According to Wikipedia, cognitive biases "are tendencies to think in certain ways that can lead to systematic deviations from a standard of rationality or good judgment, and are often studied in psychology and behavioral economics." Far more than simply an exercise in academia, cognitive biases have all sorts of practical impacts on our lives, whether or not we admit it. A very broad umbrella, cognitive bias comes in many forms, as evidenced by the fact that Wikipedia lists over 170 of them. Some of these biases are more prevalent in certain areas of life than in others. Below is an infographic from Business Insider, of all places, which is an elementary summary of what it refers to as "20 cognitive biases that screw up your decisions." But how do these cognitive biases relate to real life?

Despite its promise, the growing field of Artificial Intelligence (AI) is experiencing a variety of growing pains. In addition to the problem of bias I discussed in a previous article, there is also the'black box' problem: if people don't know how AI comes up with its decisions, they won't trust it. In fact, this lack of trust was at the heart of many failures of one of the best-known AI efforts: IBM Watson – in particular, Watson for Oncology. Experts were quick to single out the problem. "IBM's attempt to promote its supercomputer programme to cancer doctors (Watson for Oncology) was a PR disaster," says Vyacheslav Polonski, Ph.D., UX researcher for Google and founder of Avantgarde Analytics.

For the first time, the U.S. military's research wing will be allocating $2 billion to develop a new generation of Artificial Intelligence (AI) with "human-like …

That's how quickly brain damage happen when the cells get no oxygen in a stroke or in some brain injuries. Both can have tragic consequences -- paralysis, memory loss, speech difficulties and even death. But doctors can't start treatment without an initial diagnosis, and that requires reading a CT scan as soon as the test's completed. Unfortunately, that's not what usually happens, said Prashant Warier, co-founder of Qure.ai, a member of our Inception startup accelerator program. "Radiologists typically have a backlog of cases," he said.

The total global revenue opportunity for the healthcare artificial intelligence market will exceed $34 billion by 2025, according to a report from Tractica. The report notes that global software revenue from 22 key healthcare AI use cases will grow from $511.7 million in 2018 to $8.6 billion annually by 2025. More articles on artificial intelligence: Director of analytics tops list of top-paying AI jobs Indiana U School of Medicine partners with Fujifilm for AI research Dr. Eric Topol turns critical eye on landmark FDA-approved AI device

Beijing: A little more than a year ago, China released an aggressive plan to become the world's leading artificial intelligence (AI) player. But with its technological dependence on the US laid bare, it's now promoting a softer approach, calling for all nations to join hands to develop the technology. Chinese leaders, including vice premier Liu He, joined business mogul Jack Ma and executives from Google at the World Artificial Intelligence Conference in Shanghai to support a borderless approach to AI research. He called for foreign investment in the country and pledged to foster "an environment of free thinking" to support development. The tone struck by He is a far cry from the aggressive plan issued by the State Council last year with the aim of China becoming the world leader in AI by 2030, in part through government support.

There is a significant difference between automation and the use of artificial intelligence (AI) in the medtech space. Automation, within a healthcare setting, is defined as the use of hardware and software specifically programmed to save time. AI can be categorized as machine learning, meaning software and hardware working in conjunction to effectively mimic human decision-making -- just much, much faster. AI can learn outside of its programming, and the goal is for the software to make a decision of equivalent quality, compared to a human. The use of automation and AI is integral within the medtech space, as data is becoming increasingly important to manage and understand.

Anti-Trump Senator Jeff Flake, a member of the Senate Judiciary Committee, said in an interview Sunday evening that until he learns more about the sexual assault allegation regarding Brett Kavanaugh, he is "not comfortable voting yes" on Kavanaugh. It's Flakes last chance to poke President Trump and the country in the eye before he rides retires and likely finds a job in the liberal media. Jeff Flake becomes the first Republican senator to call for a pause on the Kavanaugh hearings until the Judiciary Committee hears from his accuser. Kavanaugh's accuser is a far left anti-Trump activist. Over the past few days, what appeared at first to be a merely token resistance to the nomination of Trump SCOTUS pick Brett Kavanaugh has morphed into something entirely more menacing.

IMAGE: The four newly determined personality types are based on five widely-recognized character traits. The findings challenge existing paradigms in psychology. The new study, led by Luís Amaral of the McCormick School of Engineering, will be published Sept. 17 by the journal Nature Human Behaviour. The findings potentially could be of interest to hiring managers and mental health care providers. "People have tried to classify personality types since Hippocrates' time, but previous scientific literature has found that to be nonsense," said co-author William Revelle, professor of psychology in the Weinberg College of Arts and Sciences.

Despite its promise, the growing field of Artificial Intelligence (AI) is experiencing a variety of growing pains. In addition to the problem of bias I discussed in a previous article, there is also the'black box' problem: if people don't know how AI comes up with its decisions, they won't trust it. In fact, this lack of trust was at the heart of many failures of one of the best-known AI efforts: IBM Watson – in particular, Watson for Oncology. Experts were quick to single out the problem. "IBM's attempt to promote its supercomputer programme to cancer doctors (Watson for Oncology) was a PR disaster," says Vyacheslav Polonski, PhD, UX researcher for Google and founder of Avantgarde Analytics.