Richard Gray of IQVIA says it's all in the mind(set). As companies reflect on where they have come during the last decade, they are looking for evidence that the journey is leading them in the right direction. Whilst much has changed over the last 10 years, customer healthcare professional (HCP) facing teams still remain a critical channel within most companies' go-to-market strategy. What is key in enabling their success as a key component of the ongoing promotional channel mix? "The HCP demand for a more personalised experience and expectation for digital to be part of the solution exists" The expectations of HCPs have evolved. Today, they demand a better experience from pharma and increased value from their interactions.

Data is the bedrock of all machine learning systems. As such, working with the right data collection company is critical in order to solve a supervised machine learning problem. If you don't have a particular goal or project in mind, there is a wealth of open data available on the web to practice with. However, if you're looking to tackle a specific problem, chances are you'll need to collect data yourself or work with a company that can collect data for you. There are many data collection companies that provide crowdsourcing services to help individuals and corporations gather data at scale.

The benefits of facial recognition technology and its cons are controversial issues. Many stakeholders are pointing out the pros, but there are also detractors voicing its disadvantages. There are many concerns around face recognition technology, such as invasion of privacy, abuse of power, what rogue elements within government agencies could do with it, and more. Already, the heated debate around facial recognition has caused some public relations backlashes. As a result, investors could stay clear of the technology, inhibiting its development.

"It was a one-day competition with the aim of building machine learning models and increasing the awareness of students on Artificial Intelligence.

Radiologic screening of high-risk adults reduces lung-cancer-related mortality; however, a small minority of eligible individuals undergo such screening in the United States. The availability of blood-based tests could increase screening uptake. Here researchers introduce improvements to cancer personalized profiling by deep sequencing (CAPP-Seq), a method for the analysis of circulating tumour DNA (ctDNA), to better facilitate screening applications. They show that, although levels are very low in early-stage lung cancers, ctDNA is present prior to treatment in most patients and its presence is strongly prognostic. They develop and prospectively validate a machine-learning method termed'lung cancer likelihood in plasma' (Lung-CLiP), which can robustly discriminate early-stage lung cancer patients from risk-matched controls.

Machine learning can tell different types of knot apart just by'looking' at them. For decades, mathematicians have had algorithms that calculate whether any two knots are of the same type -- that is, whether the knots can be converted into each other without cutting the string. But these algorithms are slow: the number of steps they require grows exponentially with the complexity of the knots. Liang Dai at the City University of Hong Kong and his collaborators created geometric models of the five simplest knots and fed those models into neural networks, which are computing systems modelled after the brain's networks of neurons. After training on hundreds of thousands of such models, the networks had learnt to classify knots with 99% accuracy or better.

AIoT is the major emerging trend from the survey, demonstrating the beginning of the process to build a true IoT ecosystem. Research showed that almost half (49%) of respondents already use AI in their IoT applications, with Machine Learning (ML) the most used technology (28%) followed by cloud-based AI (19%). This adoption of AI within IoT design is coupled with a growing confidence to take the lead on IoT development and an increasing number of respondents seeing themselves as innovators. However, it is still evident that some engineers (51%) are hesitant to adopt AI due to being new to the technology or because they require specialized expertise in how to implement AI in IoT applications. Other results from element14's second Global IoT Survey show that security continues to be the biggest concern designers consider in IoT implementation.

MADRID, SPAIN - MARCH 28: Health personnel are seen outside the emergency entrance of the Severo ... [ ] Ochoa Hospital on March 28, 2020 in Madrid, Spain. Spain plans to continue its quarantine measures at least through April 11. The Coronavirus (COVID-19) pandemic has spread to many countries across the world, claiming over 20,000 lives and infecting hundreds of thousands more. AI (Artificial Intelligence) has a long history, going back to the 1950s when the computer industry started. It's interesting to note that much of the innovation came from government programs, not private industry. This was all about how to leverage technologies to fight the Cold War and put a man on the moon.

Good gamers can tune out distractions and unimportant on-screen information and focus their attention on avoiding obstacles and overtaking others in virtual racing games like Mario Kart. However, can machines behave similarly in such vision-based tasks? A possible solution is designing agents that encode and process abstract concepts, and research in this area has focused on learning all abstract information from visual inputs. This however is compute intensive and can even degrade model performance. Now, researchers from Google Brain Tokyo and Google Japan have proposed a novel approach that helps guide reinforcement learning (RL) agents to what's important in vision-based tasks.

There's still no completely safe and surefire method for locating unexploded ordinance after a war is over, but researchers at Ohio State University have found a way to harness image processing algorithms, powered by machine learning, to study satellite imagery and locate hot spots where UXO are likely to be located. The researchers focused their efforts on a 100-square-kilometre area near Kampong Trabaek, Cambodia, which was the target of carpet-bombing missions carried out by the United States Air Force during the Vietnam War. The team was given access to declassified military data that revealed that 3,205 bombs had been dropped in the area between 1970 and 1973. Determining exactly how many of those bombs didn't explode has gotten harder and harder as, six decades later, nature has slowly reclaimed the country's heaviest hit areas, hiding and obscuring the craters that are counted and used to make accurate estimates. The OSU study used a two-step process to come up with a more accurate estimate of how many bombs were still left in the area.