AI means too many (different) things to too many people. We need better ways of talking – and thinking – about it. Cue, Drew Breunig, a gifted geek and cultural anthropologist, who has come up with a neat categorisation of the technology into three use cases: gods, interns and cogs. "Gods", in this sense, would be "super-intelligent, artificial entities that do things autonomously". In other words, the AGI (artificial general intelligence) that OpenAI's Sam Altman and his crowd are trying to build (at unconscionable expense), while at the same time warning that it could be an existential threat to humanity. AI gods are, Breunig says, the "human replacement use cases".

Anyone waiting for the results of a medical test knows the anxious question: "Will my life change completely when I know?" And the relief if you test negative. Today, artificial intelligence (AI) is increasingly deployed to predict life-threatening diseases. But there remains a big challenge in getting the machine learning (ML) algorithms to be precise enough--specifically, in getting the algorithms to correctly diagnose if someone is sick. Machine learning (ML) is the branch of AI where algorithms learn from datasets and get smarter in the process.

Recent surveys, studies, forecasts and other quantitative assessments of the progress of AI highlighted US consumers' interest in AI working alongside their physicians, the current porn-heavy state of deepfakes, the potential business benefits of Robotic Process Automation (RPA), and the impact of automation on incomes and employment. The internet is home to at least 14,678 deepfakes, according to a new report by DeepTrace. Funds run by computers that follow rules set by humans account for 35% of America's stockmarket, 60% of institutional equity assets and 60% of trading activity. Exchange-traded funds (etfs) and mutual funds automatically track indices of shares and bonds--in September 2019 these vehicles had $4.3trn invested in American equities, exceeding the sums actively run by humans for the first time [The Economist] Only 16% expect to reduce training budgets; 52% of the U.S. employees surveyed believe they have the necessary skills to be successful in an AI-enabled workplace, 20% saying they do not possess the right skills, and 28% reporting they aren't sure; 25% of employers and 20% of employees see a definite gap in workers' skills; 63% of U.S. employees expressed willingness to use a virtual or digital assistant to help them self-manage tasks and deadlines [Genesys surveys of 303 employers and 1,001 employees] Only 29% of finance departments that have deployed Robotic Process Automation (RPA) have utilized the technology for financial reporting; the average amount of avoidable rework in accounting departments can take up to 30% of a full-time employee's overall time. The first comprehensive review of studies published since 2012 comparing analysis of medical images by healthcare professionals and deep learning systems found humans and machines are on a par.

Silicon Valley baron Elon Musk insulted rival billionaire Mark Zuckerberg on Tuesday, escalating a tech wizard war of words over whether robots will become smart enough to kill their human creators. "His understanding of the subject is limited," Musk said in a tweet about the Facebook Inc founder whose algorithms and other technology revolutionized social media and won 2 billion monthly active users. Previously, Zuckerberg was asked about Musk's views on the dangers of robots. In his response, Zuckerberg chided "naysayers" whose "doomsday scenarios" were "irresponsible." Zuckerberg and Musk, CEO of electric car maker Tesla Inc. and rocket company SpaceX, have been waging a debate at a distance over the past few days on the dangers of artificial intelligence.