A bipartisan panel of voters weighed in on the future of artificial intelligence and growing concerns surrounding the potential dangers of the emerging technology. A U.K.-based tech expert said he is not losing sleep at night over the recent growth of artificial intelligence but argued he does have concerns over AI potentially becoming a hellish boss that oversees an employee's every move. Michael Wooldridge is a professor of computer science at the University of Oxford who has been a leading expert on AI for at least 30 years. He spoke with The Guardian this month regarding upcoming lectures he will lead this winter to demystify artificial intelligence, while noting what concerns he does have with the tech. He told the outlet that he does not share the same worries as some AI experts who warn the powerful systems could one day lead to the downfall of humanity.

It has been touted as an existential risk on a par with pandemics. But when it comes to artificial intelligence, at least one pioneer is not losing sleep over such worries. Prof Michael Wooldridge, who will be delivering this year's Royal Institution Christmas lectures, said he was more concerned AI could become the boss from hell, monitoring employees' every email, offering continual feedback and even – potentially – deciding who gets fired. "There are some prototypical examples of those tools that are available today. And I find that very, very disturbing," he said.

Robots and AI are poised to make life a lot easier for us. But that reality is causing a lot of people to lose sleep. Workers will be displaced, industries will be disrupted, and our workplaces will be changing. Guest Alexandra Levit doesn't lose sleep over this. Levit is a futurist--she looks at marketplace trends to forecast what has the most potential to cause disruptions.

This post is'not' intended to teach people how to use popular predictive modelling APIs for free. Although, to your surprise, this isn't a far fetched possibility. Trained Machine learning models are basically a function that maps feature vectors to the output variable. Upon querying with a test instance, the model predicts an outcome, assigning probability scores to all the possible classes. Google, Amazon etc provides public facing APIs to train predictive models on the subscriber's data, the model can further be used for prediction purposes .

This post is'not' intended to teach people how to use popular predictive modelling APIs for free. Although, to your surprise, this isn't a far fetched possibility. Trained Machine learning models are basically a function that maps feature vectors to the output variable. Upon querying with a test instance, the model predicts an outcome, assigning probability scores to all the possible classes. Google, Amazon etc provides public facing APIs to train predictive models on the subscriber's data, the model can further be used for prediction purposes .

This post is'not' intended to teach people how to use popular predictive modelling APIs for free. Although, to your surprise, this isn't a far fetched possibility. Trained Machine learning models are basically a function that maps feature vectors to the output variable. Upon querying with a test instance, the model predicts an outcome, assigning probability scores to all the possible classes. Google, Amazon etc provides public facing APIs to train predictive models on the subscriber's data, the model can further be used for prediction purposes .