- Ask an economist or a technology expert and they will happily tell you that decades of data reliably show automation has created more jobs than it has destroyed.
- The problem with this rose-tinted view of automation, however, is its focus on big averages that take little account of individuals’ experiences.
- As tempting as it may be to pour money into boosting automation in return for the long-awaited boost to productivity and headline economic growth, doing so without having a clear plan for retraining displaced workers would cause untold harm to millions of individuals.
- As the Institute for Public Policy Research points out , some workers are far more vulnerable than others to automation.
- If the government fails to act, the result could all too easily be a spike in unemployment and poverty in places with the lowest skilled workers – a very high price to pay for a bit of average productivity growth.
Investment in education and retraining is needed to equip people to adapt as automation shakes up their workplaces
Continue reading “Beware the unintended consequences of a robot revolution”
- Google’s neural network, software which uses statistics to approximate how the brain learns, taught itself to detect the shapes of cats and humans with more than 70% accuracy.
- “Over the last five years it’s been pretty incredible, the progress of deep [neural] nets,” says Grant Van Horn, lead competition organizer and graduate student at California Institute of Technology.
- Van Horn says this latest Google competition differs from ImageNet, which forces algorithms to identify a wide variety of objects like cars and houses and boats, because iNat requires AI to examine the “nitty-gritty details” that separate one species from another.
- On a scale from general image recognition (ImageNet) to specific (facial recognition,where most faces generally look the same and only slight variations matter), iNat lies somewhere in the middle, Van Horn says.
- Van Horn, who has specialized in building AI that distinguishes differences between birds, said that the iNat competition illustrates how AI is beginning to help people learn about the world around them, rather than just help them organize their photos, for instance.
In 2012, Google made a breakthrough: It trained its AI to recognize cats in YouTube videos. Google’s neural network, software which uses statistics to approximate how the brain learns, taught itself to detect the shapes of cats and humans with more than 70% accuracy. It was a 70% improvement over any other machine learning at the time. Five years later,…
Continue reading “Five years ago, artificial intelligence was struggling to identify cats. Now it’s trying to tackle 5000 species — Quartz”
- Google has been working on a wide range of AI-based projects lately – earlier this week, it showed off one that can identify what you’re trying to draw and surface clean clipart that resembles your doodle.
- Its latest experiment is called Sketch-RNN, and it’s a neural network system that has learned to draw on its own by looking at roughly 5.5 million sketches from people who played Pictionary with Google’s AI-powered Quick, Draw!
- By triaging sketches in 75 different categories like cats, pigs and trucks, the AI can now draw basic representations of these things when presented with hand-drawn sketches.
- Sketch-RNN can also draw without the help of a starting sketch, and can even complete sketches that a human has started, but not finished.
- Sure, the sketches aren’t exactly photorealistic, but the idea here is to ‘train a machine to draw and generalize abstract concepts in a manner similar to humans’, and Google has achieved that.
Google’s latest experiment is a neural network system that has learned to draw by looking at roughly 5.5 million sketches from people who played pictionary.
Continue reading “Google used your pictionary sketches to teach its AI to draw”
- Facebook Messenger users across the US are now being prompted to send and request money transfers by an artificial intelligence-based feature that detects when a payment is being discussed in a conversation on the social media platform and responds with a suggestion designed to help the user complete that payment.
- “M offers suggestions by popping into an open conversation to suggest relevant content and capabilities to enrich the way people communicate and get things done,” the social media giant says.
- M may make a suggestion in a conversation relevant to one of the core actions, and then the M logo and suggestion will appear — it’s that simple.”
- Facebook began testing payments through its Messenger service in July 2016.
- The social media giant also updated its Messenger chatbot platform to enable bots to accept payments without having to send shoppers to external sites to complete the checkout process in September 2016.
Facebook Messenger users across the US are now being prompted to send and request money transfers by an artificial intelligence-based feature.
Continue reading “Facebook Messenger adds payment prompts using artificial intelligence • NFC World”
- The prospect that artificial intelligence (AI) might one day surpass human intelligence is one that many people, including a number of notable personalities, are terrified of.
- As it is, deep learning machines have already shown a number of ways where they outperform humans.
- There’s one area, though, where humans are still superior, and that’s the speed at which we learn.
- Right now, humans learn at a rate that’s 10 times faster than that of a deep learning machine.
- And it is this ‘superiority’ that has kept that ‘AI taking over humans’ apocalyptic view in the background.
Google Develops A Deep Learning Machine That Could Learn As Fast As Humans
Continue reading “Google Develops A Deep Learning Machine That Could Learn As Fast As Humans”
- One of the theoretical advantages of software, artificial intelligence, algorithms, and robots is that they don’t suffer many human foibles.
- The reality, of course, is different.
- One of the problems is that, in many instances, the engineers who designed the superefficient code didn’t fully think through the impact on humans, the full possibilities of what humans could do with it, or of the capacity of their products to inflict harm or offense.
- With robots and machines becoming more integrated into the human experience, it is all the more urgent for engineers to become familiar with the works of John Donne, John Locke, and Jean-Paul Sartre.
- If we are going to empower machines, algorithms, and software to do more of the work that humans used to perform, we have to imbue them with some of the empathy and limitations that people have.
Robots are becoming chefs, and software is our new chauffeur, but their capacity for empathy leaves something to be desired.
Continue reading “Why Artificial Intelligence Needs Some Emotional Intelligence”
- When Unanimous AI developed UNU in 2015, the goal was to create artificial intelligence (AI) systems that “keep people in the loop,” amplifying human intelligence instead of replacing it.
- Unanimous AI’s March Madness bracket was able to beat all but three percent of ESPN brackets across the country on the first day of the tournament.
- The technology makes use of the collective intelligence of people — combining “knowledge, insights, and intuitions,” as Unanimous AI puts it — to develop a kind of artificial intelligence that’s inherently human.
- As Unanimous AI explains, “We empower people to act as ‘data processors’ that come together online and form an intelligent system, connected by AI algorithms.
- One day, perhaps we’ll be able to combine the intelligence of Watson with that of a swarm of medical professionals to improve healthcare, or combine the insights of an investment-making AI with a swarm of finance experts.
The collective intelligence is killing it on ESPN.
Continue reading “March Madness: A Swarm Intelligence Is Predicting the Future”