Detecting sexual orientation might not be a pertinent issue when it comes to artificial intelligence (AI), but scientists are working on it, according to a study published last week. Ever since the study came to the fore, it has been facing stiff opposition from LGBTQ groups. The study says that it can distinguish between gay and heterosexual men accurately 81 percent of the time and between gay and heterosexual women 74 percent of the time. The study has raised an uproar with LGBTQ advocacy groups such as the Gay and Lesbian Alliance Against Defamation (GLAAD) and the Human Rights Campaign (HRC). The groups have called the study "A dangerous and flawed research that could cause harm to LGBTQ people around the world."

Radiologists can scrutinize hundreds of images before identifying an area of concern in a patient's body. But a type of artificial intelligence known as deep learning could soon help medical experts pinpoint problems faster and more accurately, says Dr. Michalski, executive director of the Boston-based Center for Clinical Data Science at Massachusetts General Hospital and Brigham and Women's Hospital. Deep learning includes algorithms, or computer programs, that search for, identify and analyze problems without direction from people, though many humans still guide the algorithms today. For months, Dr. Michalski has used hundreds of thousands of medical images to train a deep-learning system to detect pulmonary nodules and strokes, measure tumors and look for traumatic injury and fractures, among other tasks. The early results "are promising," he says. "The technology really does work."

These and many other fascinating insights are from the Boston Consulting Group and MIT Sloan Management Review study published this week, Reshaping Business With Artificial Intelligence. An online summary of the report is available here. The survey is based on interviews with more than 3,000 business executives, managers, and analysts in 112 countries and 21 industries. For additional details regarding the methodology, please see page 4. The research found significant gaps between companies who have already adopted and understand Artificial Intelligence (AI) and those lagging. AI early adopters invest heavily in analytics expertise and ensuring the quality of algorithms and data can scale across their enterprise-wide information and knowledge needs.

Sophia Genetics, a big data analytics company that's using artificial intelligence (AI) to help medical professionals diagnose and treat patients through genomic analysis, has raised $30 million in a round of funding led by Balderton Capital, with participation from 360 Capital Partners, Invoke Capital, and Alychlo. Founded out of Switzerland in 2011, Sophia Genetics sells itself as "the most advanced artificial intelligence AI for data-driven medicine." Its platform learns from thousands of patients' genomic profiles to improve and expedite patient diagnosis across oncology, hereditary cancer, metabolic disorders, pediatrics, and cardiology. The company said that its AI technology is currently being used by more than 300 hospitals in 53 countries, and it has already analyzed the genomic profiles of more than 125,000 patients. Prior to now, Sophia Genetics had raised around $30 million in funding, and with its latest cash injection the company said that it plans to further develop its technology, recruit top talent, and grow hospitals' adoption of clinical genomics testing.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

Genpact announced that it has acquired TandemSeven, a Boston-headquartered company that delivers customer and digital experience innovation consulting using design thinking at its core. TandemSeven's ability to design better customer experiences complements Genpact's digital capability aimed at transforming business processes end-to-end. Terms of the deal are not disclosed. As part of its ongoing strategy to drive both digital-led innovation and transformation along with digitally-enabled intelligent operations for its clients, Genpact is investing in leading technologies that are reimagining customer experiences and transforming the way companies across many industries compete. The acquisition of TandemSeven advances that strategy and extends Genpact's ability to help its clients provide a seamless experience from the front office all the way to the back office.

Watson has made AI practitioners' lives worse by building up unnecessary hype with marketing materials that are essentially science fiction and then predictably failing to deliver anything remotely similar to customers. I just hope they don't cause another AI recession with their reckless overpromising.

One of the most significant advances for sales in this decade is the growing sophistication and accuracy of predictive analytics--the science of knowing how and why and when people buy, and the best ways to approach them in sales. Predictive analytics is, in my opinion, one of the most compelling uses of Artificial Intelligence (AI). The sales industry is beginning to catch on. However, inbound marketing and sales (along with many other functions and sectors) must continue to advance their use of AI in the ways their customers will consider useful and welcome, as opposed to invasive or "creepy." It's an interesting read and you can find the executive summary here.

Summary: With only slight tongue in cheek about the road ahead we report on the just passed House of Representative's new "Federal Automated Vehicle Policy" as well as similar policy just emerging in Germany. As a model of regulation on emerging AI technology we think they got this just about right. Just today (9/6/17) the US House of Representatives released its 116 page "Federal Automated Vehicles Policy". This still has to be reconciled and approved by the Senate but word is that shouldn't take long. Equally as interesting is that just two weeks ago the German federal government published its guidelines for Highly Automated Vehicles (HAV being the new name of choice for these vehicles).

In a prior article, we talked about IoT being the connection of the physical and the digital worlds. That is, connecting those things that were physical in nature hitherto and now find a need to be connected to the digital world. This phenomenon about things/objects/entities is also influencing the enterprises in how they are transforming and shaping themselves to survive and thrive in the fast evolving world. The enterprises across consumer, commercial, public, and industrial sectors that were born in the pre-Internet era (Honeywell, ABB, GE, Philips, Siemens, and so on) are making moves to position themselves as digitally transformed companies. More subtle are the moves being made by the Internet era companies (Google, Amazon, etc.) to integrate themselves with the physical world.