Tag Archives: austin

#437261 How AI Will Make Drug Discovery ...

If you had to guess how long it takes for a drug to go from an idea to your pharmacy, what would you guess? Three years? Five years? How about the cost? $30 million? $100 million?

Well, here’s the sobering truth: 90 percent of all drug possibilities fail. The few that do succeed take an average of 10 years to reach the market and cost anywhere from $2.5 billion to $12 billion to get there.

But what if we could generate novel molecules to target any disease, overnight, ready for clinical trials? Imagine leveraging machine learning to accomplish with 50 people what the pharmaceutical industry can barely do with an army of 5,000.

Welcome to the future of AI and low-cost, ultra-fast, and personalized drug discovery. Let’s dive in.

GANs & Drugs
Around 2012, computer scientist-turned-biophysicist Alex Zhavoronkov started to notice that artificial intelligence was getting increasingly good at image, voice, and text recognition. He knew that all three tasks shared a critical commonality. In each, massive datasets were available, making it easy to train up an AI.

But similar datasets were present in pharmacology. So, back in 2014, Zhavoronkov started wondering if he could use these datasets and AI to significantly speed up the drug discovery process. He’d heard about a new technique in artificial intelligence known as generative adversarial networks (or GANs). By pitting two neural nets against one another (adversarial), the system can start with minimal instructions and produce novel outcomes (generative). At the time, researchers had been using GANs to do things like design new objects or create one-of-a-kind, fake human faces, but Zhavoronkov wanted to apply them to pharmacology.

He figured GANs would allow researchers to verbally describe drug attributes: “The compound should inhibit protein X at concentration Y with minimal side effects in humans,” and then the AI could construct the molecule from scratch. To turn his idea into reality, Zhavoronkov set up Insilico Medicine on the campus of Johns Hopkins University in Baltimore, Maryland, and rolled up his sleeves.

Instead of beginning their process in some exotic locale, Insilico’s “drug discovery engine” sifts millions of data samples to determine the signature biological characteristics of specific diseases. The engine then identifies the most promising treatment targets and—using GANs—generates molecules (that is, baby drugs) perfectly suited for them. “The result is an explosion in potential drug targets and a much more efficient testing process,” says Zhavoronkov. “AI allows us to do with fifty people what a typical drug company does with five thousand.”

The results have turned what was once a decade-long war into a month-long skirmish.

In late 2018, for example, Insilico was generating novel molecules in fewer than 46 days, and this included not just the initial discovery, but also the synthesis of the drug and its experimental validation in computer simulations.

Right now, they’re using the system to hunt down new drugs for cancer, aging, fibrosis, Parkinson’s, Alzheimer’s, ALS, diabetes, and many others. The first drug to result from this work, a treatment for hair loss, is slated to start Phase I trials by the end of 2020.

They’re also in the early stages of using AI to predict the outcomes of clinical trials in advance of the trial. If successful, this technique will enable researchers to strip a bundle of time and money out of the traditional testing process.

Protein Folding
Beyond inventing new drugs, AI is also being used by other scientists to identify new drug targets—that is, the place to which a drug binds in the body and another key part of the drug discovery process.

Between 1980 and 2006, despite an annual investment of $30 billion, researchers only managed to find about five new drug targets a year. The trouble is complexity. Most potential drug targets are proteins, and a protein’s structure—meaning the way a 2D sequence of amino acids folds into a 3D protein—determines its function.

But a protein with merely a hundred amino acids (a rather small protein) can produce a googol-cubed worth of potential shapes—that’s a one followed by three hundred zeroes. This is also why protein-folding has long been considered an intractably hard problem for even the most powerful of supercomputers.

Back in 1994, to monitor supercomputers’ progress in protein-folding, a biannual competition was created. Until 2018, success was fairly rare. But then the creators of DeepMind turned their neural networks loose on the problem. They created an AI that mines enormous datasets to determine the most likely distance between a protein’s base pairs and the angles of their chemical bonds—aka, the basics of protein-folding. They called it AlphaFold.

On its first foray into the competition, contestant AIs were given 43 protein-folding problems to solve. AlphaFold got 25 right. The second-place team managed a meager three. By predicting the elusive ways in which various proteins fold on the basis of their amino acid sequences, AlphaFold may soon have a tremendous impact in aiding drug discovery and fighting some of today’s most intractable diseases.

Drug Delivery
Another theater of war for improved drugs is the realm of drug delivery. Even here, converging exponential technologies are paving the way for massive implications in both human health and industry shifts.

One key contender is CRISPR, the fast-advancing gene-editing technology that stands to revolutionize synthetic biology and treatment of genetically linked diseases. And researchers have now demonstrated how this tool can be applied to create materials that shape-shift on command. Think: materials that dissolve instantaneously when faced with a programmed stimulus, releasing a specified drug at a highly targeted location.

Yet another potential boon for targeted drug delivery is nanotechnology, whereby medical nanorobots have now been used to fight incidences of cancer. In a recent review of medical micro- and nanorobotics, lead authors (from the University of Texas at Austin and University of California, San Diego) found numerous successful tests of in vivo operation of medical micro- and nanorobots.

Drugs From the Future
Covid-19 is uniting the global scientific community with its urgency, prompting scientists to cast aside nation-specific territorialism, research secrecy, and academic publishing politics in favor of expedited therapeutic and vaccine development efforts. And in the wake of rapid acceleration across healthcare technologies, Big Pharma is an area worth watching right now, no matter your industry. Converging technologies will soon enable extraordinary strides in longevity and disease prevention, with companies like Insilico leading the charge.

Riding the convergence of massive datasets, skyrocketing computational power, quantum computing, cognitive surplus capabilities, and remarkable innovations in AI, we are not far from a world in which personalized drugs, delivered directly to specified targets, will graduate from science fiction to the standard of care.

Rejuvenational biotechnology will be commercially available sooner than you think. When I asked Alex for his own projection, he set the timeline at “maybe 20 years—that’s a reasonable horizon for tangible rejuvenational biotechnology.”

How might you use an extra 20 or more healthy years in your life? What impact would you be able to make?

Join Me
(1) A360 Executive Mastermind: If you’re an exponentially and abundance-minded entrepreneur who would like coaching directly from me, consider joining my Abundance 360 Mastermind, a highly selective community of 360 CEOs and entrepreneurs who I coach for 3 days every January in Beverly Hills, Ca. Through A360, I provide my members with context and clarity about how converging exponential technologies will transform every industry. I’m committed to running A360 for the course of an ongoing 25-year journey as a “countdown to the Singularity.”

If you’d like to learn more and consider joining our 2021 membership, apply here.

(2) Abundance-Digital Online Community: I’ve also created a Digital/Online community of bold, abundance-minded entrepreneurs called Abundance-Digital. Abundance-Digital is Singularity University’s ‘onramp’ for exponential entrepreneurs—those who want to get involved and play at a higher level. Click here to learn more.

(Both A360 and Abundance-Digital are part of Singularity University—your participation opens you to a global community.)

This article originally appeared on diamandis.com. Read the original article here.

Image Credit: andreas160578 from Pixabay Continue reading

Posted in Human Robots

#435658 Video Friday: A Two-Armed Robot That ...

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here’s what we have so far (send us your events!):

ICRES 2019 – July 29-30, 2019 – London, U.K.
DARPA SubT Tunnel Circuit – August 15-22, 2019 – Pittsburgh, Pa., USA
IEEE Africon 2019 – September 25-27, 2019 – Accra, Ghana
ISRR 2019 – October 6-10, 2019 – Hanoi, Vietnam
Ro-Man 2019 – October 14-18, 2019 – New Delhi, India
Humanoids 2019 – October 15-17, 2019 – Toronto, Canada
Let us know if you have suggestions for next week, and enjoy today’s videos.

I’m sure you’ve seen this video already because you read this blog every day, but if you somehow missed it because you were skiing across Antarctica (the only valid excuse we’re accepting today), here’s our video introducing HMI’s Aquanaut transforming robot submarine.

And after you recover from all that frostbite, make sure and read our in-depth feature article here.

[ Aquanaut ]

Last week we complained about not having seen a ballbot with a manipulator, so Roberto from CMU shared a new video of their ballbot, featuring a pair of 7-DoF arms.

We should learn more at Humanoids 2019.

[ CMU ]

Thanks Roberto!

The FAA is making it easier for recreational drone pilots to get near-realtime approval to fly in lightly controlled airspace.

[ LAANC ]

Self-reconfigurable modular robots are usually composed of multiple modules with uniform docking interfaces that can be transformed into different configurations by themselves. The reconfiguration planning problem is finding what sequence of reconfiguration actions are required for one arrangement of modules to transform into another. We present a novel reconfiguration planning algorithm for modular robots. The algorithm compares the initial configuration with the goal configuration efficiently. The reconfiguration actions can be executed in a distributed manner so that each module can efficiently finish its reconfiguration task which results in a global reconfiguration for the system. In the end, the algorithm is demonstrated on real modular robots and some example reconfiguration tasks are provided.

[ CKbot ]

A nice design of a gripper that uses a passive thumb of sorts to pick up flat objects from flat surfaces.

[ Paper ] via [ Laval University ]

I like this video of a palletizing robot from Kawasaki because in the background you can see a human doing the exact same job and obviously not enjoying it.

[ Kawasaki ]

This robot cleans and “brings joy and laughter.” What else do we need?

I do appreciate that all the robots are named Leo, and that they’re also all female.

[ LionsBot ]

This is less of a dishwashing robot and more of a dishsorting robot, but we’ll forgive it because it doesn’t drop a single dish.

[ TechMagic ]

Thanks Ryosuke!

A slight warning here that the robot in the following video (which costs something like $180,000) appears “naked” in some scenes, none of which are strictly objectionable, we hope.

Beautifully slim and delicate motion life-size motion figures are ideal avatars for expressing emotions to customers in various arts, content and businesses. We can provide a system that integrates not only motion figures but all moving devices.

[ Speecys ]

The best way to operate a Husky with a pair of manipulators on it is to become the robot.

[ UT Austin ]

The FlyJacket drone control system from EPFL has been upgraded so that it can yank you around a little bit.

In several fields of human-machine interaction, haptic guidance has proven to be an effective training tool for enhancing user performance. This work presents the results of psychophysical and motor learning studies that were carried out with human participant to assess the effect of cable-driven haptic guidance for a task involving aerial robotic teleoperation. The guidance system was integrated into an exosuit, called the FlyJacket, that was developed to control drones with torso movements. Results for the Just Noticeable Difference (JND) and from the Stevens Power Law suggest that the perception of force on the users’ torso scales linearly with the amplitude of the force exerted through the cables and the perceived force is close to the magnitude of the stimulus. Motor learning studies reveal that this form of haptic guidance improves user performance in training, but this improvement is not retained when participants are evaluated without guidance.

[ EPFL ]

The SAND Challenge is an opportunity for small businesses to compete in an autonomous unmanned aerial vehicle (UAV) competition to help NASA address safety-critical risks associated with flying UAVs in the national airspace. Set in a post-natural disaster scenario, SAND will push the envelope of aviation.

[ NASA ]

Legged robots have the potential to traverse diverse and rugged terrain. To find a safe and efficient navigation path and to carefully select individual footholds, it is useful to predict properties of the terrain ahead of the robot. In this work, we propose a method to collect data from robot-terrain interaction and associate it to images, to then train a neural network to predict terrain properties from images.

[ RSL ]

Misty wants to be your new receptionist.

[ Misty Robotics ]

For years, we’ve been pointing out that while new Roombas have lots of great features, older Roombas still do a totally decent job of cleaning your floors. This video is a performance comparison between the newest Roomba (the S9+) and the original 2002 Roomba (!), and the results will surprise you. Or maybe they won’t.

[ Vacuum Wars ]

Lex Fridman from MIT interviews Chris Urmson, who was involved in some of the earliest autonomous vehicle projects, Google’s original self-driving car among them, and is currently CEO of Aurora Innovation.

Chris Urmson was the CTO of the Google Self-Driving Car team, a key engineer and leader behind the Carnegie Mellon autonomous vehicle entries in the DARPA grand challenges and the winner of the DARPA urban challenge. Today he is the CEO of Aurora Innovation, an autonomous vehicle software company he started with Sterling Anderson, who was the former director of Tesla Autopilot, and Drew Bagnell, Uber’s former autonomy and perception lead.

[ AI Podcast ]

In this week’s episode of Robots in Depth, Per speaks with Lael Odhner from RightHand Robotics.

Lael Odhner is a co-founder of RightHand Robotics, that is developing a gripper based on the combination of control and soft, compliant parts to get better grasping of objects. Their work focuses on grasping and manipulating everyday human objects in everyday environments.This mimics how human hands combine control and flexibility to grasp objects with great dexterity.

The combination of control and compliance makes the RightHand robotics gripper very light-weight and affordable. The compliance makes it easier to grasp objects of unknown shape and differs from the way industrial robots usually grip. The compliance also helps in a more unstructured environment where contact with the object and its surroundings cannot be exactly predicted.

[ RightHand Robotics ] via [ Robots in Depth ] Continue reading

Posted in Human Robots

#434781 What Would It Mean for AI to Become ...

As artificial intelligence systems take on more tasks and solve more problems, it’s hard to say which is rising faster: our interest in them or our fear of them. Futurist Ray Kurzweil famously predicted that “By 2029, computers will have emotional intelligence and be convincing as people.”

We don’t know how accurate this prediction will turn out to be. Even if it takes more than 10 years, though, is it really possible for machines to become conscious? If the machines Kurzweil describes say they’re conscious, does that mean they actually are?

Perhaps a more relevant question at this juncture is: what is consciousness, and how do we replicate it if we don’t understand it?

In a panel discussion at South By Southwest titled “How AI Will Design the Human Future,” experts from academia and industry discussed these questions and more.

Wait, What Is AI?
Most of AI’s recent feats—diagnosing illnesses, participating in debate, writing realistic text—involve machine learning, which uses statistics to find patterns in large datasets then uses those patterns to make predictions. However, “AI” has been used to refer to everything from basic software automation and algorithms to advanced machine learning and deep learning.

“The term ‘artificial intelligence’ is thrown around constantly and often incorrectly,” said Jennifer Strong, a reporter at the Wall Street Journal and host of the podcast “The Future of Everything.” Indeed, one study found that 40 percent of European companies that claim to be working on or using AI don’t actually use it at all.

Dr. Peter Stone, associate chair of computer science at UT Austin, was the study panel chair on the 2016 One Hundred Year Study on Artificial Intelligence (or AI100) report. Based out of Stanford University, AI100 is studying and anticipating how AI will impact our work, our cities, and our lives.

“One of the first things we had to do was define AI,” Stone said. They defined it as a collection of different technologies inspired by the human brain to be able to perceive their surrounding environment and figure out what actions to take given these inputs.

Modeling on the Unknown
Here’s the crazy thing about that definition (and about AI itself): we’re essentially trying to re-create the abilities of the human brain without having anything close to a thorough understanding of how the human brain works.

“We’re starting to pair our brains with computers, but brains don’t understand computers and computers don’t understand brains,” Stone said. Dr. Heather Berlin, cognitive neuroscientist and professor of psychiatry at the Icahn School of Medicine at Mount Sinai, agreed. “It’s still one of the greatest mysteries how this three-pound piece of matter can give us all our subjective experiences, thoughts, and emotions,” she said.

This isn’t to say we’re not making progress; there have been significant neuroscience breakthroughs in recent years. “This has been the stuff of science fiction for a long time, but now there’s active work being done in this area,” said Amir Husain, CEO and founder of Austin-based AI company Spark Cognition.

Advances in brain-machine interfaces show just how much more we understand the brain now than we did even a few years ago. Neural implants are being used to restore communication or movement capabilities in people who’ve been impaired by injury or illness. Scientists have been able to transfer signals from the brain to prosthetic limbs and stimulate specific circuits in the brain to treat conditions like Parkinson’s, PTSD, and depression.

But much of the brain’s inner workings remain a deep, dark mystery—one that will have to be further solved if we’re ever to get from narrow AI, which refers to systems that can perform specific tasks and is where the technology stands today, to artificial general intelligence, or systems that possess the same intelligence level and learning capabilities as humans.

The biggest question that arises here, and one that’s become a popular theme across stories and films, is if machines achieve human-level general intelligence, does that also mean they’d be conscious?

Wait, What Is Consciousness?
As valuable as the knowledge we’ve accumulated about the brain is, it seems like nothing more than a collection of disparate facts when we try to put it all together to understand consciousness.

“If you can replace one neuron with a silicon chip that can do the same function, then replace another neuron, and another—at what point are you still you?” Berlin asked. “These systems will be able to pass the Turing test, so we’re going to need another concept of how to measure consciousness.”

Is consciousness a measurable phenomenon, though? Rather than progressing by degrees or moving through some gray area, isn’t it pretty black and white—a being is either conscious or it isn’t?

This may be an outmoded way of thinking, according to Berlin. “It used to be that only philosophers could study consciousness, but now we can study it from a scientific perspective,” she said. “We can measure changes in neural pathways. It’s subjective, but depends on reportability.”

She described three levels of consciousness: pure subjective experience (“Look, the sky is blue”), awareness of one’s own subjective experience (“Oh, it’s me that’s seeing the blue sky”), and relating one subjective experience to another (“The blue sky reminds me of a blue ocean”).

“These subjective states exist all the way down the animal kingdom. As humans we have a sense of self that gives us another depth to that experience, but it’s not necessary for pure sensation,” Berlin said.

Husain took this definition a few steps farther. “It’s this self-awareness, this idea that I exist separate from everything else and that I can model myself,” he said. “Human brains have a wonderful simulator. They can propose a course of action virtually, in their minds, and see how things play out. The ability to include yourself as an actor means you’re running a computation on the idea of yourself.”

Most of the decisions we make involve envisioning different outcomes, thinking about how each outcome would affect us, and choosing which outcome we’d most prefer.

“Complex tasks you want to achieve in the world are tied to your ability to foresee the future, at least based on some mental model,” Husain said. “With that view, I as an AI practitioner don’t see a problem implementing that type of consciousness.”

Moving Forward Cautiously (But Not too Cautiously)
To be clear, we’re nowhere near machines achieving artificial general intelligence or consciousness, and whether a “conscious machine” is possible—not to mention necessary or desirable—is still very much up for debate.

As machine intelligence continues to advance, though, we’ll need to walk the line between progress and risk management carefully.

Improving the transparency and explainability of AI systems is one crucial goal AI developers and researchers are zeroing in on. Especially in applications that could mean the difference between life and death, AI shouldn’t advance without people being able to trace how it’s making decisions and reaching conclusions.

Medicine is a prime example. “There are already advances that could save lives, but they’re not being used because they’re not trusted by doctors and nurses,” said Stone. “We need to make sure there’s transparency.” Demanding too much transparency would also be a mistake, though, because it will hinder the development of systems that could at best save lives and at worst improve efficiency and free up doctors to have more face time with patients.

Similarly, self-driving cars have great potential to reduce deaths from traffic fatalities. But even though humans cause thousands of deadly crashes every day, we’re terrified by the idea of self-driving cars that are anything less than perfect. “If we only accept autonomous cars when there’s zero probability of an accident, then we will never accept them,” Stone said. “Yet we give 16-year-olds the chance to take a road test with no idea what’s going on in their brains.”

This brings us back to the fact that, in building tech modeled after the human brain—which has evolved over millions of years—we’re working towards an end whose means we don’t fully comprehend, be it something as basic as choosing when to brake or accelerate or something as complex as measuring consciousness.

“We shouldn’t charge ahead and do things just because we can,” Stone said. “The technology can be very powerful, which is exciting, but we have to consider its implications.”

Image Credit: agsandrew / Shutterstock.com Continue reading

Posted in Human Robots

#432331 $10 million XPRIZE Aims for Robot ...

Ever wished you could be in two places at the same time? The XPRIZE Foundation wants to make that a reality with a $10 million competition to build robot avatars that can be controlled from at least 100 kilometers away.

The competition was announced by XPRIZE founder Peter Diamandis at the SXSW conference in Austin last week, with an ambitious timeline of awarding the grand prize by October 2021. Teams have until October 31st to sign up, and they need to submit detailed plans to a panel of judges by the end of next January.

The prize, sponsored by Japanese airline ANA, has given contestants little guidance on how they expect them to solve the challenge other than saying their solutions need to let users see, hear, feel, and interact with the robot’s environment as well as the people in it.

XPRIZE has also not revealed details of what kind of tasks the robots will be expected to complete, though they’ve said tasks will range from “simple” to “complex,” and it should be possible for an untrained operator to use them.

That’s a hugely ambitious goal that’s likely to require teams to combine multiple emerging technologies, from humanoid robotics to virtual reality high-bandwidth communications and high-resolution haptics.

If any of the teams succeed, the technology could have myriad applications, from letting emergency responders enter areas too hazardous for humans to helping people care for relatives who live far away or even just allowing tourists to visit other parts of the world without the jet lag.

“Our ability to physically experience another geographic location, or to provide on-the-ground assistance where needed, is limited by cost and the simple availability of time,” Diamandis said in a statement.

“The ANA Avatar XPRIZE can enable creation of an audacious alternative that could bypass these limitations, allowing us to more rapidly and efficiently distribute skill and hands-on expertise to distant geographic locations where they are needed, bridging the gap between distance, time, and cultures,” he added.

Interestingly, the technology may help bypass an enduring hand break on the widespread use of robotics: autonomy. By having a human in the loop, you don’t need nearly as much artificial intelligence analyzing sensory input and making decisions.

Robotics software is doing a lot more than just high-level planning and strategizing, though. While a human moves their limbs instinctively without consciously thinking about which muscles to activate, controlling and coordinating a robot’s components requires sophisticated algorithms.

The DARPA Robotics Challenge demonstrated just how hard it was to get human-shaped robots to do tasks humans would find simple, such as opening doors, climbing steps, and even just walking. These robots were supposedly semi-autonomous, but on many tasks they were essentially tele-operated, and the results suggested autonomy isn’t the only problem.

There’s also the issue of powering these devices. You may have noticed that in a lot of the slick web videos of humanoid robots doing cool things, the machine is attached to the roof by a large cable. That’s because they suck up huge amounts of power.

Possibly the most advanced humanoid robot—Boston Dynamics’ Atlas—has a battery, but it can only run for about an hour. That might be fine for some applications, but you don’t want it running out of juice halfway through rescuing someone from a mine shaft.

When it comes to the link between the robot and its human user, some of the technology is probably not that much of a stretch. Virtual reality headsets can create immersive audio-visual environments, and a number of companies are working on advanced haptic suits that will let people “feel” virtual environments.

Motion tracking technology may be more complicated. While even consumer-grade devices can track peoples’ movements with high accuracy, you will probably need to don something more like an exoskeleton that can both pick up motion and provide mechanical resistance, so that when the robot bumps into an immovable object, the user stops dead too.

How hard all of this will be is also dependent on how the competition ultimately defines subjective terms like “feel” and “interact.” Will the user need to be able to feel a gentle breeze on the robot’s cheek or be able to paint a watercolor? Or will simply having the ability to distinguish a hard object from a soft one or shake someone’s hand be enough?

Whatever the fidelity they decide on, the approach will require huge amounts of sensory and control data to be transmitted over large distances, most likely wirelessly, in a way that’s fast and reliable enough that there’s no lag or interruptions. Fortunately 5G is launching this year, with a speed of 10 gigabits per second and very low latency, so this problem should be solved by 2021.

And it’s worth remembering there have already been some tentative attempts at building robotic avatars. Telepresence robots have solved the seeing, hearing, and some of the interacting problems, and MIT has already used virtual reality to control robots to carry out complex manipulation tasks.

South Korean company Hankook Mirae Technology has also unveiled a 13-foot-tall robotic suit straight out of a sci-fi movie that appears to have made some headway with the motion tracking problem, albeit with a human inside the robot. Toyota’s T-HR3 does the same, but with the human controlling the robot from a “Master Maneuvering System” that marries motion tracking with VR.

Combining all of these capabilities into a single machine will certainly prove challenging. But if one of the teams pulls it off, you may be able to tick off trips to the Seven Wonders of the World without ever leaving your house.

Image Credit: ANA Avatar XPRIZE Continue reading

Posted in Human Robots

#431553 This Week’s Awesome Stories From ...

ROBOTS
Boston Dynamics’ Atlas Robot Does Backflips Now and It’s Full-Tilt InsaneMatt Simon | Wired “To be clear: Humanoids aren’t supposed to be able to do this. It’s extremely difficult to make a bipedal robot that can move effectively, much less kick off a tumbling routine.”

TRANSPORTATION
This Is the Tesla Semi TruckZac Estrada | The Verge“What Tesla has done today is shown that it wants to invigorate a segment, rather than just make something to comply with more stringent emissions regulations… And in the process, it’s trying to do for heavy-duty commercial vehicles what it did for luxury cars—plough forward in its own lane.”
PRIVACY AND SECURITY
Should Facebook Notify Readers When They’ve Been Fed Disinformation?Austin Carr | Fast Company “It would be, Reed suggested, the social network equivalent of a newspaper correction—only one that, with the tech companies’ expansive data, could actually reach its intended audience, like, say, the 250,000-plus Facebook users who shared the debunked YourNewsWire.com story.”
BRAIN HEALTH
Brain Implant Boosts Memory for First Time EverKristin Houser | NBC News “Once implanted in the volunteers, Song’s device could collect data on their brain activity during tests designed to stimulate either short-term memory or working memory. The researchers then determined the pattern associated with optimal memory performance and used the device’s electrodes to stimulate the brain following that pattern during later tests.”
COMPUTING
Yale Professors Race Google and IBM to the First Quantum ComputerCade Metz | New York Times “Though Quantum Circuits is using the same quantum method as its bigger competitors, Mr. Schoelkopf argued that his company has an edge because it is tackling the problem differently. Rather than building one large quantum machine, it is constructing a series of tiny machines that can be networked together. He said this will make it easier to correct errors in quantum calculations—one of the main difficulties in building one of these complex machines.”
Image Credit: Tesla Motors Continue reading

Posted in Human Robots