Mr. Minevich is a highly regarded and trusted Digital Cognitive Strategist, Artificial Intelligence expert, Venture Capitalist, and the principal founder of Going Global Ventures. Mark collaborates and advises large global enterprises both in the US and Japan (Hitachi), and is the official AI and Future of Work Advisor to the Boston Consulting Group. Currently, he serves as the strategic advisor and Global ambassador to the CEO and Chairman of New York based IPsoft Inc. Mark holds the role of senior fellow as part of the U.S. Council on Competitiveness in Washington, D.C., and maintains a position as senior adviser on Global Innovation and Technology to the United Nations Office of Project Services (UNOPS). He is an appointed member of the G20/B20's Digital Task Force, supplementing the group with expert knowledge on digitization, advanced autonomous systems, and the future of AI.

The amount of internet usage dedicated to playing video games in the US rose by 75 percent during the first week of the countrywide COVID-19 shutdown. The figure comes from a Verizon report on how internet usage has been affected by the entire country being asked to stay indoors for potentially months on end. While other forms of online entertainment saw boosts over the same period--video streaming rose 12 percent, and general web traffic rose by 20 percent--nothing came close to video games. Several other game-centric businesses saw similar bumps over the same period. The PC gaming platform Steam saw more than 20 million people playing games through the service at the same time, an all-time high.

Drones can do many things, but avoiding obstacles is not their strongest suit yet – especially when they move quickly. Although many flying robots are equipped with cameras that can detect obstacles, it typically takes from 20 to 40 milliseconds for the drone to process the image and react. It may seem quick, but it is not enough to avoid a bird or another drone, or even a static obstacle when the drone itself is flying at high speed. This can be a problem when drones are used in unpredictable environments, or when there are many of them flying in the same area. Reaction of a few milliseconds In order to solve this problem, researchers at the University of Zurich have equipped a quadcopter (a drone with four propellers) with special cameras and algorithms that reduced its reaction time down to a few milliseconds – enough to avoid a ball thrown at it from a short distance.

Though ERNIE 1.0 (released in March 2019) has been a popular model for text classification, it was ERNIE 2.0 which became the talk of the town in the latter half of 2019. Developed by tech-giant Baidu, ERNIE outperformed Google XLNet and BERT on the GLUE benchmark for English. ERNIE stands for Enhanced Representation through kNowledge IntEgration, and ERNIE 2.0 is an upgraded version of ERNIE 1.0. ERNIE 1.0 was pathbreaking in its own way – it was one of the first models to leverage Knowledge Graphs. This incorporation further enhanced training the model for advanced tasks like Relation Classification and NamedEntityRecognition (NER). Like its predecessor, ERNIE 2.0 brings another innovation to the table in the form of Continual Incremental Multi-task Learning.

I still learn new knowledge everyday with my growing passion in Data Science field. To pursue different career track as a graduating physics student there must be'Why' and'How' questions to be answered. Having been asked by a number of people about my transition from academia -- Physics to Data Science, I hope my story could answer the questions on why I decided to become a Data Scientist and how I pursued the goal, and ultimately encourage as well as inspire more people to pursue their passion. The CERN Summer Student Programme offers once-in-a-lifetime opportunity for undergraduate students of physics, computing and engineering to join one of their research projects with top scientists in multicultural teams at CERN in Geneva, Switzerland. In June 2017, I was very fortunate to be accepted to join the programme.

As domain experts, engineers are highly valuable in shaping companies' AI projects. This is especially the case in Australia. Here, 98 per cent of businesses have fewer than 20 employees, and a team of data scientists trained on statistical machine-learning methods tends to be something enjoyed by bigger organisations. The growth in AI will continue in the new year and is expected to push rapidly into industrial scenarios. Non-data scientists have access to more and more information and training centred around AI.

Imagine being scared to breathe the air around you. An unusual concept for us here in the UK, but it is a genuine concern for communities all over the world with air pollution killing an estimated seven million people every year. A team of Loughborough University computer scientists are hoping to help eradicate this fear with a new artificial intelligence (AI) system they have developed that can predict air pollution levels hours in advance. The technology is novel for a number of reasons, one being that it has the potential to provide new insight into the environmental factors that have significant impacts on air pollution levels. Professor Qinggang Meng and Dr. Baihua Li are leading the project which is focused on using AI to predict PM2.5--particulate matter of less than 2.5 microns (10-6 m) in diameter--that is often characterized as reduced visibility in cities and hazy-looking air when levels are high.

Roni Rosenfeld makes predictions for a living. Typically, he uses artificial intelligence to forecast the spread of the seasonal flu. But with the coronavirus outbreak claiming lives all over the world, he's switched to predicting the spread of Covid-19. It was the Centers for Disease Control and Prevention (CDC) that asked Rosenfeld to take on this task. As a professor of computer science at Carnegie Mellon University, he leads the machine learning department and the Delphi research group, which aims "to make epidemiological forecasting as universally accepted and useful as weather forecasting is today."

Healthcare data is projected to grow by 43 percent by 2020, to a hard-to-fathom level of 2.3 zettabytes. Eighty percent of it is unstructured and mostly unlabeled, making it hard for organizations to extract value from the datasets.[1] In the UK, the number of CT scans increased by 33% between 2013 and 2016 while the number of radiologists only increased by 3% per year. There are several studies that show that when radiologists are forced to work faster, their average interpretation error rate rises and can have a significant impact on patient care.[2] The cost of developing a new drug averages around $2.5 billion, and the process itself can take more than 10 years -- a huge barrier to the development of targeted treatments.[3] Healthcare data is projected to grow by 43 percent by 2020, to a hard-to-fathom level of 2.3 zettabytes.

In 2010, Japan's telecom operations partnered with a robotic manufacturer to develop a humanoid robot that can interact with customers and perceive human emotions. In Japan, where it is used as customer service and representative in mobile stores. Additionally, the retailer experienced a 98% increase in customer interactions, a 20% increase in foot traffic and, a 300% increase in revenue. While not the only retail robot in use, in-store robots appear to at least initially boost store interest and sales. This is a great effect that will wear off once retail robots become the norm remains to be determined.