A.I

4. AI pros will shine

At the very top of LinkedIn’s top 15 emerging jobs in the U.S. for 2020 is the AI specialist. Hiring for artificial intelligence pros of various titles (including AI and ML engineers) has grown 74 percent annually over the last four years, according to LinkedIn. “Artificial intelligence and machine learning have both become synonymous with innovation, and our data shows that’s more than just buzz,” LinkedIn said, with especially hot markets in the San Francisco Bay Area, New York, Boston, Seattle, and Los Angeles.

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3. Data governance will get sexy

2020 will be about bringing AI into production, agrees Pat Ryan, executive vice president of enterprise architecture at SPR. But that will require IT to put in work with the chief data officer’s organization. The problem, says Forrester in its 2020 AI predictions report, is “sourcing data from a complex portfolio of applications and convincing various data gatekeepers to play along.”

Next year, the luster of AI and ML will wear off as companies realize it’s not magic, but math, says Ryan. “Organizations now also know the need for high-quality data as the foundation for AI/ML, so come 2020, we’ll see a heightened sense of appreciation and need for data governance, data analysts, data engineers, and machine learning engineers.”

The goal: creating a data pipeline capable of continuous curation to drive more successful AI projects. That’s why “firms with chief data officers (CDOs) are already about 1.5 times more likely to use AI, ML, and/or deep learning for their insights initiatives than those without CDOs,” Forrester says.

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2. Operationalization will be the name of the game

AI has the potential to become the new operating system for the enterprise. “Over the last decade, organizations have been picking up AI know-how and started working with the technology, but successfully putting models into production has remained a challenge,” Gagné says. “This year will be a tipping point for the infrastructure needed to support effective deployments, providing integrated learning environments and data ecosystems that support adaptive decision making by AI.”

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1. IT leaders will get real about measuring AI impact

Here’s a sobering stat: Fewer than two out of five companies reported business gains from AI in the past three years, according to the MIT AI survey. That will need to change in the new year, given the significant investment organizations are continuing to make in AI capabilities.

One way to achieve this is to change the way we measure results. Think reporting against things like ease of use, improved processes, and customer satisfaction.

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10 AI trends to watch in 2020

What’s happening in artificial intelligence in the year ahead? Look for modeling at the edge, new attention to data governance, and continued talent wars, among key AI trends

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The AI industry could top $4 trillion by 2022. As well as being a feat of engineering and computing there are a significant amount of social and moral implications that need to be considered.

Artificial intelligence prediction for 2020.
Big changes in machine learning applications, tools, techniques, platforms, and standards are on the horizon.

It is not enough to have the information, we need to understand what it means. That’s where the analysis and intelligence come in – and that is where the information becomes a powerful instrument.

Artificial Intelligence (AI) is one of the most popular areas in computer science and engineering. AI deals with intelligent behavior, learning, and adaptation in machines, robots and body-less computer programs. AI is everywhere: search engines use it to improve answers to queries, to recognize speech, to translate languages, email programs use it to filter spam, banks use it to predict exchange rates and stock markets, doctors use it to recognize tumors, robots use it to localize themselves and obstacles, autonomous cars use it to drive, video games use it to enhance the player's experience, adaptive telescopes use it to improve image quality, smartphones use it to recognize objects / faces / gestures / voices / music, etc etc.

People are discussing the possibility of superintelligence and AI risks. Big players such as Google, Amazon, Baidu, Microsoft etc are investing billions in AI, and the AI-related job market is growing extremely rapidly.

In this exciting context the first AI master in Switzerland is offered in Lugano, profiting from the competences of the Faculty of Informatics and the Swiss AI Lab, IDSIA, Dalle Molle Institute for Artificial Intelligence, a common institute with SUPSI and one of the world's leading research institutes in this field.

The concept of AI dates back to the 1940s, and the term "artificial intelligence" was introduced at a 1956 conference at Dartmouth College. Over the next two decades, researchers developed programs that played games and did simple pattern recognition and machine learning. Cornell University scientist Frank Rosenblatt developed the Perceptron, the first artificial neural network, which ran on a 5-ton (4.5-metric ton), room-sized IBM computer that was fed punch cards.

But it wasn't until the mid-1980s that a second wave of more complex, multilayer neural networks were developed to tackle higher-level tasks, according to Honavar. In the early 1990s, another breakthrough enabled AI to generalize beyond the training experience.

In the 1990s and 2000s, other technological innovations — the web and increasingly powerful computers — helped accelerate the development of AI. "With the advent of the web, large amounts of data became available in digital form," Honavar says. "Genome sequencing and other projects started generating massive amounts of data, and advances in computing made it possible to store and access this data. We could train the machines to do more complex tasks. You couldn't have had a deep learning model 30 years ago, because you didn't have the data and the computing power."

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