The news: Alphabet X, the company’s early research and development division, has unveiled the Everyday Robot project, whose aim is to develop a “general-purpose learning robot.” The idea is to equip robots with cameras and complex machine-learning software, letting them observe the world around them and learn from it without needing to be taught every potential situation they may encounter.
For now: The early prototype robots are learning how to sort trash. It sounds mundane, but it’s tough to get robots to identify different types of objects, and then how to grasp them. Alphabet X claims that its robots are currently putting less than 5% of trash in the wrong place, versus an error rate of 20% among the office’s humans.
The big idea: Robots are expensive and confined to performing very specific, specialized tasks. Getting robots that can operate safely and autonomously in messy, complex human environments like homes or offices is one of the biggest challenges in robotics right now.
The Russian military will be going all out sci-fi, with Vladimir Putin saying the plan for boosting the Armed Forces until 2033 should focus on AI and weapons based on ‘new physical principles.’
With the introduction of a whole range of state-of-the-art arms in recent years, Russia has been “able to make a step forward compared to the world’s other military powers,” Putin said during a meeting of the Russian Security Council on Friday.
The tally of the newest weapons and hardware in the possession of the country’s Armed Forces and Navy is currently over 68 percent, he said, adding that they must be increased to at least 70 percent and maintained at that level.
Ray Kurzweil is one of the world’s leading inventors, thinkers, and futurists, with a thirty-year track record of accurate predictions. Called “the restless genius” by The Wall Street Journal and “the ultimate thinking machine” by Forbes magazine, he was selected as one of the top entrepreneurs by Inc. magazine, which described him as the “rightful heir to Thomas Edison.” PBS selected him as one of the “sixteen revolutionaries who made America.”
Ray was the principal inventor of the first CCD flat-bed scanner, the first omni-font optical character recognition, the first print-to-speech reading machine for the blind, the first text-to-speech synthesizer, the first music synthesizer capable of recreating the grand piano and other orchestral instruments, and the first commercially marketed large-vocabulary speech recognition.
Among Ray’s many honors, he received a Grammy Award for outstanding achievements in music technology; he is the recipient of the National Medal of Technology, was inducted into the National Inventors Hall of Fame, holds twenty-one honorary Doctorates, and honors from three U.S. presidents.
Ray has written five national best-selling books, including New York Times best sellers The Singularity Is Near (2005) and How To Create A Mind (2012). He is Co-Founder and Chancellor of Singularity University and a Director of Engineering at Google heading up a team developing machine intelligence and natural language understanding.
Ci2019 featured over 40 global leaders including Chief Technology Officer of Google Ray Kurzweil (USA), CEO of NESTA Geoff Mulgan CBE (UK), Chief Data and Transformation Officer at DBS Bank Paul Cobban (Singapore), A.I. Experts Professor Toby Walsh and Liesl Yearsley (USA), Co-founder of Oxford Insights Emma Martinho-Truswell (UK), Ethics leader Professor Simon Longstaff, Ethics and Culture of Robots and AI Professor Kathleen Richardson (UK), brain performance neuroscientist Dr Etienne Van Der Walt (South Africa), transdisciplinary Behavioural Scientist Dr Richard Claydon (Hong Kong), Director of the Learning Technology Research Centre Carl Smith (UK), Australia’s Chief Scientist Dr Alan Finkel AO, Deakin University Vice Chancellor Professor Jane Den Hollander, ATO’s Jane King, Innovation & Science Australia CEO Dr Charles Day, CEDA CEO Melinda Cilento, Jobs for NSW CEO Nicole Cook, Behaviour Innovation founder & CEO John Pickering, People and Performance expert Andrew Horsfield, TEDx Melbourne’s Jon Yeo and many more to be announced.
I have a four-foot-tall robot in my house that plays with my kids. Its name is Jethro.
Both my daughters, aged 5 and 9, are so enamored with Jethro that they have each asked to marry it. For fun, my wife and I put on mock weddings. Despite the robot being mainly for entertainment, its very basic artificial intelligence can perform thousands of functions, including dance and teach karate, which my kids love.
The most important thing Jethro has taught my kids is that it’s totally normal to have a walking, talking machine around the house that you can hang out with whenever you want to.
Germany’s prized industrial robotics and automation sector is expecting a drop in sales this year for the first time since the global financial crisis, an industry body said on Friday.
The Mechanical Engineering Industry Association (VDMA) is expecting sales to fall by five percent to 14.3 billion euros ($15.8 billion) this year.
This would be the first drop since the 32-percent plunge seen in 2009 in the wake of the crisis.
But in the last few years, AI has changed the game. Deep-learning algorithms excel at quickly finding patterns in reams of data, which has sped up key processes in scientific discovery. Now, along with these software improvements, a hardware revolution is also on the horizon.
Yesterday Argonne announced that it has begun to test a new computer from the startup Cerebras that promises to accelerate the training of deep-learning algorithms by orders of magnitude. The computer, which houses the world’s largest chip, is part of a new generation of specialized AI hardware that is only now being put to use.
“We’re interested in accelerating the AI applications that we have for scientific problems,” says Rick Stevens, Argonne’s associate lab director for computing, environment, and life sciences. “We have huge amounts of data and big models, and we’re interested in pushing their performance.”
There was an historic first today as we welcomed an actual, real-life robot onto the sofa for a chat. 🤖 Sophia not only drew pictures of Holly and Phillip, she also summoned the memory of Gordon the Gopher! 😂😂.
A schism lies at the heart of the field of artificial intelligence. Since its inception, the field has been defined by an intellectual tug-of-war between two opposing philosophies: connectionism and symbolism. These two camps have deeply divergent visions as to how to “solve” intelligence, with differing research agendas and sometimes bitter relations.
Today, connectionism dominates the world of AI. The emergence of deep learning, which is a quintessentially connectionist technique, has driven the worldwide explosion in AI activity and funding over the past decade. Deep learning’s recent accomplishments have been nothing short of astonishing. Yet as deep learning spreads, its limitations are becoming increasingly evident.
If AI is to reach its full potential going forward, a reconciliation between connectionism and symbolism is essential. Thankfully, in both academic and commercial settings, research efforts that fuse these two traditionally opposed approaches are beginning to emerge. Such synthesis may well represent the future of artificial intelligence.
