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) …
Maybe it stems from science fiction's depiction of malevolent robots overtaking humanity. To get to the bottom of this, Pega surveyed 5,000 consumers from North America, the United Kingdom, Japan, Germany, and France about their views on empathy and AI. Get our report and discover how you can combine AI with human ethics for better engagement.
Artificial intelligence is increasingly present in our daily lives. This new technology presents us with many opportunities and much to consider. Creating trustworthy and ethical artificial intelligence requires an understanding not only of the technology itself, but also the societal and ethical conditions present, and how to appropriately account for and assess their impact on the way AI is designed, built, and tested, and the way we interact with it. This course explains the Ethics Guidelines for Trustworthy AI created by the European Union's High-Level Expert Group on Artificial Intelligence, with insights from members of the group as well as experts from SAP. Each of them will share their own insights, examples, and areas of particular interest when it comes to artificial intelligence.
May Masoud is a Solution Specialist at SAS Canada, as part of the Data Sciences team. Leveraging her analytics background, she helps businesses visualize the potential of their data, and surface insights using modern data mining and machine learning techniques. With a Master of Business Analytics following a Bachelor in Statistics & Economics, May aims to create value at every step of the analytics lifecycle: data discovery, model build, model deployment, and business strategy. She has touched the analytics landscape in a variety of industries, whether it is oil production models for the energy sector or solving churn problems in the telecom industry. May's aim is to ubiquitize self-serve analytics and enable citizen data scientists.
When we began our 14-week tech health sprint in October 2018, we did not realize the profound lessons we would learn in just a few months. Together with federal agencies and private sector organizations, we demonstrated the power of applying artificial intelligence (AI) to open federal data. Through this collaborative process, we showed that federal data can be turned into products for real-world health applications with the potential to help millions of Americans have a better life. Joshua Di Frances, the executive director of the Presidential Innovation Fellows (PIF) program, says that this collaboration across agencies and private companies represents a new way of approaching AI and federal open data. "Through incentivizing links between government and industry via a bidirectional AI ecosystem, we can help promote usable, actionable data that benefits the American people," Di Frances said.
Artificial intelligence is set to transform global productivity, working patterns and lifestyles and create enormous wealth. Research firm Gartner expects the global AI economy to increase from about $1.2 trillion last year to about $3.9 Trillion by 2022, while McKinsey sees it delivering global economic activity of around $13 trillion by 2030. By the same year, PricewaterhouseCoopers reckons on $15.7 trillion - more than the current combined output of China and India. Tech investor Tej Kohli, however, believes the impact will be much faster and exponentially larger, however, potentially worth $150 trillion by 2025. That's nearly double the IMF's forecast of $88 trillion for global gross domestic product this year but Kohli is undaunted.
This story was co-published with ProPublica. Ariella Russcol specializes in drama at the Frank Sinatra School of the Arts in Queens, New York, and the senior's performance on this April afternoon didn't disappoint. While the library is normally the quietest room in the school, her ear-piercing screams sounded more like a horror movie than study hall. But they weren't enough to set off a small microphone in the ceiling that was supposed to detect aggression. A few days later, at the Staples Pathways Academy in Westport, Connecticut, junior Sami D'Anna inadvertently triggered the same device with a less spooky sound--a coughing fit from a lingering chest cold.
The field of artificial intelligence is exploding with projects such as IBM Watson, DeepMind's AlphaZero, and voice recognition used in virtual assistants including Amazon's Alexa, Apple's Siri, and Google's Home Assistant. Because of the increasing impact of AI on people's lives, concern is growing about how to take a sound ethical approach to future developments. Building ethical artificial intelligence requires both a moral approach to building AI systems and a plan for making AI systems themselves ethical. For example, developers of self-driving cars should be considering their social consequences including ensuring that the cars themselves are capable of making ethical decisions. Here are some major issues that need to be considered.
Ex-prime minister David Cameron has taken a job at Affiniti, one of the world's largest artificial intelligence companies, which specialises in the use of AI in sales. As chair of the company's advisory board, Mr Cameron says he will be helping support its work to transform the future of customer service and interpersonal communications. While Mr Cameron may not have predicted the UK's future in Europe, there's no doubt that backing the use of AI in sales is a better bet. "AI and machine-learning are the next evolution of the digital revolution," says Brendan Dykes, director of product marketing at customer experience and call centre technology vendor Genesys. "Like the incoming tide, you can ignore it, but it will continue to come and you can either ride the wave or be swept away by it."
We have an almost mystical faith in the ability of artificial intelligence (AI) to understand and solve problems. It's being applied across many areas of our daily lives and, as a result, the hardware to enable this is starting to populate our data centers. Data centers in themselves present an array of complex problems, including optimization and prediction. So, how about using this miracle technology to improve our facilities? Machine learning, and especially deep learning, can examine a large set of data, and find patterns within it that do not depend on the model that humans would use to understand and predict that data.
For now, the NSA is exploring the use of artificial intelligence to detect vulnerabilities. "We are experimenting and developing'self-healing networks,' where we see a vulnerability and the vulnerability is recognized rapidly and patched or mitigated," NSA Director Gen. Paul Nakasone explained in his Joint Forces Quarterly interview. Machine learning eventually could help ease the immense workload placed on each cyber staffer at the agency, Neal Ziring, NSA's Technical Director for Capabilities told CyberScoop. "We're going to need, at the very least, ML techniques to pull signal out of the noise so that the defenders, the operators can be informed [and] spend their time on the most critical events or anomalies rather than trying to make sense of this huge data space manually," Ziring said.