to-day life.

Doctors risk malpractice when not using machine learning for diagnosis and treatment planning

A group of Chinese and American researchers recently created an AI system that diagnoses common childhood illnesses, ranging from the flu to meningitis. Trained on electronic health records compiled from 1.3 million outpatient visits of almost 600,000 patients, the AI program produced diagnosis outcomes with unprecedented accuracy.

While the US health system does not tout the same level of accessible universal health data as some Chinese systems, we’ve made progress in implementing AI in medical diagnosis. Dr. Kang Zhang, chief of ophthalmic genetics at the University of California, San Diego, created his own system that detects signs of diabetic blindness, relying on both text and medical images.

With an eye to the future, Jacobstein has predicted that “we will soon see an inflection point where doctors will feel it’s a risk to not use machine learning and AI in their everyday practices because they don’t want to be called out for missing an important diagnostic signal.”

Quantum advantage will massively accelerate drug design and testing

Researchers estimate that there are 1060 possible drug-like molecules—more than the number of atoms in our solar system. But today, chemists must make drug predictions based on properties influenced by molecular structure, then synthesize numerous variants to test their hypotheses.

Quantum computing could transform this time-consuming, highly costly process into an efficient, not to mention life-changing, drug discovery protocol.

“Quantum computing is going to have a major industrial impact… not by breaking encryption,” said Jacobstein, “but by making inroads into design through massive parallel processing that can exploit superposition and quantum interference and entanglement, and that can wildly outperform classical computing.”

AI accelerates security systems’ vulnerability and defense

With the incorporation of AI into almost every aspect of our lives, cyberattacks have grown increasingly threatening. “Deep attacks” can use AI-generated content to avoid both human and AI controls.

Previous examples include fake videos of former President Obama speaking fabricated sentences, and an adversarial AI fooling another algorithm into categorizing a stop sign as a 45 mph speed limit sign. Without the appropriate protections, AI systems can be manipulated to conduct any number of destructive objectives, whether ruining reputations or diverting autonomous vehicles.

Jacobstein’s take: “We all have security systems on our buildings, in our homes, around the healthcare system, and in air traffic control, financial organizations, the military, and intelligence communities. But we all know that these systems have been hacked periodically and we’re going to see that accelerate. So, there are major business opportunities there and there are major opportunities for you to get ahead of that curve before it bites you.”

AI design systems drive breakthroughs in atomically precise manufacturing

Just as the modern computer transformed our relationship with bits and information, AI will redefine and revolutionize our relationship with molecules and materials. AI is currently being used to discover new materials for clean-tech innovations, such as solar panels, batteries, and devices that can now conduct artificial photosynthesis.

Today, it takes about 15 to 20 years to create a single new material, according to industry experts. But as AI design systems skyrocket in capacity, these will vastly accelerate the materials discovery process, allowing us to address pressing issues like climate change at record rates. Companies like Kebotix are already on their way to streamlining the creation of chemistries and materials at the click of a button.

Atomically precise manufacturing will enable us to produce the previously unimaginable.

Final Thoughts
Within just the past three years, countries across the globe have signed into existence national AI strategies and plans for ramping up innovation. Businesses and think tanks have leaped onto the scene, hiring AI engineers and tech consultants to leverage what computer scientist Andrew Ng has even called the new ‘electricity’ of the 21st century.

As AI plays an exceedingly vital role in everyday life, how will your business leverage it to keep up and build forward?

In the wake of burgeoning markets, new ventures will quickly arise, each taking advantage of untapped data sources or unmet security needs.

And as your company aims to ride the wave of AI’s exponential growth, consider the following pointers to leverage AI and disrupt yourself before it reaches you first:

Determine where and how you can begin collecting critical data to inform your AI algorithms
Identify time-intensive processes that can be automated and accelerated within your company
Discern which global challenges can be expedited by hyper-fast, all-knowing minds

Remember: good data is vital fuel. Well-defined problems are the best compass. And the time to start implementing AI is now.

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

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.

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#434786 AI Performed Like a Human on a Gestalt ...

Dr. Been Kim wants to rip open the black box of deep learning.

A senior researcher at Google Brain, Kim specializes in a sort of AI psychology. Like cognitive psychologists before her, she develops various ways to probe the alien minds of artificial neural networks (ANNs), digging into their gory details to better understand the models and their responses to inputs.

The more interpretable ANNs are, the reasoning goes, the easier it is to reveal potential flaws in their reasoning. And if we understand when or why our systems choke, we’ll know when not to use them—a foundation for building responsible AI.

There are already several ways to tap into ANN reasoning, but Kim’s inspiration for unraveling the AI black box came from an entirely different field: cognitive psychology. The field aims to discover fundamental rules of how the human mind—essentially also a tantalizing black box—operates, Kim wrote with her colleagues.

In a new paper uploaded to the pre-publication server arXiv, the team described a way to essentially perform a human cognitive test on ANNs. The test probes how we automatically complete gaps in what we see, so that they form entire objects—for example, perceiving a circle from a bunch of loose dots arranged along a clock face. Psychologist dub this the “law of completion,” a highly influential idea that led to explanations of how our minds generalize data into concepts.

Because deep neural networks in machine vision loosely mimic the structure and connections of the visual cortex, the authors naturally asked: do ANNs also exhibit the law of completion? And what does that tell us about how an AI thinks?

Enter the Germans
The law of completion is part of a series of ideas from Gestalt psychology. Back in the 1920s, long before the advent of modern neuroscience, a group of German experimental psychologists asked: in this chaotic, flashy, unpredictable world, how do we piece together input in a way that leads to meaningful perceptions?

The result is a group of principles known together as the Gestalt effect: that the mind self-organizes to form a global whole. In the more famous words of Gestalt psychologist Kurt Koffka, our perception forms a whole that’s “something else than the sum of its parts.” Not greater than; just different.

Although the theory has its critics, subsequent studies in humans and animals suggest that the law of completion happens on both the cognitive and neuroanatomical level.

Take a look at the drawing below. You immediately “see” a shape that’s actually the negative: a triangle or a square (A and B). Or you further perceive a 3D ball (C), or a snake-like squiggle (D). Your mind fills in blank spots, so that the final perception is more than just the black shapes you’re explicitly given.

Image Credit: Wikimedia Commons contributors, the free media repository.
Neuroscientists now think that the effect comes from how our visual system processes information. Arranged in multiple layers and columns, lower-level neurons—those first to wrangle the data—tend to extract simpler features such as lines or angles. In Gestalt speak, they “see” the parts.

Then, layer by layer, perception becomes more abstract, until higher levels of the visual system directly interpret faces or objects—or things that don’t really exist. That is, the “whole” emerges.

The Experiment Setup
Inspired by these classical experiments, Kim and team developed a protocol to test the Gestalt effect on feed-forward ANNs: one simple, the other, dubbed the “Inception V3,” far more complex and widely used in the machine vision community.

The main idea is similar to the triangle drawings above. First, the team generated three datasets: one set shows complete, ordinary triangles. The second—the “Illusory” set, shows triangles with the edges removed but the corners intact. Thanks to the Gestalt effect, to us humans these generally still look like triangles. The third set also only shows incomplete triangle corners. But here, the corners are randomly rotated so that we can no longer imagine a line connecting them—hence, no more triangle.

To generate a dataset large enough to tease out small effects, the authors changed the background color, image rotation, and other aspects of the dataset. In all, they produced nearly 1,000 images to test their ANNs on.

“At a high level, we compare an ANN’s activation similarities between the three sets of stimuli,” the authors explained. The process is two steps: first, train the AI on complete triangles. Second, test them on the datasets. If the response is more similar between the illusory set and the complete triangle—rather than the randomly rotated set—it should suggest a sort of Gestalt closure effect in the network.

Machine Gestalt
Right off the bat, the team got their answer: yes, ANNs do seem to exhibit the law of closure.

When trained on natural images, the networks better classified the illusory set as triangles than those with randomized connection weights or networks trained on white noise.

When the team dug into the “why,” things got more interesting. The ability to complete an image correlated with the network’s ability to generalize.

Humans subconsciously do this constantly: anything with a handle made out of ceramic, regardless of shape, could easily be a mug. ANNs still struggle to grasp common features—clues that immediately tells us “hey, that’s a mug!” But when they do, it sometimes allows the networks to better generalize.

“What we observe here is that a network that is able to generalize exhibits…more of the closure effect [emphasis theirs], hinting that the closure effect reflects something beyond simply learning features,” the team wrote.

What’s more, remarkably similar to the visual cortex, “higher” levels of the ANNs showed more of the closure effect than lower layers, and—perhaps unsurprisingly—the more layers a network had, the more it exhibited the closure effect.

As the networks learned, their ability to map out objects from fragments also improved. When the team messed around with the brightness and contrast of the images, the AI still learned to see the forest from the trees.

“Our findings suggest that neural networks trained with natural images do exhibit closure,” the team concluded.

AI Psychology
That’s not to say that ANNs recapitulate the human brain. As Google’s Deep Dream, an effort to coax AIs into spilling what they’re perceiving, clearly demonstrates, machine vision sees some truly weird stuff.

In contrast, because they’re modeled after the human visual cortex, perhaps it’s not all that surprising that these networks also exhibit higher-level properties inherent to how we process information.

But to Kim and her colleagues, that’s exactly the point.

“The field of psychology has developed useful tools and insights to study human brains– tools that we may be able to borrow to analyze artificial neural networks,” they wrote.

By tweaking these tools to better analyze machine minds, the authors were able to gain insight on how similarly or differently they see the world from us. And that’s the crux: the point isn’t to say that ANNs perceive the world sort of, kind of, maybe similar to humans. It’s to tap into a wealth of cognitive psychology tools, established over decades using human minds, to probe that of ANNs.

“The work here is just one step along a much longer path,” the authors conclude.

“Understanding where humans and neural networks differ will be helpful for research on interpretability by enlightening the fundamental differences between the two interesting species.”

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