Tag Archives: beat

#435593 AI at the Speed of Light

Posted on September 11, 2019 by Android

Neural networks shine for solving tough problems such as facial and voice recognition, but conventional electronic versions are limited in speed and hungry for power. In theory, optics could beat digital electronic computers in the matrix calculations used in neural networks. However, optics had been limited by their inability to do some complex calculations that had required electronics. Now new experiments show that all-optical neural networks can tackle those problems.

The key attraction of neural networks is their massive interconnections among processors, comparable to the complex interconnections among neurons in the brain. This lets them perform many operations simultaneously, like the human brain does when looking at faces or listening to speech, making them more efficient for facial and voice recognition than traditional electronic computers that execute one instruction at a time.

Today's electronic neural networks have reached eight million neurons, but their future use in artificial intelligence may be limited by their high power usage and limited parallelism in connections. Optical connections through lenses are inherently parallel. The lens in your eye simultaneously focuses light from across your field of view onto the retina in the back of your eye, where an array of light-detecting nerve cells detects the light. Each cell then relays the signal it receives to neurons in the brain that process the visual signals to show us an image.

Glass lenses process optical signals by focusing light, which performs a complex mathematical operation called a Fourier transform that preserves the information in the original scene but rearranges is completely. One use of Fourier transforms is converting time variations in signal intensity into a plot of the frequencies present in the signal. The military used this trick in the 1950s to convert raw radar return signals recorded by an aircraft in flight into a three-dimensional image of the landscape viewed by the plane. Today that conversion is done electronically, but the vacuum-tube computers of the 1950s were not up to the task.

Development of neural networks for artificial intelligence started with electronics, but their AI applications have been limited by their slow processing and need for extensive computing resources. Some researchers have developed hybrid neural networks, in which optics perform simple linear operations, but electronics perform more complex nonlinear calculations. Now two groups have demonstrated simple all-optical neural networks that do all processing with light.

In May, Wolfram Pernice of the Institute of Physics at the University of Münster in Germany and colleagues reported testing an all-optical “neuron” in which signals change target materials between liquid and solid states, an effect that has been used for optical data storage. They demonstrated nonlinear processing, and produced output pulses like those from organic neurons. They then produced an integrated photonic circuit that incorporated four optical neurons operating at different wavelengths, each of which connected to 15 optical synapses. The photonic circuit contained more than 140 components and could recognize simple optical patterns. The group wrote that their device is scalable, and that the technology promises “access to the high speed and high bandwidth inherent to optical systems, thus enabling the direct processing of optical telecommunication and visual data.”

Now a group at the Hong Kong University of Science and Technology reports in Optica that they have made an all-optical neural network based on a different process, electromagnetically induced transparency, in which incident light affects how atoms shift between quantum-mechanical energy levels. The process is nonlinear and can be triggered by very weak light signals, says Shengwang Du, a physics professor and coauthor of the paper.

In their demonstration, they illuminated rubidium-85 atoms cooled by lasers to about 10 microKelvin (10 microdegrees above absolute zero). Although the technique may seem unusually complex, Du said the system was the most accessible one in the lab that could produce the desired effects. “As a pure quantum atomic system [it] is ideal for this proof-of-principle experiment,” he says.

Next, they plan to scale up the demonstration using a hot atomic vapor center, which is less expensive, does not require time-consuming preparation of cold atoms, and can be integrated with photonic chips. Du says the major challenges are reducing cost of the nonlinear processing medium and increasing the scale of the all-optical neural network for more complex tasks.

“Their demonstration seems valid,” says Volker Sorger, an electrical engineer at George Washington University in Washington who was not involved in either demonstration. He says the all-optical approach is attractive because it offers very high parallelism, but the update rate is limited to about 100 hertz because of the liquid crystals used in their test, and he is not completely convinced their approach can be scaled error-free. Continue reading →

Posted in Human Robots

#435528 The Time for AI Is Now. Here’s Why

Posted on August 21, 2019 by Android

You hear a lot these days about the sheer transformative power of AI.

There’s pure intelligence: DeepMind’s algorithms readily beat humans at Go and StarCraft, and DeepStack triumphs over humans at no-limit hold’em poker. Often, these silicon brains generate gameplay strategies that don’t resemble anything from a human mind.

There’s astonishing speed: algorithms routinely surpass radiologists in diagnosing breast cancer, eye disease, and other ailments visible from medical imaging, essentially collapsing decades of expert training down to a few months.

Although AI’s silent touch is mainly felt today in the technological, financial, and health sectors, its impact across industries is rapidly spreading. At the Singularity University Global Summit in San Francisco this week Neil Jacobstein, Chair of AI and Robotics, painted a picture of a better AI-powered future for humanity that is already here.

Thanks to cloud-based cognitive platforms, sophisticated AI tools like deep learning are no longer relegated to academic labs. For startups looking to tackle humanity’s grand challenges, the tools to efficiently integrate AI into their missions are readily available. The progress of AI is massively accelerating—to the point you need help from AI to track its progress, joked Jacobstein.

Now is the time to consider how AI can impact your industry, and in the process, begin to envision a beneficial relationship with our machine coworkers. As Jacobstein stressed in his talk, the future of a brain-machine mindmeld is a collaborative intelligence that augments our own. “AI is reinventing the way we invent,” he said.

AI’s Rapid Revolution
Machine learning and other AI-based methods may seem academic and abstruse. But Jacobstein pointed out that there are already plenty of real-world AI application frameworks.

Their secret? Rather than coding from scratch, smaller companies—with big visions—are tapping into cloud-based solutions such as Google’s TensorFlow, Microsoft’s Azure, or Amazon’s AWS to kick off their AI journey. These platforms act as all-in-one solutions that not only clean and organize data, but also contain built-in security and drag-and-drop coding that allow anyone to experiment with complicated machine learning algorithms.

Google Cloud’s Anthos, for example, lets anyone migrate data from other servers—IBM Watson or AWS, for example—so users can leverage different computing platforms and algorithms to transform data into insights and solutions.

Rather than coding from scratch, it’s already possible to hop onto a platform and play around with it, said Jacobstein. That’s key: this democratization of AI is how anyone can begin exploring solutions to problems we didn’t even know we had, or those long thought improbable.

The acceleration is only continuing. Much of AI’s mind-bending pace is thanks to a massive infusion of funding. Microsoft recently injected $1 billion into OpenAI, the Elon Musk venture that engineers socially responsible artificial general intelligence (AGI).

The other revolution is in hardware, and Google, IBM, and NVIDIA—among others—are racing to manufacture computing chips tailored to machine learning.

Democratizing AI is like the birth of the printing press. Mechanical printing allowed anyone to become an author; today, an iPhone lets anyone film a movie masterpiece.

However, this diffusion of AI into the fabric of our lives means tech explorers need to bring skepticism to their AI solutions, giving them a dose of empathy, nuance, and humanity.

A Path Towards Ethical AI
The democratization of AI is a double-edged sword: as more people wield the technology’s power in real-world applications, problems embedded in deep learning threaten to disrupt those very judgment calls.

Much of the press on the dangers of AI focuses on superintelligence—AI that’s more adept at learning than humans—taking over the world, said Jacobstein. But the near-term threat, and far more insidious, is in humans misusing the technology.

Deepfakes, for example, allow AI rookies to paste one person’s head on a different body or put words into a person’s mouth. As the panel said, it pays to think of AI as a cybersecurity problem, one with currently shaky accountability and complexity, and one that fails at diversity and bias.

Take bias. Thanks to progress in natural language processing, Google Translate works nearly perfectly today, so much so that many consider the translation problem solved. Not true, the panel said. One famous example is how the algorithm translates gender-neutral terms like “doctor” into “he” and “nurse” into “she.”

These biases reflect our own, and it’s not just a data problem. To truly engineer objective AI systems, ones stripped of our society’s biases, we need to ask who is developing these systems, and consult those who will be impacted by the products. In addition to gender, racial bias is also rampant. For example, one recent report found that a supposedly objective crime-predicting system was trained on falsified data, resulting in outputs that further perpetuate corrupt police practices. Another study from Google just this month found that their hate speech detector more often labeled innocuous tweets from African-Americans as “obscene” compared to tweets from people of other ethnicities.

We often think of building AI as purely an engineering job, the panelists agreed. But similar to gene drives, germ-line genome editing, and other transformative—but dangerous—tools, AI needs to grow under the consultation of policymakers and other stakeholders. It pays to start young: educating newer generations on AI biases will mold malleable minds early, alerting them to the problem of bias and potentially mitigating risks.

As panelist Tess Posner from AI4ALL said, AI is rocket fuel for ambition. If young minds set out using the tools of AI to tackle their chosen problems, while fully aware of its inherent weaknesses, we can begin to build an AI-embedded future that is widely accessible and inclusive.

The bottom line: people who will be impacted by AI need to be in the room at the conception of an AI solution. People will be displaced by the new technology, and ethical AI has to consider how to mitigate human suffering during the transition. Just because AI looks like “magic fairy dust doesn’t mean that you’re home free,” the panelists said. You, the sentient human, bear the burden of being responsible for how you decide to approach the technology.

The time for AI is now. Let’s make it ethical.

Image Credit: GrAI / Shutterstock.com Continue reading →

Posted in Human Robots

#435161 Less Like Us: An Alternate Theory of ...

Posted on June 3, 2019 by Android

The question of whether an artificial general intelligence will be developed in the future—and, if so, when it might arrive—is controversial. One (very uncertain) estimate suggests 2070 might be the earliest we could expect to see such technology.

Some futurists point to Moore’s Law and the increasing capacity of machine learning algorithms to suggest that a more general breakthrough is just around the corner. Others suggest that extrapolating exponential improvements in hardware is unwise, and that creating narrow algorithms that can beat humans at specialized tasks brings us no closer to a “general intelligence.”

But evolution has produced minds like the human mind at least once. Surely we could create artificial intelligence simply by copying nature, either by guided evolution of simple algorithms or wholesale emulation of the human brain.

Both of these ideas are far easier to conceive of than they are to achieve. The 302 neurons of the nematode worm’s brain are still an extremely difficult engineering challenge, let alone the 86 billion in a human brain.

Leaving aside these caveats, though, many people are worried about artificial general intelligence. Nick Bostrom’s influential book on superintelligence imagines it will be an agent—an intelligence with a specific goal. Once such an agent reaches a human level of intelligence, it will improve itself—increasingly rapidly as it gets smarter—in pursuit of whatever goal it has, and this “recursive self-improvement” will lead it to become superintelligent.

This “intelligence explosion” could catch humans off guard. If the initial goal is poorly specified or malicious, or if improper safety features are in place, or if the AI decides it would prefer to do something else instead, humans may be unable to control our own creation. Bostrom gives examples of how a seemingly innocuous goal, such as “Make everyone happy,” could be misinterpreted; perhaps the AI decides to drug humanity into a happy stupor, or convert most of the world into computing infrastructure to pursue its goal.

Drexler and Comprehensive AI Services
These are increasingly familiar concerns for an AI that behaves like an agent, seeking to achieve its goal. There are dissenters to this picture of how artificial general intelligence might arise. One notable alternative point of view comes from Eric Drexler, famous for his work on molecular nanotechnology and Engines of Creation, the book that popularized it.

With respect to AI, Drexler believes our view of an artificial intelligence as a single “agent” that acts to maximize a specific goal is too narrow, almost anthropomorphizing AI, or modeling it as a more realistic route towards general intelligence. Instead, he proposes “Comprehensive AI Services” (CAIS) as an alternative route to artificial general intelligence.

What does this mean? Drexler’s argument is that we should look more closely at how machine learning and AI algorithms are actually being developed in the real world. The optimization effort is going into producing algorithms that can provide services and perform tasks like translation, music recommendations, classification, medical diagnoses, and so forth.

AI-driven improvements in technology, argues Drexler, will lead to a proliferation of different algorithms: technology and software improvement, which can automate increasingly more complicated tasks. Recursive improvement in this regime is already occurring—take the newer versions of AlphaGo, which can learn to improve themselves by playing against previous versions.

Many Smart Arms, No Smart Brain
Instead of relying on some unforeseen breakthrough, the CAIS model of AI just assumes that specialized, narrow AI will continue to improve at performing each of its tasks, and the range of tasks that machine learning algorithms will be able to perform will become wider. Ultimately, once a sufficient number of tasks have been automated, the services that an AI will provide will be so comprehensive that they will resemble a general intelligence.

One could then imagine a “general” intelligence as simply an algorithm that is extremely good at matching the task you ask it to perform to the specialized service algorithm that can perform that task. Rather than acting like a single brain that strives to achieve a particular goal, the central AI would be more like a search engine, looking through the tasks it can perform to find the closest match and calling upon a series of subroutines to achieve the goal.

For Drexler, this is inherently a safety feature. Rather than Bostrom’s single, impenetrable, conscious and superintelligent brain (which we must try to psychoanalyze in advance without really knowing what it will look like), we have a network of capabilities. If you don’t want your system to perform certain tasks, you can simply cut it off from access to those services. There is no superintelligent consciousness to outwit or “trap”: more like an extremely high-level programming language that can respond to complicated commands by calling upon one of the myriad specialized algorithms that have been developed by different groups.

This skirts the complex problem of consciousness and all of the sticky moral quandaries that arise in making minds that might be like ours. After all, if you could simulate a human mind, you could simulate it experiencing unimaginable pain. Black Mirror-esque dystopias where emulated minds have no rights and are regularly “erased” or forced to labor in dull and repetitive tasks, hove into view.

Drexler argues that, in this world, there is no need to ever build a conscious algorithm. Yet it seems likely that, at some point, humans will attempt to simulate our own brains, if only in the vain attempt to pursue immortality. This model cannot hold forever. Yet its proponents argue that any world in which we could develop general AI would probably also have developed superintelligent capabilities in a huge range of different tasks, such as computer programming, natural language understanding, and so on. In other words, CAIS arrives first.

The Future In Our Hands?
Drexler argues that his model already incorporates many of the ideas from general AI development. In the marketplace, algorithms compete all the time to perform these services: they undergo the same evolutionary pressures that lead to “higher intelligence,” but the behavior that’s considered superior is chosen by humans, and the nature of the “general intelligence” is far more shaped by human decision-making and human programmers. Development in AI services could still be rapid and disruptive.

But in Drexler’s case, the research and development capacity comes from humans and organizations driven by the desire to improve algorithms that are performing individualized and useful tasks, rather than from a conscious AI recursively reprogramming and improving itself.

In other words, this vision does not absolve us of the responsibility of making our AI safe; if anything, it gives us a greater degree of responsibility. As more and more complex “services” are automated, performing what used to be human jobs at superhuman speed, the economic disruption will be severe.

Equally, as machine learning is trusted to carry out more complex decisions, avoiding algorithmic bias becomes crucial. Shaping each of these individual decision-makers—and trying to predict the complex ways they might interact with each other—is no less daunting a task than specifying the goal for a hypothetical, superintelligent, God-like AI. Arguably, the consequences of the “misalignment” of these services algorithms are already multiplying around us.

The CAIS model bridges the gap between real-world AI, machine learning developments, and real-world safety considerations, as well as the speculative world of superintelligent agents and the safety considerations involved with controlling their behavior. We should keep our minds open as to what form AI and machine learning will take, and how it will influence our societies—and we must take care to ensure that the systems we create don’t end up forcing us all to live in a world of unintended consequences.

Image Credit: MF Production/Shutterstock.com Continue reading →

Posted in Human Robots

#435159 This Week’s Awesome Stories From ...

Posted on June 2, 2019 by Android

ARTIFICIAL INTELLIGENCE
DeepMind Can Now Beat Us at Multiplayer Games Too
Cade Metz | The New York Times
“DeepMind’s project is part of a broad effort to build artificial intelligence that can play enormously complex, three-dimensional video games, including Quake III, Dota 2 and StarCraft II. Many researchers believe that success in the virtual arena will eventually lead to automated systems with improved abilities in the real world.”

ROBOTICS
Tiny Robots Carry Stem Cells Through a Mouse
Emily Waltz | IEEE Spectrum
“Engineers have built microrobots to perform all sorts of tasks in the body, and can now add to that list another key skill: delivering stem cells. In a paper, published [May 29] in Science Robotics, researchers describe propelling a magnetically-controlled, stem-cell-carrying bot through a live mouse.” [Video shows microbots navigating a microfluidic chip. MRI could not be used to image the mouse as the bots navigate magnetically.]

COMPUTING
How a Quantum Computer Could Break 2048-Bit RSA Encryption in 8 Hours
Emerging Technology From the arXiv | MIT Technology Review
“[Two researchers] have found a more efficient way for quantum computers to perform the code-breaking calculations, reducing the resources they require by orders of magnitude. Consequently, these machines are significantly closer to reality than anyone suspected.” [The arXiv is a pre-print server for research that has not yet been peer reviewed.]

AUTOMATION
Lyft Has Completed 55,000 Self Driving Rides in Las Vegas
Christine Fisher | Engadget
“One year ago, Lyft launched its self-driving ride service in Las Vegas. Today, the company announced its 30-vehicle fleet has made 55,000 trips. That makes it the largest commercial program of its kind in the US.”

TRANSPORTATION
Flying Car Startup Alaka’i Bets Hydrogen Can Outdo Batteries
Eric Adams | Wired
“Alaka’i says the final product will be able to fly for up to four hours and cover 400 miles on a single load of fuel, which can be replenished in 10 minutes at a hydrogen fueling station. It has built a functional, full-scale prototype that will make its first flight ‘imminently,’ a spokesperson says.”

ETHICS
The World Economic Forum Wants to Develop Global Rules for AI
Will Knight | MIT Technology Review
“This week, AI experts, politicians, and CEOs will gather to ask an important question: Can the United States, China, or anyone else agree on how artificial intelligence should be used and controlled?”

SPACE
Building a Rocket in a Garage to Take on SpaceX and Blue Origin
Jackson Ryan | CNET
“While billionaire entrepreneurs like SpaceX’s Elon Musk and Blue Origin’s Jeff Bezos push the boundaries of human spaceflight and exploration, a legion of smaller private startups around the world have been developing their own rocket technology to launch lighter payloads into orbit.”

Image Credit: Kevin Crosby / Unsplash Continue reading →

Posted in Human Robots

#435145 How Big Companies Can Simultaneously Run ...

Posted on May 28, 2019 by Android

We live in the age of entrepreneurs. New startups seem to appear out of nowhere and challenge not only established companies, but entire industries. Where startup unicorns were once mythical creatures, they now seem abundant, not only increasing in numbers but also in the speed with which they can gain the minimum one-billion-dollar valuations to achieve this status.

But no matter how well things go for innovative startups, how many new success stories we hear, and how much space they take up in the media, the story that they are the best or only source of innovation isn’t entirely accurate.

Established organizations, or legacy organizations, can be incredibly innovative too. And while innovation is much more difficult in established organizations than in startups because they have much more complex systems—nobody is more likely to succeed in their innovation efforts than established organizations.

Unlike startups, established organizations have all the resources. They have money, customers, data, suppliers, partners, and infrastructure, which put them in a far better position to transform new ideas into concrete, value-creating, successful offerings than startups.

However, for established organizations, becoming an innovation champion in these times of rapid change requires new rules of engagement.

Many organizations commit the mistake of engaging in innovation as if it were a homogeneous thing that should be approached in the same way every time, regardless of its purpose. In my book, Transforming Legacy Organizations, I argue that innovation in established organizations must actually be divided into three different tracks: optimizing, augmenting, and mutating innovation.

All three are important, and to complicate matters further, organizations must execute all three types of innovation at the same time.

Optimizing Innovation
The first track is optimizing innovation. This type of innovation is the majority of what legacy organizations already do today. It is, metaphorically speaking, the extra blade on the razor. A razor manufacturer might launch a new razor that has not just three, but four blades, to ensure an even better, closer, and more comfortable shave. Then one or two years later, they say they are now launching a razor that has not only four, but five blades for an even better, closer, and more comfortable shave. That is optimizing innovation.

Adding extra blades on the razor is where the established player reigns.

No startup with so much as a modicum of sense would even try to beat the established company in this type of innovation. And this continuous optimization, both on the operational and customer facing sides, is important. In the short term. It pays the rent. But it’s far from enough. There are limits to how many blades a razor needs, and optimizing innovation only improves upon the past.

Augmenting Innovation
Established players must also go beyond optimization and prepare for the future through augmenting innovation.

The digital transformation projects that many organizations are initiating can be characterized as augmenting innovation. In the first instance, it is about upgrading core offerings and processes from analog to digital. Or, if you’re born digital, you’ve probably had to augment the core to become mobile-first. Perhaps you have even entered the next augmentation phase, which involves implementing artificial intelligence. Becoming AI-first, like the Amazons, Microsofts, Baidus, and Googles of the world, requires great technological advancements. And it’s difficult. But technology may, in fact, be a minor part of the task.

The biggest challenge for augmenting innovation is probably culture.

Only legacy organizations that manage to transform their cultures from status quo cultures—cultures with a preference for things as they are—into cultures full of incremental innovators can thrive in constant change.

To create a strong innovation culture, an organization needs to thoroughly understand its immune systems. These are the mechanisms that protect the organization and operate around the clock to keep it healthy and stable, just as the body’s immune system operates to keep the body healthy and stable. But in a rapidly changing world, many of these defense mechanisms are no longer appropriate and risk weakening organizations’ innovation power.

When talking about organizational immune systems, there is a clear tendency to simply point to the individual immune system, people’s unwillingness to change.

But this is too simplistic.

Of course, there is human resistance to change, but the organizational immune system, consisting of a company’s key performance indicators (KPIs), rewards systems, legacy IT infrastructure and processes, and investor and shareholder demands, is far more important. So is the organization’s societal immune system, such as legislative barriers, legacy customers and providers, and economic climate.

Luckily, there are many culture hacks that organizations can apply to strengthen their innovation cultures by upgrading their physical and digital workspaces, transforming their top-down work processes into decentralized, agile ones, and empowering their employees.

Mutating Innovation
Upgrading your core and preparing for the future by augmenting innovation is crucial if you want success in the medium term. But to win in the long run and be as or more successful 20 to 30 years from now, you need to invent the future, and challenge your core, through mutating innovation.

This requires involving radical innovators who have a bold focus on experimenting with that which is not currently understood and for which a business case cannot be prepared.

Here you must also physically move away from the core organization when you initiate and run such initiatives. This is sometimes called “innovation on the edges” because the initiatives will not have a chance at succeeding within the core. It will be too noisy as they challenge what currently exists—precisely what the majority of the organization’s employees are working to optimize or augment.

Forward-looking organizations experiment to mutate their core through “X divisions,” sometimes called skunk works or innovation labs.

Lowe’s Innovation Labs, for instance, worked with startups to build in-store robot assistants and zero-gravity 3D printers to explore the future. Mutating innovation might include pursuing partnerships across all imaginable domains or establishing brand new companies, rather than traditional business units, as we see automakers such as Toyota now doing to build software for autonomous vehicles. Companies might also engage in radical open innovation by sponsoring others’ ingenuity. Japan’s top airline ANA is exploring a future of travel that does not involve flying people from point A to point B via the ANA Avatar XPRIZE competition.

Increasing technological opportunities challenge the core of any organization but also create unprecedented potential. No matter what product, service, or experience you create, you can’t rest on your laurels. You have to bring yourself to a position where you have a clear strategy for optimizing, augmenting, and mutating your core and thus transforming your organization.

It’s not an easy job. But, hey, if it were easy, everyone would be doing it. Those who make it, on the other hand, will be the innovation champions of the future.

Image Credit: rock-the-stock / Shutterstock.com

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Posted in Human Robots