The world of artificial intelligence has exploded in recent years. Computers armed with AI do everything from drive cars to pick movies you'll probably like. Some have warned we're putting too much trust in computers that appear to do wondrous things. But what exactly do people mean when they talk about artificial intelligence? It's hard to find a universally accepted definition of artificial intelligence.

As the world grows increasingly connected, growing concern regarding the influence of artificial intelligence (AI) has been bubbling to the surface, affecting perceptions by industries big and small along with the general populace. Spurred on by sensationalized media predictions of AI taking over human decision-making and silver-screen tales of robot revolutions, there is a fear of allowing AI or its cousin, the Internet of Things (IoT), into our lives. Here is AI's man behind the curtain. One of the biggest sticking points is the popular – yet mistaken – notion that AI will cost people their jobs. In truth, the situation is just the opposite.

As the Chinese Professional Baseball League starts its season, one team has gotten creative about "filling the stands" during the coronavirus pandemic. The Rakuten Monkeys will play games in front of robot mannequins in the audience dressed up as fans, according to the CPBL official website. "You call that a fastball? I haven't seen anything that slow since Pentium II!" You swing like your tension-amplification mechanism was sent to the wrong 3-D printer!" "C'mon, let's score some runs!

After two virus cases were reported in February, Mayor Sergei Sobyanin authorized facial recognition software to track Chinese citizens in the capital, drawing complaints from rights groups. When the city introduced digital passes for commuters in April, tightly packed crowds formed at Metro stations as police checked smartphones individually.

One of the challenges to curbing the spread of COVID-19 is that asymptomatic individuals, or carriers, can spread the virus before they realize they are infected. In April, researchers from West Virginia University's (WVU) Rockefeller Neuroscience Institute (RNI) and WVU Medicine set out to predict symptoms before they appear using wearable rings by Oura and AI prediction models. Now, the researchers claim their digital platform can detect COVID-19 related symptoms up to three days early with over 90 percent accuracy. The approach is neuroscience-based, and it asks participants to track stress, anxiety, memory and other psychological and cognitive biometrics in the RNI app. Oura Ring collects physiological data, like body temperature, heart rate variability, resting heart rate, respiratory rate and sleep patterns.

The automation wave has overtaken IT departments everywhere making DevOps a critical piece of infrastructure technology. DevOps breeds efficiency through automating software delivery and allowing companies to push software to market faster while releasing a more reliable product. What is next for DevOps? We need to look no further than artificial intelligence and machine learning. Most organizations quickly realize the promise of AI and machine learning, but often fail to understand how they can properly harness them to improve their systems.

It's been two years since Mckinsey invented the term analytics translator, called it the'new must-have role' and predicted we'd need around 5 million of them. For the past ten years, we've struggled with the ambiguous title'data scientist', then'citizen data scientist'. Although I've seen many'data scientists' change their Linkedin titles to'analytics translator', the problem remains that no one knows what'analytics translator' really means. Mckinsey seems to have slipped this term into a Harvard Business Review article, and it has somehow taken root. What's more, people seem truly excited by the term.

The artificial intelligence model showed great promise in predicting which patients treated in U.S. Veterans Affairs hospitals would experience a sudden decline in kidney function. But it also came with a crucial caveat: Women represented only about 6% of the patients whose data were used to train the algorithm, and it performed worse when tested on women. The shortcomings of that high-profile algorithm, built by the Google sister company DeepMind, highlight a problem that machine learning researchers working in medicine are increasingly worried about. And it's an issue that may be more pervasive -- and more insidious -- than experts previously realized, new research suggests. The study, led by researchers in Argentina and published Monday in the journal PNAS, found that when female patients were excluded from or significantly underrepresented in the training data used to develop a machine learning model, the algorithm performed worse in diagnosing them when tested across across a wide range of medical conditions affecting the chest area.

Times have changed and caught most of us unprepared. It is always a part of Bolt's culture to move quickly and adapt -- and the crisis situation that is unfolding due to a pandemic definitely requires significant adaptation. This is a look from inside Bolt's data team -- data analysts, data engineers, data scientists -- as we share our experience and advice for times of crisis with all the similar teams out there. Most of the resources are thrown into surviving and, for some, even on seizing new opportunities. Data teams definitely have a role to play in this.

Recent surveys, studies, forecasts and other quantitative assessments of AI highlight the number of manufacturing jobs eliminated by robots; why robots could replace financial analysts; the very small number of organizations not evaluating or using AI today; and the debate over the usefulness of Covid-19 contact-tracing. And as data quality and diversity increase from the wearables and other internet-of-things devices, a virtuous cycle of improvements will kick in. In this world a novel coronavirus could be tracked, traced, intercepted, and cut off before it got going"--Kai-Fu Lee