If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Why own car when you can shop online and hail a Lyft when you need a ride? Instead of freaking out, Ford is preparing for the future with a self-driving car designed for carrying both packages and passengers. Previously, the company promised a fully autonomous car for ride-sharing by 2021. "We're developing self-driving technology because the world is changing rapidly," Sherif Marakby, the company's vice president of autonomous vehicles and electrification, wrote in a Medium post Tuesday morning. "For many people who live in large cities, owning a car is no longer a viable choice."
Artificial intelligence is set to be the stepping stone between driver assistance systems and truly autonomous vehicles. You'd be forgiven for thinking that fully autonomous cars were just around the corner. In some respects, of course, they are. Partial automation – along the lines of Tesla's much-publicised Autopilot – is set to become commonplace on premium cars over the next few years. Even when it comes to higher levels of autonomy, much of the required hardware is already available.
The Australian Ageing Agenda (AAA) reports that RDM Autonomous has recently opened its first satellite office in South Australia, and will be working with the IRT Group to bring autonomous cars to retirement homes. Details of the program will be revealed at the 2017 Information Technology in Aged Care (ITAC) Conference on the Gold Coast in late November, though the AAA reports that the companies plan to introduce RDM's Pod Zero (pictured) for initial testing at IRT's Kangara Waters facility in Canberra. Following the ITAC Conference, the Pod Zero will also make its way to one of the IRT Group's bases in Brisbane. Speaking with the AAA, Winston Mitchell, IRT IT project coordinator, said: "Piloting the technology on private roads within aged care communities hasn't been done before and IRT is eager to understand how driverless cars can improve residents' independence and quality of life". "Pod Zero will be programmed to safely navigate private roads within IRT Communities and residents will be able to hail Pod Zero and travel independently to appointments and social activities within their community."
Before autonomous trucks and taxis hit the road, manufacturers will need to solve problems far more complex than collision avoidance and navigation (see "10 Breakthrough Technologies 2017: Self-Driving Trucks"). These vehicles will have to anticipate and defend against a full spectrum of malicious attackers wielding both traditional cyberattacks and a new generation of attacks based on so-called adversarial machine learning (see "AI Fight Club Could Help Save Us from a Future of Super-Smart Cyberattacks"). As consensus grows that autonomous vehicles are just a few years away from being deployed in cities as robotic taxis, and on highways to ease the mind-numbing boredom of long-haul trucking, this risk of attack has been largely missing from the breathless coverage. It reminds me of numerous articles promoting e-mail in the early 1990s, before the newfound world of electronic communications was awash in unwanted spam. Back then, the promise of machine learning was seen as a solution to the world's spam problems.
In the blog "From Autonomous to Smart: Importance of Artificial Intelligence," we laid out the artificial intelligence (AI) challenges in creating "smart" edge devices: We also talked about how Moore's Law isn't going to bail us out of these challenges; that the growth of Internet of Things (IOT) data and the complexity of the problems that we are trying to address at the edge (think "smart" cars) is growing much faster than Moore's Law can accommodate. So we are going to use this blog to deep dive into the category of artificial intelligence called reinforcement learning. We are going to see how reinforcement learning might help us to address these challenges; to work smarter at the edge when brute force technology advances will not suffice. With the rapid increases in computing power, it's easy to get seduced into thinking that raw computing power can solve problems like smart edge devices (e.g., cars, trains, airplanes, wind turbines, jet engines, medical devices). Look at the dramatic increase in the number of possible moves between checkers and chess even though the board layout is exactly the same.
The concept of robotics has been in existence for a long time, with Egyptians using automated water clocks to strike the hour bell and hydraulically operated statues that could gesture and speak in 400 BC. Subsequently, there have been many such instances of robotics in the history of mankind. The first modern-day Industrial Revolution dates back to 1800s and had manufacturing processes for metals, chemicals, textiles and mining; leading to an increase in productivity and output. Robots have evolved tremendously over the years and are now being widely used in various sectors such as defence, disaster management, search and rescue operations, and the entertainment industry in the form of electronically operated toys. Automation is an extension of robotics and can be termed as the next phase of industrial revolution.
You need just two eyes and two ears to drive. Those remarkable sensors provide all the info you need to, say, know that a fire engine is coming up fast behind you, so get out of the way. Autonomous vehicles need a whole lot more than that. They use half a dozen cameras to see everything around them, radars to know how far away it all is, and at least one lidar laser scanner to map the world. Yet even that may not be enough.
While self-driving technology should one day help to eliminate most kinds of collisions, there will still be instances where the sudden coming together of car and object will be unavoidable. For example, when someone rushes into the road from a spot unseen by the vehicle's sensors, the autonomous car may have too little distance in which to stop to avoid a collision. In such a case, the technology needs to decide if it's safe to swerve out of the way or simply apply the brakes and brace for impact. Google spinoff Waymo has been thinking a lot about how best to deal with such situations. As it continues to improve its sensor technology to help its vehicle understand its surroundings and respond quickly and safely to unfolding events, it's also been considering how to deal with unavoidable collisions, whether it's with a "soft" human that could easily sustain an injury, or a harder object like another vehicle.
The auto industry seems to be ready for disruption. It is an industry that has functioned largely without changes for the past hundred years, but with the emergence of technologies such as artificial intelligence, self-driving and robotics, the basic paradigm of the industry is expected to change. Robotics, for example, has been used for a long time in the auto industry, but not at the rate that it are being applied currently. Tesla, the biggest disruptor in the automotive industry, has set the trend for increasingly robot-run factories. The Tesla Gigafactory 1 is located at a site which was previously a General Motors automotive factory that employed more than 50,000 people.