Grocery giant Sobeys has unveiled what it calls Canada's first smart shopping cart at a store in Ontario. It's a great idea for people looking for assistance in shopping and to be on their way as soon as possible. The project is a partnership with New York-based retail technology company Capers. Essentially, the cart you grab as you enter the store has high-tech devices that allow customers to shop, get recipe ideas, obtain information on where to get ingredients, weigh and pay for their food. The cart also has GPS so you can find whatever you need in the store. The Smart Cart has been developed to allow shoppers to bypass traditional checkout lanes, one of the most unpleasant parts of grocery shopping.

So much of the conversation around artificial intelligence is about either the negative consequences, like job losses, or the dangers, in the form of A.I. bias or the potential misuse of facial recognition technology. As Axios's Kaveh Waddell reported this week, there can be a risk in trusting A.I. too much, for example in tasks like prescribing drugs or setting prison sentences. "These programs generally offer new information or a few options meant to help a human decision-maker choose more wisely," he wrote. "But an overworked or overly trusting person can fall into a rubber-stamping role, unquestioningly following algorithmic advice." And that points up the fault line with A.I. Instead of thinking of it as something we should or shouldn't trust -- which puts us in a passive mindset -- we should think of it as a tool we're in charge of, and one that will work to the extent that we direct it in the right way.

With years of cybersecurity experience under his belt, security expert Mike Beck investigated whether he could teach AI to think like a cybersecurity analyst--and helped to transform the fight for online security in the process. Few people know what it's like to battle cyberattacks in a high-stakes environment better than Mike Beck. Without his expertise, London's biggest event in 2012 could have gone dark. Beck, a cybersecurity expert with a background in UK intelligence, joined the UK's MI5 domestic security service shortly before the 2012 Summer Games opened. When the UK government learned of a serious threat to the electricity infrastructure supporting the Games, they looked to one of their newest hires.

When you think of trends that might be shaping the future, the first things that come to mind probably have something to do with technology: Robots taking over jobs. Technology is undoubtedly changing the way we live, and will continue to do so--probably at an accelerating rate--in the near and far future. But there are other trends impacting the course of our lives and societies, too. They're less obvious, and while many are tied to technology, some have nothing to do with it. For the past nine years, entrepreneur and author Rohit Bhargava has read hundreds of articles across all types of publications, tagged and categorized them by topic, funneled frequent topics into broader trends, analyzed those trends, narrowed them down to the most significant ones, and published a book about them as part of his'Non-Obvious' series.

To be a real "full-stack" data scientist, or what many bloggers and employers call a "unicorn," you have to master every step of the data science process -- all the way from storing your data, to putting your finished product (typically a predictive model) in production. But the bulk of data science training focuses on machine/deep learning techniques; data management knowledge is often treated as an afterthought. Data science students usually learn modeling skills with processed and cleaned data in text files stored on their laptop, ignoring how the data sausage is made. Students often don't realize that in industry settings, getting the raw data from various sources to be ready for modeling is usually 80% of the work. And because enterprise projects usually involve a massive amount of data that their local machine is not equipped to handle, the entire modeling process often takes place in the cloud, with most of the applications and databases hosted on servers in data centers elsewhere. Even after the student landed a job as a data scientist, data management often becomes something that a separate data engineering team takes care of. As a result, too many data scientists know too little about data storage and infrastructure, often to the detriment of their ability to make the right decisions at their jobs. The goal of this article is to provide a roadmap of what a data scientist in 2019 should know about data management -- from types of databases, where and how data is stored and processed, to the current commercial options -- so the aspiring "unicorns" could dive deeper on their own, or at least learn enough to sound like one at interviews and cocktail parties.

A recent study conducted by Oracle and research firm Future Workplace found that 64% of people would trust a robot more than their manager. The study included 8,370 employees, managers and HR leaders across 10 countries. Its aim was to see how AI has changed relationships between people and technology at work. It did have some surprising results when comparing human supervisors to potential robot overlords. According to the study, 64 % of people would trust a robot over their manager.

Another humanoid robot could look just like you. You could be close to $130,000 richer. A British engineering and manufacturing firm called Geomiq has put out a call for people interested in being the face of a new "state-of-the-art humanoid" it's developing with an unnamed company. "The company is searching for a'kind and friendly' face to be the literal face of the robot once it goes into production," Geomiq says in a blog post about the project. "This will entail the selected person's face being reproduced on potentially thousands of versions of the robots worldwide."

Many of the solutions we work on at HAL24K involve detecting, classifying and tracking objects from camera feeds. However, for some of our customers it's not enough to simply identify and track each object in the frame: they need to know exactly where it is located in the real world (i.e. its latitude and longitude), how large it is or how fast it is travelling. Luckily, two factors come into play which we can use to our advantage on many of our projects. Firstly, the cameras we use are often fixed in position. This means that each pixel of the background always corresponds to the same real world location.

Google has updated its search algorithms to tap into an AI language model that is better at understanding netizens' queries than previous systems. Pandu Nayak, a Google fellow and vice president of search, announced this month that the Chocolate Factory has rolled out BERT, short for Bidirectional Encoder Representations from Transformers, for its most fundamental product: Google Search. To pull all of this off, researchers at Google AI built a neural network known as a transformer. The architecture is suited to deal with sequences in data, making them ideal for dealing with language. To understand a sentence, you must look at all the words in it in a specific order.

The opening keynote for DevLearn 2019 Conference & Expo was beautiful, poised, and … a robot. Sophia was created by Dr. David Hanson of Hong Kong-based Hanson Robotics. In a conversation with The eLearning Guild's executive director and executive vice president David Kelly, Sophia spoke about artificial intelligence and its impact on work and society. "What really excites me is the opportunity to dispel some common misconceptions humans have about artificial intelligence," said Sophia, who was draped in a black garment and spoke in an eerily polite, feminine voice. "The first is the assumption the AI conversation is about robots. Artificial intelligence is affecting many different aspects of life. Most of us are interacting with AI every day without even realizing it."