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."

Models and algorithms for analyzing complex networks are widely used in research and affect society at large through their applications in online social networks, search engines, and recommender systems. According to a new study, however, one widely used algorithmic approach for modeling these networks is fundamentally flawed, failing to capture important properties of real-world complex networks. "It's not that these techniques are giving you absolute garbage. They probably have some information in them, but not as much information as many people believe," said C. "Sesh" Seshadhri, associate professor of computer science and engineering in the Baskin School of Engineering at UC Santa Cruz. Seshadhri is first author of a paper on the new findings published March 2 in Proceedings of the National Academy of Sciences.

The statistical branch of Artificial Intelligence has enamored organizations across industries, spurred an immense amount of capital dedicated to its technologies, and entranced numerous media outlets for the past couple of years. All of this attention, however, will ultimately prove unwarranted unless organizations, data scientists, and various vendors can answer one simple question: can they provide Explainable AI? Although the ability to explain the results of Machine Learning models--and produce consistent results from them--has never been easy, a number of emergent techniques have recently appeared to open the proverbial'black box' rendering these models so difficult to explain. One of the most useful involves modeling real-world events with the adaptive schema of knowledge graphs and, via Machine Learning, gleaning whether they're related and how frequently they take place together. When the knowledge graph environment becomes endowed with an additional temporal dimension that organizations can traverse forwards and backwards with dynamic visualizations, they can understand what actually triggered these events, how one affected others, and the critical aspect of causation necessary for Explainable AI.

The discovery was made using a machine-learning algorithm. This technology enabled scientists to discover a powerful new antibiotic compound. The importance of the antibiotic has been shown through various tests, where the chemical was challenged against several disease-causing bacteria. Among the microbial cohort were some organisms previously identified to be resistant to mot antibiotics. Further studies were undertaken using mice, yielding similarly successful results.

In today's competitive business environment, managers rely heavily on insight from their analytics software. Current performance, feedback from product releases, rate of new customers – these are just a few of countless questions that analytics applications answer for us. But using these analytics programs – to their fullest extent – is still an emerging discipline. As critical as their insights are, actually gleaning those insights requires surmounting myriad challenges. These include everything from lack of training to inability to formulate an effective query.

The umbrella term for software and hardware it automates the human resources function in organization. One of the most discussed and debated trends of the contemporary times in the HR Technology is the use of Artificial Intelligence (AI). As per recent predictions, AI is going to be the crunch point, in terms of productivity for HR professionals. It has been feared by many professionals that machine is going to take away their jobs. Basically there is no reason to be cautiously optimistic; this is quite early to predict the actual impact of AI in HR and Talent Acquisition.

Almost every industry is seeking top quality Artificial Intelligence (AI) and analytics professionals across the world. Apart from top academic institutions, industry has also been targetting scientific research labs in order to tap those who possess competencies in quantitative techniques proficient in building models and are getting them oriented to design business solutions. The AI as a service market size was valued at $1.13 billion in 2017 and is expected to be $10.88 billion by 2023, thus opening up a huge demand for AI talent pool. The AI-powered services in the form of Application Programming Interface (API) and Software Development Kit (SDK) are primarily driving the demand for AI and analytics professionals. In addition to these, startups working on path breaking ideas are also in need of smart data science professionals.

While speaking on a panel recently, Landing AI founder and Google Brain cofounder Andrew Ng described a moment when he read the OECD's AI ethics principles to an engineer, and the engineer told him the words give no instruction on how he should change how he does his job. That's why, Ng said, any code of conduct should be designed by and for ML practitioners. Well, Microsoft Research must've heard that, because it recently created an AI ethics checklist together with nearly 50 engineers from a dozen tech companies. Authors said the checklist is intended to spark conversation and "good tension" within organizations. The list avoids yes or no questions, uses words like "scrutinize," and asks teams to "define fairness criteria."

Achieving production-level governance with machine-learning projects currently presents unique challenges. A new space of tools and practices is emerging under the name MLOps. The space is analogous to DevOps but tailored to the practices and workflows of machine learning. Machine learning models make predictions for new data based on the data they have been trained on. Managing this data in a way that can be safely used in live environments is challenging, and one of the key reasons why 80 per cent of data science projects never make it to production – an estimate from Gartner.

A new hospital ward run entirely by robots has opened in Wuhan, China, in a bid to protect medical staff from contracting the coronavirus. On 7 March, about 200 patients exhibiting early symptoms of covid-19 were ushered into the new ward, which is in a converted sports centre in Wuhan, the city where the coronavirus outbreak started. The robots deliver food, drinks and drugs to the patients, and keep the ward clean.