Nowcasting can play a key role in giving policymakers timelier insight to data published with a significant time lag, such as final GDP figures. Currently, there are a plethora of methodologies and approaches for practitioners to choose from. However, there lacks a comprehensive comparison of these disparate approaches in terms of predictive performance and characteristics. This paper addresses that deficiency by examining the performance of 12 different methodologies in nowcasting US quarterly GDP growth, including all the methods most commonly employed in nowcasting, as well as some of the most popular traditional machine learning approaches. Performance was assessed on three different tumultuous periods in US economic history: the early 1980s recession, the 2008 financial crisis, and the COVID crisis. The two best performing methodologies in the analysis were long short-term memory artificial neural networks (LSTM) and Bayesian vector autoregression (BVAR).

When it comes to digital transformation, the Covid crisis has provided important lessons for business leaders. Among the most compelling lessons is the potential data analytics and artificial intelligence brings to the table. "Launching a direct-to-consumer business was always on our roadmap, but we certainly hadn't planned on launching it in 30 days in the middle of a pandemic," says Michael Lindsey, chief growth officer at Frito-Lay. "The pandemic inspired our teams to move faster that we would have dreamed possible." The crisis accelerated the adoption of analytics and AI, and this momentum will continue into the 2020s, surveys show. Fifty-two percent of companies accelerated their AI adoption plans because of the Covid crisis, a study by PwC finds.

India and Pakistan have fought four wars in the past few decades, but when India faced an oxygen shortage in its hospitals during its recent COVID-19 surge, Pakistan offered to help. Finding these positive tweets, however, was not as easy as simply browsing the supportive hashtags or looking at the most popular posts. And Twitter's algorithm isn't tuned to surface the most positive tweets during a crisis. Ashique KhudaBukhsh of Carnegie Mellon University's Language Technologies Institute led a team of researchers who used machine learning to identify supportive tweets from Pakistan during India's COVID crisis. In the throes of a public health crisis, words of hope can be welcome medicine.

The decisions that we make now and in the near future will set the tone for the rest of the decade including how artificial intelligence (AI) may develop and how we will use it. It will require enlightened leadership to maximise the benefit for human society. This article is focused on providing a moment of reflection in terms of where we are and where we are going from a policy and philosophical perspective and to serve as a prelude to a more technical article on the next generation of AI that will follow. Positive use case potential for AI includes the fight against Covid -19. For example The Lancet published an article authored by Zhou et al. entitled "Artificial Intelligence in COVID-19 drug repurposing" and state that " In this Review, we introduce guidelines on how to use AI for accelerating drug repurposing or repositioning, for which AI approaches are not just formidable but are also necessary. We discuss how to use AI models in precision medicine, and as an example, how AI models can accelerate COVID-19 drug repurposing."

The decisions that we make now and in the near future will set the tone for the rest of the decade including how artificial intelligence (AI) may develop and how we will use it. It will require enlightened leadership to maximise the benefit for human society. This article is focused on providing a moment of reflection in terms of where we are and where we are going from a policy and philosophical perspective and to serve as a prelude to a more technical article on the next generation of AI that will follow. Positive use case potential for AI includes the fight against Covid -19. For example The Lancet published an article authored by Zhou et al. entitled "Artificial Intelligence in COVID-19 drug repurposing" and state that " In this Review, we introduce guidelines on how to use AI for accelerating drug repurposing or repositioning, for which AI approaches are not just formidable but are also necessary. We discuss how to use AI models in precision medicine, and as an example, how AI models can accelerate COVID-19 drug repurposing."

AI has been at the forefront of the medical profession's efforts to fight Covid-19 and treat patients during the coronavirus pandemic. Enabling healthcare providers to make fast, accurate and data-driven decisions, the technology has been producing some extraordinary outcomes. Outside the Covid crisis, machine intelligence is lending itself to hundreds of medical applications, from scanning vast numbers of people to assess their risk of dementia to accelerating the drug discovery process. Here is just a small selection of cases where the technology is revolutionising healthcare provision. Healthcare professionals are using AI-powered speech-recognition systems to update electronic patient records more quickly and accurately.

Deep reinforcement learning (DRL) has reached super human levels in complex tasks like game solving (Go and autonomous driving). However, it remains an open question whether DRL can reach human level in applications to financial problems and in particular in detecting pattern crisis and consequently dis-investing. In this paper, we present an innovative DRL framework consisting in two sub-networks fed respectively with portfolio strategies past performances and standard deviations as well as additional contextual features. The second sub network plays an important role as it captures dependencies with common financial indicators features like risk aversion, economic surprise index and correlations between assets that allows taking into account context based information. We compare different network architectures either using layers of convolutions to reduce network's complexity or LSTM block to capture time dependency and whether previous allocations is important in the modeling. We also use adversarial training to make the final model more robust. Results on test set show this approach substantially over-performs traditional portfolio optimization methods like Markowitz and is able to detect and anticipate crisis like the current Covid one.

The year of 2020 has proved to be a challenging year defined mostly across the world by a global pandemic and as a result an increasing shift towards digital. The decisions that we make now and in the near future will set the tone for the rest of the decade including how AI may develop and how we will use it. It will require enlightened leadership to maximise the benefit for human society. This article is focused on providing a moment of reflection in terms of where we are and where we are going from a policy and philosophical perspective and to serve as a prelude to a more technical article on the next generation of AI that will follow. Positive use case potential for AI includes the fight against Covid -19.

Back in June, a report from LinkedIn noted that the Covid crisis had cooled off demand for artificial intelligence skills. However, four months later, companies are still struggling with finding AI skills. Overall, more than four out of 10 enterprises now use artificial intelligence in a serious way, up one-third in just two years, a recent survey of 1,000 executives by RELX finds. Adoption accelerated in a big way over the past few months. AI technologies are being employed at 81% of businesses -- up from 48% in a similar survey conducted in 2018.

August 13, 2020: DHL Supply Chain is aiming to optimise e-fulfilment for online shops with IDEA, a solution specifically designed for warehouses. IDEA algorithm can significantly improve order-picking processes in DHL-operated warehouses. The costs involved are comparatively low since the software, developed by DHL, is compatible with most traditional warehouse management systems and can be integrated easily into existing IT infrastructure. In its first commercial deployments, the solution has reduced distance travelled by warehouse employees by up to 50 percent and increased productivity of individual DHL locations by up to 30 percent. "The Covid crisis has shown us that unforeseen volume fluctuations for individual products can put enormous stress on supply chains. Lack of clarity regarding stock levels and incorrect data on availability can lead to bottlenecks for individual products and then delays in delivering to end customers," said Markus Voss, CIO & COO at DHL Supply Chain.