Demystifying AI: Understanding the human-machine relationship


Rules are then written for the computer system to learn about all the data points and make calculations based on the rules of the road. Computer systems are programmed with machine learning algorithms and continuously learn to look at more data more quickly than any human would be able to. It might even notice lots of interactions when "Fly the Friendly Skies" ads are placed next to images of a person being brutally pulled off the plane and place more ads there! Artificial intelligence, machine learning and "self-aware systems" are real.

No reason to fear the robot revolution - TechCentral


Basically, machine learning uses algorithms that iteratively learn from data, meaning that it enables computers to find hidden insights without being explicitly programmed where to look. However, where data mining extracts information for human comprehension, machine learning uses it to detect patterns in data and to adjust its program actions accordingly. Incredibly, it's a science that is not new; it is one that was, in fact, predicted nearly 70 years ago by Alan Turing, widely considered the father of theoretical computer science and artificial intelligence. For starters, it is applicable to healthcare, as machine learning algorithms can process more information and spot more patterns than humans can, by several orders of magnitude.

How is predictive data shaping the auto industry


How is predictive data changing the automotive industry and what changes can we expect to see in the future? Connected and autonomous cars are going to benefit most from the inclusion of predictive data because their design centers on data collection and processing. As more and more connected cars hit the roads, data management is going to become an essential tool. Predictive data has already shown potential for preventative maintenance, but this same application could be used to predict software problems and security flaws as well.

Repurposing 1980s traffic systems for artificial intelligence


Video monitoring systems for traffic have been in use since the mid- 1960s, initially pioneered by Israel and the Netherlands in order to capture motorists violating traffic light regulations. But they would have been very differently-designed if computer evaluation, rather than human monitoring, had been anticipated – a problem which researchers from Carnegie Mellon are currently tackling. The sheer scale and maturity of urban traffic-monitoring systems, many of which hail back to the 1980s, are a tantalising prospect for AI in terms of generating usable data about traffic flow, but are predicated on'common sense' – currently an elusive goal for machine learning, even in terms of widely-agreed definition. In the paper Understanding Traffic Density from Large-Scale Web Camera Data four researchers consider what Fully Convolutional Networks (FCNs) might be able to achieve in terms of leveraging all this extant, live data without the need to invest in expensive and experimental new monitoring systems. City-wide traffic camera networks were originally developed as tools for municipal traffic authorities to make rough estimations of congestion, and to be apprised of serious blockages such as road accidents; any traffic flow data emerging from their use would be purely anecdotal – impossibly expensive or complicated to rationalise and analyse scientifically.

Briefing – AI, machine learning and cars


The mid- to long- term development focus include smart remote maintenance service, autonomous driving, smart traffic, and connectivity mobility hub, all of which will benefit from continued R&D investment in the fields of in-vehicle networks, cloud and big data analytics and connected car security technologies. The Swedish safety supplier also announced that it is forming a joint venture with Volvo Cars to develop software for autonomous driving and driver assistance systems. "In order for the system to acquire this information step-by-step, a range of sensors such as radars, cameras, and Surround View systems are needed," said Continental's ADAS business unit head Karl Haupt. Steering on Demand enables the transition between driver and automated driving control through safe, intuitive steering transitions for vehicles capable of SAE Level 3 - 5 automated driving.

How IoT and machine learning can make our roads safer


Ben Dickson is a software engineer and the founder of TechTalks. The transportation industry is associated with high maintenance costs, disasters, accidents, injuries and loss of life. Hundreds of thousands of people across the world are losing their lives to car accidents and road disasters every year. According to the National Safety Council, 38,300 people were killed and 4.4 million injured on U.S. roads alone in 2015. The related costs -- including medical expenses, wage and productivity losses and property damage -- were estimated at $152 billion.

IEEE Xplore: IEEE Transactions on Intelligent Transportation Systems

AITopics Original Links

Accurate and timely traffic flow information is important for the successful deployment of intelligent transportation systems. Over the last few years, traffic data have been exploding, and we have truly entered the era of big data for transportation. Existing traffic flow prediction methods mainly use shallow traffic prediction models and are still unsatisfying for many real-world applications. In recent years, the range of sensing technologies has expanded rapidly, whereas sensor devices have become cheaper. This has led to a rapid expansion in condition monitoring of systems, structures, vehicles, and machinery using sensors.

Infusing Machines with Intelligence - Part 3


As seen in Part 1 and Part 2 of this series, it is hard not to feel excited about machine learning. First, it empowers machines to teach themselves the tasks that humans can perform but find difficult to "teach" a computer via conventional coding (e.g. Secondly, it enables computers to perform tasks that far exceed human abilities, like analysing terabytes of data at lightning speed to unearth hidden patterns and make sense of them. But it is also hard not to feel some unease about the prospect of self-improving computer systems with increasingly human-like and super-human aptitudes, whether it is the threat of mass unemployment, the erosion of privacy, or simply the inability to understand, validate and trust the technologies that will increasingly impact our lives. These problems that artificial intelligence (AI) is throwing back at us are complex and multifaceted, and to tackle them requires concerted endeavours by our technologists, entrepreneurs, lawmakers and thinkers from all fields and walks of life.

How Uber Pre-Packages Machine Learning 'as a service' Across the Business


Uber has a core team providing pre-packaged machine learning algorithms'as-a-service' to its team of mobile app developers, map experts and autonomous driving teams. Head of machine learning at Uber, Danny Lange has been busy bringing to Uber a similar structure to one he built during his time at infrastructure-as-a-service (IaaS) provider Amazon Web Services (AWS). There he managed their internal machine learning platform and helped launch Amazon Machine Learning for AWS. Speaking to Computerworld UK, Lange said: "We are going to make every part of our business smarter and provide better user experiences. I run the team that offers that as an infrastructure and we have three core areas: drivers and riders taking trips, improving maps for drivers and self driving vehicles."

Can AI and Sensors Power the Next Generation of Traffic Lights? - DZone IoT


It's perhaps understandable therefore that humans struggle to program traffic lights to function effectively. Video games have been enthusiastic adopters of reinforcement learning in recent years, with algorithms used to figure out the most beneficial action at a given moment, and therefore to award the player the highest score. In traffic management, the top score is awarded when drivers are kept waiting for the shortest period of time and the shortness of queues. Like reinforcement learning, deep learning also takes inspiration from the human brain.