Ford has announced the creation of a Robotics and Artificial Intelligence Research team to help shape the future of transportation.
“The impact of robotics and artificial intelligence on the way we get around – even in just the next five to 10 years – will be enormous,” says Ken Washington, vice president of Research and Advanced Engineering and Chief Technical Officer of Ford Motor Company.
Washington says that this move aligns multiple disciplines under one team for a more concerted effort to come to understand the potential for robotics and artificial intelligence. This includes a greater focus on evaluating new sensor technologies, machine-learning methods, technical requirements for entry into global markets, and the development of personal mobility devices, drones and other aerial robotics that can enhance travel.
The new team also serves to advance projects Ford is already working on – such as autonomous vehicles.
In February 2017, Ford announced a plan to invest $1 billion during the next five years in a new artificial intelligence software company, Argo AI, which leads development of Ford’s virtual driver system – the computer platform, sensors, and algorithms – for Ford’s first-generation self-driving vehicle program. The new Robotics and Artificial Intelligence Research team will work concurrently with Argo AI and will be able to put greater emphasis on other developing uses of sensor technology and artificial intelligence, and how those developments can be used in autonomous vehicles.
“Our robotics and artificial intelligence researchers will continue to collaborate with the Argo AI team so we can someday put this promising emerging technology to work in future generations of self-driving vehicles,” says Washington.
The research team is already using the existing Ford virtual driver system for continued research without disrupting Argo AI’s ongoing production work. The team is able to use Ford’s research fleet to experiment with emerging sensing technology and try out new ways of leveraging deep learning techniques.
“This means you’ll likely see at least two separate fleets of self-driving vehicles on the road – one led by the Ford team, conducting advanced research, and another by Argo AI, developing and testing our virtual driver system for production,” explains Washington.
Research and Opportunities
The potential for autonomous vehicle technology to transform society means there’s heavy emphasis on its development, but automation and artificial intelligence can be applied in other ways as well. Ford is already using robotics in manufacturing and logistics, and the new research team will evaluate further advancements in robotics to assist in ergonomically difficult tasks.
Artificial intelligence also plays a big role as part of Ford’s Global Data and Analytic team’s support for sales, marketing and finance, so the team will look to spread the technology to drive smarter decision-making and more personalised experiences.
“Our new research team will continue the relationships we’ve built with startup companies through partnerships, investments and acquisitions,” says Washington. “The startup community is demonstrating tremendous opportunities for us with advanced sensors, deep learning, applied robotics and more, so it’s important for us to continue to foster these relationships.”
Finally, the research team will also lead projects with US universities working on robotics and artificial intelligence, including the University of Michigan, Stanford University, M.I.T., Virginia Tech, Purdue University, Texas A&M, Georgia Institute of Technology and others that Ford is currently developing relationships with. Washington says that the team is especially excited about Ford’s upcoming presence on the University of Michigan campus with the new Ford Motor Company Robotics Building.
“Ford is poised to drive into the future by expanding automation of mobility products and services,” says Washington. “This decision is driving energy with everyone on our team, as it clearly indicates the direction of Ford Motor Company. Because we understand the science of robotics and artificial intelligence, we can establish a team tasked with not just watching the future, but helping to create it.”
Project Bloodhound saved
The British project to break the world landspeed record at a site in the Northern Cape has been saved by a new backer, after it went into bankruptcy proceedings in October.
Two weeks ago, and two months after entering voluntary administration, the Bloodhound Programme Limited announced it was shutting down. This week it announced that its assets, including the Bloodhound Supersonic Car (SSC), had been acquired by an enthusiastic – and wealthy – supporter.
“We are absolutely delighted that on Monday 17th December, the business and assets were bought, allowing the Project to continue,” the team said in a statement.
“The acquisition was made by Yorkshire-based entrepreneur Ian Warhurst. Ian is a mechanical engineer by training, with a strong background in managing a highly successful business in the automotive engineering sector, so he will bring a lot of expertise to the Project.”
Warhurst and his family, says the team, have been enthusiastic Bloodhound supporters for many years, and this inspired his new involvement with the Project.
“I am delighted to have been able to safeguard the business and assets preventing the project breakup,” he said. “I know how important it is to inspire young people about science, technology, engineering and maths, and I want to ensure Bloodhound can continue doing that into the future.
“It’s clear how much this unique British project means to people and I have been overwhelmed by the messages of thanks I have received in the last few days.”
The record attempt was due to be made late next year at Hakskeen Pan in the Kalahari Desert, where retired pilot Andy Green planned to beat the 1228km/h land-speed record he set in the United States in 1997. The target is for Bloodhound to become the first car to reach 1000mph (1610km/h). A track 19km long and 500 metres wide has been prepared, with members of the local community hired to clear 16 000 tons of rock and stone to smooth the surface.
The team said in its announcement this week: “Although it has been a frustrating few months for Bloodhound, we are thrilled that Ian has saved Bloodhound SSC from closure for the country and the many supporters around the world who have been inspired by the Project. We now have a lot of planning to do for 2019 and beyond.”
Motor Racing meets Machine Learning
The futuristic car technology of tomorrow is being built today in both racing cars and
toys, writes ARTHUR GOLDSTUCK
The car of tomorrow, most of us imagine, is being built by the great automobile manufacturers of the world. More and more, however, we are seeing information technology companies joining the race to power the autonomous vehicle future.
Last year, chip-maker Intel paid $15.3-billion to acquire Israeli company Mobileye, a leader in computer vision for autonomous driving technology. Google’s autonomous taxi division, Waymo, has been valued at $45-billion.
Now there’s a new name to add to the roster of technology giants driving the future.
Amazon Web Services, the world’s biggest cloud computing service and a subsidiary of Amazon.com, last month unveiled a scale model autonomous racing car for developers to build new artificial intelligence applications. Almost in the same breath, at its annual re:Invent conference in Las Vegas, it showcased the work being done with machine learning in Formula 1 racing.
AWS DeepRacer is a 1/18th scale fully autonomous race car, designed to incorporate the features and behaviour of a full-sized vehicle. It boasts all-wheel drive, monster truck tires, an HD video camera, and on-board computing power. In short, everything a kid would want of a self-driving toy car.
But then, it also adds everything a developer would need to make the car autonomous in ways that, for now, can only be imagined. It uses a new form of machine learning (ML), the technology that allows computer systems to improve their functions progressively as they receive feedback from their activities. ML is at the heart of artificial intelligence (AI), and will be core to autonomous, self-driving vehicles.
AWS has taken ML a step further, with an approach called reinforcement learning. This allows for quicker development of ML models and applications, and DeepRacer is designed to allow developers to experiment with and hone their skill in this area. It is built on top of another AWS platform, called Amazon SageMaker, which enables developers and data scientists to build, train, and deploy machine learning quickly and easily.
Along with DeepRacer, AWS also announced the DeepRacer League, the world’s first global autonomous racing league, open to anyone who orders the scale model from AWS.
As if to prove that DeepRacer is not just a quirky entry into the world of motor racing, AWS also showcased the work it is doing with the Formula One Group. Ross Brawn, Formula 1’s managing director of Motor Sports, joined AWS CEO Andy Jassy during the keynote address at the re:Invent conference, to demonstrate how motor racing meets machine learning.
“More than a million data points a second are transmitted between car and team during a Formula 1 race,” he said. “From this data, we can make predictions about what we expect to happen in a wheel-to-wheel situation, overtaking advantage, and pit stop advantage. ML can help us apply a proper analysis of a situation, and also bring it to fans.
“Formula 1 is a complete team contest. If you look at a video of tyre-changing in a pit stop – it takes 1.6 seconds to change four wheels and tyres – blink and you will miss it. Imagine the training that goes into it? It’s also a contest of innovative minds.”
Formula 1 racing has more than 500 million global fans and generated $1.8 billion in revenue in 2017. As a result, there are massive demands on performance, analysis and information.
During a race, up to 120 sensors on each car generate up to 3GB of data and 1 500 data points – every second. It is impossible to analyse this data on the fly without an ML platform like Amazon SageMaker. It has a further advantage: the data scientists are able to incorporate 65 years of historical race data to compare performance, make predictions, and provide insights into the teams’ and drivers’ split-second decisions and strategies.
This means Formula 1 can pinpoint how a driver is performing and whether or not drivers have pushed themselves over the limit.
“By leveraging Amazon SageMaker and AWS’s machine-learning services, we are able to deliver these powerful insights and predictions to fans in real time,” said Pete Samara, director of innovation and digital technology at Formula 1.