Tag Archives: humans

#437982 Superintelligent AI May Be Impossible to ...

It may be theoretically impossible for humans to control a superintelligent AI, a new study finds. Worse still, the research also quashes any hope for detecting such an unstoppable AI when it’s on the verge of being created.

Slightly less grim is the timetable. By at least one estimate, many decades lie ahead before any such existential computational reckoning could be in the cards for humanity.

Alongside news of AI besting humans at games such as chess, Go and Jeopardy have come fears that superintelligent machines smarter than the best human minds might one day run amok. “The question about whether superintelligence could be controlled if created is quite old,” says study lead author Manuel Alfonseca, a computer scientist at the Autonomous University of Madrid. “It goes back at least to Asimov’s First Law of Robotics, in the 1940s.”

The Three Laws of Robotics, first introduced in Isaac Asimov's 1942 short story “Runaround,” are as follows:

A robot may not injure a human being or, through inaction, allow a human being to come to harm.
A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.
A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.

In 2014, philosopher Nick Bostrom, director of the Future of Humanity Institute at the University of Oxford, not only explored ways in which a superintelligent AI could destroy us but also investigated potential control strategies for such a machine—and the reasons they might not work.

Bostrom outlined two possible types of solutions of this “control problem.” One is to control what the AI can do, such as keeping it from connecting to the Internet, and the other is to control what it wants to do, such as teaching it rules and values so it would act in the best interests of humanity. The problem with the former is that Bostrom thought a supersmart machine could probably break free from any bonds we could make. With the latter, he essentially feared that humans might not be smart enough to train a superintelligent AI.

Now Alfonseca and his colleagues suggest it may be impossible to control a superintelligent AI, due to fundamental limits inherent to computing itself. They detailed their findings this month in the Journal of Artificial Intelligence Research.

The researchers suggested that any algorithm that sought to ensure a superintelligent AI cannot harm people had to first simulate the machine’s behavior to predict the potential consequences of its actions. This containment algorithm then would need to halt the supersmart machine if it might indeed do harm.

However, the scientists said it was impossible for any containment algorithm to simulate the AI’s behavior and predict with absolute certainty whether its actions might lead to harm. The algorithm could fail to correctly simulate the AI’s behavior or accurately predict the consequences of the AI’s actions and not recognize such failures.

“Asimov’s first law of robotics has been proved to be incomputable,” Alfonseca says, “and therefore unfeasible.”

We may not even know if we have created a superintelligent machine, the researchers say. This is a consequence of Rice’s theorem, which essentially states that one cannot in general figure anything out about what a computer program might output just by looking at the program, Alfonseca explains.

On the other hand, there’s no need to spruce up the guest room for our future robot overlords quite yet. Three important caveats to the research still leave plenty of uncertainty to the group’s predictions.

First, Alfonseca estimates AI’s moment of truth remains, he says, “At least two centuries in the future.”

Second, he says researchers do not know if so-called artificial general intelligence, also known as strong AI, is theoretically even feasible. “That is, a machine as intelligent as we are in an ample variety of fields,” Alfonseca explains.

Last, Alfonseca says, “We have not proved that superintelligences can never be controlled—only that they can’t always be controlled.”

Although it may not be possible to control a superintelligent artificial general intelligence, it should be possible to control a superintelligent narrow AI—one specialized for certain functions instead of being capable of a broad range of tasks like humans. “We already have superintelligences of this type,” Alfonseca says. “For instance, we have machines that can compute mathematics much faster than we can. This is [narrow] superintelligence, isn’t it?” Continue reading

Posted in Human Robots

#437971 Video Friday: Teleport Yourself Into ...

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!):

HRI 2021 – March 8-11, 2021 – [Online]
RoboSoft 2021 – April 12-16, 2021 – [Online]
Let us know if you have suggestions for next week, and enjoy today's videos.

Samsung announced some new prototype robots at CES this week. It's a fancy video, but my guess is that the actual autonomy here is minimal at best.

[ Samsung ]

Some very impressive reactive agility from Ghost Robotics' little quadruped.

[ Ghost Robotics ]

Toyota Research Institute (TRI) is researching how to bring together the instinctive reflexes of professional drivers and automated driving technology that uses the calculated foresight of a supercomputer. Using a Toyota GR Supra, TRI will learn from some of the most skilled drivers in the world to develop sophisticated vehicle control algorithms. The project’s goal is to design a new level of active safety technology for the Toyota Guardian™ approach of amplifying human driving abilities and helping keep people safe.

[ TRI ]

The end of this video features one of the most satisfying-sounding drone outtakes I've ever heard,

[ ASL ]

Reachy can now run the first humanoid VR teleoperation app available on the market. This app allows you to place yourself in the body of a humanoid robot, in VR, wherever you are in the world, to remotely operate it and carry out complex tasks. With this new functionality, Reachy is able to learn from the demonstration of the humans who control it, which makes application development even easier.

[ Pollen Robotics ]

Thanks Elsa!

Boston Dynamics has inspired some dancing robot videos recently, including this from Marco Tempest.

[ Marco Tempest ]

MOFLIN is an AI Pet created from a totally new concept. It possesses emotional capabilities that evolve like living animals. With its warm soft fur, cute sounds, and adorable movement, you’d want to love it forever. We took a nature inspired approach and developed a unique algorithm that allows MOFLIN to learn and grow by constantly using its interactions to determine patterns and evaluate its surroundings from its sensors. MOFLIN will choose from an infinite number of mobile and sound pattern combinations to respond and express its feelings. To put it in simple terms, it’s like you’re interacting with a living pet.

I like the minimalist approach. I dislike the “it’s like you’re interacting with a living pet” bit.

[ Kickstarter ]

There's a short gif of these warehouse robots going around, but here's the full video.

[ BionicHIVE ]

Vstone's Robovie-Z proves that you don't need fancy hardware for effective teleworking.

[ Vstone ]

All dual-arm robots are required, at some point, to play pool.

[ ABB ]

Volkswagen Group Components gives us a first glimpse of the real prototypes. This is one of the visionary charging concepts that Volkswagen hopes will expand the charging infrastructure over the next few years. Its task: fully autonomous charging of vehicles in restricted parking areas, like underground car parks.

To charge several vehicles at the same time, the mobile robot moves a trailer, essentially a mobile energy storage unit, to the vehicle, connects it up and then uses this energy storage unit to charge the battery of the electric vehicle. The energy storage unit stays with the vehicle during the charging process. In the meantime, the robot charges other electric vehicles.

[ Volkswagen ]

I've got a lot of questions about Moley Robotics' kitchen. But I would immediately point out that the system appears to do no prep work, which (at least for me) is the time-consuming and stressful part of cooking.

[ Moley Robotics ]

Blueswarm is a collective of fish-inspired miniature underwater robots that can achieve a wide variety of 3D collective behaviors – synchrony, aggregation/dispersion, milling, search – using only implicit communication mediated through the production and sensing of blue light. We envision this platform for investigating collective AI, underwater coordination, and fish-inspired locomotion and sensing.

[ Science Robotics ]

A team of Malaysian researchers are transforming pineapple leaves into strong materials that can be used to build frames for unmanned aircraft or drones.

[ Reuters ]

The future of facility disinfecting is here, protect your customers, and create peace of mind. Our drone sanitization spraying technology is up to 100% more efficient and effective than conventional manual spray sterilization processes.

[ Draganfly ]

Robots are no long a future technology, as small robots can be purchased today to be utilized for educational purposes. See what goes into making a modern robot come to life.

[ Huggbees ]

How does a robot dog learn how to dance? Adam and the Tested team examine and dive into Boston Dynamics' Choreographer software that was behind Spot's recent viral dancing video.

[ Tested ]

For years, engineers have had to deal with “the tyranny of the fairing,” that anything you want to send into space has to fit into the protective nosecone on top of the rocket. A field of advanced design has been looking for new ways to improve our engineering, using the centuries-old artform to dream bigger.

[ JPL ] Continue reading

Posted in Human Robots

#437964 How Explainable Artificial Intelligence ...

The field of artificial intelligence has created computers that can drive cars, synthesize chemical compounds, fold proteins, and detect high-energy particles at a superhuman level.

However, these AI algorithms cannot explain the thought processes behind their decisions. A computer that masters protein folding and also tells researchers more about the rules of biology is much more useful than a computer that folds proteins without explanation.

Therefore, AI researchers like me are now turning our efforts toward developing AI algorithms that can explain themselves in a manner that humans can understand. If we can do this, I believe that AI will be able to uncover and teach people new facts about the world that have not yet been discovered, leading to new innovations.

Learning From Experience
One field of AI, called reinforcement learning, studies how computers can learn from their own experiences. In reinforcement learning, an AI explores the world, receiving positive or negative feedback based on its actions.

This approach has led to algorithms that have independently learned to play chess at a superhuman level and prove mathematical theorems without any human guidance. In my work as an AI researcher, I use reinforcement learning to create AI algorithms that learn how to solve puzzles such as the Rubik’s Cube.

Through reinforcement learning, AIs are independently learning to solve problems that even humans struggle to figure out. This has got me and many other researchers thinking less about what AI can learn and more about what humans can learn from AI. A computer that can solve the Rubik’s Cube should be able to teach people how to solve it, too.

Peering Into the Black Box
Unfortunately, the minds of superhuman AIs are currently out of reach to us humans. AIs make terrible teachers and are what we in the computer science world call “black boxes.”

AI simply spits out solutions without giving reasons for its solutions. Computer scientists have been trying for decades to open this black box, and recent research has shown that many AI algorithms actually do think in ways that are similar to humans. For example, a computer trained to recognize animals will learn about different types of eyes and ears and will put this information together to correctly identify the animal.

The effort to open up the black box is called explainable AI. My research group at the AI Institute at the University of South Carolina is interested in developing explainable AI. To accomplish this, we work heavily with the Rubik’s Cube.

The Rubik’s Cube is basically a pathfinding problem: Find a path from point A—a scrambled Rubik’s Cube—to point B—a solved Rubik’s Cube. Other pathfinding problems include navigation, theorem proving and chemical synthesis.

My lab has set up a website where anyone can see how our AI algorithm solves the Rubik’s Cube; however, a person would be hard-pressed to learn how to solve the cube from this website. This is because the computer cannot tell you the logic behind its solutions.

Solutions to the Rubik’s Cube can be broken down into a few generalized steps—the first step, for example, could be to form a cross while the second step could be to put the corner pieces in place. While the Rubik’s Cube itself has over 10 to the 19th power possible combinations, a generalized step-by-step guide is very easy to remember and is applicable in many different scenarios.

Approaching a problem by breaking it down into steps is often the default manner in which people explain things to one another. The Rubik’s Cube naturally fits into this step-by-step framework, which gives us the opportunity to open the black box of our algorithm more easily. Creating AI algorithms that have this ability could allow people to collaborate with AI and break down a wide variety of complex problems into easy-to-understand steps.

A step-by-step refinement approach can make it easier for humans to understand why AIs do the things they do. Forest Agostinelli, CC BY-ND

Collaboration Leads to Innovation
Our process starts with using one’s own intuition to define a step-by-step plan thought to potentially solve a complex problem. The algorithm then looks at each individual step and gives feedback about which steps are possible, which are impossible and ways the plan could be improved. The human then refines the initial plan using the advice from the AI, and the process repeats until the problem is solved. The hope is that the person and the AI will eventually converge to a kind of mutual understanding.

Currently, our algorithm is able to consider a human plan for solving the Rubik’s Cube, suggest improvements to the plan, recognize plans that do not work and find alternatives that do. In doing so, it gives feedback that leads to a step-by-step plan for solving the Rubik’s Cube that a person can understand. Our team’s next step is to build an intuitive interface that will allow our algorithm to teach people how to solve the Rubik’s Cube. Our hope is to generalize this approach to a wide range of pathfinding problems.

People are intuitive in a way unmatched by any AI, but machines are far better in their computational power and algorithmic rigor. This back and forth between man and machine utilizes the strengths from both. I believe this type of collaboration will shed light on previously unsolved problems in everything from chemistry to mathematics, leading to new solutions, intuitions and innovations that may have, otherwise, been out of reach.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Image Credit: Serg Antonov / Unsplash Continue reading

Posted in Human Robots

#437940 How Boston Dynamics Taught Its Robots to ...

A week ago, Boston Dynamics posted a video of Atlas, Spot, and Handle dancing to “Do You Love Me.” It was, according to the video description, a way “to celebrate the start of what we hope will be a happier year.” As of today the video has been viewed nearly 24 million times, and the popularity is no surprise, considering the compelling mix of technical prowess and creativity on display.

Strictly speaking, the stuff going on in the video isn’t groundbreaking, in the sense that we’re not seeing any of the robots demonstrate fundamentally new capabilities, but that shouldn’t take away from how impressive it is—you’re seeing state-of-the-art in humanoid robotics, quadrupedal robotics, and whatever-the-heck-Handle-is robotics.

What is unique about this video from Boston Dynamics is the artistic component. We know that Atlas can do some practical tasks, and we know it can do some gymnastics and some parkour, but dancing is certainly something new. To learn more about what it took to make these dancing robots happen (and it’s much more complicated than it might seem), we spoke with Aaron Saunders, Boston Dynamics’ VP of Engineering.

Saunders started at Boston Dynamics in 2003, meaning that he’s been a fundamental part of a huge number of Boston Dynamics’ robots, even the ones you may have forgotten about. Remember LittleDog, for example? A team of two designed and built that adorable little quadruped, and Saunders was one of them.

While he’s been part of the Atlas project since the beginning (and had a hand in just about everything else that Boston Dynamics works on), Saunders has spent the last few years leading the Atlas team specifically, and he was kind enough to answer our questions about their dancing robots.

IEEE Spectrum: What’s your sense of how the Internet has been reacting to the video?

Aaron Saunders: We have different expectations for the videos that we make; this one was definitely anchored in fun for us. The response on YouTube was record-setting for us: We received hundreds of emails and calls with people expressing their enthusiasm, and also sharing their ideas for what we should do next, what about this song, what about this dance move, so that was really fun. My favorite reaction was one that I got from my 94-year-old grandma, who watched the video on YouTube and then sent a message through the family asking if I’d taught the robot those sweet moves. I think this video connected with a broader audience, because it mixed the old-school music with new technology.

We haven’t seen Atlas move like this before—can you talk about how you made it happen?

We started by working with dancers and a choreographer to create an initial concept for the dance by composing and assembling a routine. One of the challenges, and probably the core challenge for Atlas in particular, was adjusting human dance moves so that they could be performed on the robot. To do that, we used simulation to rapidly iterate through movement concepts while soliciting feedback from the choreographer to reach behaviors that Atlas had the strength and speed to execute. It was very iterative—they would literally dance out what they wanted us to do, and the engineers would look at the screen and go “that would be easy” or “that would be hard” or “that scares me.” And then we’d have a discussion, try different things in simulation, and make adjustments to find a compatible set of moves that we could execute on Atlas.

Throughout the project, the time frame for creating those new dance moves got shorter and shorter as we built tools, and as an example, eventually we were able to use that toolchain to create one of Atlas’ ballet moves in just one day, the day before we filmed, and it worked. So it’s not hand-scripted or hand-coded, it’s about having a pipeline that lets you take a diverse set of motions, that you can describe through a variety of different inputs, and push them through and onto the robot.

Image: Boston Dynamics

Were there some things that were particularly difficult to translate from human dancers to Atlas? Or, things that Atlas could do better than humans?

Some of the spinning turns in the ballet parts took more iterations to get to work, because they were the furthest from leaping and running and some of the other things that we have more experience with, so they challenged both the machine and the software in new ways. We definitely learned not to underestimate how flexible and strong dancers are—when you take elite athletes and you try to do what they do but with a robot, it’s a hard problem. It’s humbling. Fundamentally, I don’t think that Atlas has the range of motion or power that these athletes do, although we continue developing our robots towards that, because we believe that in order to broadly deploy these kinds of robots commercially, and eventually in a home, we think they need to have this level of performance.

One thing that robots are really good at is doing something over and over again the exact same way. So once we dialed in what we wanted to do, the robots could just do it again and again as we played with different camera angles.

I can understand how you could use human dancers to help you put together a routine with Atlas, but how did that work with Spot, and particularly with Handle?

I think the people we worked with actually had a lot of talent for thinking about motion, and thinking about how to express themselves through motion. And our robots do motion really well—they’re dynamic, they’re exciting, they balance. So I think what we found was that the dancers connected with the way the robots moved, and then shaped that into a story, and it didn’t matter whether there were two legs or four legs. When you don’t necessarily have a template of animal motion or human behavior, you just have to think a little harder about how to go about doing something, and that’s true for more pragmatic commercial behaviors as well.

“We used simulation to rapidly iterate through movement concepts while soliciting feedback from the choreographer to reach behaviors that Atlas had the strength and speed to execute. It was very iterative—they would literally dance out what they wanted us to do, and the engineers would look at the screen and go ‘that would be easy’ or ‘that would be hard’ or ‘that scares me.’”
—Aaron Saunders, Boston Dynamics

How does the experience that you get teaching robots to dance, or to do gymnastics or parkour, inform your approach to robotics for commercial applications?

We think that the skills inherent in dance and parkour, like agility, balance, and perception, are fundamental to a wide variety of robot applications. Maybe more importantly, finding that intersection between building a new robot capability and having fun has been Boston Dynamics’ recipe for robotics—it’s a great way to advance.

One good example is how when you push limits by asking your robots to do these dynamic motions over a period of several days, you learn a lot about the robustness of your hardware. Spot, through its productization, has become incredibly robust, and required almost no maintenance—it could just dance all day long once you taught it to. And the reason it’s so robust today is because of all those lessons we learned from previous things that may have just seemed weird and fun. You’ve got to go into uncharted territory to even know what you don’t know.

Image: Boston Dynamics

It’s often hard to tell from watching videos like these how much time it took to make things work the way you wanted them to, and how representative they are of the actual capabilities of the robots. Can you talk about that?

Let me try to answer in the context of this video, but I think the same is true for all of the videos that we post. We work hard to make something, and once it works, it works. For Atlas, most of the robot control existed from our previous work, like the work that we’ve done on parkour, which sent us down a path of using model predictive controllers that account for dynamics and balance. We used those to run on the robot a set of dance steps that we’d designed offline with the dancers and choreographer. So, a lot of time, months, we spent thinking about the dance and composing the motions and iterating in simulation.

Dancing required a lot of strength and speed, so we even upgraded some of Atlas’ hardware to give it more power. Dance might be the highest power thing we’ve done to date—even though you might think parkour looks way more explosive, the amount of motion and speed that you have in dance is incredible. That also took a lot of time over the course of months; creating the capability in the machine to go along with the capability in the algorithms.

Once we had the final sequence that you see in the video, we only filmed for two days. Much of that time was spent figuring out how to move the camera through a scene with a bunch of robots in it to capture one continuous two-minute shot, and while we ran and filmed the dance routine multiple times, we could repeat it quite reliably. There was no cutting or splicing in that opening two-minute shot.

There were definitely some failures in the hardware that required maintenance, and our robots stumbled and fell down sometimes. These behaviors are not meant to be productized and to be a 100 percent reliable, but they’re definitely repeatable. We try to be honest with showing things that we can do, not a snippet of something that we did once. I think there’s an honesty required in saying that you’ve achieved something, and that’s definitely important for us.

You mentioned that Spot is now robust enough to dance all day. How about Atlas? If you kept on replacing its batteries, could it dance all day, too?

Atlas, as a machine, is still, you know… there are only a handful of them in the world, they’re complicated, and reliability was not a main focus. We would definitely break the robot from time to time. But the robustness of the hardware, in the context of what we were trying to do, was really great. And without that robustness, we wouldn’t have been able to make the video at all. I think Atlas is a little more like a helicopter, where there’s a higher ratio between the time you spend doing maintenance and the time you spend operating. Whereas with Spot, the expectation is that it’s more like a car, where you can run it for a long time before you have to touch it.

When you’re teaching Atlas to do new things, is it using any kind of machine learning? And if not, why not?

As a company, we’ve explored a lot of things, but Atlas is not using a learning controller right now. I expect that a day will come when we will. Atlas’ current dance performance uses a mixture of what we like to call reflexive control, which is a combination of reacting to forces, online and offline trajectory optimization, and model predictive control. We leverage these techniques because they’re a reliable way of unlocking really high performance stuff, and we understand how to wield these tools really well. We haven’t found the end of the road in terms of what we can do with them.

We plan on using learning to extend and build on the foundation of software and hardware that we’ve developed, but I think that we, along with the community, are still trying to figure out where the right places to apply these tools are. I think you’ll see that as part of our natural progression.

Image: Boston Dynamics

Much of Atlas’ dynamic motion comes from its lower body at the moment, but parkour makes use of upper body strength and agility as well, and we’ve seen some recent concept images showing Atlas doing vaults and pullups. Can you tell us more?

Humans and animals do amazing things using their legs, but they do even more amazing things when they use their whole bodies. I think parkour provides a fantastic framework that allows us to progress towards whole body mobility. Walking and running was just the start of that journey. We’re progressing through more complex dynamic behaviors like jumping and spinning, that’s what we’ve been working on for the last couple of years. And the next step is to explore how using arms to push and pull on the world could extend that agility.

One of the missions that I’ve given to the Atlas team is to start working on leveraging the arms as much as we leverage the legs to enhance and extend our mobility, and I’m really excited about what we’re going to be working on over the next couple of years, because it’s going to open up a lot more opportunities for us to do exciting stuff with Atlas.

What’s your perspective on hydraulic versus electric actuators for highly dynamic robots?

Across my career at Boston Dynamics, I’ve felt passionately connected to so many different types of technology, but I’ve settled into a place where I really don’t think this is an either-or conversation anymore. I think the selection of actuator technology really depends on the size of the robot that you’re building, what you want that robot to do, where you want it to go, and many other factors. Ultimately, it’s good to have both kinds of actuators in your toolbox, and I love having access to both—and we’ve used both with great success to make really impressive dynamic machines.

I think the only delineation between hydraulic and electric actuators that appears to be distinct for me is probably in scale. It’s really challenging to make tiny hydraulic things because the industry just doesn’t do a lot of that, and the reciprocal is that the industry also doesn’t tend to make massive electrical things. So, you may find that to be a natural division between these two technologies.

Besides what you’re working on at Boston Dynamics, what recent robotics research are you most excited about?

For us as a company, we really love to follow advances in sensing, computer vision, terrain perception, these are all things where the better they get, the more we can do. For me personally, one of the things I like to follow is manipulation research, and in particular manipulation research that advances our understanding of complex, friction-based interactions like sliding and pushing, or moving compliant things like ropes.

We’re seeing a shift from just pinching things, lifting them, moving them, and dropping them, to much more meaningful interactions with the environment. Research in that type of manipulation I think is going to unlock the potential for mobile manipulators, and I think it’s really going to open up the ability for robots to interact with the world in a rich way.

Is there anything else you’d like people to take away from this video?

For me personally, and I think it’s because I spend so much of my time immersed in robotics and have a deep appreciation for what a robot is and what its capabilities and limitations are, one of my strong desires is for more people to spend more time with robots. We see a lot of opinions and ideas from people looking at our videos on YouTube, and it seems to me that if more people had opportunities to think about and learn about and spend time with robots, that new level of understanding could help them imagine new ways in which robots could be useful in our daily lives. I think the possibilities are really exciting, and I just want more people to be able to take that journey. Continue reading

Posted in Human Robots

#437892 This Week’s Awesome Tech Stories From ...

ENVIRONMENT
Human-Made Stuff Now Outweighs All Life on Earth
Stephanie Pappas | Scientific American
“Humanity has reached a new milestone in its dominance of the planet: human-made objects may now outweigh all of the living beings on Earth. Roads, houses, shopping malls, fishing vessels, printer paper, coffee mugs, smartphones and all the other infrastructure of daily life now weigh in at approximately 1.1 trillion metric tons—equal to the combined dry weight of all plants, animals, fungi, bacteria, archaea and protists on the planet.”

SPACE
So, It Turns Out SpaceX Is Pretty Good at Rocketing
Eric Berger | Ars Technica
“As the Sun sank toward the South Texas horizon, a fantastical-looking spaceship rose into the reddening sky. It was, in a word, epic. …This was one heck of a test-flight that addressed a number of unknowns about Starship, which is the upper stage of SpaceX’s new launch system and may one day land humans on the Moon, Mars, and beyond.”

ARTIFICIAL INTELLIGENCE
Tiny Four-Bit Computers Are All You Need to Train AI
Karen Hao | MIT Technology Review
“The work…could increase the speed and cut the energy costs needed to train deep learning by more than sevenfold. It could also make training powerful AI models possible on smartphones and other small devices, which would improve privacy by helping to keep personal data on a local device. And it would make the process more accessible to researchers outside big, resource-rich tech companies.”

ENERGY
Did Quantum Scape Just Solve a 40-Year-Old Battery Problem?
Daniel Oberhaus | Wired
“[The properties of solid state batteries] would send…energy density through the roof, enable ultra-fast charging, and would eliminate the risk of battery fires. But for the past 40 years, no one has been able to make a solid-state battery that delivers on this promise—until earlier this year, when a secretive startup called QuantumScape claimed to have solved the problem. Now it has the data to prove it.”

ROBOTICS
Hyundai Buys Boston Dynamics for Nearly $1 Billion. Now What?
Evan Ackerman | IEEE Spectrum
“I hope that Boston Dynamics is unique enough that the kinds of rules that normally apply to robotics companies (or companies in general) can be set aside, at least somewhat, but I also worry that what made Boston Dynamics great was the explicit funding for the kinds of radical ideas that eventually resulted in robots like Atlas and Spot. Can Hyundai continue giving Boston Dynamics the support and freedom that they need to keep doing the kinds of things that have made them legendary? I certainly hope so.”

BIOTECH
CRISPR and Another Genetic Strategy Fix Cell Defects in Two Common Blood Disorders
Jocelyn Kaiser | Science
“It is a double milestone: new evidence that cures are possible for many people born with sickle cell disease and another serious blood disorder, beta-thalassemia, and a first for the genome editor CRISPR. Today, in The New England Journal of Medicine (NEJM) and tomorrow at the American Society of Hematology (ASH) meeting, teams report that two strategies for directly fixing malfunctioning blood cells have dramatically improved the health of a handful of people with these genetic diseases.”

ETHICS
The Dark Side of Big Tech’s Funding for AI Research
Tom Simonite | Wired
“Timnit Gebru’s exit from Google is a powerful reminder of how thoroughly companies dominate the field, with the biggest computers and the most resources. …[Meredith] Whittaker of AI Now says properly probing the societal effects of AI is fundamentally incompatible with corporate labs. ‘That kind of research that looks at the power and politics of AI is and must be inherently adversarial to the firms that are profiting from this technology.’i”

Image credit: Karsten Winegeart / Unsplash Continue reading

Posted in Human Robots