NASA's InSight Mars lander is covered in dust in its final selfie, taken on April 24. The following month its robotic arm was put into resting position, aka "retirement pose." NASA's InSight Mars lander is covered in dust in its final selfie, taken on April 24. The following month its robotic arm was put into resting position, aka "retirement pose." The end has long been in sight for InSight, the NASA lander that's been stationed on Mars since 2018.

Mars One Ventures, the company behind an improbable plan to colonize the red planet via global reality show, confirmed last week that it is now in bankruptcy. News reports of this demise brought on a whipping storm of schadenfreude: The "Fyre Festival of Space"--as several outlets called it--had been canceled before liftoff. "It was terribly mismanaged, shortsighted and possibly even fraudulent," said the New York Post. "The false promise is over," added Forbes. Forget the fact that Mars One failed; I mean, we all knew that it would fail.

CARY – Oliver Schabenberger, to use a military term embraced by industry, is "dual hatted." And as chief operating officer as well as chief technology officer at SAS, he serves as No. 2 to CEO and co-founder Jim Goodnight. So no one other than Goodnight has a better hands-on, inside view of the 2018 landscape and beyond for the global software firm that is one of the world's biggest big data juggernauts with an industry-leading emphasis on analytics. And a big part of that future is the Internet of Things as SAS moves to create a division focusing on IoT, which is transforming tech around the world and is forecast as a multi-trillion dollar opportunity by Cisco as well as other firms. In a Q&A with WRAL Tech Wire, Schabenberger, who was promoted to the COO in 2017 after being selected as CTO the previous year, talks about the future and several key issues facing the company.

There are so many things that could go wrong when you're sleeping with a robot. Or you could accidentally push the robot off the edge of the bed and smash it into a million pieces. In my case, the robot woke me up at 5AM saying "goodnight" in Dutch and started breathing. I'm talking about Somnox, "the world's first sleep robot," as it's been touted in pretty successful Kickstarter and Indiegogo campaigns. It's actually more of a peanut-shaped pillow than a humanoid robot that can perform backflips a la Boston Dynamics. But for a machine with no arms, legs, or even a face, it actually feels pretty human.

Artificial intelligence and machine learning are considered the new new thing but Jim Goodnight sets the record straight: "That's exactly what we have been doing in statistics for years and years. We just call it building a model where these computer people call it machine learning. It's a model, it ain't nothing the machine learned." What Goodnight has been doing for 50 years is developing computer software that applies statistical methods to data to help people make better decisions. Automating these decisions is what AI is all about: "Once you have created software to make decisions and humans don't have to make them, you are allowed to call it artificial intelligence."

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.