Amazon said Wednesday it expects to begin large-scale deliveries by drone in the coming months as it unveiled its newest design for its “Prime Air” fleet.
Jeff Wilke, head of Amazon’s consumer operations, told the company’s Machine Learning, Automation, Robotics and Space conference in Las Vegas that drones would play a role in ramping up efforts to shorten delivery times for many items to just one day for Amazon Prime members.
“We’ve been hard at work building fully electric drones that can fly up to 15 miles (25 kilometers) and deliver packages under five pounds (2.3 kilos) to customers in less than 30 minutes,” Wilke said in a blog post.
Self-driving cars could revolutionise people’s lives. By the end of the next decade, or perhaps even sooner, they could radically transform public spaces and liberate us from the many problems of mass car ownership. They’ll also be much better behaved than human drivers.
Robot drivers won’t break the speed limit, jump the lights, or park where they shouldn’t. They won’t drive under the influence of drink or drugs. They’ll never get tired or behave aggressively. They won’t be distracted by changing the music or sending a text, and they’ll never be trying to impress their mates.
Driverless cars could also change the face of public spaces. Private cars are very expensive items that do absolutely nothing 95% of the time. They are economically viable only because paying a taxi driver for all your car journeys would be even more expensive. Once cars don’t need human drivers, this cost balance should tip the other way.
When Elon Musk and DARPA both hop aboard the cyborg hypetrain, you know brain-machine interfaces (BMIs) are about to achieve the impossible.
BMIs, already the stuff of science fiction, facilitate crosstalk between biological wetware with external computers, turning human users into literal cyborgs. Yet mind-controlled robotic arms, microelectrode “nerve patches”, or “memory Band-AIDS” are still purely experimental medical treatments for those with nervous system impairments.
With the Next-Generation Nonsurgical Neurotechnology (N3) program, DARPA is looking to expand BMIs to the military. This month, the project tapped six academic teams to engineer radically different BMIs to hook up machines to the brains of able-bodied soldiers. The goal is to ditch surgery altogether—while minimizing any biological interventions—to link up brain and machine.
Robert Downey Jr. doesn’t pretend to be a brilliant scientist — even though he’s played Tony Stark, aka Iron Man, for the past 11 years.
But on Tuesday night he attended Amazon’s brand new, premier, open-to-the-public Machine Learning, Automation, Robotics and Space (re: MARS) conference in Las Vegas — a room filled with AI legends, astronauts, and other dignitaries — as a keynote speaker.
He delivered a gag-filled talk that somehow weaved together the history of the Marvel Cinematic Universe, the evolution of Stark’s Iron Man suits, allusions to his own troubled history with drug addiction, the actual history of artificial intelligence and its pioneers, with a bunch of jokes using the Amazon Alexa voice and Matt Damon (including a videotaped guest appearance by Damon).
Researchers, from biochemists to material scientists, have long relied on the rich variety of organic molecules to solve pressing challenges. Some molecules may be useful in treating diseases, others for lighting our digital displays, still others for pigments, paints, and plastics. The unique properties of each molecule are determined by its structure—that is, by the connectivity of its constituent atoms. Once a promising structure is identified, there remains the difficult task of making the targeted molecule through a sequence of chemical reactions. But which ones?
Organic chemists generally work backwards from the target molecule to the starting materials using a process called retrosynthetic analysis. During this process, the chemist faces a series of complex and inter-related decisions. For instance, of the tens of thousands of different chemical reactions, which one should you choose to create the target molecule? Once that decision is made, you may find yourself with multiple reactant molecules needed for the reaction. If these molecules are not available to purchase, then how do you select the appropriate reactions to produce them? Intelligently choosing what to do at each step of this process is critical in navigating the huge number of possible paths.
Researchers at Columbia Engineering have developed a new technique based on reinforcement learning that trains a neural network model to correctly select the “best” reaction at each step of the retrosynthetic process. This form of AI provides a framework for researchers to design chemical syntheses that optimize user specified objectives such synthesis cost, safety, and sustainability. The new approach, published May 31 by ACS Central Science, is more successful (by ~60%) than existing strategies for solving this challenging search problem.
How might future changes in the structure of business and the nature of work impact the environment?
While governments around the world are wrestling with the potential for massive on-rushing technological disruption of work and the jobs market, few are extending the telescope to explore what the knock-on impacts might be for the planet. Here we explore some dimensions of the issue.
Although replacing humans with robots has a dystopian flavor, what, if any positives are there from successive waves of artificial intelligence (AI) and other exponentially developing technologies displacing jobs ranging from banker to construction worker? Clearly, the number of people working and the implications for commuting, conduct of their role and their resulting income-related domestic lifestyle all have a direct bearing on their consumption of resources and emissions footprint. However, while everyone wants to know the impact of smart automation, the reality is that we are all clueless as to the outcome over the next twenty years, as this fourth industrial revolution has only just started.
There is a dramatic variation in views on the extent to which automation technologies such as AI, robotics and 3D / 4D printing will replace humans or enable wholly new roles. For example, A 2016 McKinsey automation study reported that, with current technologies, about a third of most job activities are technologically automatable, affecting 49% of the world economy, an estimated 1.1 billion employees and $12.7 trillion in wages. China, India, Japan, and USA account for more than half of these totals. The report concluded it would be more than two decades before automation reaches 50% of current activities.
Luminous Computing, a one-year-old startup, is aiming to build a photonics chip that will handle workloads needed for AI at the speed of light. It’s a moonshot and yet, the young company already has a number of high-profile investors willing to bet on the prospect.
The company has raised $9 million in a seed round led by Bill Gates, NEO’s Ali Partovi and Luke Nosek and Steve Oskoui of Gigafund.
The round also attracted other new investors, including Travis Kalanick’s fund 10100, BoxGroup, Uber CEO Dara Khosrowshahi, and Emil Michael as well as pre-seed investors Class 5 Global, Joshua Browder, Ozmen Ventures, Schox Investments and Third Kind Venture Capital.
