Trained neural nets perform much like humans on classic psychological tests

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Neural networks were inspired by the human brain. Now AI researchers have shown that they perceive the world in similar ways.

In the early part of the 20th century, a group of German experimental psychologists began to question how the brain acquires meaningful perceptions of a world that is otherwise chaotic and unpredictable. To answer this question, they developed the notion of the “gestalt effect”—the idea that when it comes to perception, the whole is something other than the parts.

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The new weapon in the fight against crime

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Solving a murder or tracking down the perpetrators of sexual abuse often requires dogged police work. What if a machine could help detectives spot the vital clues they need?

The images on Eduardo Fidalgo’s computer show mundane scenes – a sofa scattered with pillows, a folded duvet on a bed, some children’s toys strewn across a floor. They depict views most of us would see around our own homes.

But these rather ordinary pictures are helping to build a new weapon in the fight against crime. Fidalgo and his colleagues are using the images to train a machine to spot clues in crime scene photographs.

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Researchers develop a soft robotic finger with self-perception

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Soft robotics is a rapidly growing field that has a huge amount of potential in applications where traditional rigid robots would be unsafe or unwieldy. But, building a soft robot comes with a number of unique challenges, particularly when it comes to actuation and position sensing. Fortunately, a newly-developed soft robotic finger with its own sense of self-perception may dramatically improve the situation.

This work comes from a team of researchers at the Bioinspired Robotics and Design Lab at the University of California San Diego and others around the globe. It’s intended to give soft robots the kind of positional sensing that is innately practical in rigid robots. Because a traditional robot’s frame is inflexible, it’s relatively simple to determine it’s exact position — you only need to measure the angle at each joint. But, due to their inherent flexibility, that’s not so easy with soft robots.

The solution that the researchers came up with was to use a neural network and machine learning to identify correlations between the readings from a motion capture system and flex sensors within the soft robotic finger. The flex sensors were placed somewhat arbitrarily, which would normally be extremely difficult to process through explicit programming. But, by using the neural network, the system is able to match those sensor readings to what it sees in the motion capture system.

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The big deal about an AI model that can talk like you

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Meryl Streep is pitch perfect as the narrator of the Norah Ephron novel Heartburn. In the audiobook version, Streep’s classic delivery brings alive the emotional turmoil as well as the self-deprecating wit of Rachel Samstat, who has just found out about her husband’s affair. In the Harry Potter audiobooks, it’s singer-actor Jim Dale who creates the magic.

Now let’s say you are discomforted by American and British accents. You prefer to hear Heartburn and the Potter books in voices you can relate with. You want to switch the genders of the narrators. You want the Muggles speaking in the voice of your favourite Bollywood actor. You want to be the narrator.

Those are real options a Bengaluru startup expects to offer as it develops an artificial intelligence model for cloning voices. It reckons there is massive business opportunity in impersonating voices, and not just from the growing popularity of audiobooks. Think voiceovers for ads, narrations for education-technology platforms, real-time translations, automated responses, voice assistants, smart speakers.

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A neural network can learn to organize the world it sees into concepts—just like we do

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Generative adversarial networks are not just good for causing mischief. They can also show us how AI algorithms “think.”

GANs, or generative adversarial networks, are the social-media starlet of AI algorithms. They are responsible for creating the first AI painting ever sold at an art auction and for superimposing celebrity faces on the bodies of porn stars. They work by pitting two neural networks against each other to create realistic outputs based on what they are fed. Feed one lots of dog photos, and it can create completely new dogs; feed it lots of faces, and it can create new faces.

As good as they are at causing mischief, researchers from the MIT-IBM Watson AI Lab realized GANs are also a powerful tool: because they paint what they’re “thinking,” they could give humans insight into how neural networks learn and reason. This has been something the broader research community has sought for a long time—and it’s become more important with our increasing reliance on algorithms.

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This clever AI hid data from its creators to cheat at its appointed task

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Depending on how paranoid you are, this research from Stanford and Google will be either terrifying or fascinating. A machine learning agent intended to transform aerial images into street maps and back was found to be cheating by hiding information it would need later in “a nearly imperceptible, high-frequency signal.” Clever girl!

But in fact this occurrence, far from illustrating some kind of malign intelligence inherent to AI, simply reveals a problem with computers that has existed since they were invented: they do exactly what you tell them to do.

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AI competition is the new space race

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The technology still has a long way to go despite progress in 2018. The EU, U.S. and China are all determined not to be left behind.

It’s been another year of relentless artificial-intelligence hype and incremental AI achievement. Machines still beat humans only in carefully constructed environments or at narrow tasks. The good news is that, as the technology progresses, the race for leadership is still wide open, and even Europe, where politicians fret that the continent is lagging behind China and the U.S., is still quite competitive.

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Google created AI that just needs a few snapshots to make 3D models of its surroundings

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The algorithm only needs a couple perspectives to figure out what objects look like.

Google’s new type of artificial intelligence algorithm can figure out what things look like from all angles — without needing to see them.

After viewing something from just a few different perspectives, the Generative Query Network was able to piece together an object’s appearance, even as it would appear from angles not analyzed by the algorithm, according to research published today in Science. And it did so without any human supervision or training. That could save a lot of time as engineers prepare increasingly advanced algorithms for technology, but it could also extend the abilities of machine learning to give robots (military or otherwise) greater awareness of their surroundings.

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How fast is artificial intelligence growing? Look at the key bellwethers

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AI & Big Data

One can intuitively surmise artificial intelligence (AI) is today’s hot commodity, gaining traction in businesses, academia and government in recent years. Now, there is data — all in one place — that documents growth across many indicators, including startups, venture capital, job openings and academic programs. These bellwethers were captured in the AI Index, produced under the auspices of was conceived within Stanford University’s Human-Centered AI Institute and the One Hundred Year Study on AI (AI100).

One key measure of AI development is startups and venture capital funding. From January 2015 to January 2018, active AI startups increased 2.1x, while all active startups increased 1.3x, the report states. “For the most part, growth in all active startups has remained relatively steady, while the number of AI startups has seen exponential growth,” the report’s authors add. The trickle of venture capital into AI startups, another bellwether, also turned into a torrent. VC funding for AI startups in the US increased 4.5x from 2013 to 2017. Meanwhile, VC funding for all active startups increased 2.08x.

Another key measure, job openings, accelerated in AI. While machine learning is the largest skill cited as a requirement, deep learning is growing at the fastest rate — from 2015 to 2017 the number of job openings requiring deep learning increased 35x, the report’s authors state.

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These people are not real – they were created by AI

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Machine learning algorithms are getting scary-good at creating fake images that look real.

Computers are getting better at generating fake images and video of people saying or doing things they never did in real life. The latest work from chip maker Nvidia takes this a step further by generating convincing-looking images of people who never existed in the first place—they’re AI creations, but they look incredibly real.

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The AI boom is happening all over the world, and it’s accelerating quickly

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The second annual AI Index report pulls together data and expert findings on the field’s progress and acceleration.

The rate of progress in the field of artificial intelligence is one of the most hotly contested aspects of the ongoing boom in teaching computers and robots how to see the world, make sense of it, and eventually perform complex tasks both in the physical realm and the virtual one. And just how fast the industry is moving, and to what end, is typically measured not just by actual product advancements and research milestones, but also by the prognostications and voiced concerns of AI leaders, futurists, academics, economists, and policymakers. AI is going to change the world — but how and when are still open questions.

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In a major breakthrough, Google unveils an AI that learns on its own

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Surpassing The Masters

We’ve written before about how Google is one of the most prominent tech companies leading the way when it comes to the development of artificial intelligence. As each month passes, its AI division, DeepMind, continues to reveal increasingly advanced AI capabilities, especially when it comes to AlphaGo.

This particular AI is most well-known for mastering the ancient Chinese game of Go…and subsequently defeating 18-time world champion Lee Se-dol, which happened just last year.

Since then, DeepMind has started adding imagination to its AI, and they also used gaming to teach the AI how to better manage tasks. AlphaGo even went on to defeat another top go player, Ke Jie, once again showing off its (potentially) unlimited potential to learn.

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