Surgical training in medical school residency programs has followed the apprenticeship model. The learning and assessment process is inherently subjective and time-consuming. Thus, there is a need for objective methods to assess surgical skills. Here, we use the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to systematically survey the literature on the use of Deep Neural Networks for automated and objective surgical skill assessment, with a focus on kinematic data as putative markers of surgical competency. There is considerable recent interest in deep neural networks (DNN) due to the availability of powerful algorithms, multiple datasets, some of which are publicly available, as well as efficient computational hardware to train and host them. We have reviewed 530 papers, of which we selected 25 for this systematic review. Based on this review, we concluded that DNNs are powerful tools for automated, objective surgical skill assessment using both kinematic and video data. The field would benefit from large, publicly available, annotated datasets that are representative of the surgical trainee and expert demographics and multimodal data beyond kinematics and videos.

One crucial objective of multi-task learning is to align distributions across tasks so that the information between them can be transferred and shared. However, existing approaches only focused on matching the marginal feature distribution while ignoring the semantic information, which may hinder the learning performance. To address this issue, we propose to leverage the label information in multi-task learning by exploring the semantic conditional relations among tasks. We first theoretically analyze the generalization bound of multi-task learning based on the notion of Jensen-Shannon divergence, which provides new insights into the value of label information in multi-task learning. Our analysis also leads to a concrete algorithm that jointly matches the semantic distribution and controls label distribution divergence. To confirm the effectiveness of the proposed method, we first compare the algorithm with several baselines on some benchmarks and then test the algorithms under label space shift conditions. Empirical results demonstrate that the proposed method could outperform most baselines and achieve state-of-the-art performance, particularly showing the benefits under the label shift conditions.

Artificial intelligence algorithms are increasingly adopted as decisional aides by public organisations, with the promise of overcoming biases of human decision-makers. At the same time, the use of algorithms may introduce new biases in the human-algorithm interaction. A key concern emerging from psychology studies regards human overreliance on algorithmic advice even in the face of warning signals and contradictory information from other sources (automation bias). A second concern regards decision-makers inclination to selectively adopt algorithmic advice when it matches their pre-existing beliefs and stereotypes (selective adherence). To date, we lack rigorous empirical evidence about the prevalence of these biases in a public sector context. We assess these via two pre-registered experimental studies (N=1,509), simulating the use of algorithmic advice in decisions pertaining to the employment of school teachers in the Netherlands. In study 1, we test automation bias by exploring participants adherence to a prediction of teachers performance, which contradicts additional evidence, while comparing between two types of predictions: algorithmic v. human-expert. We do not find evidence for automation bias. In study 2, we replicate these findings, and we also test selective adherence by manipulating the teachers ethnic background. We find a propensity for adherence when the advice predicts low performance for a teacher of a negatively stereotyped ethnic minority, with no significant differences between algorithmic and human advice. Overall, our findings of selective, biased adherence belie the promise of neutrality that has propelled algorithm use in the public sector.

Many times AI has been put on a pedestal as the future of x y & z, however, many seem to agree that education is a sector in particular which will see stark changes in both admin, teaching styles, personalisation and more. I had the pleasure of speaking to three individuals working in the field, including, Vinod Bakthavachalam, Senior Data Scientist at Coursera, Kian Katanforoosh, Lecturer at Stanford University & Sergey Karayev, Co-Founder and CTO of Gradescope. We began by having Sergey of Gradescope walk us through his product, which has been recently acquired by turnitin. The concept, it seemed was formed from the simple and widespread issue of both lack of consistency, lack of insight through time constraint and delayed feedback on academic work. Sergey found that scanning the papers onto an online interface when paired with a rubric can allow for accurate marking in seconds across several papers.

The difference between robotics and automation is almost nonexistent and yet has a huge difference in everything from trade shows, marketing, publications to academic conferences and journals. This week, the difference was expressed as an opportunity in the Dear Colleague Letter below from Professor Ken Goldberg, CITRIS CPAR and UC Berkeley, who suggested that students whose papers were rejected from ICRA, revise them for CASE, the Conference on Automation Science and Engineering. This opportunity was expressed beautifully in the title quote from Professor Raja Chatila, ex President of IEEE Robotics and Automation Society and current President of IEEE Global Society on Ethics of Autonomous and Intelligent Systems. "One robot on Mars is robotics, ten robots on Mars is automation." Over 2000 papers were declined by ICRA today, including many that can be effectively revised for another conference such as IEEE CASE (deadline 15 March).

Why universities will need to digitalise to survive Dave Sherwood 27 February 2021 Universities, and the role they play in society, are under threat from the impact of the ongoing pandemic. While rarely a sector in financial crisis, university leaders in seven of the higher education systems in Europe now predict a fall in core national funding as a result of COVID-19, compounding the huge hits universities have taken on rental and commercial services and contractual research. Fourteen national university sectors in Europe have also predicted a fall in income from international students, with travel restrictions limiting student mobility. Estimates of losses to the United Kingdom university sector range from £3 billion (US4.2 billion) to £19 billion (US$26.7 billion) per year as a result of the coronavirus, while the picture is no less bleak across the pond. The University of Michigan alone anticipates losses of up to US$1 billion this year across its three campuses.

Post-pandemic, some of universities' teaching practices may never return. In parallel, artificial intelligence (AI) is becoming so capable it could start changing how we learn. Covid, perversely, may herald a renaissance for online learning. Most digital learning today is terrible, resembling "bad television", as frequent collaborator professor Alex Pentland of MIT puts it. According to a 2019 study, only 3 per cent of students who start an online class finish it.

If you've been keeping up with the Kaggle News, you may be familiar with the Mechanisms of Action competition by the Laboratory for Innovation Science at Harvard recently closed. I'm proud to say that my partner, Andy Wang, and I managed to place in the top 4% -- 152nd out of 4,373 teams. What's interesting, though, is that we're relatively new to Kaggle competition. In terms of machine learning, we're not exactly professionals -- we're both students that have picked up Python and machine learning from online courses and tutorials. We didn't get gold, of course.

Konstantin Klemmer is a PhD student at the University of Warwick working at the intersection of machine learning and geographic data. He also serves as the Communications Chair for Climate Change AI. We talked about his research and the Climate Change AI organisation. Climate Change AI (CCAI) is a volunteer run organisation that catalyses impactful work at the intersection of climate change and machine learning by providing education and infrastructure, building a community, and advancing discourse. We also run a forum and regular community events like our fortnightly happy hour.

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