Artificial intelligence promises to make hiring an unbiased utopia. Employee referrals, a process that tends to leave underrepresented groups out, still make up a bulk of companies' hires. Recruiters and hiring managers also bring their own biases to the process, studies have found. "Identifying high-potential candidates is very subjective," says Alan Todd, CEO of CorpU, a technology platform for leadership development. "People pick who they like based on unconscious biases."

Students from Fast.ai, a small organization that runs free machine-learning courses online, just created an AI algorithm that outperforms code from Google's researchers, according to an important benchmark. Fast.ai's success is important because it sometimes seems as if only those with huge resources can do advanced AI research. Fast.ai consists of part-time students keen to try their hand at machine learning--and perhaps transition into a career in data science. It rents access to computers in Amazon's cloud. But Fast.ai's team built an algorithm that beats Google's code, as measured using a benchmark called DAWNBench, from researchers at Stanford.

Artificial intelligence will have a profound impact on jobs and worker re-training. Industry analyst and CXOTalk host, Michael Krigsman, explores this crucial issue with two experts during an informative and important episode. Shirley Malcom is Head of Education and Human Resources Programs of the American Association for the Advancement of Science (AAAS). The directorate includes AAAS programs in education, activities for underrepresented groups, and public understanding of science and technology. Dr. Malcom was head of the AAAS Office of Opportunities in Science from 1979 to 1989.

On a sunny Monday afternoon in Oakland, AI4All alum Ananya Karthik gathered a few dozen girls to show them how to use the Deep Dream Generator program to fuse images together and create a unique piece of art. OAKLAND -- Through connections made at summer camp, high school students Aarvu Gupta and Lili Sun used artificial intelligence to create a drone program that aims to detect wildfires before they spread too far. Rebekah Agwunobi, a rising high school senior, learned enough to nab an internship at the Massachusetts Institute of Technology's Media Lab, working on using artificial intelligence to evaluate the court system, including collecting data on how judges set bail. Both projects stemmed from the Oakland, Calif.-based nonprofit AI4All, which will expand its outreach to young under-represented minorities and women with a $1 million grant from Google.org, the technology giant's philanthropic arm announced Friday. Artificial intelligence is becoming increasingly commonplace in daily life, found in everything from Facebook's face detection feature for photos to Apple's iPhone X facial recognition.

For many years, the main goal of companies is to collect as much user data as possible. Dealing with all these and new incoming data quickly and effectively is impossible without intelligent systems. This is why companies desperately need to harness AI technologies to come to the top place among competitors – and the sooner the better. However, the challenge is that modern AI systems are "idiot savants" as Gurdeep Singh Pall of Microsoft put it in one of his talks. "They are great at what they do, but if you don't use them correctly, it's a disaster."

Artificial intelligence promises to make hiring an unbiased utopia. Employee referrals, a process that tends to leave underrepresented groups out, still make up a bulk of companies' hires. Recruiters and hiring managers also bring their own biases to the process, studies have found, often choosing people with the "right-sounding" names and educational background. Across the pipeline, companies lack racial and gender diversity, with the ranks of underrepresented people thinning at the highest levels of the corporate ladder. Fewer than 5% of CEOs at Fortune 500 companies are women--and there are only three black CEOs.

When applied to everyday business interactions, predictive models developed using machine learning can have a dramatic impact on customer experience, patient care, forecasting, public safety, risk management, and many other aspects of public and private sector operations. If you've been putting off using machine learning because you believe you don't have the required talent and tools available, you've waited long enough! In It's Machine Learning, Not Rocket Science!, InterSystems Senior Sales Engineer Anton Umnikov shows how you can finally dive into machine learning. In today's world, with all the advancements in available tools and libraries, machine learning no longer belongs only in the specialist domains of data scientists. Software engineers in your IT organization are already equipped to work with machine learning and deliver business value.

Artificial intelligence promises to make hiring an unbiased utopia. Employee referrals, a process that tends to leave underrepresented groups out, still make up a bulk of companies' hires. Recruiters and hiring managers also bring their own biases to the process, studies have found, often choosing people with the "right-sounding" names and educational background. Across the pipeline, companies lack racial and gender diversity, with the ranks of underrepresented people thinning at the highest levels of the corporate ladder. Fewer than 5 percent of chief executive officers at Fortune 500 companies are women, and that number will shrink further in October when Pepsi CEO Indra Nooyi steps down.

AI is shaping the world, who will be shaping AI? The future may be uncertain, but one certainty is that today's youth will be central to shaping it. Acorn Aspiration's mission is to empower and assist teens in learning and shaping the future of AI for positive contributions to the world. More than 1500 young people have participated in Acorn's Bootcamps/Hackathons, Accelerator programs, and conferences to date. Acorn's latest initiative, TeensInAI, was launched at the UN AI for Good Summit in May, and followed by a 5-day Bootcamp/ Hackathon.

There are so many online classes available from sites like Coursera, edX, and Udacity that companies don't know what content to offer their employees. And once companies do choose a learning program, it's tough for them to figure out what skills their employees pick up and to what degree they've mastered them. They need an objective metric to evaluate proficiency. A new AI-powered tool developed by Coursera aims to be that metric. The feature lets companies that subscribe to its training programs see which of their employees are earning top scores in Coursera classes; how their employees' skills measure up to their competitors'; and what courses would help fill any knowledge gaps.