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Are AI Clusters a key to overcoming the UK productivity gap?

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Are AI Clusters a key to overcoming the UK productivity gap?

The UK has a well-documented decades-long 20% labour productivity gap with the US, Germany and France.  With Brexit potentially creating extra challenges in overcoming this competitive disadvantage, the UK could fall even further behind.  But if you are an optimist it could also be a moment for the UK to take a giant leap forward as it has done in the past when confronted with no other choice.

Britain has been very good at doing this recently but it could be even better and Brexit could galvanise a new era of even more productive collaboration between public institutions and private business.   There are many sectors of the UK economy that have bright sparks of world class performance but McKinsey Global Institute Productivity research shows very clearly they are held back by vexing bottlenecks.  We believe this can be overcome. We decided to begin working to advance some already very strong work on creating industry clusters.  We could have chosen any one of many industry sectors to provide a foundation for our thinking but as it happens we already have a cluster of knowledge, connections and momentum in the AI space, we are taking a stab at working on how AI could be a backbone technology to drive more rapid development of many high performing industry clusters in the UK and indeed a high performing cluster of AI suppliers in the UK.

We believe the rise of Artificial Intelligence could be a springboard for rapid productivity growth in two ways:  AI is a functional industry unto itself and AI is also an increasingly fundamental element of any high performing industry from high tech to Entertainment and even Art.   A strong pure AI cluster is already developing in London spurred on by an imperative to develop tools for Fintech, Drug Discovery and many other applications.  The basic functionality of these tools is transferrable to other industries and the reverse is true also: AI applications designed for specific situations can be generalised to provide the backbone for applications in other industry sectors. We think this is a model the UK should support with a big coordinated push from the public and private sectors.

However, it should not be done indiscriminately and it should be done in clusters because spread too thinly the net effect will be minimal and there has to be a well thought through drive to support strong clusters that can raise the game to world class levels and then float all other boats in its wake.  Why else are clusters important?  If you measure the productivity of two businesses running exactly the same processes but one is in close proximity to other related businesses and another is isolated, chances are the first one is going to produce more than the second one and scale up faster.  It is well documented that clusters co-located with leading research universities enable iterative development of ideas and an easy flow of people from academe into local industry. Furthermore, research by Sherry Coutu shows scaled up businesses are much more productive in spawning new startups.  Clusters create powerful virtuous feedback and AI can act as a turbocharger across many sectors.

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Artificial Intelligence

China plans new era of sea power with unmanned AI submarines

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China is planning to upgrade its naval power with unmanned AI submarines that aim to provide an edge over the fleets of their global counterparts.

A report by the South China Post on Sunday revealed Beijing’s plans to build the automated subs by the early 2020s in response to unmanned weapons being developed in the US.

The subs will be able to patrol areas in the South China Sea and Pacific Ocean that are home to disputed military bases.

While the expected cost of the submarines has not been disclosed, they’re likely to be cheaper than conventional submarines as they do not require life-supporting apparatus for humans. However, without a human crew, they’ll also need to be resilient enough to be at sea without onboard repairs possible.

The XLUUVs (Extra-Large Unmanned Underwater Vehicles) are much bigger than current underwater vehicles, will be able to dock as any other conventional submarine, and will carry a large amount of weaponry and equipment.

As a last resort, they could be used in automated ‘suicide’ attacks that scuttle the vessel but causes damage to an enemy’s ship that may or not be manned.

“The AI has no soul. It is perfect for this kind of job,” said Lin Yang, Chief Scientist on the project. “[An AI sub] can be instructed to take down a nuclear-powered submarine or other high-value targets. It can even perform a kamikaze strike.”

The AI element of the submarines will need to carry out many tasks including navigating often unpredictable waters, following patrol routes, identifying friendly or hostile ships, and making appropriate decisions.

It’s the decision-making that will cause the most concern as the AI is being designed not to seek input during the course of a mission.

The international norm being promoted by AI researchers is that any weaponised AI system will require human input to ultimately make a decision. Any news that China is following a policy of creating weaponised AIs that do not require human input should be of global concern.

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Artificial Intelligence

AI robots will solve underwater infrastructure damage checks

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Robots will be paired with a versatile AI that can quickly adapt to unpredictable conditions when examining underwater infrastructure.

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.

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Artificial Intelligence

Technology Trends That Will Dominate 2018

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  1. AI permeation. Artificial intelligence (AI), largely manifesting through machine learning algorithms, isn’t just getting better. It isn’t just getting more funding. It’s being incorporated into a more diverse range of applications. Rather than focusing on one goal, like mastering a game or communicating with humans, AI is starting to make an appearance in almost every new platform, app, or device, and that trend is only going to accelerate in 2018. We’re not at techno-pocalypse levels (and AI may never be sophisticated enough for us to reach that point), but by the end of 2018, AI will become even more of a mainstay in all forms of technology.

  2. Digital centralization. Over the past decade, we’ve seen the debut of many different types of devices, including smartphones, tablets, smart TVs, and dozens of other “smart” appliances. We’ve also come to rely on lots of individual apps in our daily lives, including those for navigation to even changing the temperature of our house. Consumers are craving centralization; a convenient way to manage everything from as few devices and central locations as possible. Smart speakers are a good step in the right direction, but 2018 may influence the rise of something even better.

  3. 5G preparation. Though tech timelines rarely play out the way we think, it’s possible that we could have a 5G network in place—with 5G phones—by the end of 2019. 5G internet has the potential to be almost 10 times faster than 4G, making it even better than most home internet services. Accordingly, it has the potential to revolutionize how consumers use internet and how developers think about apps and streaming content. 2018, then, is going to be a year of massive preparation for engineers, developers, and consumers, as they gear up for a new generation of internet.

  4. Data overload. By now, every company in the world has realized the awesome power and commoditization of consumer data, and in 2018, data collection is going to become an even higher priority. With consumers talking to smart speakers throughout their day, and relying on digital devices for most of their daily tasks, companies will soon have access to—and start using—practically unlimited amounts of personal data. This has many implications, including reduced privacy, more personalized ads, and possibly more positive outcomes, such as better predictive algorithms in healthcare.

 

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