This week, Dessa launched Atlas as an open source platform for developing deep learning projects. The deep learning startup Dessa was acquired by Square in February 2020. Let's see what features this beta version of their deep learning platform has to offer. Atlas has a Python SDK, CLI, GUI and scheduler on board. These aspects should help reduce the effort of managing infrastructure and increase the speed of developing deep learning models.

Nowadays we are hearing a lot of buzz around artificial intelligence or AI and robotics. We have even felt their presence in our surroundings in one or the other way. The technology sounds new but its seeds have been long sown. Do you know that AI found its way into film-business around 100 years ago? Yes, the 1927 release Metropolis depicts AI that takes the form of a humanoid robot with an intent on taking over the titular mega-city by inciting chaos.

A research team has proposed non-contrast thoracic chest CT scans as an effective tool for detecting, quantifying, and tracking COVID-19. As of March 16, the COVID-19 pandemic had a confirmed infection total of more than 170,000 people around the globe. The speed of transmission of COVID-19 has surprised the world and had a massive impact on people's daily lives and the global economy. To accelerate COVID-19 detection and support efforts to combat the epidemic, researchers from RADLogics, Tel-Aviv University, New York Mount Sinai Hospital and University of Maryland School of Medicine developed an AI-based approach designed to help identify infected patients and quantify disease burden by analyzing thoracic CT (Computer Tomography, aka CAT) exams. Data sources for new epidemic diseases such as COVID-19 remain limited, as does expertise.

Artificial intelligence is transforming market research. US companies are predicted to spend over $31 billion in 2020 to better understand their customers, their markets, their competitors and the perception of their brand. AI is fueling this increase in spending, up from $16 billion in 2019. Digital transformation initiatives are integrating digital technology into all areas of the business, transforming how the business operates and delivers value to its customers. Since technology is rapidly evolving, this transformation is driven by a cultural change, not a single process or initiative, that requires experimentation, learning and failure.

Our ability to collect and record information in a digital form has exploded as has our adoption of AI systems, which use data to make decisions. But data isn't neutral, and sexism, racism and other forms of discrimination are showing up in our data products. Catherine D'Ignazio, an assistant professor of urban science and planning at the Massachusetts Institute of Technology (MIT), argues we need to do better. Along with Lauren Klein, who directs the Digital Humanities Lab at Emory University, she is the co-author of the new book Data Feminism, which charts a course for a more equitable data science. D'Ignazio also directs MIT's new Data and Feminism lab, which seeks to use data and computation to counter oppression.

Elmer is now in use on Sperry's entire North American non-stop track monitoring fleet, including ATS rail-road vehicles. Sperry Rail has been active in non-destructive testing for rail faults for more than 90 years. In a drive to deliver greater efficiency in track monitoring, the supplier has developed Artificial Intelligence (AI) and machine learning applications that it is now installing in regular service. As demand for track access grows, infrastructure managers from around the world face a daily battle to keep their railways operational during limited maintenance intervals. Predictive and preventative rather than interval-based maintenance is the Holy Grail for infrastructure managers.

Dr. Alex Antic is a trusted and experienced Data Science leader, with a proven record of delivering innovative, successful and sustainable projects in government, industry and startups (including Sports Analytics), that leverage data and Machine Learning/Deep Learning capabilities to deliver actionable insights. Dr. Antic is currently serving as an Academic at ANU, whilst also being a Principal Data Scientist for Federal Agency. Before his current role at ANU, Alex was a quantitative analyst at hedge fund and investment bank. He has also worked with the actuarial teams in the marketing analytics. Because of this unique career path with the commercial experience, he understands the business needs well as well as able to be pragmatics with the commercial perspective whilst working at different environment.

In my previous blog post, I claimed that "AI is not magic." In this post, my goal is to discuss how neural networks learn, and show that AI isn't a crystal ball or magic, just science and some very slick mathematics. I'll keep this very high level. Let's start with a hypothetical scenario. Suppose we are building an app to identify hot dogs. Take a picture and the app will tell you if it's a hotdog or not.

On March 16, 2020, the White House Office of Science and Technology Policy (OSTP) announced the availability of an open research dataset on COVID-19, as well as stated a call to action for the nation's artificial intelligence (AI) researchers to help scientists fight the disease. Established in 1976, the OSTP provides scientific and technological advice to the President and the Executive Office, among other duties. The CORD-19 (COVID-19 Open Research Dataset) is the result of a collaboration between the Allen Institute for Artificial Intelligence (AI2), Microsoft, the National Library of Medicine at the National Institutes of Health (NIH), Georgetown University's Center for Security and Emerging Technology (CSET), and the Chan Zuckerberg Initiative in response to a request by the White House Office of Science and Technology Policy. Together these institutions issued a joint call to action to the world's AI researchers to create text and data mining tools to help accelerate COVID-19 research. The Allen Institute's Semantic Scholar has an adaptive feed on COVID-19 research.

Interpol concluded a partnership with a South Korean artificial intelligence startup aimed at tracking and combating cryptocurrency-related crimes on the darknet. The organisation will use a new data analysing engine to track illegal activity. The darknet has been a pain in the neck for the police and law enforcement agencies for years. Criminals steal people's confidential information and sell it for illegal purposes, at the same time making it nearly impossible to track or catch them. Despite the blockchain network is completely transparent and all transactions are traceable, it doesn't matter as long as the parties conducting the transaction remain anonymous.