Alien: Earth begins where most Alien stories end: with a crew of blue-collar workers realizing that they are, and have always been, doomed. Deemed expendable by their employers over the monsters in the cargo hold (at least the crew of the USCSS Maginot, unlike the Nostromo, knew the monsters were the mission), they are made mortally aware of their place at the bottom of several food chains at once. With the FX show's fifth episode, cheekily titled "In Space, No One …," creator Noah Hawley takes us back to the Maginot's corridors to give viewers a rendition of Alien in miniature, retrofitting the sturdy bones of Ridley Scott's seminal film to his own ends. This may sound like a cynical enterprise, but it's par for the course for Alien. As Slate's own Sam Adams has noted, the series is Hollywood's greatest non-franchise, a collection of films (and comic books and video games) constantly remixing a few primary colors into compelling new shades.

Can we imagine a scenario in which the different anxieties aroused by George Romero's horror film Night of the Living Dead and Stanley Kubrick's sci-fi dystopia 2001: A Space Odyssey merge? How might a monster that combined our fear of becoming something less than human with our fear of increasingly "intelligent" machines appear to us and what might it say? There is one work – of both horror and science fiction – that imagines such a monster. Published almost exactly 150 years before Romero and Kubrick released their movies, it is a book in which physical deformity and technological mutiny coalesce, creating a monster that is both a zombie and AI, or something in between the two. A gothic fiction, it is also described by some literary historians as the first science-fiction novel.

Peggy and John Havlik, co-author, “The Leadership Killer: Reclaiming Humility in an Age of Arrogance” returns to the show to talk about hubris. He says hubris is the root of all evil that makes good leaders go bad. They also discuss: Traits commonly seen in good coaches or leaders. The importance of showing empathy and owning up to mistakes today. One piece of advice for leaders today. (07.07.20 - #676) IoT, Internet of Things, Peggy Smedley, artificial intelligence, machine learning, big data, digital transformation, cybersecurity, blockchain, 5G cloud, sustainability, future of work, podcast

When Google Flu Trends was launched in 2009, Google's chief economist, Hal Varian, explained that search trends could be used to "predict the present." At the time, the notion that useful patterns and insights could be extracted from large-scale search query data made perfect sense. After all, many users' digital journeys begin with a search query -- including 8 out of 10 people seeking health-related information. So what could possibly go wrong? The answer is infamous in the business and data science communities.

Discussion on use of artificial intelligence (AI) and health specifically is ubiquitous in the medical and lay press reflecting the perception that it has enormous potential to reduce the personal and global burden of many long-term medical conditions. Currently diabetes appears to be the poster child for the application of AI in health care for a number of reasons.1 Worldwide, the number of adults and children developing diabetes continues to rise in parallel with global access to smartphone technologies. On a daily basis, personal data from people living with diabetes are continuously created and logged. Although the main variable of interest is glucose, with the rise in consumer tracking technologies, glucose data are being supplemented with additional information related to nutrition, physical activity, and sleep. With the increasing availability of additional sensor technologies for physiological monitoring including smart insulin pens, social media, and records of internet searches, the diabetes data pool will continue to grow.2,3

In 1797, at the dawn of the industrial age, Goethe wrote "The Sorcerer's Apprentice," a poem about a magician in training who, through his arrogance and half-baked powers, unleashes a chain of events that he could not control.

About 20 years later, a young Mary Shelley answered a dare to write a ghost story, which she shared at a small gathering at Lake Geneva. Her story would go on to be published as a novel, "Frankenstein; or, the Modern Prometheus," on Jan. 1, 1818. Both are stories about our powers to create things that take on a life of their own. Goethe's poem comes to a climax when the apprentice calls out in a panic: While the master fortunately returns just in time to cancel the treacherous spell, Shelley's tale doesn't end so nicely: Victor Frankenstein's monster goes on a murderous rampage, and his creator is unable to put a stop to the carnage. That's the question we face on the 200th anniversary of "Frankenstein," as we find ourselves grappling with the unintended consequences of our creations on Facebook, to artificial intelligence and human genetic engineering.

Microsoft Corp. and its competitors should eschew artificial intelligence systems that replace people instead of maximizing their time, Chief Executive Officer Satya Nadella said in an interview Monday. "The fundamental need of every person is to be able to use their time more effectively, not to say, 'let us replace you'," Nadella said in an interview at the DLD conference in Munich. "This year and the next will be the key to democratizing AI. The most exciting thing to me is not just our own promise of AI as exhibited by these products, but to take that capability and put it in the hands of every developer and every organization." Nadella is pushing Microsoft into consumer and industrial applications of software that can make inferences about its environment.

An interesting sampling of thoughts about the future of AI, the obstacles to "human-level" artificial intelligence, and how we might overcome those obstacles is found in the business week article with a link below). I find several interesting issues in the article. In this post, we explore the first; viz., the idea of "human-level" intelligence implicitly assumes that intelligence has levels. Within a very specific framework, it might make sense to talk about levels. For instance, if you are building a machine vision program to recognize hand-printed characters, and you have a very large sample of such hand printed characters to test on, then, it makes sense to measure your improvement in terms of accuracy.