Artificial Intelligence

Softbank’s robot Pepper is set to be the first non-human to testify in front of the UK Parliament to give evidence about the fourth industrial revolution. Pepper will be attempting to explain topics such as AI and robotics to The Commons Education Select Committee. “If we’ve got the march of the robots, we perhaps need the march of the robots to our select committee to give evidence,” Committee chair Robert Halfon told  Tes . “The fourth industrial revolution is possibly the most important challenge facing our nation over the next 10, 20, to 30 years.” AI and robotics will drastically change our societies, and not always for the better. There will be serious challenges ahead. It’s rare to hear of AI being discussed without  the potential impact on jobs . Low-skilled workers, in particular, are most threatened by automation replacement. The Select Committee will be looking to understand what impacts the fourth industrial revolution will have and how the negatives ...

Scientists at Imperial College London have developed a system that can treat patients with sepsis and, by using artificial intelligence, analyse the records of 100,000 hospital patients in intensive care units (ICU) to assess the best course of treatment. The tool, which is called AI Clinician, can be used alongside medical professionals to help doctors better understand the best treatment strategy. Sepsis, which is commonly known as blood poisoning, kills around 44,000 people every year in the UK. The product uses a process called reinforcement learning wherein robots learn taking decisions to solve a problem. The researchers conducted a study wherein they reviewed US patient records from 130 intensive care units (ICUs) over a 15-year period to explore whether the AI system’s recommendations might have been able to improve patient outcomes, compared with standard care. Now they are hoping to use the AI Clinician tool in ICUs in the UK. Professor Anthony Gordon, senior author f...

WHAT IS MEDICAL ARTIFICIAL INTELLIGENCE? Informing clinical decision making through insights from past data is the essence of evidence-based medicine. Traditionally, statistical methods have approached this task by characterising patterns within data as mathematical equations, for example, linear regression suggests a ‘line of best fit’. Through ‘machine learning’ (ML), AI provides techniques that uncover complex associations which cannot easily be reduced to an equation. For example, neural networks represent data through vast numbers of interconnected neurones in a similar fashion to the human brain. This allows ML systems to approach complex problem solving just as a clinician might — by carefully weighing evidence to reach reasoned conclusions. However, unlike a single clinician, these systems can simultaneously observe and rapidly process an almost limitless number of inputs. For example, an AI-driven smartphone app now capably handles the task of triaging 1.2 mil...