Though Elon Musk has famously warned humanity about the dangers of artificial intelligence, his shareholders might be well-served by having an algorithm on Tesla's board of directors. In recent years, Tesla has become a cautionary tale for how difficult it is for part-time directors to oversee charismatic, strong-willed CEOs--especially ones who are the founding visionaries of their companies. Given how Elon Musk has landed the company in hot water with the Securities and Exchange Commission with his erratic tweets and mocking disregard for the regulatory regime dictating the proper behavior of a publicly traded company, it's little wonder that Tesla's board has been accused of being "asleep at the wheel." Perhaps their seeming unwillingness to rein him in is due to the Tesla directors' personal loyalty to Musk. Or maybe they simply don't want to spend the time to "preapprove" Musk's tweets about the company, especially with the less conventional hours and fast pace the CEO keeps.

Thinking about the fantastic pie-in-the-sky future is always a fun exercise. I, too, want a self-driving car. But some weeks, it's clear everyone needs to come down to earth. This was one of them. Tesla sued two other electric vehicle companies focusing on self-driving for trade secret theft, proving that building this tech will be a grind.

The patentability of artificial intelligence (AI) has been increasingly scrutinized in light of the surge in AI technology development and the ambiguity regarding the interpretation of software-related patents. The Federal Circuit has gradually refined the criteria for determining subject matter eligibility for software-related patents, and based in part on such jurisprudence, earlier this year the U.S. Patent and Trademark Office (USPTO) released revised guidance on examining patent subject matter eligibility under 35 U.S.C. §101. See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Considering the advances in AI technology and intellectual property law, how do these recent developments shape the outlook of AI patentability?

In a March 21 Slatest, Mark Joseph Stern misstated that the April 2019 Wisconsin Supreme Court election could give Democratic justices a majority. That opportunity will not arise until the 2020 election. Due to an editing error, a March 20 Future Tense Newsletter incorrectly stated that the National Institute of Standards and Technology has been using nonconsensually obtained images to train its Facial Recognition Verification Testing program. The NIST does not develop or train facial recognition systems. It provides independent government evaluations of prototype face recognition technologies.

LIKU baby humanoid robots are demonstrated on the Torooc Inc. stand on the opening day of the MWC Barcelona in Barcelona, Spain, on Monday, Feb. 25, 2019. At the wireless industry's biggest conference, over 100,000 people are set to see the latest innovations in smartphones, artificial intelligence devices and autonomous drones exhibited by more than 2,400 companies. On February 11, 2019, President Trump signed an executive order on Maintaining American Leadership in Artificial Intelligence and in February 2019, a survey by Protiviti called Artificial Intelligence and Machine Learning indicated that only 16% of business leaders surveyed are getting significant value from advanced artificial intelligence (AI) in their companies. The report also found that companies of all sizes and across industries are investing heavily in advanced AI with an average of $36M spent in the fiscal year 2018. Of those same companies surveyed, 10% plan to increase their budgets over the next two years.

Last June, a team at Harvard Medical School and MIT showed that it's pretty darn easy to fool an artificial intelligence system analyzing medical images. Researchers modified a few pixels in eye images, skin photos and chest X-rays to trick deep learning systems into confidently classifying perfectly benign images as malignant. These so-called "adversarial attacks" implement small, carefully designed changes to data--in this case pixel changes imperceptible to human vision--to nudge an algorithm to make a mistake. That's not great news at a time when medical AI systems are just reaching the clinic, with the first AI-based medical device approved in April and AI systems besting doctors at diagnosis across healthcare sectors. Now, in collaboration with a Harvard lawyer and ethicist, the same team is out with an article in the journal Science to offer suggestions about when and how the medical industry might intervene against adversarial attacks.

I saw "Apollo 11" in the Los Angeles suburb of Alhambra, sitting in an IMAX theatre with ten or so other freelancers and retirees who could see a documentary about NASA in the middle of a Thursday. The director and editor, Todd Douglas Miller, tells the story of the moon launch using archival footage, including a trove of 70-mm. The film has no voice-over narration. Instead its story is relayed by the newscasts of Walter Cronkite and the radio transmissions of Edwin (Buzz) Aldrin, Neil Armstrong, Michael Collins, and their interlocutors on Earth. The result is a visual museum about America in July, 1969, in which Aldrin's famous 16-mm.

Artificial intelligence raises exciting possibilities for healthcare, but are companies promising more than they can deliver? But artificial intelligence's potential also comes with an incredible level of hype. "AI has the most transformative potential of anything I've seen in my life, and I graduated medical school 40 years ago. It's the biggest thing I've ever seen by far," prominent cardiologist and author Dr. Eric Topol told Medical Design & Outsourcing. "But it's more in promise than it is in reality."

To my readers it will appear as though I am writing some article on old Greek mythology, but you will soon realize that the world remains the same the more it changes. Recently Ali Rahimi, a researcher in artificial intelligence at Google, compared machine learning with alchemy. Later a few technology journalists, more than ever before, started writing about the relationship between technology and alchemy. Alchemy is about using the "trial and error" method and coming out with a formula (mostly secret or something that cannot be deconstructed). Similarly, in machine learning a model is designed out of data, this model constantly learns and produces an output but nobody know how decisions are made.

Walking around without being constantly identified by AI could soon be a thing of the past, legal experts have warned. The use of facial recognition software could signal the end of civil liberties if the law doesn't change as quickly as advancements in technology, they say. Software already being trialled around the world could soon be adopted by companies and governments to constantly track you wherever you go. Shop owners are already using facial recognition to track shoplifters and could soon be sharing information across a broad network of databases, potentially globally. Previous research has found that the technology isn't always accurate, mistakenly identifying women and individuals with darker shades of skin as the wrong people.