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) …
Giving robots the ability to operate in the real world has been, and continues to be, one of the most difficult tasks in AI research. Since 1987, researchers at Carnegie Mellon University have been investigating one such task. Their research has been focused on using adaptive, vision-based systems to increase the driving performance of the Navlab line of on-road mobile robots. This research has led to the development of a neural network system that can learn to drive on many road types simply by watching a human teacher. This article describes the evolution of this system from a research project in machine learning to a robust driving system capable of executing tactical driving maneuvers such as lane changing and intersection navigation.
The automobile is being dismantled, reimagined, and rebuilt in Silicon Valley. Intel's proposed $15.3 billion acquisition of Mobileye, an Israeli company that supplies carmakers with a computer-vision technology and advanced driver assistance systems, offers a chance to measure the scale of this rebuild. In particular, it shows how valuable on-the-road data is likely to be in the evolution of automated driving. While the price tag might seem steep, especially with so many players in automated driving today, Mobileye has some key technological strengths and strategic advantages. It's also developing new technologies that could help solidify this position.
Over the past few years, the term "deep learning" has firmly worked its way into business language when the conversation is about Artificial Intelligence (AI), Big Data and analytics. And with good reason – it is an approach to AI which is showing great promise when it comes to developing the autonomous, self-teaching systems which are revolutionizing many industries. Deep Learning is used by Google in its voice and image recognition algorithms, by Netflix and Amazon to decide what you want to watch or buy next, and by researchers at MIT to predict the future. The ever-growing industry which has established itself to sell these tools is always keen to talk about how revolutionary this all is. But what exactly is it?
What Is The Difference Between Deep Learning, Machine Learning and AI? Over the past few years, the term "deep learning" has firmly worked its way into business language when the conversation is about Artificial Intelligence (AI), Big Data and analytics. And with good reason – it is an approach to AI which is showing great promise when it comes to developing the autonomous, self-teaching systems which are revolutionizing many industries. Deep Learning is used by Google in its voice and image recognition algorithms, by Netflix and Amazon to decide what you want to watch or buy next, and by researchers at MIT to predict the future. The ever-growing industry which has established itself to sell these tools is always keen to talk about how revolutionary this all is.
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."
One of the latest trends in the world of commercial vehicle design and engineering is'machine learning', a term that's used across the wider automotive world and is rapidly transforming the way in which certain vehicle systems are engineered and built. Vehicle manufacturers are keen to get hold of people with machine learning experience and bring them on board for the betterment of future products. It's also being touted as fundamental to the development of driverless vehicles. But what is machine learning? Essentially, it's a type of computerised artificial intelligence (AI) that allows the interpretation of large amounts of data, on a scale far in advance of anything humans could do.
Some labor economists have viewed Polanyi's Paradox as a major barrier for AI, arguing it implies a limit on its potential to automate human jobs. Indeed, the automation of driving has been a major challenge for AI research over the past decade. Thus, the automation of driving would be hugely beneficial, saving lives and preventing injuries on a massive scale. In the balance, life saving and injury prevention must take precedence, and we have a moral imperative to develop and deploy automated driving.
Two recent accidents involving Tesla's Autopilot system may raise questions about how computer systems based on learning should be validated and investigated when something goes wrong. But machine learning techniques are increasingly used to train automotive systems, especially to recognize visual information. For example, a deep learning neural network can be trained to recognize dogs in photographs or video footage with remarkable accuracy provided it sees enough examples. A team at Princeton designed an automated driving system based largely on deep learning.
Besides supporting internal customers in truck design and engineering, the analytics group uses advanced statistics and machine learning techniques to benefit its external customers. The model predicts failures for more than 40,000 combinations of diagnostic trouble codes (DTCs) by make, model and year of vehicle. When alerts are found for International trucks, its customer service group can address the problem directly with the fleet customer. The team used the technique to analyze the usage patterns of 100,000 vehicles by engine operating hours, miles, idling time, etc.
How do you satisfy the "one button" trick to help solve the menu and button configuration dilemma on the vehicle dashboard? One reason I chose to move into the automotive group at SAS is their proven approaches and experience with applying machine learning techniques to help these situations. Driverless vehicles, connected cars, e-hailing, car sharing, and other innovative offerings are reshaping our industry. In the case of getting the dashboard to work intuitively, conveniently and effectively with the driver, machine learning techniques are a wise choice.