IBM is aiming to make Watson Data Platform a de facto operating system for data science in the months ahead as it compiles various functions and parts and combines them into a cloud-based effort to make data more consumable. How can the cloud help CIOs to make the most of the information their firms collect? Today, Watson Data Platform is a collection of services used to prepare, store, ingest and analyze data and then allow customers to build applications on top of it. These various parts, acquired and built on IBM's Bluemix, combine to allow various players in the data science food chain to play a role in solving business problems. In October and November, IBM is likely to finish stitching Watson Data Platform together into a cohesive operating system, said Derek Schoettle, general manager for IBM Watson Data Platform.
Last year, InformationWeek published a high-level introduction to deep learning that was meant to explain the basics of the technology to CIOs and IT managers. Since then, interest in deep learning has skyrocketed, so now seems like a good time to revisit the topic with a deeper dive into the technology. Enterprises have been spending a lot of money on deep learning and related technologies -- and they are about to spend much more. According to IDC, spending on artificial intelligence (AI), which includes deep learning, will likely grow from an estimated $24.0 billion in 2018 to $77.6 billion in 2022. In other words, AI investments will triple in just four years.
Drones are very much on CIOs' radars, but are unlikely to reach mainstream usage anytime soon. Analyst Gartner recently unveiled its 2017 hype cycle for emerging technologies, which is often seen as a good measure of the technologies we can expect to see affecting businesses -- and their IT spending activity -- a few years from now. So what did CIOs learn this year? This ebook, based on the latest ZDNet/TechRepublic special feature, analyzes our original research to pinpoint how organizations are spending their tech dollars in 2018 and what priorities they're focusing on. It also offers advice on ways to build a practical and effective budget that supports the business.
The emerging technologies on the Gartner Inc. Hype Cycle for Emerging Technologies, 2017 reveal three distinct megatrends that will enable businesses to survive and thrive in the digital economy over the next five to 10 years. Artificial intelligence (AI) everywhere, transparently immersive experiences and digital platforms are the trends that will provide unrivaled intelligence, create profoundly new experiences and offer platforms that allow organizations to connect with new business ecosystems. The Hype Cycle for Emerging Technologies report is the longest-running annual Gartner Hype Cycle, providing a cross-industry perspective on the technologies and trends that business strategists, chief innovation officers, R&D leaders, entrepreneurs, global market developers and emerging-technology teams should consider in developing emerging-technology portfolios. The Emerging Technologies Hype Cycle is unique among most Gartner Hype Cycles because it garners insights from more than 2,000 technologies into a succinct set of compelling emerging technologies and trends. This Hype Cycle specifically focuses on the set of technologies that is showing promise in delivering a high degree of competitive advantage over the next five to 10 years (see Figure 1).
Machine learning, deep learning and the connected home have reached the peak of the Hype Cycle, while blockchain and commercial drones have begun the slide into the'trough of disillusionment'. According to Gartner's 2017 Hype Cycle for Emerging Technologies, augmented reality and virtual reality have begun to climb the'plateau of productivity' as they approach mainstream adoption. Gartner is tipping artificial intelligence technologies will be the most disruptive class of technologies over the next decade, giving organisations the ability to harness data insights to adapt to new situations and solve problems. But for certain areas mainstream adoption of AI is still a decade away. For example Gartner is projecting that it will take more than 10 years for AI in marketing to reach the Plateau of Productivity.