In the interview, Ms. Huang relates some interesting patterns she has observed with regards to AI adoption. The sorts of companies BMW iVentures is seeing and investing in are primarily using AI to focus on streamlining workflows, optimizing processes, and reducing overall costs. Since AI holds the ability to analyze complex datasets and identify data patterns very quickly, it can provide fast results and identify very specific needs or circumstances without necessarily relying on a team of people who need to try to process more than they can reliably count on. Already, AI has managed to identify trends that have helped to innovate the ways that companies do business, by providing customized customer interactions and identifying needs for clients. The biggest struggle with data, particularly in the automotive industry where the actual process of taking customer feedback and turning that into a future product can take several years, is ensuring that the data being referenced is still relevant.
According to the Automotive Council UK (ACUK) "… in the East Midlands and Yorkshire… Over a third of automotive manufacturers produce components. Read more: Mark Casci: Can cannabis save the high street? A quarter produce commercial vehicles, one fifth are aftermarket suppliers." In June 2017, the ACUK's report "Growing the Automotive Supply Chain: Local Vehicle Content Analysis" found "…cars manufactured in Britain are becoming more British…" A main reason quoted in the report was "the parts sourced by UK car manufacturers from UK first-tier suppliers has increased from 36 per cent in 2011, to 44 per cent in 2017." This is of course great news for the UK – but we would be foolish to ignore the advancements in technology, including artificial intelligence (AI) and how it has infiltrated a large part of our lives, domestically, commercially and politically.
The race to fully autonomous vehicles is on. In April, Elon Musk declared that Tesla should have over a million level 5 autonomous vehicles manufactured by 2020. To clarify, that means over a million cars equipped with the necessary hardware capable of driving with no help from a driver. In addition, government approvals will be necessary (read: mandatory) long before self-driving Teslas will be commonplace. In addition, Musk also sparked some lively debate when he commented that Tesla will not be relying on lidar, the laser sensor technology that self-driving cars from many other companies (most notably Google's Waymo) currently depend on for "seeing" lines on the road, pedestrians, and more.
The automotive industry isn't just being driven by people -- it's also driven by data, particularly as automobile manufacturers move toward autonomous, self-driving vehicles. Last year, Waymo cars drove 1.2 million miles in California. Meanwhile, Tesla, with its Autopilot program, is actively collecting data from hundreds of thousands of vehicles to predict how its cars might perform autonomously. So far the company has collected hundreds of millions of miles worth of data. What are these autonomous vehicle manufacturers doing with all of that data?
What I saw didn't look very much like the future -- or at least the automated one you might imagine. The offices could have been call centers or payment processing centers. One was a timeworn former apartment building in the middle of a low-income residential neighborhood in western Kolkata that teemed with pedestrians, auto rickshaws and street vendors. In facilities like the one I visited in Bhubaneswar and in other cities in India, China, Nepal, the Philippines, East Africa and the United States, tens of thousands of office workers are punching a clock while they teach the machines. Tens of thousands more workers, independent contractors usually working in their homes, also annotate data through crowdsourcing services like Amazon Mechanical Turk, which lets anyone distribute digital tasks to independent workers in the United States and other countries.
Object detection is a computer vision technique whose aim is to detect objects such as cars, buildings, and human beings, just to mention a few. The objects can generally be identified from either pictures or video feeds. Object detection has been applied widely in video surveillance, self-driving cars, and object/people tracking. In this piece, we'll look at the basics of object detection and review some of the most commonly-used algorithms and a few brand new approaches, as well. Object detection locates the presence of an object in an image and draws a bounding box around that object.
The automotive industry is one of the most high-tech industries in the world – so a headline finding in a report published this week was, on the face of it, somewhat surprising. Capgemini's report – Accelerating Automotive's AI Transformation – found that during 2018, the number of companies in the industry deploying AI "at scale" grew only marginally by 3%. This reflected that just 10% of respondents surveyed said that their organizations were deploying AI-driven initiatives across the entirety of its operations "with full scope and scale," during 2018, compared to 7% in 2017. The relatively slow pace of growth is evidence that "the industry has not made significant progress in AI-driven transformation since 2017", the report concludes – a surprising finding given the scale of investment and enthusiasm shown by industry leaders. I spoke to one of the report's authors, Capgemini's Ingo Finck, who told me "To an extent, I did find this surprising, because from the discussions we've been having with these companies we see that the vast majority – more than 80% - mention AI in their core strategy. "It's clearly a strategic factor for them, so yes … we were surprised by the relatively slow growth rate." Before we start delving into the possible reasons for this slow uptake, it's worth noting that there is a key geographic variation: In China, the number of automotive companies working at scale with AI almost doubled, from 5% to 9%. This is explained to some extent by the comparatively "open" approach taken by China's AI giants, such as Baidu's development of the open source Apollo platform. This has involved it partnering with over 130 other businesses and organizations. Finck explains that the slow growth demonstrated in other regions could be down to the fact that organizations are taking a more mature approach to AI deployment. This might mean they are moving away from "try everything and see what works" methodologies, towards focusing on proven use cases that can then be scaled. Another disparity is apparent when we consider the sizes of the businesses that are reporting growth in AI deployments. "We can see that the smaller companies are struggling more with AI – whereas with larger companies [with revenue of $10 billion plus] the adoption rate is higher.
Every drop of seawater contains thousands of cells that can reveal the diversity of life in our ocean. Using a self-contained robotic laboratory and an autonomous underwater vehicle, MBARI scientists and engineers are developing advanced collection techniques that may one day simplify the jobs of biologists and resource managers. A recent study confirms that autonomously collected samples of environmental DNA (eDNA) are equivalent to samples collected by people using traditional, manual methods. A growing body of research indicates that wildlife surveys using eDNA analyses can be as (or more) accurate than simply using traditional methods. As such, eDNA assessments appear to offer a very promising and cost-effective means for monitoring biodiversity, which presents an attractive proposition for researchers as well as resource managers who study ocean ecosystems.
Jaguar Land Rover is trailing an in-car system that changes temperature, music and lighting in response to a driver's mood. The system gauges a driver's mood with a driver-facing camera and biometric sensing, and adjusts the heating, ventilation and air conditioning, media and ambient lighting to help tackle stress and tiredness. "Personalisation settings could include changing the ambient lighting to calming colours if the system detects the driver is under stress, selecting a favourite playlist if signs of weariness are identified, and lowering the temperature in response to yawning or other signs of tiring," the company said. The systems uses AI to get to know the owners moods better over time, Jaguar Land Rover added. "In time the system will learn a driver's preference and make increasingly tailored adjustments," the company said.
One of China's newest autonomous vehicle makers, Neolix, recently put self-driving microvans into action as it looks to scale up its solution to the country's logistics puzzle made more complex by a surge in online shopping. The Beijing-based startup, barely a year old, has already deployed the vehicles in the capital and other cities, but it faces stiff competition from a crowded field where other players, especially e-commerce groups, are racing to develop similar robovans. "Operating 10,000 units will be an industry milestone and it is crucial [for us] to achieve it," said Yu Enyuan, 45, Neolix's founder and chief executive. Neolix's ambition is to replace the roughly 40 million vehicles providing so-called last-mile logistics in China, a market projected to be 3 trillion yuan ($428 billion). These home deliveries are now handled mainly by two- and three-wheel electric motorbikes, zigzagging through neighborhoods to carry everything from milk tea to mattresses.