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) …
Corporate investment in artificial intelligence-powered customer care solutions has nearly doubled in the past 12 months alone, according to a recent study by customer experience consulting firm COPC and Execs in the Know, a global community of customer experience professionals. The research, presented in a report titled "The CX Journey: Understanding Corporate Strategies and Best Practices," found that since 2017 companies have dramatically increased their budgets for use of artificial intelligence (AI)-powered solutions for customer service. The number of companies using such technologies increased from 17 percent in 2017 to more than 30 percent at the end of last year, according to the data. An unrelated study from Gartner, though, shows even greater adoption of AI in customer service. The research firm's data found that 37 percent of customer service leaders are piloting or using artificial intelligence bots and virtual customer assistants (VCAs).
Within the next decade, healthcare will see emerging technologies including artificial intelligence, cloud computing, predictive analytics and blockchain spurring billions of dollars in value increases, according to a new McKinsey & Company report on this tech-driven "era of exponential growth." For these innovations to impact areas like clinical productivity, care delivery and waste reduction, though, certain value pools will need to be disrupted across the entire industry. Here are four possible disruptive changes that could transform healthcare in the coming years, according to McKinsey. More articles about AI: How AI can enhance clinical productivity IBM Research using self-driving car tech to promote seniors' wellbeing Bill calls for $2.2B in federal AI funding
Storage is no longer an afterthought when it comes to systems of any reasonable scale. This is not to say significant thought has not gone into various storage architectures for HPC and large-scale enterprise but the options are expanding. Now, with AI and its latency and bandwidth requirements added to the mix, storage is more diverse than its been for the last decade. All-flash arrays, advances in NVMe over fabrics, new protocols and data movement innovations to keep accelerators fed, and a rethink of traditional parallel file systems are all pushing storage in new directions. Despite all the freshness in terms of approaches and technologies, there are still questions about where to start that hinge on the workloads at hand.
T-Mobile prides itself on being a disruptor in the world of wireless communications, always thinking creatively about the relationship it wants to have with its consumers. That includes the company's approach to using AI for customer service. Using the predictive capabilities of machine learning to improve customer service is a great example of AI augmenting human abilities. T-Mobile sees it as an opportunity to serve customers better and faster, benefiting not just the company and its service agents but also enriching the customer experience and creating stronger human-to-human connections. "Most industries have looked to use AI and machine learning to build more sophisticated Interactive Voice Response (IVR) systems and chatbots as a means to deflect for as long as possible the interaction between a human customer service agent and the customer," says Cody Sanford, executive vice president and chief information officer at T-Mobile.
"The Personalization team makes deciding what to play next on Spotify easier and more enjoyable for every listener. We seek to understand the world of music and podcasts better than anyone else so that we can make great recommendations to every individual person and keep the world listening. Everyday, hundreds of millions of people all over the world use the products we build which include destinations like "Home" and "Search" as well as original playlists such as "Discover Weekly" and "Daily Mix."
A new contract with the Massachusetts Institute of Technology (MIT) will bring airmen from across Air Force career fields to work with researchers on artificial intelligence technology. The project will focus on research in AI projects including decision support, maintenance and logistics, talent management, medical readiness, situational awareness, business operations and disaster relief, according to a news release. The effort is part of the service's science and technology strategy. Similar partnerships around the U.S. focus on other innovations.
Landing multi-rotor drones smoothly is difficult. Complex turbulence is created by the airflow from each rotor bouncing off the ground as the ground grows ever closer during a descent. This turbulence is not well understood nor is it easy to compensate for, particularly for autonomous drones. That is why takeoff and landing are often the two trickiest parts of a drone flight. Drones typically wobble and inch slowly toward a landing until power is finally cut, and they drop the remaining distance to the ground.
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There have been huge advancements in recent years in the area of AI "deepfakes", or fake photos or videos of humans created using neural networks. Fake videos of a person usually require a large number of photos of that individual, but Samsung has figured out how to create realistic talking heads from as little as a single portrait photo. In a newly published paper titled, "Few-Shot Adversarial Learning of Realistic Neural Talking Head Models," a team of researchers at the Samsung AI Center in Moscow, Russia, share their new system that has this "few-shot capability." Once it's familiar with human faces, it's able to create talking heads of previously unseen people using one or a few shots of that person. For each photo, the AI is able to detect various "landmarks" on the face -- things like the eyes, nose, mouth, and various lengths and shapes.