ML models are increasingly deployed in settings with real world interactions such as vehicles, but unfortunately, these models can fail in systematic ways. To prevent errors, ML engineering teams monitor and continuously improve these models. We propose a new abstraction, model assertions, that adapts the classical use of program assertions as a way to monitor and improve ML models. Model assertions are arbitrary functions over a model's input and output that indicate when errors may be occurring, e.g., a function that triggers if an object rapidly changes its class in a video. We propose methods of using model assertions at all stages of ML system deployment, including runtime monitoring, validating labels, and continuously improving ML models. For runtime monitoring, we show that model assertions can find high confidence errors, where a model returns the wrong output with high confidence, which uncertainty-based monitoring techniques would not detect. For training, we propose two methods of using model assertions. First, we propose a bandit-based active learning algorithm that can sample from data flagged by assertions and show that it can reduce labeling costs by up to 40% over traditional uncertainty-based methods. Second, we propose an API for generating "consistency assertions" (e.g., the class change example) and weak labels for inputs where the consistency assertions fail, and show that these weak labels can improve relative model quality by up to 46%. We evaluate model assertions on four real-world tasks with video, LIDAR, and ECG data.

Natural Language Processing (NLP) helps empower intelligent machines by enhancing a better understanding of the human language for linguistic-based human-computer communication. Recent developments in computational power and the advent of large amounts of linguistic data have heightened the need and demand for automating semantic analysis using data-driven approaches. The utilization of data-driven strategies is pervasive now due to the significant improvements demonstrated through the usage of deep learning methods in areas such as Computer Vision, Automatic Speech Recognition, and in particular, NLP. This survey categorizes and addresses the different aspects and applications of NLP that have benefited from deep learning. It covers core NLP tasks and applications and describes how deep learning methods and models advance these areas. We further analyze and compare different approaches and state-of-the-art models.

Neural-symbolic computing has now become the subject of interest of both academic and industry research laboratories. Graph Neural Networks (GNN) have been widely used in relational and symbolic domains, with widespread application of GNNs in combinatorial optimization, constraint satisfaction, relational reasoning and other scientific domains. The need for improved explainability, interpretability and trust of AI systems in general demands principled methodologies, as suggested by neural-symbolic computing. In this paper, we review the state-of-the-art on the use of GNNs as a model of neural-symbolic computing. This includes the application of GNNs in several domains as well as its relationship to current developments in neural-symbolic computing.

The Morin and Scott laboratories are seeking a Postdoctoral Fellow to take a leadership role in an ongoing effort to resolve the molecular aetiology of aggressive lymphoid cancers using genomic techniques. This individual will work closely with a team of bioinformaticians, biostatisticians and clinician-scientists in a highly productive and stimulating research environment at a world-class research facility in Vancouver, Canada. The successful applicant will apply cutting-edge bioinformatic techniques to analyze hundreds of terabytes of high-throughput sequencing data produced from clinical cancer samples, namely RNA-seq (bulk/single cell), whole exome, whole genome, and circulating tumour DNA sequencing data. This position demands a strong background in bioinformatics, computational biology or data science. Detailed knowledge of cancer biology (particularly non-Hodgkin lymphomas) would be an asset.

Someday soon, oil refineries may trade in crude oil for agricultural waste like corn stalks or renewable plants like switchgrass in order to produce sustainable biofuels. But we're not there quite yet; converting those products into usable chemicals on a large scale requires efficient catalytic reactions, which researchers are still hunting for. Recently, Conway Assistant Professor Reid Van Lehn and his colleagues in the Department of Chemical and Biological Engineering have found a way to speed up the process of finding suitable reaction conditions using machine learning, which may help the era of biofuels come a little bit sooner. One of the ways to convert lignocellulosic biomass into usable fuels is via acid-catalyzed reactions, which usually take place in water. It's often a slow process, but research has shown that the addition of certain organic cosolvents can increase reaction rates 100-fold or more.

However, let's add a few evaluation & visualization parts before doing so – for you to visually appreciate model progress. First, we add a numerical evaluation using model.evaluate

Based on our interactions and the results of this study, we expect to see organizations not only adopt AI – but scale it across their enterprises, by building/developing their own AI, or putting ready-made AI applications to work. For example, according to the survey, 40% of respondents currently deploying AI said they are developing proof-of-concepts for specific AI-based or AI-assisted projects, and 40% are using pre-built AI applications, such as chatbots and virtual agents. I see the excitement building with clients every day. Consider just a couple of recent examples. Legal software developer LegalMation has leveraged IBM Watson and our natural language processing technology to help attorneys automate some of the most mundane litigation tasks, speeding, for example, the written discovery process from multiple hours to a few minutes.

This story is part of a collection of pieces on how we work today, from video conferencing to using productivity apps for off-label purposes to Silicon Valley culture. When Tony Huffman stepped away from the production line at the Denso auto part factory in Battle Creek, Michigan, to talk with WIRED earlier this month, the workers he supervised were still being watched--but not by a human. A camera over each station captured workers' movements as they assembled parts for auto heat-management systems. The video was piped into machine-learning software made by a startup called Drishti, which watched workers' movements and calculated how long each person took to complete their work. "In the past, we would take a line that was struggling and bring a bunch of people down with stopwatches to try and make it better," Huffman says--at least for problems that seemed serious enough to justify the time and expense.

Robotic Process Automation (RPA), which allows for the automation of the tasks of workers, has been one of the hottest categories in tech. The reason is actually simple: the ROI (Return on Investment) has generally been fairly high. Yet there are some nagging issues. And perhaps the biggest is the scaling of the technology. But AI (Artificial Intelligence) is likely to help out. To see how, consider one of the leaders in the space, Automation Anywhere.

It is put to us on that first Monday morning as an exciting innovation which will streamline our processes and free up time for the important stuff. This happens in our morning "huddle cuddle", where at 9.15am on the dot our manager has us gather around in a loose circle and run through the day ahead – how many pieces of work there are to be processed, which queues will be prioritised, who is going to take lunch when. This is the kind of place where our team of nine refers to each other as family. The kind of place with an A4 printout stuck to the kitchenette fridge that says "if Britney Spears can get through 2007, you can get through today". The job is a data-processing role at a small member-owned health insurance fund.