These types of situations are what programs such as the MIT-developed Moral Machine have been looking to unlock. The site has for the past four years canvassed public opinion by presenting a series of ethical dilemmas: should an autonomous car protect its passenger for instance, or a pedestrian, in a particular scenario?

The wider point, however, is that such autonomy – or to put it more romantically, machines thinking for themselves – requires vast amounts of data. Data which, to date, has not been calibrated. Would you entrust your safety to your vehicle?

This is an analogy which Dorian Selz, co-founder and CEO of Squirro, uses to explain why the great AI revolution might take longer than many think. “I think we’re going to see massive advances in the underlying technology over the next 10 years – we are probably going to see some level of breakthrough to have some low level of intelligence in these machines,” Selz tells AI News. “I can imagine that – but it will take time until it is really adopted widely.”

For the CEO of an AI platform provider, this may be an interesting position to take – something which Selz accepts. Yet Squirro’s business case may give an indication. The company’s modus operandi is  taking organisations’ unstructured data sets – everything from emails, to market reports, to PowerPoint presentations and Word documents – and utilising what it calls ‘augmented intelligence’ to surface greater insights.

This collaborative process not only leans on the human factor, but takes time to appreciate. “Most companies have ratios of 90% to 99% of all data they generate or acquire never being used beyond the first use,” explains Salz. “That is the equivalent of buying a car, driving it from the dealership back to your garage, and then throwing away the key and never driving that car again.

“If you say data is the new oil, at the same time companies are not making use of that data,” he adds. “What works wonderfully well for numbers doesn’t work at all for text in an Excel.”

Selz knows of which he speaks with regard to how companies use and stockpile data. Squirro is the fourth company he has co-built, with a previous success being Swiss homegrown search engine local.ch. With Squirro, which promises benefits from customer and service insights to cognitive search, the goal is for this data to complement business processes and working methods.

Take an accounts department, for example, whereby the data can be able to show not just whether a client has paid or not, but any relevant news or market research which could impact their ability to pay. In the supply chain, issues can be identified several levels down the chain which may impact the direct supplier.

“If you look at any company today, small or big, their IT landscape is effectively a combination of boxed applicastions – a supply chain system at the back of the company, an ERP system in the middle, some level of CRM at the front and support systems in the background,” explains Selz. “The bigger the company, the more of that stuff you have, but it’s still essentially a landscape of boxed IT applications.

“The future we see is a future where you’re going to see a machine learning-driven layer that weaves all these different data silos together,” Selz adds. “We foresee a future where these types of intimately weaved informational fabrics become the new norm in any enterprise.

“It doesn’t replace you – I don’t believe in that at all. But it will support you in decision making.”

One customer which exemplifies this ‘weaving’ of data is Brookson, a UK-based local accountancy firm for contractors. The company had already been using Squirro to classify data coming in to expedite the administrative and call centre process, but the next step was particularly noteworthy: using the technology to assess future customer relationships. Based on a roadmap created from previous customers, good, bad or indifferent, every new interaction can be assessed and forecast using pre-defined journeys.

“I love to retell that story, because it shows that AI per se is not just for the big boys,” says Selz. “As a nimble, agile, SME enterprise, you can use this type of technology to really fundamentally put yourself in a better position in the marketplace.”

This is an interesting point; plenty of research, not least a study from VC company Work-Bench in 2018, has argued that bigger companies are at a natural advantage with AI because they can hoover up the best talent. But as AI expertise does not automatically equal big businesses, big effects are not the results of such technology yet, Selz argues – going back to his original theme around transformation.

“Everybody talks about these big intelligence machines that do these fantastic things for companies and automate,” he explains. “Transform entire industries? I don’t really believe that.

“If you look at many machine learning techniques in use today, from the more simplistic ones like SVM, Bayesian algorithms, all the way to more sophisticated deep learning models, at the end, they’re not really intelligent,” Selz adds. “It’s pattern matching at the power of whatever computer you have.”

Selz notes that ‘none of the lovely AI engines had foreseen the coronavirus’. Given the reactive, rather than proactive, steps taken by governments worldwide, it wasn’t just the machines who were flat-footed. Looking at various stories assessing AI’s impact on Covid-19, many argue its role is to assess what will happen next – rather, assessing the damage once the horse has bolted – with a Singapore research unit predicting last month, with extreme caution, that the pandemic will end globally by December.

This informs Selz’s overall theory that we are nowhere near ready for primetime in B2B just yet. “It’s outright dangerous to think such an AI engine can suddenly transform your business if it is so limited in its capabilities and capacity,” he says.

“A customer relationship is more than what you have in your data sets,” Selz adds. “Even in retail volume segments, no one has a fully digitised customer relationship yet. If you don’t have it fully digitised, every aspect of it, why would you entrust some anonymous algorithm call method to evaluate and effectively decide on the future of that relationship?

“No sane person would do that.”

Editor’s note: You can find out more about augmented intelligence at AIM, a virtual event hosted by Squirro on June 23-25.

 Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London, and Amsterdam to learn more. Co-located with the IoT Tech Expo, , and Cyber Security & .

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The startup has also developed a cloud-based platform in order to manage some of the challenges that could not be addressed within the car.

According to FiveAI, the latest round of funding will be used to turn these cloud and in-car systems into products. The aim is to later on collaborate with carmakers to develop these platforms into vehicles.

In October, the company announced the launch of commuter research trials for its self-driving car technology along with motor insurance firm Direct Line. As part of the Streetwise Consortium, a 19 kilometre autonomous driving research route that featured human passengers was launched in Croydon and Bromley to test out the technology. Participants were later on asked for insight on their experiences.

FiveAI CEO Stan Boland said at the time: “Shared, self-driving vehicle services promise a better way for people to get around, we’ll be working with forward-thinking partners to make these services a reality in European urban environments. The lessons learned through Streetwise provide an important step towards that goal.”

This is by no means the only funding taking place in this space right now. Waymo announced earlier this month that it had raised $2.25 billion (£1.75bn) in a fundraising round led by Silver Lake, Canada Pension Plan Investment Board and Mubadala Investment Company, while autonomous vehicle technology company Pony.ai secured $400m from Toyota, with the company aiming to strengthen its partnership with Toyota in mobility services.

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In a tweet, Musk wrote: “Tesla will hold a super fun AI party/hackathon at my house with the Tesla AI/autopilot team in about four weeks. Invitations going out soon.”

The hackathon will focus on the AI behind Tesla’s problematic AutoPilot feature which has been reported to accelerate erratically. Tesla has denied the claims but the hackathon suggests the company at least wants to make AutoPilot more robust.

Ahead of the hackathon announcement, Musk called for developers to join Tesla’s AI team which, he says, reports directly to him.

Talking up the opportunity, Musk highlighted that Tesla will soon have over a million connected vehicles worldwide. Every Tesla is fitted with the sensors and computing power needed for full self-driving which “is orders of magnitude more than everyone else combined”.

Musk says that an individual’s educational background is irrelevant. However, before you get too excited you can just waltz into Tesla, you will be required to pass a “hardcore coding test”.

Python is used “for rapid iteration” at Tesla to build neural networks before the code is converted into “C++/C/raw metal driver code” for the speed required for such critical tasks as piloting a vehicle.

As part of that need for speed, Tesla is also taking on the challenge of building its own AI chips. Musk says Tesla is seeking world-class chip designers to join the company’s teams based in Palo Alto and Austin.

Musk has been vocal about his fears of AI – calling it a potentially existential threat if left unchecked. However, he is also well aware of its opportunities (if that’s of any surprise given how it’s helping to inflate his and investors’ wallets).

“My actions, not just words, show how critically I view (benign) AI,” Musk wrote.

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This year, Baidu’s stock price has dropped 36 percent. Li used the Game of Thrones phrase that “winter is coming” following a first-quarter loss.

Baidu has since not only been asking employees to fly economy rather than business, and not to stay in five-star hotels, but even to reduce the number of paper towels and cups they’re using in the office.

AI is expected to open up new revenue avenues for Baidu amid slowing growth in its traditional search business. Li said he’s an “optimist” when it comes to AI’s ability to make lives better.

Li was speaking at the plenary session of the sixth World Internet Conference (WIC) in Wuzhen, east China’s Zhejiang province.

During the conference, Li made a rather bold prediction:

“Artificial intelligence will not destroy human beings but will give people eternal life. Everything every person has said and done, even people’s memories, emotions and consciousness, can be digitally stored on network disks or the cloud.

Machines can learn people’s way of thinking. When there are new problems, people can talk to future generations across time and space via technology.”

Baidu, or any other company, is likely some way off from achieving Li’s prediction – but it’s a fascinating insight about AI’s potential direction from a tech leader nonetheless.

Self-driving cars, particularly robotaxis, is one area where Baidu is investing heavily. Last month, the company launched public trials in the Hunan province consisting of 45 autonomous taxis.

Li highlights that self-driving vehicles don’t just provide an opportunity itself but also has a knock-on effect of requiring “urban transport infrastructure, especially the software, to be updated.”

Baidu is also expanding its influence and sharing its AI expertise globally.

In July, AI News reported that Baidu will help develop Intel’s Nervana neural processor. The increasing scrutiny from the US over Chinese influence in Western companies and infrastructure, leading to an increasing number of Chinese firms being added to an ‘entity list’, may reduce such collaborations.

China’s growth and influence is hard to ignore, especially in AI. China’s State Council released a roadmap two years ago aimed at making the country a global AI leader by 2030 as it ramps up competition with the US.

With the full backing of the Chinese government, it’s understandable why Li continues to be bullish about Baidu’s AI investments.

(Image Credit: Fortune Global Forum 2013 by Stefen Chow/Fortune Global Forum under CC BY-ND 2.0 license)

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Announced during this year’s Baidu Create conference, the companies say they will focus on areas such as the smart home, intelligent connectivity, smart mobility, and e-commerce.

Geely is a privately held global automotive group headquartered in the city of Hangzhou and is most well-known for owning the Volvo and Lotus car brands.

The long-term goal of Baidu and Geely is to accelerate the adoption of connected vehicles in both China and the rest of the world. Li Shufu, Chairman of Zhejiang Geely Holding Group (Geely Holding), and Robin Li, Founder and CEO of Baidu, were at the announcement.

Li said: “The major advances in the history of mobility are accompanied by new changes brought about by cutting-edge technology. Geely Holding, through their years of achievements and innovations, has become a leader in China’s automotive industry.

“This strategic cooperation between Baidu and Geely, focusing on AI technology empowerment, will not only bring about more natural and convenient human-car interaction but also accelerate the intelligentisation of the automobile and mobility service industry, helping the Chinese automotive industry become a leader in the era of smart mobility.”

Shufu and Li interacted with Geely’s latest connected SUV, the Bo Yue Pro, on-stage during the event. The intelligent new vehicle is equipped with Baidu’s AI system through Geely’s SKU19 Smart Ecosystem which helps to connect people, cars, and homes.

Geely has plans to roll out the SKU19 across its portfolio.

Shufu commented: “China’s intelligent connected vehicle technology is now at the forefront of the world, bringing consumers a safe and convenient travel experience. Baidu is a leader within the AI industry in China, and also a pioneer in promoting industrial intelligence with their globally leading AI R&D capability and deep technical insight in the field of intelligent driving.

“Through strategic cooperation and resource collaboration, the two sides are committed to jointly building an intelligent mobility ecosphere to lead the future, and create a brand-new experience for users.”

Baidu continues to double-down on its AI efforts. Just last week, AI News reported that Baidu will lend its AI expertise to help with the development of Intel’s Nervana neural processor.

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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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Roads, especially while shared with human drivers, are unpredictable places. Training a self-driving car for every possible situation is a monumental task.

The AI developed by Microsoft and MIT compares the action taken by humans in a given scenario to what the driverless car’s own AI would do. Where the human decision is more optimal, the vehicle’s behaviour is updated for similar future occurrences.

Ramya Ramakrishnan, an author of the report, says:

“The model helps autonomous systems better know what they don’t know.

Many times, when these systems are deployed, their trained simulations don’t match the real-world setting [and] they could make mistakes, such as getting into accidents.

The idea is to use humans to bridge that gap between simulation and the real world, in a safe way, so we can reduce some of those errors.”

For example, if an emergency vehicle is approaching then a human driver should know to let them pass if safe to do so. These situations can get complex dependent on the surroundings.

On a country road, allowing the vehicle to pass could mean edging onto the grass. The last thing you, or the emergency services, want a driverless car to do is to handle all country roads the same and swerve off a cliff edge.

Humans can either ‘demonstrate’ the correct approach in the real world, or ‘correct’ by sitting at the wheel and taking over if the car’s actions are incorrect. A list of situations is compiled along with labels whether its actions were deemed acceptable or unacceptable.

The researchers have ensured a driverless car AI does not see its action as 100 percent safe even if the result has been so far. Using the Dawid-Skene machine learning algorithm, the AI uses probability calculations to spot patterns and determine if something is truly safe or still leaves the potential for error.

We’re yet to reach a point where the technology is ready for deployment. Thus far, the scientists have only tested it with video games. It offers a lot of promise, however, to help ensure driverless car AIs can one day safely respond to all situations.

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Achieving this means greater reliance on sensors, data and processing power. And that’s true whether you’re developing for highly automated driving in which the driver is supported in functions such as navigation, security and image recognition or whether you’re working on fully automated driving (FAD) where the driver is essentially just a passenger.

The building blocks of an automated vehicle

A deep learning foundation for advanced driver assistance systems (ADAS) is an essential component of a safe autonomous vehicle, but there are many other attributes to consider, including:

• The ability to process enormous amounts of environmental data
• The ability to sense surroundings, including:
  • Creating access to hardware accelerators for developing common computer vision routines
  • Providing inference data gathered from cameras
  • Methods to extract features from video and track them

Technology inside the car… and out

It’s important to remember that a range of technologies both inside and outside the car are responsible for powering autonomous vehicles. In particular:

• Data Centre: One autonomous car generates a volume of data equivalent to almost 3,000 people, making the data centre critical for storing, sharing and protecting the data generated from deep learning algorithms
• 5G: 5G connectivity delivers ultra-low latency at gigabit speeds and high bandwidth. This allows for intelligent and agile networks to give priority to the safety-first devices needed for an automated vehicle
• Human Machine Interface: That means a software-defined cockpit that consolidates cluster displays with in-vehicle infotainment systems. The cockpit should include media management, security systems and cloud connections. Ultimately, this design should build trust between the driver and vehicle

With automated vehicles set to rely more on sensors, data, and processing power, it’s important not to overlook the importance of in-vehicle computing. Intel’s portfolio of power-efficient processors, field-programmable gate arrays (FPGAs), and software are designed to deliver high-compute performance per watt.

Get started today

Request access to the Intel GO Automotive SDK Beta to enjoy a comprehensive toolkit to build high-performing, power-efficient designs for in-vehicle and cloud-based data centre platforms.

If you are a data scientist, system designer or developer of autonomous driving solutions, this SDK will help you to maximise hardware performance, optimise systems and applications and advance perception sensor and deep learning algorithms. The SDK includes several workflow modules and optimisation tools, some of which are specialised for automotive development.

Getting the right hardware and software in place now will enable developers to build safer, smarter autonomous vehicles. Visit the Intel® Developer Zone to view the full range of tips and tools available to support emerging autonomous driving capabilities and help developers create their own driving experiences.

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