Pepper will be attempting to explain topics such as AI and robotics to The Commons Education Select Committee.

“If we’ve got the march of the robots, we perhaps need the march of the robots to our select committee to give evidence,” Committee chair Robert Halfon told Tes.

“The fourth industrial revolution is possibly the most important challenge facing our nation over the next 10, 20, to 30 years.”

AI and robotics will drastically change our societies, and not always for the better. There will be serious challenges ahead.

It’s rare to hear of AI being discussed without the potential impact on jobs. Low-skilled workers, in particular, are most threatened by automation replacement.

The Select Committee will be looking to understand what impacts the fourth industrial revolution will have and how the negatives can be reduced. They’re hoping Pepper can help, though it all feels somewhat a gimmick.

“This is not about someone bringing an electronic toy robot and doing a demonstration,” said Mr Halfon. “It’s about showing the potential of robotics and artificial intelligence and the impact it has on skills.”

Pepper is equipped with four microphones, two HD cameras, and a touchscreen on its chest for displaying information when needed.

We caught up with Pepper at MWC earlier this year:

Fortunately, Pepper is experienced with public speaking after featuring on stages around the world. The robot could perhaps even offer advice to some leading tech company CEOs on coming across more human.

 Interested in hearing industry leaders discuss subjects like this? Attend the co-located AI & , and >

(Image Credit: Pepper at Opening Ceremony of the 28th Tokyo International Film Festival by Dick Thomas Johnson under CC BY 2.0)

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The so-called ‘Defense Advanced Research Projects Agency’ researches a range of innovative new technologies. Many of these advancements will have an impact beyond defence.

As such, DARPA’s funding initiative will be open to applications beyond the defence community. That means any AI project you’re involved with could be in the running to receive a nice cash boost.

DARPA is calling its campaign ‘AI Next’ and applications are now open. Previous similar initiatives from the agency have been wildly successful.

Back in 2004, DARPA held ‘Grand Challenges’ with a $2 million first-place prize. Challengers had to race autonomous vehicles.

In the first year’s race, there were no winners. Just a year later, five vehicles crossed the finishing line. The initiative was lauded for creating a huge leap for driverless cars.

Fast-forward three years and those vehicles were successfully navigating traffic as part of the ‘Urban Challenge’ by DARPA.

In fact, even Siri (yes, the one which may be on your iPhone right now) was first created in part from DARPA funding. We’re talking before it was acquired by Apple, back when it was developed by SRI in Menlo Park.

DARPA is looking to make a similar leap in artificial intelligence with its AI Next initiative. The agency is focusing on the ‘third wave’ of AI which adds “contextual reasoning” (otherwise known as common sense to most of us).

That means future AIs will be able to analyse scenarios they’ve not been specifically trained for, much like humans.

Beyond simply being able to recognise there’s a person stood on the beach with a bottle of water, for example, it could notice things such as sand on the knees and equipment lying around which suggests the individual is hydrating after a game of volleyball.

This category in DARPA’s initiative is aptly called ‘Machine Common Sense’. Others include ‘Learning with Less Labels’ which aims to reduce the cost of labelling data, and ‘Lifelong Learning Machines’ which focuses on machines’ ability to transfer what’s been learnt from one environment into another.

You can apply for the funding here if your project is suitable.

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Some of a nation’s most vital infrastructure hides beneath the water. The difficulty in accessing most of it, however, makes important damage checks infrequent.

Sending humans down requires significant training and can take several weeks to recover due to the often extreme depths. There are far more underwater structures than skilled divers to inspect them.

Robots have been designed to carry out some of these dangerous tasks. The problem is until now they’ve lacked the smarts to deal with the unpredictable and rapidly-changing nature of underwater conditions.

Researchers from Stevens Institute of Technology are working on algorithms which enable these underwater robots to check and protect infrastructure.

Their work is led by Brendan Englot, Professor of Mechanical Engineering at Stevens.

“There are so many difficult disturbances pushing the robot around, and there is often very poor visibility, making it hard to give a vehicle underwater the same situational awareness that a person would have just walking around on the ground or being up in the air,” says Englot.

Englot and his team are using reinforcement learning for training algorithms. Rather than use an exact mathematical model, the robot performs actions and observes whether it helps to attain its goal.

Through a case of trial-and-error, the algorithm is updated with the collected data to figure out the best ways to deal with changing underwater conditions. This will enable the robot to successfully manoeuvre and navigate even in previously unmapped areas.

A robot was recently sent on a mission to map a pier in Manhattan.

“We didn’t have a prior model of that pier,” says Englot. “We were able to just send our robot down and it was able to come back and successfully locate itself throughout the whole mission.”

The robots use sonar for data, widely regarded as the most reliable for undersea navigation. It works similar to a dolphin’s echolocation by measuring how long it takes for high-frequency chirps to bounce off nearby structures.

A pitfall with this approach is you’re only going to be able to receive imagery similar to a grayscale medical ultrasound. Englot and his team believe that once a structure has been mapped out, a second pass by the robot could use a camera for a high-resolution image of critical areas.

For now, it’s early days but Englot’s project is an example of how AI is enabling a new era for robotics that improves efficiency while reducing the risks to humans.

What are your thoughts on the use of AI-powered robots for underwater checks?

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