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The Impact of Artificial Intelligence in 2018: Seven Predictions


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Artificial intelligence has been described both as a radical force for good and an enabler of evil whose risks are not yet fully understood. But is it just a lot of hype or a transformative technology poised for impact? Here, seven IT execs weigh in on the AI debate and offer their predictions for the changes we may see in 2018.

  1. The AI debate shifts from “is it good or evil” to “is it ever going to be good enough”: If 2017 was the year where the warnings from Elon Musk and Stephen Hawking about the potential evil from AI clashed with predictions from Mark Zuckerberg and Bill Gates on its potential good, 2018 will be the year when the debate shifts to its practical utility. Much like other technologies that were lauded for their world-changing potential and then fizzled as the fog of the hype cleared, early adopters will find themselves disappointed by AI’s obvious limits. The broader public—familiar with Alexa, Siri, and Google Home—will be similarly disillusioned as the experts acknowledge that there is only so much that AI will be able to do, and for really complex problems, a new paradigm will be needed. — Michel Morvan of Cosmo Tech
  2. AI will guide us through the trees. Despite the hype, AI has demonstrated value in industries across the board - from agriculture to biotech to manufacturing. AI is just beginning to ingest data to power services and offerings, in turn providing information necessary for better decision-making.  AI’s success will continue in the new year, specifically in a new area: troubleshooting. Expect to see an impact on troubleshooting for operators, data centers, etc. as AI helps individuals tackle the day-to-day issues, enabling them to focus on critical problems that AI itself can’t help. In 2018, AI will guide and augment humans in solving hard problems as it further cements its value-add as a human cognitive partner, guiding us through the trees to make more impactful decisions. — Ash Munshi, CEO, Pepperdata
  3. Semantic Technology will become the AI Interpreter: As artificial intelligence becomes the new consumer-facing UI for many businesses, semantic technology will emerge as the necessary interpreter. Conversational AI will need precise understanding of the communication from humans and extract meaning from the communication. Artificial intelligence in combination with semantic technology is ideally suited to address this challenge. — Jans Aasman, CEO of Franz
  4. AI will be a creativity enabler: The role of the data analyst is changing thanks to artificial intelligence. AI is allowing marketers to focus once again on the creative art of marketing—the days of data wrangling are coming to an end. With studies indicating that up to 80% of an analyst’s daily routine was relegated to data cleansing and preparation, 2018 will be the year where that 80/20 rule gets flipped upside down. The new AI-based approach to marketing technology will effectively create a win/win for both analysts and marketers alike. — Leah Pope, CMO for Datorama
  5. The button disappears and AI becomes the app interface: Developers need to figure out what data is really important to their business application, how to watch and learn from transactions, what business decisions would most benefit from this kind of proactive AI, and start experimenting. Embedded AI can predict what you need, deliver info and functionality via the right medium at the right place and time, including before you need it, and automate many tasks you do manually today. Some examples: an expense approvals app watches your pattern of approving expense reports, starts to auto-approve 99% of expense reports and only brings to your attention the rare report that requires your attention; an analytics app understands the underlying data, questions asked so far by the business user, questions that other business users in the company might have asked of the same data set, and each day provides a new insight that the analyst might not have thought of.  Siddhartha Agarwal, VP, Product Management & Strategy, Oracle
  6. AI goes mainstream: Enterprises have spent the past few years educating themselves on various artificial intelligence frameworks and tools. But as AI goes mainstream, it will move beyond just small scale experiments run by data scientists in an ad hoc manner to being automated and operationalized. The complexity of technologies used for data-driven machine and deep learning has meant that data scientists spend less time coding and building algorithms and more time configuring and administering databases and data management systems. And as enterprises move forward with operationalizing AI, they will look for products and tools to automate, manage and streamline the entire machine learning and deep learning lifecycle. Data scientists need to focus on the code and algorithms and not automating and operationalizing the process. In 2018, investments in AI lifecycle management will increase, and technologies that house the data and supervise the process will mature. Kinetica CTO and co-founder Nima Negahban
  7. AI will not transform the enterprise in the near future: Previous predictions and claims about the direct impact of AI on enterprises have been overblown. There is excessive hype around how AI will lead us to new discoveries and medical breakthroughs. However, those expecting AI to be the ultimate truth conveyer are mistaken. It will be very hard to design a model that can determine unbiased truth, because human biaswhether explicitly or implicitly—will be coded into these data analytics systems and reinforce existing beliefs and prejudices. With that said, there are certain applications where systems can make better decisions in a shorter amount of time than humans, such as in the case of autonomous vehicles. In 2018 we will begin to see real use cases of the power of AI appear in our everyday livesit just isn’t ready to be the shining star for the enterprise quite yet. Only half of the Global 2000 offer fully digital products. So, despite all of the buzz around digital transformation, there's a lot of catch-up to be done before many of these companies can even consider looking at advanced developments such as AI. Christian Beedgen, CTO, Sumo Logic

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