For many people, the word "automation" conjures up dystopian scenes of humans versus machines. A future in which people set aside our differences to oppose the sleek, metallic products of our own engineering. Few but growth-minded business types get a warm-and-fuzzy feeling of optimism when the word "automation" comes up. There's virtually no job that won't be touched by artificial intelligence (A.I.) and robotics. According to a recent Ball State study, robots and A.I. accounted for around 87 percent of job loss in the United States between 2000 and 2010.

On the dash to the subway one morning, I grabbed an AMNY and proceeded to toss away the advertising insert, as usual. But the headline stopped me from dropping it in the trash: "What's Next? I work at THIRTEEN and the day before, I had attended a meeting about digital transformation, innovation and new technology. We're in the business of broadcast and video, but our discussion touched on all of the new ways content is being delivered, including voice assistants that pair with smart speakers for the home, like Alexa, Google Assistant and Siri. So instead of reading the AMNY news of the day, I combed what the future might bring, according to the sponsored content insert (from NewsDay, for P.C. Projections of what artificial intelligence will do in our homes were impressive but also made me just a little uncomfortable.

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The period between November through February is pretty interesting for the field of medical imaging -- two major conferences bring out the latest developments in the field during this period. The Radiological Society of North America (RSNA) concluded its four-day annual meeting at the end of November 2018, the largest radiology event globally. Indeed, as healthcare looks to futuristic technologies, so does radiology. February saw the Healthcare Information and Management Systems Society (HIMSS) conference, where again technology for medical imaging is at display. From both the conferences, here's Frost & Sullivan's take on the top imaging technologies and how they will continue to evolve.

Old days of hard and not always profitable human labor is over, Smart Farming powered by Machine Learning with its high-precision algorithms is a new concept emerging today. Aiming to increase the quantity and quality of products, this cutting-edge movement makes sustainable productivity growth for everyone working in the agriculture realm. Farming goes digital and now we are observing 4th Agricultural Revolution. Everyday machines learn to solve complicated tasks, and they are doing it better with time. So, what is Machine learning applications in farming today and why should farmers care?

Recognizing that organizations are slow to adopt AI, due in part to rising data complexities, IBM (NYSE: IBM) today announced new innovations that further advance its Watson Anywhere approach to scaling AI across any cloud, and a host of clients who are leveraging the strategy to bring AI to their data, wherever it resides. "We collaborate with clients every day and around the world on their data and AI challenges, and this year we tackled one of the big drawbacks to scaling AI throughout the enterprise – vendor lock-in," said Rob Thomas, General Manager, IBM Data and AI. "When we introduced the ability to run Watson on any cloud, we opened up AI for clients in ways never imagined. Today, we pushed that even further adding even more capabilities to our Watson products running on Cloud Pak for Data." Increasing data complexity, as well as data preparation, skills shortages, and a lack of data culture are combining to slow AI adoption at a time when interest in AI continues to climb.

There is no bigger industry on our planet than food and agriculture, with a consistent, loyal customer base of 7 billion. In fact, the World Bank estimates that food and agriculture comprise about 10% of the global GDP, meaning that, food and agriculture would be valued at about $8 trillion globally based on the projected global GDP of $88 trillion for 2019. On the food front, a record $1.71 trillion was spent on food and beverages in 2018 at grocery stores and other retailers and away-from-home meals and snacks in the United States alone. During the same year, 9.7% of Americans' disposable personal income was spent on food -- 5% at home and 4.7% away from home -- a percentage that has remained steady amidst economic changes over the past 20 years. However, despite a stalwart customer base, the food industry is facing unprecedented challenges in production, demand and regulations stemming from consumer trends.

An intriguing set of questions that is being explored by researchers across the globe and is being discussed and brainstormed in various organisations and think tanks is: "what is the future of work"; "how forthcoming AI and Automation revolution will impact on the nature and structure of work"; and "what would be the impact of these changes on the fabric of society from social, economic and political perspectives". In a 2013 study "The Future of Employment: How Susceptible are Jobs to Computerisation?" researchers Dr Carl Benedikt Frey and Dr Michael Osborne made an important observation: about 47% jobs in the US will be lost to automation. Dr Carl Frey is the co-director of programme on technology and employment at Oxford Martin School at Oxford University. His research focuses on "how advances in digital technology are reshaping the nature of work and jobs and what that might mean for the future". In 2016, he was named the 2nd most influential young opinion leader by the Swedish business magazine Veckans Affärer.

Machine learning algorithms--also known as artificial intelligence (AI)--can better detect sleep apnea compared with traditional linear approaches, according to a study being presented at the CHEST Annual Meeting 2019. The researchers included 620 patients who were referred to a sleep lab in a suburban community sleep center. Researchers collected information on 12 select parameters: height, weight, waist, hip, body mass index, age, neck side, Modified Friedman stage, snoring, Epworth sleepiness scale, sex, and daytime sleepiness. During phase I, researchers used a binary particle swarm optimization technique to select the best sub-features that characterize sleep apnea. In phase II, they built an artificial neural network model based on a feedforward algorithm to detect sleep apnea.

The world of artificial intelligence is beset by hype and hubris. But make no mistake: AI has the potential to make businesses more efficient and faster. Yet, as transformative technologies keep getting smarter and cheaper, businesses' return on innovation investment has declined by 27 per cent over the last five years, according to a report from Accenture. To Ray Eitel-Porter, who heads up Accenture Applied Intelligence in the UK, the evidence points to one problem: companies don't know how to make the most of AI and data analytics, and how they can apply to business problems. AI is a transformative technology, so businesses should be ambitious and think about solutions that would yield ten times the impact of traditional investments – not just a ten per cent improvement.