Data science has surged to the forefront of the data ecosystem, with demonstrable business value derived from the numerous expressions of Artificial Intelligence currently being adopted in the enterprise. It represents the nucleus of the power of predictive analytics, and the extension of data culture throughout modern organizations. Consequently, data science trends are more impactful than those in other data management domains, which is why its increasing consumerization (beyond the realm of data scientists) is perhaps the most meaningful vector throughout IT today. "You can't find the data scientist talent to build models? Well, how about if those models can be built by a business analyst with one mouse click and one API call?" asked Oliver Schabenberger, CTO and COO at SAS, in conversation with AI Business.

AI is a hotly debated topic in every conversation, so much so that we have moved from saying'there is an app for that' to'there is an AI for that'. Oliver Schabenberger, chief operating officer and chief technology officer at SAS, observes how AI has permeated everyday discourse in recent years. Yet, AI has not always been talked about this way. An overhype of the technology led to'AI winter' in the 1980s, he says in his keynote at the Analytics Experience conference this week in Milan. During cocktail gatherings, saying one worked in AI could kill a conversation.

Deep learning is all the rage these days, and is driving a surge in interest around artificial intelligence. However, despite the advantages that deep neural networks can bring for certain applications, the actual use cases for deep learning in the real world remain narrow, as traditional machine learning methods continue to lead the way. The rapid ascent of deep learning is arguably one of the least expected technological phenomena to have occurred in the past five years. While neural networks have been around for decades, it wasn't until the University of Toronto's Geoff Hinton paired those techniques with a new computational paradigm (GPU) and the availability of huge amounts of training data to yield what we now know as deep learning. The result is that deep neural network technology has evolved "at a lightening rate," Nvidia CEO Jenson Huang said earlier this year.

What happens when you put a neural network in charge of a rules-based marketing automation solution? Would the AI emerge victorious, or does the human driver still have a thing or two to show the talented mimicker? It's an interesting question, to be sure, but more importantly, and it's an experiment that the folks at SAS – which still uses rules-based approaches in some of its analytics offerings -- actually ran, and the results might surprise you. "It beat our system," SAS Executive Vice President Oliver Schabenberger said during the SAS Analytics Experience conference held last week in San Diego, California. The result forced Schabenberger, who also holds the title of CTO and COO, to inquire about the cause. "Why is it the AI system works better than what our best minds can put together?" he said.

While the job losses generate the most interest and headlines, the losses only tell part of the story. Dig a little bit deeper into the hype cycle and you'll see Gartner also predicts AI will create 2.3 million jobs by 2020, driving a net gain of 500,000 new jobs. The question is no longer whether AI will fundamentally change the workplace. The true question is how companies can successfully use AI in ways that enables, not replaces, the human workforce, helping to make humans faster, more efficient and more productive. Andy Peart is chief strategy and marketing officer at Barcelona, Spain-based Artificial Solutions, a global specialist in natural language interaction.

As it moves to embed artificial intelligence and machine learning throughout its product line, SAS executives say the company will look to its growing base of partners to bring domain expertise to make that artificial intelligence actually intelligent. At its Global Forum event in Denver last week, the company announced that AI would become a common feature across its offerings. And CMO Randy Guard told ChannelBuzz.ca at the event that the biggest untapped opportunity for the company's partners is in connecting that AI capability with their own knowledge. "If you're going to solve a problem an AI product, you've got to bring your domain expertise, and marry the two of those together," Guard said. "As we start embedding AI and intelligence in everything we do, our partner community needs to recognize how that can be applied to the business scenarios they're solving for their customers."

While the job losses generate the most interest and headlines, the losses only tell part of the story. Dig a little bit deeper into the hype cycle and you'll see Gartner also predicts AI will create 2.3 million jobs by 2020, driving a net gain of 500,000 new jobs. The question is no longer whether AI will fundamentally change the workplace. The true question is how companies can successfully use AI in ways that enables, not replaces, the human workforce, helping to make humans faster, more efficient and more productive. Andy Peart is chief strategy and marketing officer at Barcelona, Spain-based Artificial Solutions, a global specialist in natural language interaction.

Are killer machines coming to annihilate mankind? Are we headed for a dystopian future where robots are our overlords? Are the Cylons already among us? Are concerns voiced by industry icons such as Elon Musk, who has donated millions to The Future of Life Institute, warranted? Oliver Schabenberger recently added a more measured voice to this debate in this commentary piece that he wrote for InformationWeek, pointing out that machines "are not surpassing us in thinking or learning."

Washington D.C. – In its most basic form, artificial intelligence is an algorithm that is trained to learn via the data that is fed to it. But what happens when that data is full of bias? "In traditional model building, even with good data we can introduce biases by not constructing the right variables or picking up nuances. A model is a representative of the mechanism that generated the data. So if we don't represent that mechanism correctly, then we are not forecasting correctly, but forecasting something else," explains Oliver Schabenberger, chief technology officer and executive vice president of SAS Institute Inc. to Canadian media at the Analytics Experience 2017.

Washington D.C. – In its most basic form, artificial intelligence is an algorithm that is trained to learn via the data that is fed to it. But what happens what that data is full of bias? "In traditional model building, even with good data we can introduce biases by not constructing the right variables or picking up nuances. A model is a representative of the mechanism that generated the data. So if we don't represent that mechanism correctly, then we are not forecasting correctly, but forecasting something else," explains Oliver Schabenberger, chief technology officer and executive vice president of SAS Institute Inc. to Canadian media at the Analytics Experience 2017.