Recently, someone from the editorial team for Kite, an AI autocomplete for Python, reached out to see if I would share some of their content. Since I thought the tool looked awesome, I figured I'd help them out. After some chatting, we decided on this data science article by Kirit Thadaka. How often do you think you're touched by data science in some form or another? Finding your way to this article likely involved a whole bunch of data science (whooaa).

Recently, someone from the editorial team for Kite, an AI autocomplete for Python, reached out to see if I would share some of their content. Since I thought the tool looked awesome, I figured I'd help them out. After some chatting, we decided on this data science article by Kirit Thadaka. How often do you think you're touched by data science in some form or another? Finding your way to this article likely involved a whole bunch of data science (whooaa).

Hah! That's funny, I was just looking for information about this project too. However, I was one of the guys involved (in Robokoneko project, more specifically). Explaining what actually went wrong is a bit lengthy process and goes deeply into philosophy of mind, so to put this as short as possible: what do you think could be achieved in terms of understanding human mind by randomly evolving neural networks? If a being was created, fully aware of its existence, would you be able to tell what led to forming its consciousness? Their goals were absolutely pointless.

Imagine being able to know if you have Parkinson's disease, multiple sclerosis, liver failure, Crohn's diseases, pulmonary hypertension, chronic kidney disease, or any number of cancers based on a simple, non-invasive test of your breath. Breath analyzers to detect alcohol have been around for well over half a century--why not apply the same concept to detect diseases? A global team of scientists from universities in Israel, France, Latvia, China and the United States have developed an artificial intelligence (AI) system to detect 17 diseases from exhaled breath with 86 percent accuracy. The research team led by Professor Hassam Haick of the Technion-Israel Institute of Technology collected breath samples from 1404 subjects with either no disease (healthy control) or one of 17 different diseases. The disease conditions include lung cancer, colorectal cancer, head and neck cancer, ovarian cancer, bladder cancer, prostate cancer, kidney cancer, gastric cancer, Crohn's disease, ulcerative colitis, irritable bowel syndrome, idiopathic Parkinson's, atypical Parkinson ISM, multiple sclerosis, pulmonary hypertension, pre-eclampsia toxemia, and chronic kidney disease.

Understanding the behavior of artificial intelligence(AI) agents is one of the pivotal challenges of the next decade of AI. Interpretability or explainability are some of the terms often used to describe methods that provide insights about the behavior of AI programs. Until today, most of the interpretability techniques have focused on exploring the internal structure of deep neural networks. Recently, a group of AI researchers from the Massachusetts Institute of Technology(MIT) are exploring a radical approach that attempts to explain the behavior of AI observing them in the same we study human or animal behavior. They group the ideas in this area under the catchy name of machine behavior which promises to be one of the most exciting fields in the next few years of AI.

Two years after Vishal Sikka stepped down as the CEO and executive vice chairman of Infosys, an Indian IT services company, he has launched a new venture in artificial intelligence. Vianai, a Palo Alto, Calif.-based startup, last week announced its arrival with $50 million in seed financing. Sikka believes AI has the potential not just to transform business but also to "amplify humanity," as he puts it. He sees AI as a force multiplier that can tackle issues ranging from climate change to self-improvement. "I would love to see tens of millions of people to be able to build intelligent systems, and billions to be able to bring basic intelligence into anything that they do," he says. Sikka, who holds a Ph.D. in AI from Stanford University, demonstrated the Vianai platform at a keynote address at the Oracle OpenWorld conference on September 17. Before his tenure at Infosys, he spent 12 years at German software company SAP, where he was last a member of its executive board. Sikka also serves on the supervisory board of the BMW Group and as an advisor at the Stanford Institute for Human-Centered Intelligence (HAI).

Technology developed by Kerry-based Dairymaster, which uses AI to optimise milking, was named winner of an Enterprise Ireland innovation award. Ahead of the National Ploughing Championships today (17 September), the 2019 Enterprise Ireland Innovation Arena Awards were announced. The pre-event is designed to highlight some of the latest agritech and agri-engineering projects underway in the country. The overall winner was the Mission Control system developed by Kerry-based Dairymaster, which has introduced artificial intelligence (AI) to rotary milking and includes an advanced cow recognition system called'CowNow'. The OptiCruise technology incorporated into the design adjusts the speed of milking so that milking time is optimised.

Rayfield Byrd knows when it's time to wake up every morning. The 68-year-old Oakland, Cal., resident hears a voice from the living room offering a cheery good morning. A little after 8 a.m. each day, a small yellow robot named Mabu asks Byrd how he's doing. Byrd has Type 2 diabetes and congestive heart failure, and about three years ago, he had surgery to implant a microvalve in his heart to keep his blood flowing properly. To stay healthy, he takes four medications a day and needs to exercise regularly.

Outside Germany, one of BMW's key research organizations for business and manufacturing technology is the IT Innovation and Research Center. With bases both in Silicon Valley, as well as Greenville, South Carolina, the IT center carries out research for systems and tools across the enterprise, including financial services, sales and marketing, engineering, quality, HR, production and logistics. It is part of the carmaker's central BMW Group IT department led from Munich, which coordinates the company's enterprise and manufacturing IT backbone. Similar to other laboratory locations across BMW, the IT center operates to a large extent in research mode. It has a strong connection, for example, to Clemson University, with whom it shares a campus at the International Center for Automotive Research, working closely with engineering and software professors and students.

This is a story about how something I learned on a routine plane journey changed my understanding of how we should use artificial intelligence (AI) in the workplace -- why we should use it to support human decision making. Humans need help to make good choices. We respond to context and to our own unconscious bias as much as to data. Presenting people with facts often isn't enough; they need something called "choice architecture" to help to nudge them toward the logical next step. Here is an example from my own experience of choice depending on context.