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) …
Toyota has suspended its'Chauffeur' self-driving car tests on public roads in the US, following the first ever death involving a fully autonomous vehicle. Citing Sunday evening's tragic incident, in which a Volvo SUV in autonomous mode struck and killed a woman in Tempe, Arizona, the firm says it is pausing the program with its test drivers' emotional well-being in mind, according to Bloomberg. At the time of the crash, a human backup driver was sitting behind the wheel to supervise the self-driving Uber's operations. Toyota has suspended its self-driving car tests following the first death involving an autonomous vehicle. Rafaela Vasquez was behind the wheel of the self-driving Volvo SUV which struck 49-year-old Elaine Herzberg.
While the healthcare industry becomes increasingly adept at applying clinical and claims data to improve care, it has largely ignored other data sources that provide the greatest opportunity to positively impact health and cost at scale. The dependence on this limited data set originates in the system's orientation toward "sick care" -- treating illness. To radically improve health care, we need to apply consumer demographic and lifestyle data in ways that help the health care industry shift its focus from providing sick care to partnering with people (rather than "patients") to help them stay well. The government and private sector have dedicated enormous capital and energy to building electronic health record and claims systems to automate and record sick-care transactions. This digitization supports consistent quality of care and payment accuracy, but the data is primarily retrospective – it tells a story of what has been.
Britain is pushing ahead with tests of self-driving cars on public roads despite mounting public concern over safety after a pedestrian was killed by one in the US. The country's biggest carmaker, Jaguar Land Rover, has been experimenting with autonomous cars on roads in the Midlands and is set to demonstrate more of the cars' features, including emergency braking assistance, on urban streets this week. Government-backed trials using small autonomous vehicles in south London are due to end on Friday, with organisers reporting widespread public unease about the implications for road safety and cybersecurity. A self-driving Uber car killed a woman in Tempe, Arizona on Sunday night – the first time a self-driving vehicle has killed someone that was not its occupant. Elaine Herzberg, 49, was wheeling her bicycle when she was struck by the Volvo, and later died of her injuries in hospital.
Who really owns your Internet of Things data? In a world where more and more objects are coming online and vendors are getting involved in the supply chain, how can you keep track of what's yours and what's not? Data management and data rights sharing are critical aspects of the IoT ecosystem debate. For businesses, Industry 4.0 is a new way to think about data across processes. It is also about real-time process integration with digital technology, "intelligence anywhere," and distributed intelligence.
It's still unclear to what degree Uber's vehicle was responsible for the tragedy. Tempe's police chief has said he doesn't believe Uber is at fault, but the department isn't responsible for determining the fault in any crash. The incident came not long after Toyota and Uber had formed a partnership, and just days after a report claiming that Uber was hoping to sell its self-driving tech to Toyota. This isn't likely to put deals in jeopardy, but it may lead to more caution from both companies as they minimize the chances of a repeat incident.
One of the most notable areas where machine learning is burgeoning and delivering fast-paced impact is improving conversion and it's easy to see why, considering the value machine learning can add to the performance optimisation process. Businesses already on the AI bandwagon are certainly stealing a march of their competitors – and successful machine learning cases are definitely not just the remit of large dot coms. The addition of AI-powered machine learning can not only take the time and hassle out of poring through masses of data, but also finds new ways to monetise this data and deliver personalisation at scale. By providing faster and more intelligent feedback, it's allowing more time for performance teams to experiment and apply creativity to greater effect; and this is the stuff that will deliver a fast return and greater impact on the bottom line. So the question is – what's stopping more uptake of machine learning in revolutionising digital product performance?
We have a tendency to blame technology when things go wrong. I'm the first to admit that after years of working in the technology industry I've become more and more annoyed with the technology I use. As artificial intelligence (AI) capabilities have emerged in my smart phone keeping me on schedule, telling me how to get somewhere, or generally keeping me in line, I've gotten conditioned to technology just working. That's when I want to throw that phone, espresso machine, laptop, or home security pad into a blender. AI pioneers have provided us with a glimpse of and conditioned us to ams bient AI, making it hard to break up with each other.
The advent of automated machine learning platforms has expanded the access and availability of algorithmic interpretation over the past several years. But how do the different machine learning platforms stack up from a performance perspective? That's the question that researchers from Arizona State University sought to answer. As the market for machine learning platforms expands, users are naturally inclined to seek sources of information to rank and rate the various options that are available to them. Which systems are the easiest to use?
Typically when deriving insights from a study, there is a good amount of effort needed to get your hands dirty, sift around in the data, and then come back up to the surface with something that is hopefully meaningful. With AI market research tools, insights can be delivered in a digestible format in real-time- as the data churns in. Further, AI market research tools can enrich insights by continuously aggregating data. Not to mention that machine learning algorithms make research tools smarter and more precise the more data they're exposed to.
Numerical linear algebra is concerned with the practical implications of implementing and executing matrix operations in computers with real data. It is an area that requires some previous experience of linear algebra and is focused on both the performance and precision of the operations. In this post, you will discover the fast.ai Computational Linear Algebra for Coders Review Photo by Ruocaled, some rights reserved. The course "Computational Linear Algebra for Coders" is a free online course provided by fast.ai.