For long periods of its history, artificial intelligence has lurked in the hinterland of science, often unloved and unfunded -- but two Nobel prizes in one week suggest its time in the sunshine has finally arrived. First on Tuesday, Geoffrey Hinton and John Hopfield won the physics prize for their pioneering work in creating the foundations of modern AI. Then on Wednesday, David Baker, John Jumper and Demis Hassabis shared the chemistry prize for work revealing the secrets of proteins through AI.

Eileen Yu began covering the IT industry when Asynchronous Transfer Mode was still hip and e-commerce was the new buzzword. Currently an independent business technology journalist and content specialist based in Singapore, she has over 20 years of industry experience with various publications including ZDNet, IDG, and Singapore Press Holdings. If applied inappropriately, artificial intelligence (AI) can bring more harm than good. But, it can offer a much-needed helping hand when humans are unable to find comfort from their own kind. AI hasn't always gotten a good rep.

Once again, artificial intelligence shows its potential for sifting through massive amounts of medical test data to deliver actionable results, this time with COVID-19 screening in hospitals and emergency departments. We've written numerous posts about AI applications in medicine, often for diagnostics. For example, we covered AI assisting with autism spectrum disorder diagnosis at the University of California Davis and Google Health's success with AI deep learning to improve breast cancer detection. A group of researchers from Oxford University and Harvard University developed two AI models for COVID-19 early-detection using routinely collected data in hospital emergency departments (EDs) and hospital admissions. Information is available in a research study preprint from medRxiv and bioRxiv.

Soon you won't need one of NVIDIA's tiny Jetson systems if you want to tap into its AI smarts for smaller devices. At its GPU Technology Conference (GTC) today, the company announced it'll be bringing its open source Deep Learning Architecture (NVDLA) over to ARM's upcoming Project Trillium platform, which is focused on mobile AI. Specifically, NVDLA will help developers by accelerating inferencing, the processing of using trained neural networks to perform specific tasks. While it's a surprising move for NVIDIA, which typically relies on its own closed platforms, it makes a lot of sense. NVIDIA already relies on ARM designs for its Jetson and Tegra systems.

We investigate methods for parameter learning from incomplete data that is not missing at random. Likelihood-based methods then require the optimization of a profile likelihood that takes all possible missingness mechanisms into account. Optimzing this profile likelihood poses two main difficulties: multiple (local) maxima, and its very high-dimensional parameter space. In this paper a new method is presented for optimizing the profile likelihood that addresses the second difficulty: in the proposed AI&M (adjusting imputation and mazimization) procedure the optimization is performed by operations in the space of data completions, rather than directly in the parameter space of the profile likelihood. We apply the AI&M method to learning parameters for Bayesian networks. The method is compared against conservative inference, which takes into account each possible data completion, and against EM. The results indicate that likelihood-based inference is still feasible in the case of unknown missingness mechanisms, and that conservative inference is unnecessarily weak. On the other hand, our results also provide evidence that the EM algorithm is still quite effective when the data is not missing at random.