Content provided by IBM with Insider Studios. In the summer of 1956, 10 scientists and mathematicians gathered at New Hampshire's Dartmouth College to brainstorm a new concept Assistant Professor John McCarthy called "artificial intelligence." According to the original proposal for the research project, McCarthy--along with fellow organizers from Harvard, Bell Labs and IBM--wanted to explore the idea of programming machines to use language and solve problems for humans while improving over time. It would be years before these lofty objectives were met, but the summer workshop is credited with launching the field of artificial intelligence (AI). Sixty years later, cognitive scientists, data analysts, UX designers and countless others are doing everything those pioneering scientists hoped for--and more.

The presence of artificial intelligence-powered IT is growing in healthcare. More and more CIOs and caregivers are finding new uses for powerful AI to help its human users in the delivery of care, both behind the scenes and at the point of care. Dashboards are popular tools for healthcare executives and caregivers to use to track and measure technologies in action. And AI is no different in this respect, though it is very different from other technologies in its complexity. So some forward-looking CIOs are putting together AI dashboards, or beginning to think about such dashboards and what would make them useful tools.

The Propeller spirometer and app uses advanced analytics to help patients identify triggers, symptoms, trends and other personalized insights. Also, Propeller's Air is an open API that uses machine learning from Propeller devices and environmental sources and can predict how asthma may be affected by local environmental conditions.

I have completed Bachelor of Engineering (mechanical stream). I am currently working in the aerospace service industry and am eagerly looking forward to switching to Data Science. Two reasons behind this thought are: I am pretty good in mathematical and analytical skills, while Data Science is one of the remunerative jobs and this industry is expected to grow exponentially. What is your suggestion on this? Being a non-programming professional, can I learn Data Science?

BI, or business intelligence, is changing, as a result of the IoT (Internet of Things). Data is a new kind of currency in today's connected world, and it seems like just about everything we do in life and business generates data and it's not just humans that are generating data. We are seeing all kinds of connected machines and smart devices generate unfathomable amounts of information every minute of every day. The real question is what happens to all of this data? Answer: it depends on the context, but a lot of it falls by the wayside.

Back in 2015, drone-maker DJI began putting some of its image-stabilization technology into hand-held camera gimbals. The first, called the Osmo, was well-received among the prosumer crowd, because it let users capture unshaky video on either a smartphone or on the device itself. Since then, DJI has released a series of updates to the Osmo, including the smartphone-supporting Osmo Mobile. Now DJI is putting out its most pocketable Osmo ever--suitably named the Osmo Pocket. It has a tiny color touchscreen, so you can see and control your video capture directly from the device, or you can attach your smartphone via USB-C or Lightning and use your phone as a viewfinder.

Nov 28, 2018 Ginny Engholm Danny Iny, on How to Future-Proof Your Job Digital disruption is transforming the world of work. The skills that workers will need in the long term are changing at a rapid pace, and this is causing employees -- and employers -- a lot of anxiety. Workers fear being replaced by tech for good reason -- a 2017 report from PricewaterhouseCoopers estimated that 38 percent of U.S. jobs are at risk of being replaced by automation by the early 2030s. Some industries, such as transportation, wholesale, manufacturing and retail, are particularly vulnerable to automation. But no industry is safe.

Deep learning is a concept that most people know is valuable but do not understand. Global industries, however, are seeing it's worth and investing heavily in the technology; the global deep learning market is expected to reach $10.2 billion by 2025. What are the nuances and intricacies that make deep learning a practical solution to some of today's most complex problems? And how can this technology be understood by more, making it a less intimidating and more approachable topic? "One reason why deep learning is a confusing concept to many is that it is often used alongside the terms machine learning (ML) and artificial intelligence (AI). Deep learning (DL) is a subset of ML, which is itself a subset of AI," explains Jennifer Roubaud, the VP of UK and Ireland for Dataiku.

At the heart of the discipline of artificial intelligence is the idea that one day we'll be able to build a machine that's as smart as a human. Such a system is often referred to as an artificial general intelligence, or AGI, which is a name that distinguishes the concept from the broader field of study. It also makes it clear that true AI possesses intelligence that is both broad and adaptable. To date, we've built countless systems that are superhuman at specific tasks, but none that can match a rat when it comes to general brain power. But despite the centrality of this idea to the field of AI, there's little agreement among researchers as to when this feat might actually be achievable.

In the era of the digital world, mobile and social media have become major channels for marketing. A fundamental difference with the digital era from the pre-Internet era is the concept of personal space. Mobile phone users consider their device to be their personal space and are not keen on intrusions into this space which are not relevant. Furthermore, this digital era is one of big data. With social media, mobile and internet activities continuously generating a huge amount of data, marketers have access to insights in different forms, coming from different sources and generated at different velocities.