Collaborating Authors


Monetize data, the most valuable asset of Machine Learning


The data associated with machine learning can be extremely valuable, but, Kimberley Bayliss of Haseltine Lake Kempner writes in this co-edited article, before it can be monetized, there are some major issues to be resolved. One of the things I hear over and over again from inventors is that data is the most valuable asset in machine learning (ML). After all, an ML model is only as good as the quality and quantity of data on which it is trained. If data is really that valuable, the burning question is whether it can be successfully protected and monetized. Just as employees must be aware when they access a trade secret, and the responsibilities that come with it, employees must also be aware of their responsibilities when accessing and using company data.

How Is AI Changing the Environment for the Better? - Innovation & Tech Today


Significant investments and research developments in artificial intelligence (AI) have made the technology a powerful asset in many industries -- including environmental studies. AI isn't a new technology, but businesses and consumers feel its impact and witness it seep into everyday life. AI is becoming more advanced and autonomous, and it's also broader in its use and impact. More use cases for AI are emerging, and if implemented responsibly, it can greatly benefit society. It's likely to play a role in tackling issues like climate change -- but how? Here's how AI is expected to impact the environment and usher in positive changes for a more sustainable future. It's critical to understand the breadth of environmental problems right now.

EEOC Issues Guidance on Artificial Intelligence and Disability Discrimination Under the ADA


The ADA, which applies to employers with 15 or more employees, prohibits discrimination based on disability and requires reasonable accommodations to allow qualified individuals with disabilities to be evaluated for or perform a job. The EEOC's guidance on AI explains how, in the absence of safeguards, an employer's use of certain software tools to select new employees, monitor performance, determine pay or promotions, or administer or score tests may violate these ADA provisions. This Compliance Overview provides the EEOC's guidance for employers. Employers now have a wide variety of computer-based tools available to assist them in hiring workers, monitoring worker performance, determining pay or promotions, and establishing the terms and conditions of employment. Employers may utilize these tools to save time and effort, increase objectivity or decrease bias. When this occurs, employers may risk violating federal equal employment opportunity (EEO) laws that protect individuals with disabilities.

Ethical Principles of Facial Recognition Technology


The sheer potential of facial recognition technology in various fields is almost unimaginable. However, certain errors that commonly creep into its functionality and a few ethical considerations need to be addressed before its most elaborate applications can be realized. An accurate facial recognition system uses biometrics to map facial features from a photograph or video. It compares the information with a database of known faces to find a match. Facial recognition can help verify a person's identity, but it also raises privacy issues. A few decades back, we could not have predicted that facial recognition would go on to become a near-indispensable part of our lives in the future.

An Autonomous Car Blocked a Fire Truck Responding to an Emergency


On an early April morning, around 4 am, a San Francisco Fire Department truck responding to a fire tried to pass a doubled-parked garbage truck by using the opposing lane. But a traveling autonomous vehicle, operated by the General Motors subsidiary Cruise without anyone inside, was blocking its path. While a human might have reversed to clear the lane, the Cruise car stayed put. The fire truck only passed the blockage when the garbage truck driver ran from their work to move their vehicle. "This incident slowed SFFD response to a fire that resulted in property damage and personal injuries," city officials wrote in a filing submitted to the California Public Utilities Commission.

Artificial Intelligence in China


In the fifth of a series of blogs from our global offices, we provide a overview of key trends in artificial intelligence in China. What is China's strategy for Artificial Intelligence? In March 2021, the Chinese government released the Outline of the 14th Five-Year Plan of the National Economic and Social Development of the People's Republic of China and Vision 2035. This includes more than 50 references to "[artificial] intelligence", reflecting China aims to develop of a new generation of information technology powered by artificial intelligence. Specifically, China intends to drive industry through science and technology projects to develop cutting-edge fundamental theories and algorithms, create specialized chips and build open-source algorithm platforms such as deep learning frameworks.

When I First Saw Elon Musk for Who He Really Is


On a beautiful day in May 2015, I drove the 13 hours from my home in Portland, Oregon, to Harris Ranch, California, halfway between San Francisco and Los Angeles. At the time, Tesla was touting a battery-swap station that could send Tesla drivers on their way in a fully powered vehicle in less than the time it takes to fill up a car with gas. Overtaken by curiosity, I had decided to spend a long Memorial Day weekend in California's Central Valley to see if Elon Musk's latest bit of dream weaving could stand up to reality. There, amid the pervasive stench of cow droppings from a nearby feedlot, I discovered that Tesla's battery swap station was not in fact being made available to owners who regularly drove between California's two largest cities. Instead, the company was running diesel generators to power additional Superchargers (the kind that take 30 to 60 minutes to recharge a battery) to handle the holiday rush, their exhaust mingling with the unmistakable smell of bullshit.

What are the latest applications of Machine Learning?


In this technologically advanced era, the demand for machine learning experts is growing rapidly as industrialists have already started using this technology for different purposes. As there is a skill shortage in this field, several job opportunities exist. It is a complex technical process that teaches computers to learn from data without being explicitly programmed. This technology also teaches computers to analyze data and get the work done without any human involvement. Though the technology is not new, people are adopting this technology nowadays. Let's know about modern-day applications of machine learning.

Clearview AI Says It's Bringing Facial Recognition to Schools


Clearview AI, the surveillance firm notoriously known for harvesting some 20 billion face scans off of public social media searches, said it may bring its technology to schools and other private businesses. In an interview with Reuters on Tuesday, the company revealed it's working with a U.S. company selling visitor management systems to schools. That reveal came around the same time as a horrific shooting at Robb Elementary School in Uvalde, Texas that tragically left 19 children and two teachers dead. Though Clearview wouldn't provide more details about the education-linked companies to Gizmodo, other facial recognition competitors have spent years trying to bring the tech to schools with varying levels of success and pushback. New York state even moved to ban facial recognition in schools two years ago.

Data on Machine Learning Described by Researchers at University of New South Wales (Learning from machines to close the gap between funding and expenditure in the Australian National Disability Insurance Scheme): Machine Learning


By a News Reporter-Staff News Editor at Insurance Daily News -- New research on artificial intelligence is the subject of a new report. According to news reporting originating from Canberra, Australia, by NewsRx correspondents, research stated, "The Australian National Disability Insurance Scheme (NDIS) allocates funds to participants for purchase of services." Our news reporters obtained a quote from the research from University of New South Wales: "Only one percent of the 89,299 participants spent all of their allocated funds with 85 participants having failed to spend any, meaning that most of the participants were left with unspent funds. The gap between the allocated budget and realised expenditure reflects misallocation of funds. Thus we employ alternative machine learning techniques to estimate budget and close the gap while maintaining the aggregate level of spending. Three experiments are conducted to test the machine learning models in estimating the budget, expenditure and the resulting gap; compare the learning rate between machines and humans; and identify the significant explanatory variables."