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) …
In the past few years, you might have noticed the increasing pace at which vendors are rolling out "platforms" that serve the AI ecosystem, namely addressing data science and machine learning (ML) needs. The "Data Science Platform" and "Machine Learning Platform" are at the front lines of the battle for the mind share and wallets of data scientists, ML project managers, and others that manage AI projects and initiatives. If you're a major technology vendor and you don't have some sort of big play in the AI space, then you risk rapidly becoming irrelevant. But what exactly are these platforms and why is there such an intense market share grab going on? The core of this insight is the realization that ML and data science projects are nothing like typical application or hardware development projects.
Marc Andreessen famously said that "Software is eating the world" and everyone gushed into the room. This was as much a writing on the wall for many traditional enterprises as it was wonderful news for the software industry. Still no one actually understood what he meant. "Today, the world's largest bookseller, Amazon, is a software company -- its core capability is its amazing software engine for selling virtually everything online, no retail stores necessary. On top of that, while Borders was thrashing in the throes of impending bankruptcy, Amazon rearranged its web site to promote its Kindle digital books over physical books for the first time. Now even the books themselves are software."
Trillion-dollar projections on the expanding size of the market are urging companies to capitalize on the Industrial IoT (IIoT). For many, however, it remains unclear how industries should apply IIoT to begin making the hyper-efficient and agile factory of the future a reality. As the Fourth Industrial Revolution transforms manufacturing and material handling, enterprises continue to look for ways to create value from converging technologies. But what are the steps that companies need to take to put together an effective agenda of action? I find it essential that the implementation of the industrial internet is incorporated into the company's strategy and business development.
Rome wasn't built in a day. It has taken years for computers to exhibit the level of intelligence they do today and be able to produce text that sounds and read human-like. It's time to appreciate this revolutionary journey. In 2007, The first step was taken by Robbie Allen, who was a veteran engineer at Cisco. He created an online college basketball website that automatically published game reviews, real-time updates, recaps, and incidents of injury.
Contrary to what some data scientists may like to believe, we can never reduce the world to mere numbers and algorithms. When it comes down to it, decisions are made by humans, and being an effective data scientist means understanding both people and data. When OPower, a software company, wanted to get people to use less energy, they provided customers with plenty of stats about their electricity usage and cost.
A leading expert in artificial intelligence has issued a stark warning against the use of race- and gender-biased algorithms for making critical decisions. Across the globe, algorithms are beginning to oversee various processes from job applications and immigration requests to bail terms and welfare applications. Military researchers are even exploring whether facial recognition technology could enable autonomous drones to identify their own targets. However, University of Sheffield computer expert Noel Sharkey told the Guardian that such algorithms are'infected with biases' and cannot be trusted. Calling for a halt on all AI with the potential to change people's lives, Professor Sharkey instead advocates for vigorous testing before they are used in public.
A US health insurance giant is using an AI system to monitor whether patients with chronic diseases are skipping their medication. Cigna's technology, Health Connect 360, will be rolled out to millions of Americans next month. But experts fear the technology will be used to cancel policies or avoid paying up if patients are found to be missing or incorrectly taking prescriptions. Doctors and nurses will be able to constantly keep an eye on patients' health and step in when they have cause for concern. For example, an alert may be triggered if patients forget to pick up their prescription or miss an appointment.
As the digital transformation of businesses and services continues with full force, artificial intelligence (AI) has become somewhat of a buzzword in the technology sector. While it's true that we haven't quite reached the level of technology sophistication often shown off in Hollywood blockbusters, there already are a variety of use cases where machine learning algorithms are being deployed to improve different aspects of our daily lives. Below, we look at four industries that are reaping the rewards of using AI and what this might mean for the future. Healthcare is one of the most promising areas likely to be transformed significantly by AI and machine learning. This is because this technology can quickly go through large amounts of data and find patterns that humans might miss.
This Machine Learning basics video will help you understand what is Machine Learning, what are the types of Machine Learning - supervised, unsupervised & reinforcement learning, how Machine Learning works with simple examples, and will also explain how Machine Learning is being used in various industries. Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, put simply, the iterative aspect of machine learning is the ability to adapt to new data independently. This is possible as programs learn from previous computations and use "pattern recognition" to produce reliable results.
With the help of artificial intelligence, BP says it needs 40% fewer workers to keep its natural gas ... [ ] flowing in Wyoming. A visitor to one of BP's natural gas fields in Wyoming a few years ago might have noticed an odd sight: smartphones in plastic bags tied to pumps with zip ties. This was an early test of a multistate initiative by the oil giant to link a network of Wi-Fi sensors to an artificial intelligence system--one that now operates the Wamsutter field in Wyoming with far less human oversight than before. Artificial intelligence has come to the oil patch, accelerating a technical change that is transforming the conditions for the oil and gas industry's 150,000 U.S. workers. Giant energy companies like Shell and BP are investing billions to bring artificial intelligence to new refineries, oilfields and deepwater drilling platforms.