An autonomous Chevrolet Bolt is tested at General Motors' Technical Center in Warren, Michigan. When General Motors bought Cruise Automation for $581 million one year ago it was a head-turning deal that brought together a massive, traditional Midwestern carmaker with a tiny San Francisco startup. This quirky pairing may be working as Cruise CEO Kyle Vogt says automated GM vehicles controlled by Cruise technology ar going to be deployed "very quickly." Vogt, who began dabbling in self-driving car technology as a student at MIT in the early 2000s and has been called a "robot guru," has kept a low public profile the past few months amid nonstop announcements by others in the driverless car space. But in a recent conversation with Forbes he gave an update on Cruise's relationship with GM, progress on their automated vehicle program and the rush of competitive activity.

We now know that artificial intelligence (AI) can play a winning game of poker. But, alas, poker is a game of deceit, where one needs to remain calm and hold back any traces of emotions. In today's workplaces, it also helps to remain calm and hold back on emotions -- to an extent. But how many successful business leaders go through their days with constant poker faces? Alas, AI can't bring any of that good stuff to the table as of yet, and that is making it a hard sell.

Ask many interviewers about their least favorite part of the job and they'll almost always point to transcription. It can take hours to turn even a short chat into text, which is a serious pain for everyone from reporters to police interrogators. China tech giant Baidu may have a smarter approach: artificial intelligence. It just released a beta for SwiftScribe, a transcription app that uses a neural network to make sense of speech. The software not only promises relatively accurate speech-to-text processing thanks to training on "thousands of hours" of recordings, but learns from edits. It should account more for how people actually speak, saving you from making a load of edits.

A superhuman skill once the preserve of comic book heroes could soon become a reality. Scientists have used a combination of brain scanning and artificial intelligence to read the minds of'criminals' to determine whether they are guilty of knowingly committing a crime. This is the first time that neurobiological readings alone have been used to determine guilt, according to the study, and the findings could impact how we judge criminal responsibility in the future. A 2013 study found that researchers could predict how likely prisoners were to re-offend through brain scans. A team of neuroscientists at the Mind Research Network in Albuquerque studied a group of 96 male prisoners shortly before they were due to be released.

The vast range of focal points at Monday's panel highlight to us that we're still pretty far from a clear definition of just what artificial intelligence is. Owusu lamented how "AI remains surprisingly ill-defined in the popular consciousness," yet this in itself shows us that the potential of the technology and its applications in our world are yet to be fully realized. "Why are we seeing so much AI now?" Owusu asked of the panel, who discussed the advances in computing power and the accessibility of data. The Google panel representative himself highlighted Google's strong belief in the benevolence of open source AI research as conducive to major advancements in both its own research and a palpable social good. A clear takeaway from the panel, and from the overall Mobile World Congress experience, is that AI is not a technology of the future – it's transforming our world today.

Dimensionality reduction is a critical component of any solution dealing with massive data collections. Being able to sift through a mountain of data efficiently in order to find the key descriptive, predictive and explanatory features of the collection is a fundamental required capability for coping with the Big Data avalanche. Identifying the most interesting dimensions of data is especially valuable when visualizing high-dimensional (high-variety) big data and when telling your data's story. There is a "good news, bad news" angle here. First, the bad news: the human capacity for visualizing multiple dimensions is very limited: 3 or 4 dimensions are manageable; 5 or 6 dimensions are possible; but more dimensions are difficult-to-impossible to assimilate.

Machine learning is a field that threatens to both augment and undermine exactly what it means to be human, and it's becoming increasingly important that you--yes, you--actually understand it. I don't think you should need to have a technical background to know what machine learning is or how it's done. Too much of the discussion about this field is either too technical or too uninformed, and, through this blog, I hope to level the playing field. This is for smart, ambitious people who want to know more about machine learning but who don't care about the esoteric statistical and computational details underlying the field. You don't need to know any math, statistics, or computer science to read and understand it.

Domino's Pizza has been a company willing to embrace technological change over the past few years. In 2015, it announced that customers could order pizza just by texting a pizza emoji to Domino's. Last November, it delivered a pizza by drone to a couple in New Zealand, claiming that it will be faster and safer as a drone delivery would not have to worry about traffic. Now Domino's is taking another step towards the future as it announced the DRU platform, which ZDNet describes as "an artificial intelligence (AI)-based technology that will allow customers to order a pizza using their voice." Customers could use their phone, computers, or virtual assistants like Amazon Alexa to order pizza, find opening hours, and browse through a menu. Domino's has been developing the DRU platform for years, and most companies obviously do not have the resources to make their own virtual assistants.

Human error is a troubling problem in healthcare for hospitals, healthcare providers, and for patients and their families. A report published by The BMJ last year noted that medical errors claim 250,000 lives each year, making it the third leading cause of death in the U.S. The need to address this issue in a meaningful way has led to the development of numerous clinical decision support tools and efforts to improve care team communication. Artificial intelligence can play a critical role in improving patient safety as well. In a talk at SXSW in Austin this week on the future of AI, Eric Horvitz, Microsoft Research Laboratory Technical Fellow and managing director, highlighted a few examples of how AI can be used to learn from adverse events and prevent them. Horvitz said he was excited by the potential applications from AI's adoption in healthcare, especially applications where it could protect and assist physicians, much like a safety net.

Adobe, one of the world's largest and most powerful software companies, is trying something new: It's applying machine learning and image recognition to graphic and web design. In an unnamed project, the company has created tools that automate designers' tasks, like cropping photos and designing web pages. The new project, which uses Adobe's AI and machine learning program Sensei and integrates into the Adobe Experience Manager CMS, will debut at the company's Sneaks competition later in March. While Adobe hasn't committed to integrating it into any of its products, it's one of the most ambitious attempts to marry machine learning and graphic design to date. There have been efforts to use AI in the design world before--for instance, Wix's Advance Design Intelligence and automated projects like Mark Maker, but Adobe's is notable because of the company's sheer reach in the design world.