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As a human you instinctively know that a leopard is closer to a cat than a motorbike, but the way we train most AI makes them oblivious to these kinds of relations. Building the concept of similarity into our algorithms could make them far more capable, writes the author of a new paper in Science Robotics.
Convolutional neural networks have revolutionized the field of computer vision to the point that machines are now outperforming humans on some of the most challenging visual tasks. But the way we train them to analyze images is very different from the way humans learn, says Atsuto Maki, an associate professor at KTH Royal Institute of Technology.
“Imagine that you are two years old and being quizzed on what you see in a photo of a leopard,” he writes. “You might answer ‘a cat’ and your parents might say, ‘yeah, not quite but similar’.”
In contrast, the way we train neural networks rarely gives that kind of partial credit. They are typically trained to have very high confidence in the correct label and consider all incorrect labels, whether ”cat” or “motorbike,” equally wrong. That’s a mistake, says Maki, because ignoring the fact that something can be “less wrong” means you’re not exploiting all of the information in the training data.
Even when models are trained this way, there will be small differences in the probabilities assigned to incorrect labels that can tell you a lot about how well the model can generalize what it has learned to unseen data.
If you show a model a picture of a leopard and it gives “cat” a probability of five percent and “motorbike” one percent, that suggests it picked up on the fact that a cat is closer to a leopard than a motorbike. In contrast, if the figures are the other way around it means the model hasn’t learned the broad features that make cats and leopards similar, something that could potentially be helpful when analyzing new data.
If we could boost this ability to identify similarities between classes we should be able to create more flexible models better able to generalize, says Maki. And recent research has demonstrated how variations of an approach called regularization might help us achieve that goal.
Neural networks are prone to a problem called “overfitting,” which refers to a tendency to pay too much attention to tiny details and noise specific to their training set. When that happens, models will perform excellently on their training data but poorly when applied to unseen test data without these particular quirks.
Regularization is used to circumvent this problem, typically by reducing the network’s capacity to learn all this unnecessary information and therefore boost its ability to generalize to new data. Techniques are varied, but generally involve modifying the network’s structure or the strength of the weights between artificial neurons.
More recently, though, researchers have suggested new regularization approaches that work by encouraging a broader spread of probabilities across all classes. This essentially helps them capture more of the class similarities, says Maki, and therefore boosts their ability to generalize.
One such approach was devised in 2017 by Google Brain researchers, led by deep learning pioneer Geoffrey Hinton. They introduced a penalty to their training process that directly punished overconfident predictions in the model’s outputs, and a technique called label smoothing that prevents the largest probability becoming much larger than all others. This meant the probabilities were lower for correct labels and higher for incorrect ones, which was found to boost performance of models on varied tasks from image classification to speech recognition.
Another came from Maki himself in 2017 and achieves the same goal, but by suppressing high values in the model’s feature vector—the mathematical construct that describes all of an object’s important characteristics. This has a knock-on effect on the spread of output probabilities and also helped boost performance on various image classification tasks.
While it’s still early days for the approach, the fact that humans are able to exploit these kinds of similarities to learn more efficiently suggests that models that incorporate them hold promise. Maki points out that it could be particularly useful in applications such as robotic grasping, where distinguishing various similar objects is important.
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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.
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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As technology continues to progress, the possibility of an abundant future seems more likely. Artificial intelligence is expected to drive down the cost of labor, infrastructure, and transport. Alternative energy systems are reducing the cost of a wide variety of goods. Poverty rates are falling around the world as more people are able to make a living, and resources that were once inaccessible to millions are becoming widely available.
But such a life presents fuel for the most common complaint against abundance: if robots take all the jobs, basic income provides us livable welfare for doing nothing, and healthcare is a guarantee free of charge, then what is the point of our lives? What would motivate us to work and excel if there are no real risks or rewards? If everything is simply given to us, how would we feel like we’ve ever earned anything?
Time has proven that humans inherently yearn to overcome challenges—in fact, this very desire likely exists as the root of most technological innovation. And the idea that struggling makes us stronger isn’t just anecdotal, it’s scientifically validated.
For instance, kids who use anti-bacterial soaps and sanitizers too often tend to develop weak immune systems, causing them to get sick more frequently and more severely. People who work out purposely suffer through torn muscles so that after a few days of healing their muscles are stronger. And when patients visit a psychologist to handle a fear that is derailing their lives, one of the most common treatments is exposure therapy: a slow increase of exposure to the suffering so that the patient gets stronger and braver each time, able to take on an incrementally more potent manifestation of their fears.
Different Kinds of Struggle
It’s not hard to understand why people might fear an abundant future as a terribly mundane one. But there is one crucial mistake made in this assumption, and it was well summarized by Indian mystic and author Sadhguru, who said during a recent talk at Google:
Stomach empty, only one problem. Stomach full—one hundred problems; because what we refer to as human really begins only after survival is taken care of.
This idea is backed up by Maslow’s hierarchy of needs, which was first presented in his 1943 paper “A Theory of Human Motivation.” Maslow shows the steps required to build to higher and higher levels of the human experience. Not surprisingly, the first two levels deal with physiological needs and the need for safety—in other words, with the body. You need to have food, water, and sleep, or you die. After that, you need to be protected from threats, from the elements, from dangerous people, and from disease and pain.
Maslow’s Hierarchy of Needs. Photo by Wikimedia User:Factoryjoe / CC BY-SA 3.0
The beauty of these first two levels is that they’re clear-cut problems with clear-cut solutions: if you’re hungry, then you eat; if you’re thirsty, then you drink; if you’re tired, then you sleep.
But what about the next tiers of the hierarchy? What of love and belonging, of self-esteem and self-actualization? If we’re lonely, can we just summon up an authentic friend or lover? If we feel neglected by society, can we demand it validate us? If we feel discouraged and disappointed in ourselves, can we simply dial up some confidence and self-esteem?
Of course not, and that’s because these psychological needs are nebulous; they don’t contain clear problems with clear solutions. They involve the external world and other people, and are complicated by the infinite flavors of nuance and compromise that are required to navigate human relationships and personal meaning.
These psychological difficulties are where we grow our personalities, outlooks, and beliefs. The truly defining characteristics of a person are dictated not by the physical situations they were forced into—like birth, socioeconomic class, or physical ailment—but instead by the things they choose. So a future of abundance helps to free us from the physical limitations so that we can truly commit to a life of purpose and meaning, rather than just feel like survival is our purpose.
The Greatest Challenge
And that’s the plot twist. This challenge to come to grips with our own individuality and freedom could actually be the greatest challenge our species has ever faced. Can you imagine waking up every day with infinite possibility? Every choice you make says no to the rest of reality, and so every decision carries with it truly life-defining purpose and meaning. That sounds overwhelming. And that’s probably because in our current socio-economic systems, it is.
Studies have shown that people in wealthier nations tend to experience more anxiety and depression. Ron Kessler, professor of health care policy at Harvard and World Health Organization (WHO) researcher, summarized his findings of global mental health by saying, “When you’re literally trying to survive, who has time for depression? Americans, on the other hand, many of whom lead relatively comfortable lives, blow other nations away in the depression factor, leading some to suggest that depression is a ‘luxury disorder.’”
This might explain why America scores in the top rankings for the most depressed and anxious country on the planet. We surpassed our survival needs, and instead became depressed because our jobs and relationships don’t fulfill our expectations for the next three levels of Maslow’s hierarchy (belonging, esteem, and self-actualization).
But a future of abundance would mean we’d have to deal with these levels. This is the challenge for the future; this is what keeps things from being mundane.
As a society, we would be forced to come to grips with our emotional intelligence, to reckon with philosophy rather than simply contemplate it. Nearly every person you meet will be passionately on their own customized life journey, not following a routine simply because of financial limitations. Such a world seems far more vibrant and interesting than one where most wander sleep-deprived and numb while attempting to survive the rat race.
We can already see the forceful hand of this paradigm shift as self-driving cars become ubiquitous. For example, consider the famous psychological and philosophical “trolley problem.” In this thought experiment, a person sees a trolley car heading towards five people on the train tracks; they see a lever that will allow them to switch the trolley car to a track that instead only has one person on it. Do you switch the lever and have a hand in killing one person, or do you let fate continue and kill five people instead?
For the longest time, this was just an interesting quandary to consider. But now, massive corporations have to have an answer, so they can program their self-driving cars with the ability to choose between hitting a kid who runs into the road or swerving into an oncoming car carrying a family of five. When companies need philosophers to make business decisions, it’s a good sign of what’s to come.
Luckily, it’s possible this forceful reckoning with philosophy and our own consciousness may be exactly what humanity needs. Perhaps our great failure as a species has been a result of advanced cognition still trapped in the first two levels of Maslow’s hierarchy due to a long history of scarcity.
As suggested in the opening scenes in 2001: A Space Odyssey, our ape-like proclivity for violence has long stayed the same while the technology we fight with and live amongst has progressed. So while well-off Americans may have comfortable lives, they still know they live in a system where there is no safety net, where a single tragic failure could still mean hunger and homelessness. And because of this, that evolutionarily hard-wired neurotic part of our brain that fears for our survival has never been able to fully relax, and so that anxiety and depression that come with too much freedom but not enough security stays ever present.
Not only might this shift in consciousness help liberate humanity, but it may be vital if we’re to survive our future creations as well. Whatever values we hold dear as a species are the ones we will imbue into the sentient robots we create. If machine learning is going to take its guidance from humanity, we need to level up humanity’s emotional maturity.
While the physical struggles of the future may indeed fall to the wayside amongst abundance, it’s unlikely to become a mundane world; instead, it will become a vibrant culture where each individual is striving against the most important struggle that affects all of us: the challenge to find inner peace, to find fulfillment, to build meaningful relationships, and ultimately, the challenge to find ourselves.
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The Microbots Are on Their Way
Kenneth Chang | The New York Times
“Like Frankenstein, Marc Miskin’s robots initially lie motionless. Then their limbs jerk to life. But these robots are the size of a speck of dust. Thousands fit side-by-side on a single silicon wafer similar to those used for computer chips, and, like Frankenstein coming to life, they pull themselves free and start crawling.”
Why the ‘Post-Natural’ Age Could Be Strange and Beautiful
Lauren Holt | BBC
“As long as humans have existed, we have been influencing our planet’s flora and fauna. So, if humanity continues to flourish far into the future, how will nature change? And how might this genetic manipulation affect our own biology and evolutionary trajectory? The short answer: it will be strange, potentially beautiful and like nothing we’re used to.”
Watch This Wild 3D-Printed Lung Air Sac Breathe
Amanda Kooser | CNET
“A research team led by bioengineers at the University of Washington and Rice University developed an open-source technique for bioprinting tissues ‘with exquisitely entangled vascular networks similar to the body’s natural passageways for blood, air, lymph and other vital fluids.’i”
A New Camera Can Photograph You From 45 Kilometers Away
Emerging Technology from the arXiv | MIT Technology Review
“Conventional images taken through the telescope show nothing other than noise. But the new technique produces images with a spatial resolution of about 60 cm, which resolves building windows.”
The Search for the Kryptonite That Can Stop CRISPR
Antonio Regalado | MIT Technology Review
“CRISPR weapons? We’ll leave it to your imagination exactly what one could look like. What is safe to say, though, is that DARPA has asked Doudna and others to start looking into prophylactic treatments or even pills you could take to stop gene editing, just the way you can swallow antibiotics if you’ve gotten an anthrax letter in the mail.”
The Holy Grail of Robotics: Inside the Quest to Build a Mechanical Human Hand
Luke Dormehl | Digital Trends
“For real-life roboticists, building the perfect robot hand has long been the Holy Grail. It is the hardware yin to the software yang of creating an artificial mind. Seeking out the ultimate challenge, robotics experts gravitated to recreating what is one of the most complicated and beautiful pieces of natural engineering found in the human body.”
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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.
Within just the past three years, countries across the globe have signed into existence national AI strategies and plans for ramping up innovation. Businesses and think tanks have leaped onto the scene, hiring AI engineers and tech consultants to leverage what computer scientist Andrew Ng has even called the new ‘electricity’ of the 21st century.
As AI plays an exceedingly vital role in everyday life, how will your business leverage it to keep up and build forward?
In the wake of burgeoning markets, new ventures will quickly arise, each taking advantage of untapped data sources or unmet security needs.
And as your company aims to ride the wave of AI’s exponential growth, consider the following pointers to leverage AI and disrupt yourself before it reaches you first:
Determine where and how you can begin collecting critical data to inform your AI algorithms
Identify time-intensive processes that can be automated and accelerated within your company
Discern which global challenges can be expedited by hyper-fast, all-knowing minds
Remember: good data is vital fuel. Well-defined problems are the best compass. And the time to start implementing AI is now.
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