https://youtube.com/watch?v=YwPUcG3eiFc
TechCrunch: “You’ll soon be coming home and a robot will greet you at the door.”
VentureBeat: “Read to kids, host video chats, take pictures, recognize faces and objects, connect to smart home devices, and secure the home by roaming around and video taping everything.”
How deepmind’s memory trick helps AI learn faster.
While AI systems can match many human capabilities, they take 10 times longer to learn. Now, by copying the way the brain works, Google DeepMind has built a machine that is closing the gap. “Our experiments show that neural episodic control requires an order of magnitude fewer interactions with the environment,” they say.
Intelligent machines have humans in their sights. Deep-learning machines already have superhuman skills when it comes to tasks such as face recognition, video-game playing, and even the ancient Chinese game of Go. So it’s easy to think that humans are already outgunned.
Historian Yuval Noah Harari makes a bracing prediction: just as mass industrialization created the working class, the AI revolution will create a new unworking class.
The most important question in 21st-century economics may well be: What should we do with all the superfluous people, once we have highly intelligent non-conscious algorithms that can do almost everything better than humans?
This is not an entirely new question. People have long feared that mechanization might cause mass unemployment. This never happened, because as old professions became obsolete, new professions evolved, and there was always something humans could do better than machines. Yet this is not a law of nature, and nothing guarantees it will continue to be like that in the future. The idea that humans will always have a unique ability beyond the reach of non-conscious algorithms is just wishful thinking. The current scientific answer to this pipe dream can be summarized in three simple principles:
This means we need new ways to grow food that are smarter and helps regulate our use of land, water and energy in order to feed the planet and avoid a global food crisis.
Researchers at Carnegie Mellon University’s Robotics Institute believe the answer lies in sensors, artificial intelligence (AI) and robots.
In a new initiative called FarmView, researchers are working to combine sensors, robotics and artificial intelligence to create a fleet of mobile field robots they hope will improve plant breeding and crop-management practices.
Continue reading “How Sensors, Robotics And Artificial Intelligence Will Transform Agriculture” »
As I’m sure many in the technology industry have thought today, there should have been a way to avoid the Oscars Envelopegate. But, is artificial intelligence the answer to all of our human error problems? A recent Accenture report found that the introduction and further development of AI could boost labor productivity by 40% by 2035. It seems as if banks have already picked up on this, as was seen last year with RBS’ replacement of human employees with automated services. News announced this week also suggests that artificial intelligence will become a central part of anything a technology organisation will do in the future. Will we see the same in the financial technology sector?
The relationship between man and machine is expected to be the naissance of a type of work that could potentially double annual economic growth, according to Accenture. Chief technology officer Paul Daugherty highlighted that “AI is poised to transform business in ways we’ve not seen since the impact of computer technology in the late 20th century.” He went on to explain in the report that artificial intelligence, with the help of cloud computing and analytics, is already starting to change the way that people work.
The weekend saw the UK government announce that they are planning to launch a review into the value of robotics in the country’s aim to become world technology leader. £17.3 million would be invested into university research of AI technologies such as Apple’s Siri, Amazon’s Alexa and driverless cars, as reported by The Independent. The article also drew from the Accenture report and said that artificial intelligence could add around £654 billion to the UK economy.
Continue reading “Artificial Intelligence: Removing The Human From Fintech” »
From AlphaGo’s historic victory against world champion Lee Sedol to DeepStack’s sweeping win against professional poker players, artificial intelligence is clearly on a roll.
Part of the momentum comes from breakthroughs in artificial neural networks, which loosely mimic the multi-layer structure of the human brain. But that’s where the similarity ends. While the brain can hum along on energy only enough to power a light bulb, AlphaGo’s neural network runs on a whopping 1,920 CPUs and 280 GPUs, with a total power consumption of roughly one million watts—50,000 times more than its biological counterpart.
Extrapolate those numbers, and it’s easy to see that artificial neural networks have a serious problem—even if scientists design powerfully intelligent machines, they may demand too much energy to be practical for everyday use.
Continue reading “New Artificial Synapse Bridges the Gap to Brain-Like Computers” »
Most chat bots are dumb. No one wants to message a soulless stack of if-then statements just to order a pizza when a half-decent app or website interface can do the same job in half the time.
Chat assistants are a different matter. Rather than actively bugging you for information in a back-and-forth no one enjoys having, chat assistants lurk in the background of the conversations you’re already having and glean little details that might help later. It’s the approach Google is taking with their aptly named Assistant.
Niles, a company in Y Combinator’s Winter 2017 batch, wants to be your company’s chat assistant — an alternative to that internal wiki that every company has and no one uses. It sits in Slack and tries to learn the answers to the questions that your team is tired of hearing for the billionth time.
It’s not practical to test drive autonomous vehicles enough to prove that they are safe, says RAND’s Nidhi Kalra. What barriers must be overcome to get self-driving cars safely on the streets? r.rand.org/3umk
