Deep learning, a variation of Machine Learning (ML), represents the major driver toward artificial intelligence(AI), reports Gartner. Gartner's 2017 Hype Cycle for Emerging Technologies notes deep learning is receiving additional attention because it harnesses cognitive domains that were previously the exclusive territory of humans, mainly image and voice recognition and text understanding. Today, most common use cases of ML through deep learning are in image, text and audio processing -- but increasingly also in predicting demand, determining deficiencies around service and product quality, detecting new types of fraud, streaming analytics on data in motion, and providing predictive or even prescriptive maintenance. Gartner's advice for harnessing deep learning and related technologies around Machine Learning include starting with simple business problems for which there is consensus about the expected outcomes, and gradually moving toward complex business scenarios.

From advanced Machine Learning and intelligent apps to digital twins and conversational systems, AI is just breaking out of an emerging state with substantial disruptive potential across all industries, says Gartner. All the top technology companies are spending millions each year on AI and cyber security -- from Microsoft to Google, from Cisco to Symantec, including the big name anti-virus companies. However, in the last few years, there has been an increase in startups around security tools that tout machine learning and AI (Darktrace, Cylance, AlienVault, etc.). You can look at this trend by checking out Gartner's Top 10 Strategic Technology Trends for 2017, 2016, and 2015.

There was little or no conversation or questioning around cleansing, prep, transforms, feature engineering, feature selection, model selection, and absolutely none about about hyperparameter tuning. The key issue is that I can clean, prep, transform, engineer features, select features, and run 10 or more model types simultaneously in less than 60 minutes (sometimes a lot less) and get back a nice display of the most accurate and robust model along with exportable code in my selection of languages. Although SAS and SPSS provided very deep discounts to colleges and universities each instructor had to pay several thousand dollars for the teaching version and each student had to pay a few hundred dollars (eventually there were student web based versions that were free but the instructor still had to pay). About the author: Bill Vorhies is Editorial Director for Data Science Central and has practiced as a data scientist and commercial predictive modeler since 2001.

As the AI era continues to unfold, the idea of a future driven by artificial intelligence can evoke mixed emotions.Microsoft (NASDAQ: MSFT) co-founder and Chairman Bill Gates referred to AI as the "holy grail" in computer science. On the other hand, and taking a far more apocalyptic perspective, Tesla CEO Elon Musk likened the development of artificial intelligence technologies to "summoning the demon." Regardless of our feelings on the matter, artificial intelligence is set to play an increasingly prominent role in our day-to-day lives. Let's examine 9 statistics that speak to how artificial intelligence is set to become one of the most important technology trends of our lifetimes. In a survey of corporate executives, 32% of respondents said voice recognition software like Apple'sSiri, Alphabet's (NASDAQ: GOOG) (NASDAQ: GOOGL) Google Assistant, and Amazon.com's

Gartner states that its predictions "examine three fundamental effects of continued digital innovation", comprising experience and engagement, business innovation and secondary effects resulting from increased digital capabilities. By 2020, algorithms will positively alter the behaviour of more than 1 billion global workers: algorithms can positively alter human behaviour, augmenting human intelligence with the large collective memory bank containing knowledge that has been socialised and put to the test, with this to help workers "remember" anything or be informed of just-in-time knowledge they have never even experienced, leaving them to objectively complete the task at hand, while also better appreciating life as it unveils. Through 2020, IoT will increase data centre storage demand by less than 3 per cent: of the roughly 900 exabytes of data centre hard-disk drive and solid-state drive capacity forecast to ship in 2020, IoT discrete sensor storage will represent only 0.4 per cent, with storage from multimedia sensors consuming another 2 per cent, indicating IoT can scale and deliver important data-driven business value and insight while remaining manageable from a storage infrastructure standpoint. By 2020, 40 per cent of employees can cut their healthcare costs by wearing a fitness tracker: companies will increasingly appoint fitness program managers, working closely with human resource leaders, including fitness trackers in wellness programs as part of a broader employee engagement initiative.

IBM's virtual health care assistant powered by Watson, which has memorized most of the world's medical journals and texts, is an example of this. Enterprise is already feeling the impact of AI assistants like IBM's Watson health care tool, but Gartner says these systems will become more "conversational." Powered by machine learning and AI, such systems will learn how to adapt to humans and vice versa. Gartner believes five major focal points will enable the new capabilities and business models of digital business, including information systems, customer experience, analytics and intelligence, the IoT, and business ecosystems.

Key players in machine learning include big cloud players like Amazon (NASDAQ:AMZN), Microsoft (NASDAQ:MSFT), and IBM. A recent study by AI language company Narrative Science found that 80% of executives believed that AI solutions boosted worker performance and created new jobs. Nuance developed the technology that powers Apple's (NASDAQ:AAPL) Siri, which preceded other voice assistants like Alphabet's (NASDAQ:GOOG) (NASDAQ:GOOGL) Google Now, Microsoft's Cortana, and Amazon's Alexa. The Motley Fool owns shares of and recommends Alphabet (A shares), Alphabet (C shares), Amazon.com, The Motley Fool owns shares of Microsoft and has the following options: long January 2018 90 calls on Apple and short January 2018 95 calls on Apple.

The company this morning unveiled Tetration Analytics, which it said is designed to gather "telemetry from hardware and software sensors, and then analyse the information using advanced machine learning techniques". However, it is being heavily promoted as a kind of "time machine" for the data centre. "This is a good sign for Cisco Tetration," Warrilow said. Gartner sees Tetration occupying a space in a broader market Gartner calls IT operations analytics (ITOA).

The security threat landscape continues to evolve not just in scale, but, more importantly, in sophistication. Despite a range of advancements in the industry to safeguard against increasingly bold and intricate threats, organizations have struggled to keep pace with the technologies and techniques employed by those responsible for such attacks. As companies continue to increase their digital footprints, "identify and diagnose" capabilities are not enough to remediate against a growing fundamental business challenge for organizations of all shapes and sizes. We spoke with Avivah Litan, vice president and distinguished analyst at Gartner, about the development of advanced security analytics and important considerations for organizations looking to implement machine learning to defend against an array of internal and external security threats. Q: How are analytics and machine learning changing the current security landscape?

So I have a dashboard with reports I can interact with to understand why something happens, and that typically reduces the numbers of manual steps before I can make a decision and take some actions. Whether it's IoT, big data or analytics, companies have a lot more data to base their decisions on, and data-driven decision making sounds obvious. More positively, Avanade's new study of smart technologies says business leaders globally expect to be using digital assistants and automated intelligence for problem solving, analysing data, collaborating and making decisions – and they also expect them to increase revenues by more than a third. Certainly, early adopters that Accenture has spoken to who are using machine learning to improve the way they manage customer service, financial resources and risk and compliance, in sales and marketing and in developing new areas of business found "significant, even exponential, business gains" in costs, revenue and customer performance, by using a mix of what Oberoi calls "perceptual intelligence" using natural language and voice biometrics, advanced analytics and business decision support .