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#435196 Avatar Love? New ‘Black Mirror’ ...

This week, the widely-anticipated fifth season of the dystopian series Black Mirror was released on Netflix. The storylines this season are less focused on far-out scenarios and increasingly aligned with current issues. With only three episodes, this season raises more questions than it answers, often leaving audiences bewildered.

The episode Smithereens explores our society’s crippling addiction to social media platforms and the monopoly they hold over our data. In Rachel, Jack and Ashley Too, we see the disruptive impact of technologies on the music and entertainment industry, and the price of fame for artists in the digital world. Like most Black Mirror episodes, these explore the sometimes disturbing implications of tech advancements on humanity.

But once again, in the midst of all the doom and gloom, the creators of the series leave us with a glimmer of hope. Aligned with Pride month, the episode Striking Vipers explores the impact of virtual reality on love, relationships, and sexual fluidity.

*The review contains a few spoilers.*

Striking Vipers
The first episode of the season, Striking Vipers may be one of the most thought-provoking episodes in Black Mirror history. Reminiscent of previous episodes San Junipero and Hang the DJ, the writers explore the potential for technology to transform human intimacy.

The episode tells the story of two old friends, Danny and Karl, whose friendship is reignited in an unconventional way. Karl unexpectedly appears at Danny’s 38th birthday and reintroduces him to the VR version of a game they used to play years before. In the game Striking Vipers X, each of the players is represented by an avatar of their choice in an uncanny digital reality. Following old tradition, Karl chooses to become the female fighter, Roxanne, and Danny takes on the role of the male fighter, Lance. The state-of-the-art VR headsets appear to use an advanced form of brain-machine interface to allow each player to be fully immersed in the virtual world, emulating all physical sensations.

To their surprise (and confusion), Danny and Karl find themselves transitioning from fist-fighting to kissing. Over the course of many games, they continue to explore a sexual and romantic relationship in the virtual world, leaving them confused and distant in the real world. The virtual and physical realities begin to blur, and so do the identities of the players with their avatars. Danny, who is married (in a heterosexual relationship) and is a father, begins to carry guilt and confusion in the real world. They both wonder if there would be any spark between them in real life.

The brain-machine interface (BMI) depicted in the episode is still science fiction, but that hasn’t stopped innovators from pushing the technology forward. Experts today are designing more intricate BMI systems while programming better algorithms to interpret the neural signals they capture. Scientists have already succeeded in enabling paralyzed patients to type with their minds, and are even allowing people to communicate with one another purely through brainwaves.

The convergence of BMIs with virtual reality and artificial intelligence could make the experience of such immersive digital realities possible. Virtual reality, too, is decreasing exponentially in cost and increasing in quality.

The narrative provides meaningful commentary on another tech area—gaming. It highlights video games not necessarily as addictive distractions, but rather as a platform for connecting with others in a deeper way. This is already very relevant. Video games like Final Fantasy are often a tool for meaningful digital connections for their players.

The Implications of Virtual Reality on Love and Relationships
The narrative of Striking Vipers raises many novel questions about the implications of immersive technologies on relationships: could the virtual world allow us a safe space to explore suppressed desires? Can virtual avatars make it easier for us to show affection to those we care about? Can a sexual or romantic encounter in the digital world be considered infidelity?

Above all, the episode explores the therapeutic possibilities of such technologies. While many fears about virtual reality had been raised in previous seasons of Black Mirror, this episode was focused on its potential. This includes the potential of immersive technology to be a source of liberation, meaningful connections, and self-exploration, as well as a tool for realizing our true identities and desires.

Once again, this is aligned with emerging trends in VR. We are seeing the rise of social VR applications and platforms that allow you to hang out with your friends and family as avatars in the virtual space. The technology is allowing for animation movies, such as Coco VR, to become an increasingly social and interactive experience. Considering that meaningful social interaction can alleviate depression and anxiety, such applications could contribute to well-being.

Techno-philosopher and National Geographic host Jason Silva points out that immersive media technologies can be “engines of empathy.” VR allows us to enter virtual spaces that mimic someone else’s state of mind, allowing us to empathize with the way they view the world. Silva said, “Imagine the intimacy that becomes possible when people meet and they say, ‘Hey, do you want to come visit my world? Do you want to see what it’s like to be inside my head?’”

What is most fascinating about Striking Vipers is that it explores how we may redefine love with virtual reality; we are introduced to love between virtual avatars. While this kind of love may seem confusing to audiences, it may be one of the complex implications of virtual reality on human relationships.

In many ways, the title Black Mirror couldn’t be more appropriate, as each episode serves as a mirror to the most disturbing aspects of our psyches as they get amplified through technology. However, what we see in uplifting and thought-provoking plots like Striking Vipers, San Junipero, and Hang The DJ is that technology could also amplify the most positive aspects of our humanity. This includes our powerful capacity to love.

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#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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#435145 How Big Companies Can Simultaneously Run ...

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.

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#435070 5 Breakthroughs Coming Soon in Augmented ...

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

In this third installment of my Convergence Catalyzer series, I’ll be synthesizing key insights from my annual entrepreneurs’ mastermind event, Abundance 360. This five-blog series looks at 3D printing, artificial intelligence, VR/AR, energy and transportation, and blockchain.

Today, let’s dive into virtual and augmented reality.

Today’s most prominent tech giants are leaping onto the VR/AR scene, each driving forward new and upcoming product lines. Think: Microsoft’s HoloLens, Facebook’s Oculus, Amazon’s Sumerian, and Google’s Cardboard (Apple plans to release a headset by 2021).

And as plummeting prices meet exponential advancements in VR/AR hardware, this burgeoning disruptor is on its way out of the early adopters’ market and into the majority of consumers’ homes.

My good friend Philip Rosedale is my go-to expert on AR/VR and one of the foremost creators of today’s most cutting-edge virtual worlds. After creating the virtual civilization Second Life in 2013, now populated by almost 1 million active users, Philip went on to co-found High Fidelity, which explores the future of next-generation shared VR.

In just the next five years, he predicts five emerging trends will take hold, together disrupting major players and birthing new ones.

Let’s dive in…

Top 5 Predictions for VR/AR Breakthroughs (2019-2024)
“If you think you kind of understand what’s going on with that tech today, you probably don’t,” says Philip. “We’re still in the middle of landing the airplane of all these new devices.”

(1) Transition from PC-based to standalone mobile VR devices

Historically, VR devices have relied on PC connections, usually involving wires and clunky hardware that restrict a user’s field of motion. However, as VR enters the dematerialization stage, we are about to witness the rapid rise of a standalone and highly mobile VR experience economy.

Oculus Go, the leading standalone mobile VR device on the market, requires only a mobile app for setup and can be transported anywhere with WiFi.

With a consumer audience in mind, the 32GB headset is priced at $200 and shares an app ecosystem with Samsung’s Gear VR. While Google Daydream are also standalone VR devices, they require a docked mobile phone instead of the built-in screen of Oculus Go.

In the AR space, Lenovo’s standalone Microsoft’s HoloLens 2 leads the way in providing tetherless experiences.

Freeing headsets from the constraints of heavy hardware will make VR/AR increasingly interactive and transportable, a seamless add-on whenever, wherever. Within a matter of years, it may be as simple as carrying lightweight VR goggles wherever you go and throwing them on at a moment’s notice.

(2) Wide field-of-view AR displays

Microsoft’s HoloLens 2 leads the AR industry in headset comfort and display quality. The most significant issue with their prior version was the limited rectangular field of view (FOV).

By implementing laser technology to create a microelectromechanical systems (MEMS) display, however, HoloLens 2 can position waveguides in front of users’ eyes, directed by mirrors. Subsequently enlarging images can be accomplished by shifting the angles of these mirrors. Coupled with a 47 pixel per degree resolution, HoloLens 2 has now doubled its predecessor’s FOV. Microsoft anticipates the release of its headset by the end of this year at a $3,500 price point, first targeting businesses and eventually rolling it out to consumers.

Magic Leap provides a similar FOV but with lower resolution than the HoloLens 2. The Meta 2 boasts an even wider 90-degree FOV, but requires a cable attachment. The race to achieve the natural human 120-degree horizontal FOV continues.

“The technology to expand the field of view is going to make those devices much more usable by giving you bigger than a small box to look through,” Rosedale explains.

(3) Mapping of real world to enable persistent AR ‘mirror worlds’

‘Mirror worlds’ are alternative dimensions of reality that can blanket a physical space. While seated in your office, the floor beneath you could dissolve into a calm lake and each desk into a sailboat. In the classroom, mirror worlds would convert pencils into magic wands and tabletops into touch screens.

Pokémon Go provides an introductory glimpse into the mirror world concept and its massive potential to unite people in real action.

To create these mirror worlds, AR headsets must precisely understand the architecture of the surrounding world. Rosedale predicts the scanning accuracy of devices will improve rapidly over the next five years to make these alternate dimensions possible.

(4) 5G mobile devices reduce latency to imperceptible levels

Verizon has already launched 5G networks in Minneapolis and Chicago, compatible with the Moto Z3. Sprint plans to follow with its own 5G launch in May. Samsung, LG, Huawei, and ZTE have all announced upcoming 5G devices.

“5G is rolling out this year and it’s going to materially affect particularly my work, which is making you feel like you’re talking to somebody else directly face to face,” explains Rosedale. “5G is critical because currently the cell devices impose too much delay, so it doesn’t feel real to talk to somebody face to face on these devices.”

To operate seamlessly from anywhere on the planet, standalone VR/AR devices will require a strong 5G network. Enhancing real-time connectivity in VR/AR will transform the communication methods of tomorrow.

(5) Eye-tracking and facial expressions built in for full natural communication

Companies like Pupil Labs and Tobii provide eye tracking hardware add-ons and software to VR/AR headsets. This technology allows for foveated rendering, which renders a given scene in high resolution only in the fovea region, while the peripheral regions appear in lower resolution, conserving processing power.

As seen in the HoloLens 2, eye tracking can also be used to identify users and customize lens widths to provide a comfortable, personalized experience for each individual.

According to Rosedale, “The fundamental opportunity for both VR and AR is to improve human communication.” He points out that current VR/AR headsets miss many of the subtle yet important aspects of communication. Eye movements and microexpressions provide valuable insight into a user’s emotions and desires.

Coupled with emotion-detecting AI software, such as Affectiva, VR/AR devices might soon convey much more richly textured and expressive interactions between any two people, transcending physical boundaries and even language gaps.

Final Thoughts
As these promising trends begin to transform the market, VR/AR will undoubtedly revolutionize our lives… possibly to the point at which our virtual worlds become just as consequential and enriching as our physical world.

A boon for next-gen education, VR/AR will empower youth and adults alike with holistic learning that incorporates social, emotional, and creative components through visceral experiences, storytelling, and simulation. Traveling to another time, manipulating the insides of a cell, or even designing a new city will become daily phenomena of tomorrow’s classrooms.

In real estate, buyers will increasingly make decisions through virtual tours. Corporate offices might evolve into spaces that only exist in ‘mirror worlds’ or grow virtual duplicates for remote workers.

In healthcare, accuracy of diagnosis will skyrocket, while surgeons gain access to digital aids as they conduct life-saving procedures. Or take manufacturing, wherein training and assembly will become exponentially more efficient as visual cues guide complex tasks.

In the mere matter of a decade, VR and AR will unlock limitless applications for new and converging industries. And as virtual worlds converge with AI, 3D printing, computing advancements and beyond, today’s experience economies will explode in scale and scope. Prepare yourself for the exciting disruption ahead!

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#435056 How Researchers Used AI to Better ...

A few years back, DeepMind’s Demis Hassabis famously prophesized that AI and neuroscience will positively feed into each other in a “virtuous circle.” If realized, this would fundamentally expand our insight into intelligence, both machine and human.

We’ve already seen some proofs of concept, at least in the brain-to-AI direction. For example, memory replay, a biological mechanism that fortifies our memories during sleep, also boosted AI learning when abstractly appropriated into deep learning models. Reinforcement learning, loosely based on our motivation circuits, is now behind some of AI’s most powerful tools.

Hassabis is about to be proven right again.

Last week, two studies independently tapped into the power of ANNs to solve a 70-year-old neuroscience mystery: how does our visual system perceive reality?

The first, published in Cell, used generative networks to evolve DeepDream-like images that hyper-activate complex visual neurons in monkeys. These machine artworks are pure nightmare fuel to the human eye; but together, they revealed a fundamental “visual hieroglyph” that may form a basic rule for how we piece together visual stimuli to process sight into perception.

In the second study, a team used a deep ANN model—one thought to mimic biological vision—to synthesize new patterns tailored to control certain networks of visual neurons in the monkey brain. When directly shown to monkeys, the team found that the machine-generated artworks could reliably activate predicted populations of neurons. Future improved ANN models could allow even better control, giving neuroscientists a powerful noninvasive tool to study the brain. The work was published in Science.

The individual results, though fascinating, aren’t necessarily the point. Rather, they illustrate how scientists are now striving to complete the virtuous circle: tapping AI to probe natural intelligence. Vision is only the beginning—the tools can potentially be expanded into other sensory domains. And the more we understand about natural brains, the better we can engineer artificial ones.

It’s a “great example of leveraging artificial intelligence to study organic intelligence,” commented Dr. Roman Sandler at Kernel.co on Twitter.

Why Vision?
ANNs and biological vision have quite the history.

In the late 1950s, the legendary neuroscientist duo David Hubel and Torsten Wiesel became some of the first to use mathematical equations to understand how neurons in the brain work together.

In a series of experiments—many using cats—the team carefully dissected the structure and function of the visual cortex. Using myriads of images, they revealed that vision is processed in a hierarchy: neurons in “earlier” brain regions, those closer to the eyes, tend to activate when they “see” simple patterns such as lines. As we move deeper into the brain, from the early V1 to a nub located slightly behind our ears, the IT cortex, neurons increasingly respond to more complex or abstract patterns, including faces, animals, and objects. The discovery led some scientists to call certain IT neurons “Jennifer Aniston cells,” which fire in response to pictures of the actress regardless of lighting, angle, or haircut. That is, IT neurons somehow extract visual information into the “gist” of things.

That’s not trivial. The complex neural connections that lead to increasing abstraction of what we see into what we think we see—what we perceive—is a central question in machine vision: how can we teach machines to transform numbers encoding stimuli into dots, lines, and angles that eventually form “perceptions” and “gists”? The answer could transform self-driving cars, facial recognition, and other computer vision applications as they learn to better generalize.

Hubel and Wiesel’s Nobel-prize-winning studies heavily influenced the birth of ANNs and deep learning. Much of earlier ANN “feed-forward” model structures are based on our visual system; even today, the idea of increasing layers of abstraction—for perception or reasoning—guide computer scientists to build AI that can better generalize. The early romance between vision and deep learning is perhaps the bond that kicked off our current AI revolution.

It only seems fair that AI would feed back into vision neuroscience.

Hieroglyphs and Controllers
In the Cell study, a team led by Dr. Margaret Livingstone at Harvard Medical School tapped into generative networks to unravel IT neurons’ complex visual alphabet.

Scientists have long known that neurons in earlier visual regions (V1) tend to fire in response to “grating patches” oriented in certain ways. Using a limited set of these patches like letters, V1 neurons can “express a visual sentence” and represent any image, said Dr. Arash Afraz at the National Institute of Health, who was not involved in the study.

But how IT neurons operate remained a mystery. Here, the team used a combination of genetic algorithms and deep generative networks to “evolve” computer art for every studied neuron. In seven monkeys, the team implanted electrodes into various parts of the visual IT region so that they could monitor the activity of a single neuron.

The team showed each monkey an initial set of 40 images. They then picked the top 10 images that stimulated the highest neural activity, and married them to 30 new images to “evolve” the next generation of images. After 250 generations, the technique, XDREAM, generated a slew of images that mashed up contorted face-like shapes with lines, gratings, and abstract shapes.

This image shows the evolution of an optimum image for stimulating a visual neuron in a monkey. Image Credit: Ponce, Xiao, and Schade et al. – Cell.
“The evolved images look quite counter-intuitive,” explained Afraz. Some clearly show detailed structures that resemble natural images, while others show complex structures that can’t be characterized by our puny human brains.

This figure shows natural images (right) and images evolved by neurons in the inferotemporal cortex of a monkey (left). Image Credit: Ponce, Xiao, and Schade et al. – Cell.
“What started to emerge during each experiment were pictures that were reminiscent of shapes in the world but were not actual objects in the world,” said study author Carlos Ponce. “We were seeing something that was more like the language cells use with each other.”

This image was evolved by a neuron in the inferotemporal cortex of a monkey using AI. Image Credit: Ponce, Xiao, and Schade et al. – Cell.
Although IT neurons don’t seem to use a simple letter alphabet, it does rely on a vast array of characters like hieroglyphs or Chinese characters, “each loaded with more information,” said Afraz.

The adaptive nature of XDREAM turns it into a powerful tool to probe the inner workings of our brains—particularly for revealing discrepancies between biology and models.

The Science study, led by Dr. James DiCarlo at MIT, takes a similar approach. Using ANNs to generate new patterns and images, the team was able to selectively predict and independently control neuron populations in a high-level visual region called V4.

“So far, what has been done with these models is predicting what the neural responses would be to other stimuli that they have not seen before,” said study author Dr. Pouya Bashivan. “The main difference here is that we are going one step further and using the models to drive the neurons into desired states.”

It suggests that our current ANN models for visual computation “implicitly capture a great deal of visual knowledge” which we can’t really describe, but which the brain uses to turn vision information into perception, the authors said. By testing AI-generated images on biological vision, however, the team concluded that today’s ANNs have a degree of understanding and generalization. The results could potentially help engineer even more accurate ANN models of biological vision, which in turn could feed back into machine vision.

“One thing is clear already: Improved ANN models … have led to control of a high-level neural population that was previously out of reach,” the authors said. “The results presented here have likely only scratched the surface of what is possible with such implemented characterizations of the brain’s neural networks.”

To Afraz, the power of AI here is to find cracks in human perception—both our computational models of sensory processes, as well as our evolved biological software itself. AI can be used “as a perfect adversarial tool to discover design cracks” of IT, said Afraz, such as finding computer art that “fools” a neuron into thinking the object is something else.

“As artificial intelligence researchers develop models that work as well as the brain does—or even better—we will still need to understand which networks are more likely to behave safely and further human goals,” said Ponce. “More efficient AI can be grounded by knowledge of how the brain works.”

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