The internet of things (IoT) is increasingly becoming a key component of many companies' data-driven transformation strategies. Indeed, organizations that have embraced IoT are already seeing benefits such as improved operational processes, better inventory management, and enhanced equipment maintenance -- to name a few. But a successful IoT strategy is more than just connecting a bunch of devices and sensors to the internet and gathering data from these "things." IT must establish the ability to effectively analyze the vast amounts of data IoT creates in order to make sense of it and gain real business insights. That's why an analytics strategy for IoT should be a top priority for any company looking to get the most out of all the connectivity.

I am regularly asked to summarize my many posts. I thought it would be a good idea to publish on this blog, every Monday, some of the most relevant articles that I have already shared with you on my social networks. Today I will share some of the most relevant articles about Artificial Intelligence and in what form you can find it in today's life. I will also comment on the articles. The purpose of the Association for the Advancement of Artificial Intelligence, according to its bylaws, is twofold.

Between discarded packaging, shipping fees, inventory shortages and damaged merchandise, returns cost retailers a fortune. In 2017, retail returns cost roughly $350 billion, and they were projected to rise to $550 million in 2020. While some returns are inevitable (with reasons ranging from defective products to late deliveries), others are entirely avoidable. In fact, 46% of shoppers surveyed in Narvar's 2019 The State of Online Returns report said their No. 1 reason for returning products was incorrect size, fit or color. Only 3% intentionally bought multiple items knowing they'd return some.

Results of a survey of data professionals show that about 1 out of 5 are women. Women are paid less than their male counterparts yet both women and men have similar levels of education. Ways of improving gender diversity in the field of data science are offered. Even though women make up about half of the total workforce in the US, those numbers hide the disparities in some occupational domains. As you can see in Figure 1, while women make up about half of the life, physical and social science occupations in the US, they only account for 25% and 17% of the professionals in computer and mathematical occupations and architecture and engineering occupations, respectively.

This week, we learned a lot more about the inner workings of AI fairness and ethics operations at Facebook and Google and how things have gone wrong. On Monday, a Google employee group wrote a letter asking Congress and state lawmakers to pass legislation to protect AI ethics whistleblowers. That letter cites VentureBeat reporting about the potential policy outcomes of Google firing former Ethical AI team co-lead Timnit Gebru. It also cites research by UC Berkeley law professor Sonia Katyal, who told VentureBeat, "What we should be concerned about is a world where all of the most talented researchers like [Gebru] get hired at these places and then effectively muzzled from speaking. And when that happens, whistleblower protections become essential."

Justin Lane is an Oxford University-trained artificial intelligence (AI) expert and entrepreneur with no patience for fluffy theories. That led to some fascinating fieldwork in Northern Ireland, where he studied Irish Republican Army and Ulster Defence Association extremists up close. Ultimately, he applied his humanities research to AI programming and agent-based computer simulations. Somehow, he managed to enter undergrad in Baltimore, Md. as a Green Party liberal and emerge from England's ivory towers as a Second Amendment advocate. He now describes himself as a political moderate with "a libertarian flavor." When I first met him, Lane was working at the Center for Mind and Culture in Boston.

Civil liberties activists are suing a company that provides facial recognition services to law enforcement agencies and private companies around the world, contending that Clearview AI illegally stockpiled data on 3 billion people without their knowledge or permission. The lawsuit, filed in Alameda County Superior Court in the San Francisco bay area, says the New York company violates California's constitution and seeks a court order to bar it from collecting biometric information in California and requiring it to delete data on Californians. The lawsuit says the company has built "the most dangerous" facial recognition database in the nation, has fielded requests from more than 2,000 law enforcement agencies and private companies and has amassed a database nearly seven times larger than the FBI's. Separately, the Chicago Police Department stopped using the New York company's software last year after Clearview AI was sued in Cook County by the American Civil Liberties Union. The California lawsuit was filed by four activists and the groups Mijente and Norcal Resist.

Today, machine learning and artificial intelligence systems, trained by data, have become so effective that many of the largest and most well-respected companies in the world use them almost exclusively to make mission-critical business decisions. The outcome of a loan, insurance or job application, or the detection of fraudulent activity is now determined using processes that involve no human involvement whatsoever. In a past life, I worked on machine learning infrastructure at Uber. From estimating ETAs to dynamic pricing and even matching riders with drivers, Uber relies on machine learning and artificial intelligence to enhance customer happiness and increase driver satisfaction. Frankly, without machine learning, I question whether Uber would exist as we know it today.

The protests across the U.S. and around the globe in the wake of the murder of George Floyd have raised awareness about structural inequalities. Though the specific focus has been on police brutality, scholars, activists, and artists are sounding the alarm on how systemic racism has been amplified in other areas like the tech industry, through communication and surveillance technology. In Coded Bias, a documentary by Shalini Kantayya, the director follows MIT Media Lab researcher and Algorithmic Justice League founder Joy Buolamwini as she discovers one of the fundamental problems with facial recognition. While working on a facial recognition art project, Buolamwini realizes that the computer vision software was having trouble tracking her face, but it worked fine when she put on a white mask. It was just the latest evidence of the type of bias that's baked into facial recognition and A.I. systems These technologies often connect back to the dark historical practices of racialized surveillance, eugenics, or physiognomy.

Otologic Technologies, Inc., a Wisconsin-based health-tech startup developing an artificial intelligence (AI) system to improve treatment of ear disease, announced the issuance of US Patent No. 10,932,662, "System and Method of Otoscopy Image Analysis to Diagnose Ear Pathology." The patent explains a novel artificial intelligence system to help doctors better diagnose ear disease. "One of the biggest challenges in diagnosing ear disease is the difficult nature of an ear exam," said Aaron Moberly, MD, associate professor of otolaryngology at The Ohio State University and one of the inventors of the technology. "Even experienced doctors can have trouble with a live ear exam, as patients are usually uncomfortable and the view can be obstructed. In 2015, Dr. Moberly began an ongoing collaboration with Metin Gurcan, PhD, an artificial intelligence (AI) expert at The Ohio State University.