Science How Many Kids Will Have to Die for RFK Jr. to Be Stopped? Enter your email to receive alerts for this author. You can manage your newsletter subscriptions at any time. You're already subscribed to the aa_Mary_Harris newsletter. You can manage your newsletter subscriptions at any time.

To halt the influx of bogus Ernie bot apps from surfacing in the App Store, Chinese technology company Baidu has filed a lawsuit against Apple and many app developers. Baidu is suing Apple and the creators of imitation Ernie bot apps in a lawsuit that was launched on Friday in Beijing Haidian People's Court. It aims to compel Apple to remove the problematic bogus apps and prevent app developers from distributing them. In its lawsuit, Baidu claimed that it has filed claims against Apple and the creators of the imitators of its Ernie bot in Beijing Haidian People's Court. In a statement published by its authorized "Baidu AI" WeChat account, Baidu stated that "Ernie does not currently have any official apps."

Automation of the labor force was feared for a long time. In 2017, a website sprung up to answer a question long on the minds of many: Will robots take my job? The creators based it on Bureau of Labor Statistics data and a 2013 research paper from Oxford University about "the susceptibility of jobs to computerization." Things have moved quickly since; even the term "computerization" now sounds desperately out of date. If you plug "journalist" into the site's search bar, for example, the site reveals an "automation risk score" of 9 percent.

"Is AI Coming For Commercial Art?" rendered by Stable Diffusion, prompted by Rob Salkowitz Earlier this summer, a piece generated by an AI text-to-image application won a prize in a state fair art competition, prying open a Pandora's Box of issues about the encroachment of technology into the domain of human creativity and the nature of art itself. As fascinating as those questions are, the rise of AI-based image tools like Dall-E, Midjourney and Stable Diffusion, which rapidly generate detailed and beautiful images based on text descriptions supplied by the user, pose a much more practical and immediate concern: They could very well hold a shiny, photorealistically-rendered dagger to the throats of hundreds of thousands of commercial artists working in the entertainment, videogame, advertising and publishing industries, according to a number of professionals who have worked with the technology. How impactful would this be to the global creative economy that runs on spectacular imagery? Think about the 10 minutes of credits at the end of every modern Hollywood blockbuster. Same with videogames, where commercial artists hone their skills for years to score plum jobs like concept artist and character designer.

Even before the recent George Floyd case, there has been much debate about the extent to which claims of systemic racism are supported by statistical evidence. For example (Ross 2015) claims that unarmed blacks are 3.5 times more likely to be shot by police than unarmed whites when adjusting for relative differences in population size. However, (Fryer 2016) - formally published later as (Fryer 2019) - found that there was no such racial disparity when the data were conditioned on people being stopped by police, and there was a similar conclusion in (Patty and Hanson 2020) that was produced in direct response to public concerns about the Floyd case. In response to Fryer, (Ross, Winterhalder, and McElreath 2018) argued that Fryer's analysis was compromised because it was essentially an example of Simpson's paradox (Simpson 1951; Bickel, Hammel, and O'Connell 1975; Fenton, Neil, and Constantinou 2019) whereby conclusions based on pooled statistics are reversed when drilling down into relevant subcategories. A new paper (Knox, Lowe, and Mummolo 2020) explains why Simpson's paradox is not the only statistical explanation for the apparently contradictory conclusions of Ross and Fryer.

Every Machine Learning engineer must know how to use Facets for their project -- The No Code AI Tool. Facets, a project from Google Research, is being used to visualise datasets, find interesting relationships, and clean them for machine learning. The No Code movement is on the rise and an increasing number of companies expect their engineers to quickly deliver results using pre-existing tools. From building web pages in minutes to creating mobile apps from a simple spreadsheet, no-code does it all. The proponents of building products quickly are pushing hard for the no-code movement precisely because it lets you get to the state of the art in a matter of hours instead of weeks.

Bias will continue to be a fundamental concern for businesses hoping to adopt artificial intelligence software, according to senior executives from IBM and Salesforce, two of the leading companies selling such A.I.-enabled tools. Companies have become increasingly wary that hidden biases in the data used to train A.I. systems may result in outcomes that unfairly--and in some cases illegally--discriminate against protected groups, such as women and minorities. For instance, some facial recognition systems have been found to be less accurate at differentiating between dark-skinned faces as opposed to lighter-skinned ones, because the data used to train such systems contained far fewer examples of dark-skinned people. In one of the most notorious examples, a system used by some state judicial systems to help decide whether to grant bail or parole was more likely to rate black prisoners as having a higher risk of re-offending than white prisoners with similar criminal records. "Bias is going to be one of the fundamental issues of A.I. in the future," Richard Socher, the chief scientist at software company Salesforce, said.

I always am seeking out arguments against my present beliefs (or models of reality). Gary Marcus has a new essay titled "Deep Learning: A Critical Appraisal" where he points out all the many flaws of Deep Learning. Marcus has a vested interest in seeing Deep Learning fail, after all, he wrote a book in 2001, which he still is very proud of, that disparaged the nascent Artificial Neural Network research back then. Marcus is very motivated to point out the lack of success of neural networks at every opportunity. His latest essay is one in his many attempts to claim higher understanding by criticism.

There's that little hop in its gait, the way it looks tentative as it springs forward from its haunches, the not-exactly-straight trajectory of its path. Except this isn't an injured animal. And even with two broken legs, this hexapod can figure out how to keep going. Which means that what looks like a slightly sad (if persistent) hunk of metal making its way across a hard floor represents something much bigger, actually. New research published on Wednesday in Nature finds that machines can change their behavior to adapt to being broken--they can learn and iterate based on self-reflection.

With fewer police officers conducting traffic stops and fewer reasons to pull autonomous cars over, autonomous vehicles may offer more significant benefits than letting you Netflix-and-chill: diminished racial profiling in traffic stops. Law enforcement officials are able to exercise considerable discretion when pulling cars over--how fast is too fast over the speed limit, what qualifies as distracted driving, what qualifies as driving too close to the next car. Many violations permitting that discretion will be largely eliminated after autonomous cars are widespread, meaning the ability of offending police officers to racially profile during traffic stops will be curtailed. Other technologies that remove human discretion from traffic violations are already producing similar results. For example, stoplight cameras reduce the disparity between the racial composition of the communities where they're installed and the people who are fined compared to similar citations issued by police officers.