Fox News Flash top headlines for June 7 are here. Check out what's clicking on Foxnews.com Videos of Boston Dynamics' robots have been the stuff of both awe and inspiration, as well as nightmares. Now, it appears the robots will be doing more than just performing parkour or dancing around on YouTube. According to The Verge, who interviewed Boston Dynamics' CEO Marc Raibert at Amazon's Re:MARS conference in Las Vegas, Spot, the company's dog-like robot and arguably its cutest machine, will be available for purchase "within months" and certainly before the end of 2019.
We're in the midst of a wave of excitement around AI such as hasn't been seen for a few decades. But those previous periods of inflated expectations led to troughs of disappointment. This time is (mostly) different. Applications of AI such as predictive analytics are already decreasing costs and improving reliability of industrial machinery. Pattern recognition can equal or exceed the ability of human experts in some domains.
AI and ML are starting to transform business. Gartner estimates that AI technology will generate $3.9 trillion in business value by 2022. Soon, companies that do not invest in AI risk falling behind competitively. But having said that, organizations should not adopt AI just to stay on trend. It's crucial to build an AI business case, assess the company's AI readiness and create the program in the right way.
Leave it to the folks at Google to devise AI capable of predicting which machine learning models will produce the best results. In a newly-published paper ("Off-Policy Evaluation via Off-Policy Classification") and blog post, a team of Google AI researchers propose what they call "off-policy classification," or OPC, which evaluates the performance of AI-driven agents by treating evaluation as a classification problem. The team notes that their approach -- a variant of reinforcement learning, which employs rewards to drive software policies toward goals -- works with image inputs and scales to tasks including vision-based robotic grasping. "Fully off-policy reinforcement learning is a variant in which an agent learns entirely from older data, which is appealing because it enables model iteration without requiring a physical robot," writes Robotics at Google software engineer Alexa Irpan. "With fully off-policy RL, one can train several models on the same fixed dataset collected by previous agents, then select the best one."
There is an ongoing worldwide pop culture phenomenon which has recently engulfed the entire world, and of course you know what I'm talking about: data science! But you probably knew that. While the story in Endgame revolves more or less around the Infinity Stones -- as has the entire MCU for quite a while -- and their role in saving nothing less than the entire universe (or half of it, anyways), the practice of data science actually also has something to learn from their powers. I know you don't believe me, but let's take a look. Don't forget, Thanos was a bit of a data scientist himself.
Selma Turki is on a mission to ensure more women understand the impact of new technologies on their careers and engage with the change from the planning process. Her work as EY EMEIA leader, cognitive solutions, advisory means that she works with clients to find solutions using new technologies. When we met to discuss the challenges in addressing gender diversity in the tech space, she shared her views on where the problems lie. The most common problem cited for AI lies in programming. Gartner estimates by 2022, 85% of AI projects will have incorrect outcomes due to biases in data or algorithms.
Machine learning is a proven technique for helping organizations to improve their security posture because it helps automate whatever can be automated. "Machine learning is a way to use technology that allows you to do more with less," says John Matthews, CIO of ExtraHop Networks. "I'm so tired of the AI buzzword bingo," Matthews says. Or maybe it's because ML has never enjoyed its own Terminator franchise or been linked to the overthrow of human civilization. But on the upside, machine learning makes it easier for organizations to be better at information security, and especially at the basics.
By the end of 2019, 40% of all Digital Transformation (DX) initiatives will be related to artificial intelligence (AI). Your organization needs an enterprise-wide AI Strategy to prioritize when and where to implement AI and how to use it. IDC's new eBook outlines four foundational elements that a successful AI strategy needs.
A new MIT-developed technique enables robots to quickly identify objects hidden in a three-dimensional cloud of data, reminiscent of how some people can make sense of a densely patterned "Magic Eye" image if they observe it in just the right way. Robots typically "see" their environment through sensors that collect and translate a visual scene into a matrix of dots. Think of the world of, well, "The Matrix," except that the 1s and 0s seen by the fictional character Neo are replaced by dots -- lots of dots -- whose patterns and densities outline the objects in a particular scene. Conventional techniques that try to pick out objects from such clouds of dots, or point clouds, can do so with either speed or accuracy, but not both. With their new technique, the researchers say a robot can accurately pick out an object, such as a small animal, that is otherwise obscured within a dense cloud of dots, within seconds of receiving the visual data.
Otter CEO and Silicon Valley-based serial entrepreneur Sam Liang created a cloud-based artificial intelligence engine to power its automated speech to text transcription service. In my 25-plus-year career--and counting!--as a journalist, I've done thousands of interviews and attended even more meetings, either face to face or over the phone which, for lots of them, I had to manually transcribe the conversations to make sure that I got my interlocutors' comments right--a chore that I dreaded. So when my friend Marie Domingo introduced me to Otter.ai at the TechCrunch Disrupt conference in San Francisco last fall--she was handling their public relations at that time--I was, of course, curious to know more about how this mobile app works but highly skeptical that it would actually help me. After more than six months using the free version of the service that includes 600 minutes of free transcriptions per month, Otter.ai Not only does the app (for iOS and Android) do an excellent job in transcribing my live interviews and meetings from speech to text with great accuracy--and letting me focus on the actual conversations rather than taking notes verbatim--but it made the notes totally searchable which is an amazing time saver when I'm looking for specific keywords.