According to a new study, listening to, understanding, and inspiring colleagues can account for as much as 31 per cent of their effectiveness and so Ford will be rolling out emotional intelligence training for its employees in Europe.
In the movies, James Bond solves problems with his fists and the help of super-smart Q – whose futuristic and deadly gadgets help do the talking. Being a good listener is not part of the package.
But just as Bond’s real-life counterparts are now required to show a full set of social skills, so that is the case with Q’s automotive counterparts, the engineers who are working on the cars of the future.
Ford will be rolling out emotional intelligence training for its employees in Europe, through a course with RWTH Aachen University, the company’s research partner in Germany.
“Traditionally, engineers have been seen as individual contributors and there remains a major focus on technical skills, knowledge and imagination,” said Prof. Richard Boyatzis of Case Western Reserve University in Ohio. “But contrary to common perception, engineers do not work alone. They work in multidisciplinary teams with diverse clients. The ability to work with others is an important consideration.”
According to a new first-of-its-kind study, listening to, understanding, and inspiring colleagues can account for as much as 31 per cent of their effectiveness. This was a key finding of research undertaken with Ford engineers – and their colleagues and reports – who were asked whether they love their workplace, and how they cooperate and discuss ideas for the future. The study also found it was possible to predict how enthusiastic engineers would be about projects, just by knowing how those projects were communicated.
“Emotional intelligence is about being able to identify emotions, in yourself and others, and to know how to handle and manage them,” said Rocio Luna, team coach and mediator, Training and Consulting e. V., Ford of Europe. “We’re looking to train our engineers to be better at recognising their own feelings and at reading those of others – so they can best handle when an angry person might cause problems, a happy person is keen to collaborate, and a stressed person wants to talk.”
In an age where the importance of artificial intelligence and robots is increasing, people are expected to focus on skills and capabilities that artificial intelligence has trouble replicating – understanding, motivating, and interacting with human beings.
Ford has a network of ten engineering and research centres around the world – including Merkenich in Germany, Dunton in England and Golcuk in Turkey – which harness the talents of 25,000 engineers. The company recently opened the Merkenich Innovation Hub that offers employees on‑demand dedicated workshops; training and ideation sessions; anonymous access to research and tailored, in-depth information; as well as patent consultation.
“In many engineering schools and programs, emotional and social intelligence is given cursory attention, like tolerating your crazy uncle at a wedding after he has had too much to drink. But companies are facing a motivational crisis; with three out of four employees not feeling engaged at work. Our research shows just how much emotions matter,” Prof. Boyatzis added.
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.