Speaking to investors and media at Volvo’s annual event for the capital-markets community, Volvo Group CEO Martin Lundstedt said:

“Partnership is the new leadership. If we are to succeed in the future with speed, quality, and safety – and to gain benefits of autonomous driving – we need to partner up with the best guys. In this world of unknowns, you need a partnership built on trust.”

Volvo is the world’s second-largest truckmaker after Daimler. The carmaker demonstrated its first cabin-less autonomous truck called Vera last year.

By applying AI to trucks, Volvo hopes to disrupt industries from public and freight transport, to forestry and construction.

Jensen Huang, NVIDIA founder and CEO, commented:

“Trucking is the world’s largest network – a network that through online shopping puts practically anything, anywhere in the world, quickly within our reach.

The latest breakthroughs in AI and robotics bring a new level of intelligence and automation to address the transportation challenges we face. We are thrilled to partner with Volvo Group to reinvent the future of trucking.”

The deal with Volvo is a particular boost for Nvidia and its AI technology for self-driving cars after Tesla ditched the firm last year. Tesla CEO Elon Musk said the company would focus on creating its own chips.

Nvidia debuted its ‘Xavier’ processors for the company’s DRIVE autonomous car platform last year. Xavier was in development for over four years, represents the work of over 2,000 engineers, features more than nine billion transistors, and Nvidia claims it’s the most complex system-on-a-chip (SoC) ever created

During a conference call in August last year, Huang said: “It’s super hard to build Xavier and all the software stack on top of it. If it doesn’t turn out for whatever reason for them [Tesla] you can give me a call and I’d be more than happy to help.”

Nvidia has made a name for itself as the maker of powerful GPUs, traditionally for gaming purposes. The company has been increasingly shifting gears into other computation-heavy areas like AI and machine learning.

Automotive chips accounted for $641 million of Nvidia’s $11.7 billion in revenue in its most recent fiscal year.

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Drive.ai is full of skilled personnel after being founded in 2016 by a pioneering team of graduates from Stanford’s AI lab. With AI talent in short supply, it seems Apple is considering purchasing a whole company to get the skills on its side.

Apple would not get any of Drive.ai’s intellectual property as part of the deal, only the minds behind it. While it’s unclear what Apple is considering paying for the startup, the company was believed to be valued at ~$200 million during a VC round in 2017.

The fact Apple is scoping out talent from a driverless car firm shows Cupertino’s continued interest in the area.

Back in January, Apple pulled around 200 employees off its self-driving car project which made some people question whether Cupertino had the ability (and resolve) to break into such an established industry with a minefield of regulatory needs that vary from location-to-location.

A self-driving car being tested by Apple had a rear-end collision with another vehicle last year. Fortunately, unlike Uber’s self-driving car incident, there were no injuries.

During an interview with CNN last November, Apple CEO Tim Cook expressed that his company is focused on the software side of autonomous vehicles. Acquiring talent from Drive.ai is a surefire way of boosting Apple’s in-house expertise in applying machine learning to cars.

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In the keynote session, titled ‘What is the true adoption of AI’, Hogan provided real-world examples of how the technology is being used and enabled by Nvidia’s GPUs. But first, he highlighted the momentum we’re seeing in AI.

“Many governments have announced investments in AI and how they’re going to position themselves,” comments Hogan. “Countries around the world are starting to invest in very large infrastructures.”

The world’s most powerful supercomputers are powered by Nvidia GPUs. ORNL Summit, the current fastest, uses an incredible 27,648 GPUs to deliver over 144 petaflops of performance. Vast amounts of computational power is needed for AI which puts Nvidia in a great position to capitalise.

“The compute demands of AI are huge and beyond what anybody has seen within a standard enterprise environment before,” says Hogan. “You cannot train a neural network on a standard CPU cluster.”

Nvidia started off by creating graphics cards for gaming. While that’s still a big part of what the company does, Hogan says the company pivoted towards AI back in 2012.

A great deal of the presentation was spent on autonomous vehicles, which is unsurprising given the demand and Nvidia’s expertise in the field. Hogan highlights that you simply cannot train driverless cars using CPUs and provided a comparison in cost, size, and power consumption.

“A new type of computing is starting to evolve based around GPU architecture called ‘dense computing’ – the ability to build systems that are highly-powerful, huge amounts of computational scale, but actually contained within a very small configuration,” explains Hogan.

Autonomous car manufacturers need to train petabytes of data per day, reiterate their models, and deploy them again in order to get those vehicles to market.

Nvidia has a machine called the DGX-2 which delivers two petaflops of performance. “That is one server that’s equivalent to 800 traditional servers in one box.”

Nvidia has a total of 370 autonomous vehicles which Hogan says covers most of the world’s automotive brands. Many of these are investing heavily and rushing to deliver at least ‘Level 2’ driverless cars in the 2020-21 timeframe.

“We have a fleet of autonomous cars,” says Hogan. “It’s not our intention to compete with Uber, Daimler or BMW, but the best way of us helping our customers enable that is by trying it ourselves.”

“All the work our customers do we’ve also done ourselves so we understand the challenges and what it takes to do this.”

Real-world impact

Hogan notes how AI is a “horizontal capability that sits across organisations” and is “an enabler for many, many things”. It’s certainly a challenge to come up with examples of industries that cannot be improved to some degree through AI.

Following autonomous cars, Nvidia sees the next mass scaling of AI happening in healthcare (which our dear readers already know, of course.)

Hogan provides the natural example of the UK’s National Health Service (NHS) which has vast amounts of patient data. Bringing this data together and having an AI make sense of it can unlock valuable information to improve healthcare.

AIs which can make sense of medical imaging on a par with, or even better, than some doctors are starting to become available. However, they are still 2D images that are alien to most people.

Hogan showed how AI is able to turn 2D imagery into 3D models of the organs which are easier to understand. In the GIF below, we see a radiograph of a heart being turned into a 3D model:

We’ve also heard about how AI is helping with the field of genomics, assisting in finding cures for human diseases. Nvidia GPUs are used for Oxford Nanopore’s MinIT handheld which enables DNA sequencing of things such as plants to be conducted in-the-field.

In a blog post last year, Nvidia explained how MinIT uses AI for basecalling:

“Nanopore sequencing measures tiny ionic currents that pass through nanoscale holes called nanopores. It detects signal changes when DNA passes through these holes. This captured signal produces raw data that requires signal processing to determine the order of DNA bases – known as the ‘sequence.’ This is called basecalling.

This analysis problem is a perfect match for AI, specifically recurrent neural networks. Compared with previous methods, RNNs allow for more accuracy in time-series data, which Oxford Nanopore’s sequencers are known for.”

Hogan notes how, in many respects, eCommerce paved the way for AI. Data collected for things such as advertising helps to train neural networks. In addition, eCommerce firms have consistently aimed to improve and optimise their algorithms for things such as recommendations to attract customers.

“All that data, all that Facebook information that we’ve created, has enabled us to train networks,” notes Hogan.

Brick-and-mortar retailers are also being improved by AI. Hogan gives the example of Walmart which is using AI to improve their demand forecasting and keep supply chains running smoothly.

In real-time, Walmart is able to see where potential supply challenges are and take action to avoid or minimise. The company is even able to see where weather conditions may cause issues.

Hogan says this has saved Walmart tens of billions of dollars. “This is just one example of how AI is making an impact today not just on the bottom line but also the overall performance of the business”.

Accenture is now detecting around 200 million cyber threats per day, claims Hogan. He notes how protecting against such a vast number of evolving threats is simply not possible without AI.

“It’s impossible to address that, look at it, prioritise it, and action it in any other way than applying AI,” comments Hogan. “AI is based around patterns – things that are different – and when to act and when not to.”

While often we hear about what AI could one day be used for, Hogan’s presentation was a fascinating insight into how Nvidia is seeing it making an impact today or in the not-so-distant future.

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As the UK prepares to depart the EU, the Chancellor of the Exchequer is boosting funding of industries where the UK is already emerging a leader to help solidify its position on the world stage.

£75 million will be set aside for artificial intelligence to build on the existing success stories from the likes of Cambridge-based DeepMind — which was acquired by Google for £400 million back in 2014. Up to £20 million will support companies developing AI services while £45 million will support increasing the number of PhD students studying AI each year.

Part of the reason for the lack of electric vehicle adoption in many European countries is the lack of supporting infrastructure. £400 million will be invested in charging points along with a further £100 million to boost purchases of clean cars.

This infrastructure will also help with the deployment of driverless cars — but the biggest hurdle remains setting insurance and regulatory policies. Speaking to our sister publication IoT News back in January, Jeremy Dalton, CTO of TravelSpirit, noted the Department for Transport’s work to create an environment for autonomous vehicles to be developed with little hindrance.

5G will enable greater connectivity and open up new possibilities for the IoT. Driverless cars and their connected features will likely require this next-generation mobile technology for its greatly reduced latency and increased reliability. A further £35 million will be made available to ensure rail passengers have reliable and “lightning-speed” connections during journeys. Trials will begin on the Trans-Pennine route which connects Leeds, Manchester, and Liverpool.

Other related announcements in the budget includes £76 million to boost digital and construction skills, £9 million to fund an advisory board for safe and ethical innovation, and £100 million to train an additional 8,000 qualified computer science teachers (supported by a new National Centre for Computing.)

The government funding is alongside increasing private investment in AI and IoT. According to Deloitte’s new Digital Disruption Index, 85 percent of senior executives plan to invest in artificial intelligence and the Internet of Things by 2020. More than half of the organisations surveyed expect to invest over £10 million in digital technologies such as AI, cloud, robotics, analytics, blockchain, the IoT, and virtual and augmented reality.

Prime Minister Theresa May announced on Monday a 1.7 billion pound fund to help regenerate cities and a 2.3 billion pound boost to research and development spending, due in 2021/22.

“This is a new long-term approach to shaping a stronger and fairer economy for decades to come,” May said in a Times newspaper article.

Whether it’s private or public investment, there’s little debate over the importance of giving these industries all the financial support they need to grow and thrive.

Are you impressed with the funding in the budget of future industries? Share your thoughts in the comments.

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