Few recent technological developments have garnered as much fear and optimism as artificial intelligence (AI). Perhaps AI has captured the popular imagination because it requires us to reflect on what makes us fundamentally human, both in terms of our experiences and our capabilities. AI presents us with stark visions of the future. Visions which can be grouped into two now-familiar over-simplifications: a utopian version where the mundane tasks of work and life are delegated to machines, and a dystopian version where automation heralds a new age of mass unemployment and human misery. These tropes are caricatures, of course, but the promise and the perils of AI are very real.

Developing responsible artificial intelligence is about more than just good engineering, said Alka Patel, chief of responsible AI with the U.S. Department of Defense's Joint Artificial Intelligence Center (JAIC). Responsible AI requires well-trained professionals working with the data processing, she said, which requires bridging the gap in AI literacy. The Department of Defense has a strong safety culture, Ms. Patel said, and that is critical to the JAIC's approach to AI policies and processes. At the beginning of the pandemic, for example, there were major concerns around grocery store supply chains, so the JAIC developed a prototype AI tool to better predict when certain zip codes may begin panic-buying. This tool also helps better inform the U.S. military's response to crises.

So far insurers have seen healthcare use plummet since the onset of the COVID-19 pandemic. But experts are concerned about a wave of deferred care that could hit as patients start to return to patients and hospitals, and putting insurers on the hook for an unexpected surge of healthcare spending. Artificial intelligence and machine learning could lend insurers a hand. "We are using the AI approaches to try to protect future cost bubbles," said Colt Courtright, chief data and analytics officer at Premera Blue Cross, during a session with Fierce AI Week on Wednesday. He noted that people are not going in and getting even routine cancer screenings.

Lately I've been exploring deep learning algorithms, and automating system with Artificial Intelligence. Plus, I received a couple of emails asking me about programming skills for AI. So, with those questions in mind, here is a simple introduction to artificial intelligence. AI or artificial intelligence is the process of using software to perform human tasks. It is considered to be a branch of machine learning, and sophisticated algorithms are used to do everything from automating repetitive tasks to creating self-learning sentient systems.

Can a selfie help predict your health risks? The doc.ai app helps people to answer that question by applying artificial intelligence (AI) to health records provided by users. The company has attracted millions of dollars from strategic investors and uses novel techniques to engage customers. The doc.ai platform uses AI to help people enroll in clinical trials, determine the healthiest places they have lived and eventually see what their genetic data can tell them about health and longevity. The startup has a major deal with Anthem, Inc. the nation's second-largest health insurer, operating Blue Cross and Blue Shield plans in 14 states.

After decades of promise and hype, artificial intelligence has finally reached a tipping point of market acceptance. Every day we can read about the latest AI advances and applications from startups and large companies. AI was the star of the 2018 Consumer Electronic Show earlier this year in Las Vegas. But, despite its market acceptance, a recent McKinsey report found that AI adoption is still at an early, experimental stage, especially outside the tech sector. Based on a survey of over 3,000 AI-aware C-level executives across 10 countries and 14 sectors, the report found that 20 percent of respondents had adopted AI at scale in a core part of their business, 40 percent were partial adopters or experimenters, while another 40 percent were still waiting to take their first steps.

Artificial intelligence technologies could reshape economies and societies, but more powerful algorithms do not automatically yield improved business or societal outcomes. Human-centered design thinking can help organizations get the most out of cognitive technologies. Today's artificial intelligence (AI) revolution has been made possible by the big data revolution. The machine learning algorithms researchers have been developing for decades, when cleverly applied to today's web-scale data sets, can yield surprisingly good forms of intelligence. For instance, the United States Postal Service has long used neural network models to automatically read handwritten zip code digits.

Health care is moving away from the traditional fee-for-service model to an outcomes-based model that takes a more comprehensive view of treatment and prevention. For health insurers, this means reacting dynamically and changing business models to stay responsive to market demands. New developments in cognitive computing can help insurers stay on top of changing consumer and regulatory expectations. Historically, health insurance has been a volume game. Insurers understood their business in terms of the number of claims processed, but rarely looked at other variables, including how much expense and repetitive work goes into claims management or how specific groups of policyholders use their health insurance.

The market for AI applications is white hot with huge potential, but that potential needs to be tempered by a heavy dose of realism about the capabilities and business value of artificial intelligence technology, according to industry analysts. "It's sort of captured the imagination of the world in general, but the danger we have with AI is expectations getting too high," said Mike Gualtieri, an analyst with Forrester Research. From the early days of computing, the story of AI applications has always been one of early excitement, huge hype and inevitable bust. Every decade or so, some advance in computing power has led to speculation that machines capable of replicating some aspect of human thought were right around the corner. But each time the challenges proved too difficult, and the technology was not ready.

The market for AI applications is white hot with huge potential, but that potential needs to be tempered by a heavy dose of realism, according to industry analysts. "It's sort of captured the imagination of the world in general, but the danger we have with AI is expectations getting too high," Mike Gualtieri, an analyst with Forrester Research, said. From the early days of computing, the story of AI applications has always been one of early excitement, huge hype and inevitable bust. Every decade or so, some advance in computing power has led to speculation that machines capable of replicating some aspect of human thought were right around the corner. But each time the challenges proved too difficult, and the technology was not ready.