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- “We are spurred on by the impossible.”
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When Vu Van began her MBA studies at Stanford University, she realized that her pronunciation of English words was holding her back. Her solution? Develop a mobile app that uses AI to help people improve their pronunciation.
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- Artificial intelligence isn’t just about replacing humans with computers; the best managers will find ways to use AI to augment their workers.
- Tim O’Reilly: How smart managers should use artificial intelligence
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- In the video, a compilation of clips from the O’Reilly Next:Economy Summit 2015, Tim O’Reilly talks with AI leaders who see an essential role for humans in the AI-enabled world.
Artificial intelligence isn’t just about replacing humans with computers; the best managers will find ways to use AI to augment their workers. In this video, a compilation of clips from the O’Reilly Next:Economy Summit 2015, Tim O’Reilly talks with AI leaders who see an essential role for humans in the AI-enabled world.
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- StatusToday is an Employee Insights Platform to ensure security, engagement and productivity, through patent-pending AI that understands human behavior.
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The AI-driven solution has been proven to streamline risk management and supplement leadership in business settings. Our technology offers…
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- Turing Learning would simultaneously learn how to interrogate and how to paint.”
- Dr Gross explained the advantage of the approach, called ‘Turing
- The learning robots that succeed in fooling an interrogator – making it believe their motion data were genuine – receive a reward.”
- Learning’, is that humans no longer need to tell machines what to look for. “
It is now possible for machines to learn how natural or artificial systems work by simply observing them, without being told what to look for, according to researchers at the University of Sheffield.
This could mean advances in the world of technology with machines able to predict, among other things, human behaviour.
The discovery takes inspiration from the work of pioneering computer scientist Alan Turing, who proposed a test, which a machine could pass if it behaved indistinguishably from a human. In this test, an interrogator exchanges messages with two players in a different room: one human, the other a machine.
The interrogator has to find out which of the two players is human. If they consistently fail to do so – meaning that they are no more successful than if they had chosen one player at random – the machine has passed the test, and is considered to have human-level intelligence.
Dr Roderich Gross from the Department of Automatic Control and Systems Engineering at the University of Sheffield, said: "Our study uses the Turing test to reveal how a given system – not necessarily a human – works. In our case, we put a swarm of robots under surveillance and wanted to find out which rules caused their movements. To do so, we put a second swarm – made of learning robots – under surveillance too. The movements of all the robots were recorded, and the motion data shown to interrogators."
He added: "Unlike in the original Turing test, however, our interrogators are not human but rather computer programs that learn by themselves. Their task is to distinguish between robots from either swarm. They are rewarded for correctly categorising the motion data from the original swarm as genuine, and those from the other swarm as counterfeit. The learning robots that succeed in fooling an interrogator – making it believe their motion data were genuine – receive a reward."
Dr Gross explained the advantage of the approach, called ‘Turing Learning’, is that humans no longer need to tell machines what to look for.
"Imagine you want a robot to paint like Picasso. Conventional machine learning algorithms would rate the robot’s paintings for how closely they resembled a Picasso. But someone would have to tell the algorithms what is considered similar to a Picasso to begin with. Turing Learning does not require such prior knowledge. It would simply reward the robot if it painted something that was considered genuine by the interrogators. Turing Learning would simultaneously learn how to interrogate and how to paint."
Dr Gross said he believed Turing Learning could lead to advances in science and technology.
"Scientists could use it to discover the rules governing natural or artificial systems, especially where behaviour cannot be easily characterised using similarity metrics," he said.
"Computer games, for example, could gain in realism as virtual players could observe and assume characteristic traits of their human counterparts. They would not simply copy the observed behaviour, but rather reveal what makes human players distinctive from the rest."
The discovery could also be used to create algorithms that detect abnormalities in behaviour. This could prove useful for the health monitoring of livestock and for the preventive maintenance of machines, cars and airplanes.
Turing Learning could also be used in security applications, such as for lie detection or online identity verification.
So far, Dr Gross and his team have tested Turing Learning in robot swarms but the next step is to reveal the workings of some animal collectives such as schools of fish or colonies of bees. This could lead to a better understanding of what factors influence the behaviour of these animals, and eventually inform policy for their protection.
Source: University of Sheffield
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Google DeepMind is launching a project to reduce the time it takes doctors to prepare treatment for head and neck cancers. Alphabet’s London-based artificial intelligence division has partnered… Continue reading “Google DeepMind wants to use machine learning to help treat certain cancers”
- Solution Architecture – focuses on solving specific business problems, and combines one or more applications built to deliver the complete solution.
- There are several benefits to implementing a Zeta Architecture in your organization
- We determined that we would have significantly more agility by implementing the Zeta Architecture with MapR, which will allow us to better serve customers in a more real-time and cost-effective way.”
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- Neural network settings
- Genetic algorithm settings
- Game settings
- By Tomasz Rewak
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Machine learning games. Use combination of genetic algorithms and neural networks to control the behaviour of in-game objects.
- Core to Drive.ai’ s approach is using deep learning across the board in its autonomous driving system, which means they’re teaching their self-driving cars somewhat like how you’d teach a human.
- Drive.ai uses deep learning to teach self-driving cars – and to give them a voice
- While increasing the safety of driving means building an effective ‘left brain’ for a self-driving car, so to speak, it also means addressing the ‘right brain,’ too.
- Uber and Volvo put $300M into building self-driving cars ready for sale by 2021
- Startup Drive.ai is revealing its product and strategy for the first time, and the autonomous driving tech company is looking not only to create the best hardware and software to enable self-driving cars, but also to make sure those cars communicate with people outside of the car in the most effective way possible.
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@TechCrunch: “Drive․ai uses deep learning to teach self-driving cars – and to give … by @etherington”
Startup Drive.ai is revealing its product and strategy for the first time, and the autonomous driving tech company is looking not only to create the best..
Drive.ai uses deep learning to teach self-driving cars – and to give them a voice