Just last night, the British Retail Consortium released UK retail sales figures that showed that the rise in inflation is already starting to bite, with non-food high street sales suffering the worst fall in nearly six years.
In this environment, retailers are increasingly looking to AI to better understand and target their customers. For example, using IBM Watson’s expert shopper, North Face can help you find the perfect jacket for your next adventure. On the L’Oreal app, you can upload a selfie to virtually try on makeup. What are the other innovations coming on the horizon? What does this mean for us as customers?
BCG projects that as many as 1.5 trillion km traveled in the U.S. in 2030 will be in shared, self-driving electric cars. The shift will begin gradually in the early 2020s and could happen even faster if innovations in technology and breakthroughs in pricing accelerate. A typical Chicago consumer could cut his commuting cost by $7,000 a year by ditching a personal car in favor a shared autonomous electric vehicle. Auto companies will face wrenching changes as more than 5 million conventional cars are replaced by an estimated 4.7 million autonomous electric vehicles by 2030.
Kate Darling is a leading figure in the emerging field of robot ethics that she helped and is currently helping to define at the MIT Media-Lab. In this podcast Kate discusses the emotional connection between robots and human and the ramifications that we have not planned for that are just around the corner.
Past data used to train machine learning algorithms is often biased against certain subpopulations. Hence it is important for machine learning predictors to account for this to avoid perpetuating discriminatory practices.
A team of researchers at The Alan Turing Institute proposed an approach methods to analyse the fairness of such algorithms and see whether they contain hidden discrimination. Their approach called “counterfactual fairness” aims to address fairness through causal relationships rather than relying entirely on correlations, like the spurious one between race and arrest for marijuana possession.
To illustrate this, the team analysed a similar policing example in the paper using stop and frisk data from New York City.
Building upon Quick, Draw! (another of Google’s AI Experiments) Google have designed AutoDraw. Instead of telling you what you’ve drawn (Quick Draw!) it offers a suggestions of a user generate image that… well… looked better than my doodles every time. While not perfect it still offers some moments of genuine “wow” but could easily eat up a few hours.
Last year, Tesla announced plans to acquire German engineering firm Grohmann Engineering and reconstitute it as “Tesla Advanced Automation Germany.” Now, the carmaker is using all of its new possession’s resources to gear up for Model 3 production. CEO Elon Musk wants the company to be building 500,000 cars a year next year, the more affordable Model 3 will make up the majority of that total. Tesla already has hundreds of thousands of reservations for the car, but the company has also missed every one of its deadlines for new-car launches so far.
Can Machine Learning be used as a cyber security tool? As government agencies are beginning to turn over security to automated systems that can teach themselves, the idea that hackers can sneakily influence those systems is becoming the latest (and perhaps the greatest) new concern for cybersecurity professionals.
Terrorism and security are constant news topics nowadays. Cornell University researchers are developing a system to enable teams of robots to share information as they move around, and if necessary, interpret what they see. This would allow the robots to conduct surveillance as a single entity with many eyes. What does this mean for the future?
Integrating payments capabilities into messaging apps could be the key to making chatbots the next big platform, according to Kik CEO Ted Livingston. Without integrated payments chatbots provide some useful and engaging experiences, but it is difficult for brands to drive revenue. If chat apps can monetize chatbot interactions the way Apple did for the App Store, it could one day generate up to $32 billion in revenue for the platforms, according to an earlier report by BI Intelligence.