Tag Archives: taught
#431392 What AI Can Now Do Is Remarkable—But ...
Major websites all over the world use a system called CAPTCHA to verify that someone is indeed a human and not a bot when entering data or signing into an account. CAPTCHA stands for the “Completely Automated Public Turing test to tell Computers and Humans Apart.” The squiggly letters and numbers, often posted against photographs or textured backgrounds, have been a good way to foil hackers. They are annoying but effective.
The days of CAPTCHA as a viable line of defense may, however, be numbered.
Researchers at Vicarious, a Californian artificial intelligence firm funded by Amazon founder Jeff Bezos and Facebook’s Mark Zuckerberg, have just published a paper documenting how they were able to defeat CAPTCHA using new artificial intelligence techniques. Whereas today’s most advanced artificial intelligence (AI) technologies use neural networks that require massive amounts of data to learn from, sometimes millions of examples, the researchers said their system needed just five training steps to crack Google’s reCAPTCHA technology. With this, they achieved a 67 percent success rate per character—reasonably close to the human accuracy rate of 87 percent. In answering PayPal and Yahoo CAPTCHAs, the system achieved an accuracy rate of greater than 50 percent.
The CAPTCHA breakthrough came hard on the heels of another major milestone from Google’s DeepMind team, the people who built the world’s best Go-playing system. DeepMind built a new artificial-intelligence system called AlphaGo Zero that taught itself to play the game at a world-beating level with minimal training data, mainly using trial and error—in a fashion similar to how humans learn.
Both playing Go and deciphering CAPTCHAs are clear examples of what we call narrow AI, which is different from artificial general intelligence (AGI)—the stuff of science fiction. Remember R2-D2 of Star Wars, Ava from Ex Machina, and Samantha from Her? They could do many things and learned everything they needed on their own.
Narrow AI technologies are systems that can only perform one specific type of task. For example, if you asked AlphaGo Zero to learn to play Monopoly, it could not, even though that is a far less sophisticated game than Go. If you asked the CAPTCHA cracker to learn to understand a spoken phrase, it would not even know where to start.
To date, though, even narrow AI has been difficult to build and perfect. To perform very elementary tasks such as determining whether an image is of a cat or a dog, the system requires the development of a model that details exactly what is being analyzed and massive amounts of data with labeled examples of both. The examples are used to train the AI systems, which are modeled on the neural networks in the brain, in which the connections between layers of neurons are adjusted based on what is observed. To put it simply, you tell an AI system exactly what to learn, and the more data you give it, the more accurate it becomes.
The methods that Vicarious and Google used were different; they allowed the systems to learn on their own, albeit in a narrow field. By making their own assumptions about what the training model should be and trying different permutations until they got the right results, they were able to teach themselves how to read the letters in a CAPTCHA or to play a game.
This blurs the line between narrow AI and AGI and has broader implications in robotics and virtually any other field in which machine learning in complex environments may be relevant.
Beyond visual recognition, the Vicarious breakthrough and AlphaGo Zero success are encouraging scientists to think about how AIs can learn to do things from scratch. And this brings us one step closer to coexisting with classes of AIs and robots that can learn to perform new tasks that are slight variants on their previous tasks—and ultimately the AGI of science fiction.
So R2-D2 may be here sooner than we expected.
This article was originally published by The Washington Post. Read the original article here.
Image Credit: Zapp2Photo / Shutterstock.com Continue reading
#430867 Amazon’s robots: Job destroyers or ...
Every day is graduation day at Amazon Robotics. Here's where the more than 100,000 orange robots that glide along the floors of various Amazon warehouses are made and taught their first steps. Continue reading
#430652 The Jobs AI Will Take Over First
11th July 2017: The robotic revolution is set to cause the biggest transformation in the world’s workforce since the industrial revolution. In fact, research suggests that over 30% of jobs in Britain are under threat from breakthroughs in artificial intelligence (AI) technology.
With pioneering advances in technology many jobs that weren’t considered ripe for automation suddenly are. RS Components have used PWC Data to reveal how many jobs per sector are at risk of being taken by robots by 2030, a mere 13 years away. Did you think you were exempt from the robot revolution?
The top three sectors who are most exposed to the threats of robots are Transport and Storage, Manufacturing and Wholesale and Retail with 56%, 46% and 44% risk of automation respectively. The PWC report states that the differentiating factor between losing jobs to automation probability is education; those with a GCSE-level education or lower face a 46% risk, whilst those with undergraduate degrees or higher face a 12% risk. If a job is repetitive, physical and requires minimum effort to train for, this will have a higher likelihood to become automated by machines.
The manufacturing industry has the 3rd highest likelihood potential at 46.6%, shortly behind Transportation and Storage (56.4%) and Water, Sewage and Waste Management (62.6%). Although the manufacturing sector has the 3rd highest likelihood, it has the second largest number of jobs at risk of being taken by robots; an astonishing 1.22 million jobs are at risk in the near future. Repetitive manual labour and routine tasks can be taught to fixed machines and mimicked easily, saving employers both time and money.
The three sectors least at risk are Education, Health and Social and Agriculture, Forestry and Fishing with 9%, 17% and 19% risk of automation respectively. These operations are non-repetitive and consist of characteristics that cannot be taught and are harder to replicate with AI and robotics.
These are not the only fields where the introduction of AI will have an impact on employment prospects; Administrative and Support Services, Accommodation and Food Services, Finance and Insurance, Construction, Real Estate, Public Administration and Defence, and Arts and Entertainment are not out of the woods either.
The future is not all doom and gloom. Automation is set to boost productivity to enable workers to focus on higher value, more rewarding jobs; leaving repetitive and uncomplicated ones to the robots. An increase in sectors that are less easy to automate is also expected due to lower running costs. Wealth and spending will also be boosted by the initiation of AI seizing work. Also, there are just some things AI cannot learn so these jobs will be safe.
In some sectors half of the jobs could be taken by a fully automated system. Is your job next?
The post The Jobs AI Will Take Over First appeared first on Roboticmagazine. Continue reading