Tag Archives: Defense

#435172 DARPA’s New Project Is Investing ...

Posted on June 6, 2019 by Android

When Elon Musk and DARPA both hop aboard the cyborg hypetrain, you know brain-machine interfaces (BMIs) are about to achieve the impossible.

BMIs, already the stuff of science fiction, facilitate crosstalk between biological wetware with external computers, turning human users into literal cyborgs. Yet mind-controlled robotic arms, microelectrode “nerve patches”, or “memory Band-Aids” are still purely experimental medical treatments for those with nervous system impairments.

With the Next-Generation Nonsurgical Neurotechnology (N3) program, DARPA is looking to expand BMIs to the military. This month, the project tapped six academic teams to engineer radically different BMIs to hook up machines to the brains of able-bodied soldiers. The goal is to ditch surgery altogether—while minimizing any biological interventions—to link up brain and machine.

Rather than microelectrodes, which are currently surgically inserted into the brain to hijack neural communication, the project is looking to acoustic signals, electromagnetic waves, nanotechnology, genetically-enhanced neurons, and infrared beams for their next-gen BMIs.

It’s a radical departure from current protocol, with potentially thrilling—or devastating—impact. Wireless BMIs could dramatically boost bodily functions of veterans with neural damage or post-traumatic stress disorder (PTSD), or allow a single soldier to control swarms of AI-enabled drones with his or her mind. Or, similar to the Black Mirror episode Men Against Fire, it could cloud the perception of soldiers, distancing them from the emotional guilt of warfare.

When trickled down to civilian use, these new technologies are poised to revolutionize medical treatment. Or they could galvanize the transhumanist movement with an inconceivably powerful tool that fundamentally alters society—for better or worse.

Here’s what you need to know.

Radical Upgrades
The four-year N3 program focuses on two main aspects: noninvasive and “minutely” invasive neural interfaces to both read and write into the brain.

Because noninvasive technologies sit on the scalp, their sensors and stimulators will likely measure entire networks of neurons, such as those controlling movement. These systems could then allow soldiers to remotely pilot robots in the field—drones, rescue bots, or carriers like Boston Dynamics’ BigDog. The system could even boost multitasking prowess—mind-controlling multiple weapons at once—similar to how able-bodied humans can operate a third robotic arm in addition to their own two.

In contrast, minutely invasive technologies allow scientists to deliver nanotransducers without surgery: for example, an injection of a virus carrying light-sensitive sensors, or other chemical, biotech, or self-assembled nanobots that can reach individual neurons and control their activity independently without damaging sensitive tissue. The proposed use for these technologies isn’t yet well-specified, but as animal experiments have shown, controlling the activity of single neurons at multiple points is sufficient to program artificial memories of fear, desire, and experiences directly into the brain.

“A neural interface that enables fast, effective, and intuitive hands-free interaction with military systems by able-bodied warfighters is the ultimate program goal,” DARPA wrote in its funding brief, released early last year.

The only technologies that will be considered must have a viable path toward eventual use in healthy human subjects.

“Final N3 deliverables will include a complete integrated bidirectional brain-machine interface system,” the project description states. This doesn’t just include hardware, but also new algorithms tailored to these system, demonstrated in a “Department of Defense-relevant application.”

The Tools
Right off the bat, the usual tools of the BMI trade, including microelectrodes, MRI, or transcranial magnetic stimulation (TMS) are off the table. These popular technologies rely on surgery, heavy machinery, or personnel to sit very still—conditions unlikely in the real world.

The six teams will tap into three different kinds of natural phenomena for communication: magnetism, light beams, and acoustic waves.

Dr. Jacob Robinson at Rice University, for example, is combining genetic engineering, infrared laser beams, and nanomagnets for a bidirectional system. The $18 million project, MOANA (Magnetic, Optical and Acoustic Neural Access device) uses viruses to deliver two extra genes into the brain. One encodes a protein that sits on top of neurons and emits infrared light when the cell activates. Red and infrared light can penetrate through the skull. This lets a skull cap, embedded with light emitters and detectors, pick up these signals for subsequent decoding. Ultra-fast and utra-sensitvie photodetectors will further allow the cap to ignore scattered light and tease out relevant signals emanating from targeted portions of the brain, the team explained.

The other new gene helps write commands into the brain. This protein tethers iron nanoparticles to the neurons’ activation mechanism. Using magnetic coils on the headset, the team can then remotely stimulate magnetic super-neurons to fire while leaving others alone. Although the team plans to start in cell cultures and animals, their goal is to eventually transmit a visual image from one person to another. “In four years we hope to demonstrate direct, brain-to-brain communication at the speed of thought and without brain surgery,” said Robinson.

Other projects in N3 are just are ambitious.

The Carnegie Mellon team, for example, plans to use ultrasound waves to pinpoint light interaction in targeted brain regions, which can then be measured through a wearable “hat.” To write into the brain, they propose a flexible, wearable electrical mini-generator that counterbalances the noisy effect of the skull and scalp to target specific neural groups.

Similarly, a group at Johns Hopkins is also measuring light path changes in the brain to correlate them with regional brain activity to “read” wetware commands.

The Teledyne Scientific & Imaging group, in contrast, is turning to tiny light-powered “magnetometers” to detect small, localized magnetic fields that neurons generate when they fire, and match these signals to brain output.

The nonprofit Battelle team gets even fancier with their ”BrainSTORMS” nanotransducers: magnetic nanoparticles wrapped in a piezoelectric shell. The shell can convert electrical signals from neurons into magnetic ones and vice-versa. This allows external transceivers to wirelessly pick up the transformed signals and stimulate the brain through a bidirectional highway.

The magnetometers can be delivered into the brain through a nasal spray or other non-invasive methods, and magnetically guided towards targeted brain regions. When no longer needed, they can once again be steered out of the brain and into the bloodstream, where the body can excrete them without harm.

Four-Year Miracle
Mind-blown? Yeah, same. However, the challenges facing the teams are enormous.

DARPA’s stated goal is to hook up at least 16 sites in the brain with the BMI, with a lag of less than 50 milliseconds—on the scale of average human visual perception. That’s crazy high resolution for devices sitting outside the brain, both in space and time. Brain tissue, blood vessels, and the scalp and skull are all barriers that scatter and dissipate neural signals. All six teams will need to figure out the least computationally-intensive ways to fish out relevant brain signals from background noise, and triangulate them to the appropriate brain region to decipher intent.

In the long run, four years and an average $20 million per project isn’t much to potentially transform our relationship with machines—for better or worse. DARPA, to its credit, is keenly aware of potential misuse of remote brain control. The program is under the guidance of a panel of external advisors with expertise in bioethical issues. And although DARPA’s focus is on enabling able-bodied soldiers to better tackle combat challenges, it’s hard to argue that wireless, non-invasive BMIs will also benefit those most in need: veterans and other people with debilitating nerve damage. To this end, the program is heavily engaging the FDA to ensure it meets safety and efficacy regulations for human use.

Will we be there in just four years? I’m skeptical. But these electrical, optical, acoustic, magnetic, and genetic BMIs, as crazy as they sound, seem inevitable.

“DARPA is preparing for a future in which a combination of unmanned systems, AI, and cyber operations may cause conflicts to play out on timelines that are too short for humans to effectively manage with current technology alone,” said Al Emondi, the N3 program manager.

The question is, now that we know what’s in store, how should the rest of us prepare?

Image Credit: With permission from DARPA N3 project. 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

We are a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for us to earn fees by linking to Amazon.com and affiliated sites. Continue reading →

Posted in Human Robots

#435098 Coming of Age in the Age of AI: The ...

Posted on May 15, 2019 by Android

The first generation to grow up entirely in the 21st century will never remember a time before smartphones or smart assistants. They will likely be the first children to ride in self-driving cars, as well as the first whose healthcare and education could be increasingly turned over to artificially intelligent machines.

Futurists, demographers, and marketers have yet to agree on the specifics of what defines the next wave of humanity to follow Generation Z. That hasn’t stopped some, like Australian futurist Mark McCrindle, from coining the term Generation Alpha, denoting a sort of reboot of society in a fully-realized digital age.

“In the past, the individual had no power, really,” McCrindle told Business Insider. “Now, the individual has great control of their lives through being able to leverage this world. Technology, in a sense, transformed the expectations of our interactions.”

No doubt technology may impart Marvel superhero-like powers to Generation Alpha that even tech-savvy Millennials never envisioned over cups of chai latte. But the powers of machine learning, computer vision, and other disciplines under the broad category of artificial intelligence will shape this yet unformed generation more definitively than any before it.

What will it be like to come of age in the Age of AI?

The AI Doctor Will See You Now
Perhaps no other industry is adopting and using AI as much as healthcare. The term “artificial intelligence” appears in nearly 90,000 publications from biomedical literature and research on the PubMed database.

AI is already transforming healthcare and longevity research. Machines are helping to design drugs faster and detect disease earlier. And AI may soon influence not only how we diagnose and treat illness in children, but perhaps how we choose which children will be born in the first place.

A study published earlier this month in NPJ Digital Medicine by scientists from Weill Cornell Medicine used 12,000 photos of human embryos taken five days after fertilization to train an AI algorithm on how to tell which in vitro fertilized embryo had the best chance of a successful pregnancy based on its quality.

Investigators assigned each embryo a grade based on various aspects of its appearance. A statistical analysis then correlated that grade with the probability of success. The algorithm, dubbed Stork, was able to classify the quality of a new set of images with 97 percent accuracy.

“Our algorithm will help embryologists maximize the chances that their patients will have a single healthy pregnancy,” said Dr. Olivier Elemento, director of the Caryl and Israel Englander Institute for Precision Medicine at Weill Cornell Medicine, in a press release. “The IVF procedure will remain the same, but we’ll be able to improve outcomes by harnessing the power of artificial intelligence.”

Other medical researchers see potential in applying AI to detect possible developmental issues in newborns. Scientists in Europe, working with a Finnish AI startup that creates seizure monitoring technology, have developed a technique for detecting movement patterns that might indicate conditions like cerebral palsy.

Published last month in the journal Acta Pediatrica, the study relied on an algorithm to extract the movements from a newborn, turning it into a simplified “stick figure” that medical experts could use to more easily detect clinically relevant data.

The researchers are continuing to improve the datasets, including using 3D video recordings, and are now developing an AI-based method for determining if a child’s motor maturity aligns with its true age. Meanwhile, a study published in February in Nature Medicine discussed the potential of using AI to diagnose pediatric disease.

AI Gets Classy
After being weaned on algorithms, Generation Alpha will hit the books—about machine learning.

China is famously trying to win the proverbial AI arms race by spending billions on new technologies, with one Chinese city alone pledging nearly $16 billion to build a smart economy based on artificial intelligence.

To reach dominance by its stated goal of 2030, Chinese cities are also incorporating AI education into their school curriculum. Last year, China published its first high school textbook on AI, according to the South China Morning Post. More than 40 schools are participating in a pilot program that involves SenseTime, one of the country’s biggest AI companies.

In the US, where it seems every child has access to their own AI assistant, researchers are just beginning to understand how the ubiquity of intelligent machines will influence the ways children learn and interact with their highly digitized environments.

Sandra Chang-Kredl, associate professor of the department of education at Concordia University, told The Globe and Mail that AI could have detrimental effects on learning creativity or emotional connectedness.

Similar concerns inspired Stefania Druga, a member of the Personal Robots group at the MIT Media Lab (and former Education Teaching Fellow at SU), to study interactions between children and artificial intelligence devices in order to encourage positive interactions.

Toward that goal, Druga created Cognimates, a platform that enables children to program and customize their own smart devices such as Alexa or even a smart, functional robot. The kids can also use Cognimates to train their own AI models or even build a machine learning version of Rock Paper Scissors that gets better over time.

“I believe it’s important to also introduce young people to the concepts of AI and machine learning through hands-on projects so they can make more informed and critical use of these technologies,” Druga wrote in a Medium blog post.

Druga is also the founder of Hackidemia, an international organization that sponsors workshops and labs around the world to introduce kids to emerging technologies at an early age.

“I think we are in an arms race in education with the advancement of technology, and we need to start thinking about AI literacy before patterns of behaviors for children and their families settle in place,” she wrote.

AI Goes Back to School
It also turns out that AI has as much to learn from kids. More and more researchers are interested in understanding how children grasp basic concepts that still elude the most advanced machine minds.

For example, developmental psychologist Alison Gopnik has written and lectured extensively about how studying the minds of children can provide computer scientists clues on how to improve machine learning techniques.

In an interview on Vox, she described that while DeepMind’s AlpahZero was trained to be a chessmaster, it struggles with even the simplest changes in the rules, such as allowing the bishop to move horizontally instead of vertically.

“A human chess player, even a kid, will immediately understand how to transfer that new rule to their playing of the game,” she noted. “Flexibility and generalization are something that even human one-year-olds can do but that the best machine learning systems have a much harder time with.”

Last year, the federal defense agency DARPA announced a new program aimed at improving AI by teaching it “common sense.” One of the chief strategies is to develop systems for “teaching machines through experience, mimicking the way babies grow to understand the world.”

Such an approach is also the basis of a new AI program at MIT called the MIT Quest for Intelligence.

The research leverages cognitive science to understand human intelligence, according to an article on the project in MIT Technology Review, such as exploring how young children visualize the world using their own innate 3D models.

“Children’s play is really serious business,” said Josh Tenenbaum, who leads the Computational Cognitive Science lab at MIT and his head of the new program. “They’re experiments. And that’s what makes humans the smartest learners in the known universe.”

In a world increasingly driven by smart technologies, it’s good to know the next generation will be able to keep up.

Image Credit: phoelixDE / Shutterstock.com Continue reading →

Posted in Human Robots

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

Posted on April 27, 2019 by Android

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.

Join Me
Abundance-Digital Online Community: I’ve created a Digital/Online community of bold, abundance-minded entrepreneurs called Abundance-Digital. Abundance-Digital is my ‘onramp’ for exponential entrepreneurs – those who want to get involved and play at a higher level. Click here to learn more.

Image Credit: Yurchanka Siarhei / Shutterstock.com Continue reading →

Posted in Human Robots

#434837 In Defense of Black Box AI

Posted on April 17, 2019 by Android

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

*Responses have been edited for length and style

Image Credit: Chingraph / Shutterstock.com Continue reading →

Posted in Human Robots