As technology is an industry that eats its young, it's rare to come across providers that have been around for more than a human generation. Among the household names today, IBM and SAS are the only ones spanning back over 40 years, with the difference that SAS has been continually growing and, after its early years, profitable. So the company, still under the leadership of founder Dr. Jim Goodnight, continues to forge its own path. Dr. Goodnight opened up last week's SAS analyst gathering with a demonstration using Alexa as the front end for SAS analytics. There's a bit of irony or coincidence here: SAS was founded just after the initial run of Star Trek, and 40 years later, the CEO gives a Star Trek-like demo of his products.

Didi Chuxing President Jean Liu speaks at the WSJ D Live technology conference in Laguna Beach, California, on Oct. 25, 2016. The market for Silicon Valley engineers with expertise in artificial intelligence and automated driving tech keeps getting tighter. Didi Chuxing, the ride-hailing giant backed by Apple that forced Uber to abandon its efforts to win over the Chinese market last year, just opened an R&D center in the self-driving car capital with a big "help wanted" sign in the window. Mountain View, California-based Didi Labs will focus on "AI-based security and intelligent driving technologies," the company said on March 8. It will be led by computer scientist Fengmin Gong, vice president of the Didi Research Institute and a co-founder of Palo Alto Networks.

All the regression theory developed by statisticians over the last 200 years (related to the general linear model) is useless. Regression can be performed as accurately without statistical models, including the computation of confidence intervals (for estimates, predicted values or regression parameters). The non-statistical approach is also more robust than theory described in all statistics textbooks and taught in all statistical courses. It does not require Map-Reduce when data is really big, nor any matrix inversion, maximum likelihood estimation, or mathematical optimization (Newton algorithm). It is indeed incredibly simple, robust, easy to interpret, and easy to code (no statistical libraries required).

For the past 30 years, Neuroscientist Lisa Barrett has been wondering what we've gotten so wrong about emotion and the brain. Sesame Street shows its itty bitty viewers what a picture-perfect sad face looks like – and how we ought to sound when we get mad. NBA teams trying to draft the next LeBron draw upon their own set of facial cues, hoping to assess a player's character or their "team chemistry." Barrett has always been bugged by this idea that our emotions should look or feel a certain way. As an eager graduate student in psychology, she set out to investigate–hoping the scientific method would help guide her back in the right direction.

A versatile GAN(generative adversarial network) implementation focused on scalability and ease-of-use. Configuration in HyperGAN uses JSON files. You can create a new config by running hypergan train. A hypergan configuration contains multiple encoders, multiple discriminators, multiple loss functions, and a single generator. A generator is responsible for projecting an encoding (sometimes called z space) to an output (normally an image).

Written by Tom Mayor, national strategy leader for consulting firm KPMG's Industrial Manufacturing practice, and Todd Dubner, a principal in KPMG's Strategy practice. This is one in a series of periodic guest columns by industry thought leaders. Stop and think for a moment: How do you program a Class 8 truck to drive down the interstate? How do you go from there to instructing it to maneuver a 53-foot trailer in an urban area? How many millions or perhaps billions of lines of computer code would be required to figure out all the decisions necessary to make these day-to-day driving functions successful in an autonomous vehicle?

Autonomous or driverless vehicles are a hot topic on the AI scene right now. Google, Volvo, Tesla, Uber… these are just some of the big names in the race to prove that driverless or autonomous vehicles are better and maybe even safer than human-driven vehicles. I was at a family event recently and two guests were chatting about the Artificial Intelligence (AI) component of driverless or autonomous vehicles and more specifically, how these vehicles are currently unable to detect human movement at high speed. One cited the example of a child stepping into the road whilst a vehicle was approaching at high speed. Some debate ensued about the width of lanes in the road (surrounding the vehicle) and the impact they have on the judgement of the driverless/autonomous vehicles.

SAN FRANCISCO - Google paraded executives from Disney, Verizon,The Home Depot and others out today to boast about the infrastructure and enterprise readiness of its cloud services. The kickoff to Google Cloud Next showcased companies that are using Google's capabilities for artificial intelligence, machine learning, analytics and offsite data storage. The main message was that Google is enterprise savvy and can deliver superior results to competitors thanks to its heavy investment in smarter computing. Google describes the conference as "an immersive event that brings together executives, customers, partners, developers, IT decision makers and Google engineers to build the future of the cloud." Running through Friday at Moscone Center West here, opening day also featured news about a new partnership with SAP and the acquisition of Kaggle.

Machine learning is the next big thing, says Google with its acquisition of AI site Kaggle. It doesn't take artificial intelligence to know Google thinks machine learning will be central to your future. After all, the Silicon Valley powerhouse has been busy creating self-teaching tech that can translate languages, vamp with you on piano, and politely crush you at the ancient Chinese game of Go. "Over time, the computer itself -- whatever its form factor -- will be an intelligent assistant helping you through your day," Google CEO Sundar Pichai wrote in his first-ever letter to shareholders, last year. "We will move from mobile first to an AI first world." Now Google has taken another step toward that future.

She speaks with Bloomberg's Caroline Hyde on "Bloomberg Technology."