Tag Archives: neuroscience
#434260 The Most Surprising Tech Breakthroughs ...
Development across the entire information technology landscape certainly didn’t slow down this year. From CRISPR babies, to the rapid decline of the crypto markets, to a new robot on Mars, and discovery of subatomic particles that could change modern physics as we know it, there was no shortage of headline-grabbing breakthroughs and discoveries.
As 2018 comes to a close, we can pause and reflect on some of the biggest technology breakthroughs and scientific discoveries that occurred this year.
I reached out to a few Singularity University speakers and faculty across the various technology domains we cover asking what they thought the biggest breakthrough was in their area of expertise. The question posed was:
“What, in your opinion, was the biggest development in your area of focus this year? Or, what was the breakthrough you were most surprised by in 2018?”
I can share that for me, hands down, the most surprising development I came across in 2018 was learning that a publicly-traded company that was briefly valued at over $1 billion, and has over 12,000 employees and contractors spread around the world, has no physical office space and the entire business is run and operated from inside an online virtual world. This is Ready Player One stuff happening now.
For the rest, here’s what our experts had to say.
DIGITAL BIOLOGY
Dr. Tiffany Vora | Faculty Director and Vice Chair, Digital Biology and Medicine, Singularity University
“That’s easy: CRISPR babies. I knew it was technically possible, and I’ve spent two years predicting it would happen first in China. I knew it was just a matter of time but I failed to predict the lack of oversight, the dubious consent process, the paucity of publicly-available data, and the targeting of a disease that we already know how to prevent and treat and that the children were at low risk of anyway.
I’m not convinced that this counts as a technical breakthrough, since one of the girls probably isn’t immune to HIV, but it sure was a surprise.”
For more, read Dr. Vora’s summary of this recent stunning news from China regarding CRISPR-editing human embryos.
QUANTUM COMPUTING
Andrew Fursman | Co-Founder/CEO 1Qbit, Faculty, Quantum Computing, Singularity University
“There were two last-minute holiday season surprise quantum computing funding and technology breakthroughs:
First, right before the government shutdown, one priority legislative accomplishment will provide $1.2 billion in quantum computing research over the next five years. Second, there’s the rise of ions as a truly viable, scalable quantum computing architecture.”
*Read this Gizmodo profile on an exciting startup in the space to learn more about this type of quantum computing
ENERGY
Ramez Naam | Chair, Energy and Environmental Systems, Singularity University
“2018 had plenty of energy surprises. In solar, we saw unsubsidized prices in the sunny parts of the world at just over two cents per kwh, or less than half the price of new coal or gas electricity. In the US southwest and Texas, new solar is also now cheaper than new coal or gas. But even more shockingly, in Germany, which is one of the least sunny countries on earth (it gets less sunlight than Canada) the average bid for new solar in a 2018 auction was less than 5 US cents per kwh. That’s as cheap as new natural gas in the US, and far cheaper than coal, gas, or any other new electricity source in most of Europe.
In fact, it’s now cheaper in some parts of the world to build new solar or wind than to run existing coal plants. Think tank Carbon Tracker calculates that, over the next 10 years, it will become cheaper to build new wind or solar than to operate coal power in most of the world, including specifically the US, most of Europe, and—most importantly—India and the world’s dominant burner of coal, China.
Here comes the sun.”
GLOBAL GRAND CHALLENGES
Darlene Damm | Vice Chair, Faculty, Global Grand Challenges, Singularity University
“In 2018 we saw a lot of areas in the Global Grand Challenges move forward—advancements in robotic farming technology and cultured meat, low-cost 3D printed housing, more sophisticated types of online education expanding to every corner of the world, and governments creating new policies to deal with the ethics of the digital world. These were the areas we were watching and had predicted there would be change.
What most surprised me was to see young people, especially teenagers, start to harness technology in powerful ways and use it as a platform to make their voices heard and drive meaningful change in the world. In 2018 we saw teenagers speak out on a number of issues related to their well-being and launch digital movements around issues such as gun and school safety, global warming and environmental issues. We often talk about the harm technology can cause to young people, but on the flip side, it can be a very powerful tool for youth to start changing the world today and something I hope we see more of in the future.”
BUSINESS STRATEGY
Pascal Finette | Chair, Entrepreneurship and Open Innovation, Singularity University
“Without a doubt the rapid and massive adoption of AI, specifically deep learning, across industries, sectors, and organizations. What was a curiosity for most companies at the beginning of the year has quickly made its way into the boardroom and leadership meetings, and all the way down into the innovation and IT department’s agenda. You are hard-pressed to find a mid- to large-sized company today that is not experimenting or implementing AI in various aspects of its business.
On the slightly snarkier side of answering this question: The very rapid decline in interest in blockchain (and cryptocurrencies). The blockchain party was short, ferocious, and ended earlier than most would have anticipated, with a huge hangover for some. The good news—with the hot air dissipated, we can now focus on exploring the unique use cases where blockchain does indeed offer real advantages over centralized approaches.”
*Author note: snark is welcome and appreciated
ROBOTICS
Hod Lipson | Director, Creative Machines Lab, Columbia University
“The biggest surprise for me this year in robotics was learning dexterity. For decades, roboticists have been trying to understand and imitate dexterous manipulation. We humans seem to be able to manipulate objects with our fingers with incredible ease—imagine sifting through a bunch of keys in the dark, or tossing and catching a cube. And while there has been much progress in machine perception, dexterous manipulation remained elusive.
There seemed to be something almost magical in how we humans can physically manipulate the physical world around us. Decades of research in grasping and manipulation, and millions of dollars spent on robot-hand hardware development, has brought us little progress. But in late 2018, the Berkley OpenAI group demonstrated that this hurdle may finally succumb to machine learning as well. Given 200 years worth of practice, machines learned to manipulate a physical object with amazing fluidity. This might be the beginning of a new age for dexterous robotics.”
MACHINE LEARNING
Jeremy Howard | Founding Researcher, fast.ai, Founder/CEO, Enlitic, Faculty Data Science, Singularity University
“The biggest development in machine learning this year has been the development of effective natural language processing (NLP).
The New York Times published an article last month titled “Finally, a Machine That Can Finish Your Sentence,” which argued that NLP neural networks have reached a significant milestone in capability and speed of development. The “finishing your sentence” capability mentioned in the title refers to a type of neural network called a “language model,” which is literally a model that learns how to finish your sentences.
Earlier this year, two systems (one, called ELMO, is from the Allen Institute for AI, and the other, called ULMFiT, was developed by me and Sebastian Ruder) showed that such a model could be fine-tuned to dramatically improve the state-of-the-art in nearly every NLP task that researchers study. This work was further developed by OpenAI, which in turn was greatly scaled up by Google Brain, who created a system called BERT which reached human-level performance on some of NLP’s toughest challenges.
Over the next year, expect to see fine-tuned language models used for everything from understanding medical texts to building disruptive social media troll armies.”
DIGITAL MANUFACTURING
Andre Wegner | Founder/CEO Authentise, Chair, Digital Manufacturing, Singularity University
“Most surprising to me was the extent and speed at which the industry finally opened up.
While previously, only few 3D printing suppliers had APIs and knew what to do with them, 2018 saw nearly every OEM (or original equipment manufacturer) enabling data access and, even more surprisingly, shying away from proprietary standards and adopting MTConnect, as stalwarts such as 3D Systems and Stratasys have been. This means that in two to three years, data access to machines will be easy, commonplace, and free. The value will be in what is being done with that data.
Another example of this openness are the seemingly endless announcements of integrated workflows: GE’s announcement with most major software players to enable integrated solutions, EOS’s announcement with Siemens, and many more. It’s clear that all actors in the additive ecosystem have taken a step forward in terms of openness. The result is a faster pace of innovation, particularly in the software and data domains that are crucial to enabling comprehensive digital workflow to drive agile and resilient manufacturing.
I’m more optimistic we’ll achieve that now than I was at the end of 2017.”
SCIENCE AND DISCOVERY
Paul Saffo | Chair, Future Studies, Singularity University, Distinguished Visiting Scholar, Stanford Media-X Research Network
“The most important development in technology this year isn’t a technology, but rather the astonishing science surprises made possible by recent technology innovations. My short list includes the discovery of the “neptmoon”, a Neptune-scale moon circling a Jupiter-scale planet 8,000 lightyears from us; the successful deployment of the Mars InSight Lander a month ago; and the tantalizing ANITA detection (what could be a new subatomic particle which would in turn blow the standard model wide open). The highest use of invention is to support science discovery, because those discoveries in turn lead us to the future innovations that will improve the state of the world—and fire up our imaginations.”
ROBOTICS
Pablos Holman | Inventor, Hacker, Faculty, Singularity University
“Just five or ten years ago, if you’d asked any of us technologists “What is harder for robots? Eyes, or fingers?” We’d have all said eyes. Robots have extraordinary eyes now, but even in a surgical robot, the fingers are numb and don’t feel anything. Stanford robotics researchers have invented fingertips that can feel, and this will be a kingpin that allows robots to go everywhere they haven’t been yet.”
BLOCKCHAIN
Nathana Sharma | Blockchain, Policy, Law, and Ethics, Faculty, Singularity University
“2017 was the year of peak blockchain hype. 2018 has been a year of resetting expectations and technological development, even as the broader cryptocurrency markets have faced a winter. It’s now about seeing adoption and applications that people want and need to use rise. An incredible piece of news from December 2018 is that Facebook is developing a cryptocurrency for users to make payments through Whatsapp. That’s surprisingly fast mainstream adoption of this new technology, and indicates how powerful it is.”
ARTIFICIAL INTELLIGENCE
Neil Jacobstein | Chair, Artificial Intelligence and Robotics, Singularity University
“I think one of the most visible improvements in AI was illustrated by the Boston Dynamics Parkour video. This was not due to an improvement in brushless motors, accelerometers, or gears. It was due to improvements in AI algorithms and training data. To be fair, the video released was cherry-picked from numerous attempts, many of which ended with a crash. However, the fact that it could be accomplished at all in 2018 was a real win for both AI and robotics.”
NEUROSCIENCE
Divya Chander | Chair, Neuroscience, Singularity University
“2018 ushered in a new era of exponential trends in non-invasive brain modulation. Changing behavior or restoring function takes on a new meaning when invasive interfaces are no longer needed to manipulate neural circuitry. The end of 2018 saw two amazing announcements: the ability to grow neural organoids (mini-brains) in a dish from neural stem cells that started expressing electrical activity, mimicking the brain function of premature babies, and the first (known) application of CRISPR to genetically alter two fetuses grown through IVF. Although this was ostensibly to provide genetic resilience against HIV infections, imagine what would happen if we started tinkering with neural circuitry and intelligence.”
Image Credit: Yurchanka Siarhei / Shutterstock.com Continue reading
Posted in Human Robots
#433928 The Surprising Parallels Between ...
The human mind can be a confusing and overwhelming place. Despite incredible leaps in human progress, many of us still struggle to make our peace with our thoughts. The roots of this are complex and multifaceted. To find explanations for the global mental health epidemic, one can tap into neuroscience, psychology, evolutionary biology, or simply observe the meaningless systems that dominate our modern-day world.
This is not only the context of our reality but also that of the critically-acclaimed Netflix series, Maniac. Psychological dark comedy meets science fiction, Maniac is a retro, futuristic, and hallucinatory trip that is filled with hidden symbols. Directed by Cary Joji Fukunaga, the series tells the story of two strangers who decide to participate in the final stage of a “groundbreaking” pharmaceutical trial—one that combines novel pharmaceuticals with artificial intelligence, and promises to make their emotional pain go away.
Naturally, things don’t go according to plan.
From exams used for testing defense mechanisms to techniques such as cognitive behavioral therapy, the narrative infuses genuine psychological science. As perplexing as the series may be to some viewers, many of the tools depicted actually have a strong grounding in current technological advancements.
Catalysts for Alleviating Suffering
In the therapy of Maniac, participants undergo a three-day trial wherein they ingest three pills and appear to connect their consciousness to a superintelligent AI. Each participant is hurled into the traumatic experiences imprinted in their subconscious and forced to cope with them in a series of hallucinatory and dream-like experiences.
Perhaps the most recognizable parallel that can be drawn is with the latest advancements in psychedelic therapy. Psychedelics are a class of drugs that alter the experience of consciousness, and often cause radical changes in perception and cognitive processes.
Through a process known as transient hypofrontality, the executive “over-thinking” parts of our brains get a rest, and deeper areas become more active. This experience, combined with the breakdown of the ego, is often correlated with feelings of timelessness, peacefulness, presence, unity, and above all, transcendence.
Despite being not addictive and extremely difficult to overdose on, regulators looked down on the use of psychedelics for decades and many continue to dismiss them as “party drugs.” But in the last few years, all of this began to change.
Earlier this summer, the FDA granted breakthrough therapy designation to MDMA for the treatment of PTSD, after several phases of successful trails. Similar research has discovered that Psilocybin (also known as magic mushrooms) combined with therapy is far more effective than traditional forms of treatment to treat depression and anxiety. Today, there is a growing and overwhelming body of research that proves that not only are psychedelics such as LSD, MDMA, or Psylicybin effective catalysts to alleviate suffering and enhance the human condition, but they are potentially the most effective tools out there.
It’s important to realize that these substances are not solutions on their own, but rather catalysts for more effective therapy. They can be groundbreaking, but only in the right context and setting.
Brain-Machine Interfaces
In Maniac, the medication-assisted therapy is guided by what appears to be a super-intelligent form of artificial intelligence called the GRTA, nicknamed Gertie. Gertie, who is a “guide” in machine form, accesses the minds of the participants through what appears to be a futuristic brain-scanning technology and curates customized hallucinatory experiences with the goal of accelerating the healing process.
Such a powerful form of brain-scanning technology is not unheard of. Current levels of scanning technology are already allowing us to decipher dreams and connect three human brains, and are only growing exponentially. Though they are nowhere as advanced as Gertie (we have a long way to go before we get to this kind of general AI), we are also seeing early signs of AI therapy bots, chatbots that listen, think, and communicate with users like a therapist would.
The parallels between current advancements in mental health therapy and the methods in Maniac can be startling, and are a testament to how science fiction and the arts can be used to explore the existential implications of technology.
Not Necessarily a Dystopia
While there are many ingenious similarities between the technology in Maniac and the state of mental health therapy, it’s important to recognize the stark differences. Like many other blockbuster science fiction productions, Maniac tells a fundamentally dystopian tale.
The series tells the story of the 73rd iteration of a controversial drug trial, one that has experienced many failures and even led to various participants being braindead. The scientists appear to be evil, secretive, and driven by their own superficial agendas and deep unresolved emotional issues.
In contrast, clinicians and researchers are not only required to file an “investigational new drug application” with the FDA (and get approval) but also update the agency with safety and progress reports throughout the trial.
Furthermore, many of today’s researchers are driven by a strong desire to contribute to the well-being and progress of our species. Even more, the results of decades of research by organizations like MAPS have been exceptionally promising and aligned with positive values. While Maniac is entertaining and thought-provoking, viewers must not forget the positive potential of such advancements in mental health therapy.
Science, technology, and psychology aside, Maniac is a deep commentary on the human condition and the often disorienting states that pain us all. Within any human lifetime, suffering is inevitable. It is the disproportionate, debilitating, and unjust levels of suffering that we ought to tackle as a society. Ultimately, Maniac explores whether advancements in science and technology can help us live not a life devoid of suffering, but one where it is balanced with fulfillment.
Image Credit: xpixel / Shutterstock.com Continue reading
Posted in Human Robots
#433776 Why We Should Stop Conflating Human and ...
It’s common to hear phrases like ‘machine learning’ and ‘artificial intelligence’ and believe that somehow, someone has managed to replicate a human mind inside a computer. This, of course, is untrue—but part of the reason this idea is so pervasive is because the metaphor of human learning and intelligence has been quite useful in explaining machine learning and artificial intelligence.
Indeed, some AI researchers maintain a close link with the neuroscience community, and inspiration runs in both directions. But the metaphor can be a hindrance to people trying to explain machine learning to those less familiar with it. One of the biggest risks of conflating human and machine intelligence is that we start to hand over too much agency to machines. For those of us working with software, it’s essential that we remember the agency is human—it’s humans who build these systems, after all.
It’s worth unpacking the key differences between machine and human intelligence. While there are certainly similarities, it’s by looking at what makes them different that we can better grasp how artificial intelligence works, and how we can build and use it effectively.
Neural Networks
Central to the metaphor that links human and machine learning is the concept of a neural network. The biggest difference between a human brain and an artificial neural net is the sheer scale of the brain’s neural network. What’s crucial is that it’s not simply the number of neurons in the brain (which reach into the billions), but more precisely, the mind-boggling number of connections between them.
But the issue runs deeper than questions of scale. The human brain is qualitatively different from an artificial neural network for two other important reasons: the connections that power it are analogue, not digital, and the neurons themselves aren’t uniform (as they are in an artificial neural network).
This is why the brain is such a complex thing. Even the most complex artificial neural network, while often difficult to interpret and unpack, has an underlying architecture and principles guiding it (this is what we’re trying to do, so let’s construct the network like this…).
Intricate as they may be, neural networks in AIs are engineered with a specific outcome in mind. The human mind, however, doesn’t have the same degree of intentionality in its engineering. Yes, it should help us do all the things we need to do to stay alive, but it also allows us to think critically and creatively in a way that doesn’t need to be programmed.
The Beautiful Simplicity of AI
The fact that artificial intelligence systems are so much simpler than the human brain is, ironically, what enables AIs to deal with far greater computational complexity than we can.
Artificial neural networks can hold much more information and data than the human brain, largely due to the type of data that is stored and processed in a neural network. It is discrete and specific, like an entry on an excel spreadsheet.
In the human brain, data doesn’t have this same discrete quality. So while an artificial neural network can process very specific data at an incredible scale, it isn’t able to process information in the rich and multidimensional manner a human brain can. This is the key difference between an engineered system and the human mind.
Despite years of research, the human mind still remains somewhat opaque. This is because the analog synaptic connections between neurons are almost impenetrable to the digital connections within an artificial neural network.
Speed and Scale
Consider what this means in practice. The relative simplicity of an AI allows it to do a very complex task very well, and very quickly. A human brain simply can’t process data at scale and speed in the way AIs need to if they’re, say, translating speech to text, or processing a huge set of oncology reports.
Essential to the way AI works in both these contexts is that it breaks data and information down into tiny constituent parts. For example, it could break sounds down into phonetic text, which could then be translated into full sentences, or break images into pieces to understand the rules of how a huge set of them is composed.
Humans often do a similar thing, and this is the point at which machine learning is most like human learning; like algorithms, humans break data or information into smaller chunks in order to process it.
But there’s a reason for this similarity. This breakdown process is engineered into every neural network by a human engineer. What’s more, the way this process is designed will be down to the problem at hand. How an artificial intelligence system breaks down a data set is its own way of ‘understanding’ it.
Even while running a highly complex algorithm unsupervised, the parameters of how an AI learns—how it breaks data down in order to process it—are always set from the start.
Human Intelligence: Defining Problems
Human intelligence doesn’t have this set of limitations, which is what makes us so much more effective at problem-solving. It’s the human ability to ‘create’ problems that makes us so good at solving them. There’s an element of contextual understanding and decision-making in the way humans approach problems.
AIs might be able to unpack problems or find new ways into them, but they can’t define the problem they’re trying to solve.
Algorithmic insensitivity has come into focus in recent years, with an increasing number of scandals around bias in AI systems. Of course, this is caused by the biases of those making the algorithms, but underlines the point that algorithmic biases can only be identified by human intelligence.
Human and Artificial Intelligence Should Complement Each Other
We must remember that artificial intelligence and machine learning aren’t simply things that ‘exist’ that we can no longer control. They are built, engineered, and designed by us. This mindset puts us in control of the future, and makes algorithms even more elegant and remarkable.
Image Credit: Liu zishan/Shutterstock Continue reading
Posted in Human Robots