After watching dotNetConf videos over the last couple of weeks, I've been really excited to try out some of the new image classification techniques in Visual Studio. The dotNetConf keynote included a section from Bri Actman, who is a Program Manager on the .NET Team (the relevant section is on YouTube from 58m16 to 1hr06m35s). This section showed how developers can integrate various ML techniques and code into their projects using the ModelBuilder tool in Visual Studio – in her example, photographs of the outdoors were classified according to what kind of weather they showed. As well as the keynote, there's another relevant dotNetConf talk by Cesar de la Torre which is also available here on what's new in ML.NET And the way to integrate this into my project looks very straightforward – right click on the project - Add Machine Learning - and choose what type of scenario you want to use, as shown in the screenshot below. I've highlighted the feature that I'm really interested in – image classification.

The TAIAO project (Time-Evolving Data Science / Artificial Intelligence for Advanced Open Environmental Science) will advance the state-of-the-art in environmental data science by developing new machine learning methods for time series and data streams that are able to deal with large quantities of big data in real time, which are tailored to deal with data collected on the New Zealand environment. We will build a new open source framework to implement machine learning on time series data, provide an open available repository with datasets to improve reproducibility in environmental data science, and build capability in fundamental and applied data science, accessible to all New Zealanders. This programme is a new collaboration between the Universities of Waikato, Auckland and Canterbury, Beca and MetService and includes world-leading data scientists, data engineers, and environmental scientists. We will work with regional councils, iwi and co-governance entities to implement the methods we develop to support governance and management decisions with analyses based on large volumes of data that they cannot currently process. We will also make use of our existing strong international collaborations to grow our own data science capabilities and attract top international researchers to work with us on challenging data science problems.

Machine reading comprehension(MRC) has attracted significant amounts of research attention recently, due to an increase of challenging reading comprehension datasets. In this paper, we aim to improve a MRC model's ability to determine whether a question has an answer in a given context (e.g. the recently proposed SQuAD 2.0 task). Our solution is a relation module that is adaptable to any MRC model. The relation module consists of both semantic extraction and relational information. We first extract high level semantics as objects from both question and context with multi-head self-attentive pooling. These semantic objects are then passed to a relation network, which generates relationship scores for each object pair in a sentence. These scores are used to determine whether a question is non-answerable. We test the relation module on the SQuAD 2.0 dataset using both BiDAF and BERT models as baseline readers. We obtain 1.8% gain of F1 on top of the BiDAF reader, and 1.0% on top of the BERT base model. These results show the effectiveness of our relation module on MRC

Noninvasive MR-guided focused ultrasound (MRgFUS) treatments are promising alternatives to the surgical removal of malignant tumors. A significant challenge is assessing the treated tissue immediately after MRgFUS procedures. Although current clinical assessment uses the immediate nonperfused volume (NPV) biomarker derived from contrast enhanced imaging, the use of contrast agent prevents continuing MRgFUS treatment if margins are not adequate. In addition, the NPV has been shown to provide variable accuracy for the true treatment outcome as evaluated by follow-up biomarkers. This work presents a novel, noncontrast, learned multiparametric MR biomarker that is conducive for intratreatment assessment. MRgFUS ablations were performed in a rabbit VX2 tumor model. Multiparametric MRI was obtained both during and immediately after the MRgFUS ablation, as well as during follow-up imaging. Segmentation of the NPV obtained during follow-up imaging was used to train a neural network on noncontrast multiparametric MR images. The NPV follow-up segmentation was registered to treatment-day images using a novel volume-conserving registration algorithm, allowing a voxel-wise correlation between imaging sessions. Contrasted with state-of-the-art registration algorithms that change the average volume by 16.8%, the presented volume-conserving registration algorithm changes the average volume by only 0.28%. After registration, the learned multiparametric MR biomarker predicted the follow-up NPV with an average DICE coefficient of 0.71, outperforming the DICE coefficient of 0.53 from the current standard of NPV obtained immediately after the ablation treatment. Noncontrast multiparametric MR imaging can provide a more accurate prediction of treated tissue immediately after treatment. Noncontrast assessment of MRgFUS procedures will potentially lead to more efficacious MRgFUS ablation treatments.

--Building spatiotemporal activity models for people's activities in urban spaces is important for understanding the ever-increasing complexity of urban dynamics. With the emergence of Geo-T agged Social Media (GTSM) records, previous studies demonstrate the potential of GTSM records for spatiotemporal activity modeling. However, they ignore Non-GeoT agged Social Media (NGTSM) records, which generally account for the majority of posts (e.g., more than 95% in Twitter), and could represent a great source of information to alleviate the sparsity of GTSM records. Furthermore, in the current spatiotemporal embedding techniques, less focus has been given to the users, who exhibit spatially motivated behaviors. T o bridge this research gap, this work proposes USTAR, a novel online learning method for User-guided SpatioT emporal Activity Representation, which (1) embeds locations, time, and text along with users into the same embedding space to capture their correlations; (2) uses a novel collaborative filtering approach based on two different empirically studied user behaviors to incorporate both NGTSM and GTSM records in learning; and (3) introduces a novel sampling technique to learn spatiotemporal representations in an online fashion to accommodate recent information into the embedding space, while avoiding overfitting to recent records and frequently appearing units in social media streams. Our results show that USTAR substantially improves the state-of-the-art for region retrieval and keyword retrieval and its potential to be applied to other downstream applications such as local event detection. With urbanization, more than half of the today's world population (exactly 55.7% as of 2019 1) live in urban areas. It is projected that the urbanization trend will gradually increase over the next few decades. As a result, it is not only difficult to tackle urban challenges (e.g., controlling traffic congestion, controlling environmental pollution), it is difficult for people in urban areas to find the most suitable activities and places at the right time. For instance, consider an inhabitant in a highly urbanized city like Melbourne. What is the best time to visit Mount Buller, a snowy mountain near Melbourne, for skiing? Up until the early 2000s 2, it was almost impossible to model these complex urban dynamics due to the lack of reliable data sources.

In this work, we study how the large-scale pretrain-finetune framework changes the behavior of a neural language generator. We focus on the transformer encoder-decoder model for the open-domain dialogue response generation task. We find that after standard fine-tuning, the model forgets important language generation skills acquired during large-scale pre-training. We demonstrate the forgetting phenomenon through a detailed behavior analysis from the perspectives of context sensitivity and knowledge transfer. Adopting the concept of data mixing, we propose an intuitive fine-tuning strategy named "mix-review". We find that mix-review effectively regularize the fine-tuning process, and the forgetting problem is largely alleviated. Finally, we discuss interesting behavior of the resulting dialogue model and its implications.

Increasingly, AI is adopted by our banks and our bosses, by our cars and our courts. Across the board, implicit bias remains a significant and complex problem. Several examples have become emblematic of the ways in which implicit bias can channel AI in a prejudiced direction. The Nikon camera that kept asking whether Taiwanese American blogger Joz Wang and her family members were "blinking" while they were taking photographs, for instance, or the time when Google Photos tagged two black friends as "gorillas." Or take the example of Google search results.

Techfest is the annual science and technology festival of Indian Institute of Technology Bombay.[1] It also refers to the independent body of students who organize this event along with many other social initiatives and outreach programs around the year. Techfest is known for hosting a variety of events that include competitions, exhibitions, lectures as well as workshops. Started in 1998 with the aim of providing a platform for the Indian student community to develop and showcase their technical prowess, it has now grown into Asia's Largest Science and Technology Festival[2] with a footfall of 1.75 lakhs in its latest edition.[3][4][5] The activities culminate in a grand three-day event in the campus of IIT Bombay which attracts people from all over the World, including students, academia, corporates and the general public.[6] The very first edition of Techfest was in 1998. The underlying spirit of Techfest was "to promote technology and scientific thinking and innovation" a motto that has been followed by every Techfest since. Techfest '98 also set the broad outlines of Techfest in the form of competitions, lectures, workshops, and exhibitions which went on to become a standard feature at every Techfest. Entrepreneurship also made an appearance in the 1999 and 2000 editions. Technoholix--Techfest in the Dark, showcasing technological entertainment at the end of each day as well as the hub of on the spot activities, made their debut during these years. Techfest 2001-2002 saw the incorporation of IIT Bombay's department oriented events like Yantriki, Chemsplash and Last Straw. Students from G H Raisoni College of Engineering got the Engineering Excellence Award for best design.

AI can be harnessed in a wide range of economic sectors and situations to contribute to managing environmental impacts and climate change.Some examples of application include: AI-infused clean distributed energy grids, precision agriculture, sustainable supply chains, environmental monitoring and enforcement, and enhanced weather and disaster prediction and response. Research by PwC UK, commissioned by Microsoft, models the economic impact of AI's application to manage the environment, across four sectors – agriculture, water, energy and transport. It estimates that using AI for environmental applications could contribute up to $5.2 trillion USD to the global economy in 2030, a 4.4% increase relative to business as usual. In parallel the application of AI levers could reduce worldwide greenhouse gas (GHG) emissions by 4% in 2030, an amount equivalent to 2.4 Gt CO2e – equivalent to the 2030 annual emissions of Australia, Canada and Japan combined. At the same time as productivity improvements, AI could create 38.2 million net new jobs across the global economy offering more skilled occupations as part of this transition.

According to Philips' Annual Future Health Index (FHI) 2019 report, Singapore's healthcare professionals are not yet leveraging Artificial Intelligence (AI) to its full potential for treatment and diagnosis. In the report, it is revealed that healthcare professionals in Singapore are using AI technology more for improving the accuracy and efficiency of administrative tasks such as staffing and patient scheduling (37%) than for diagnosis (28%), flagging patient anomalies (26%) and facilitating remote patient monitoring (25%). The report states that emerging countries that are leading the way for AI use in diagnosis globally with nearly half (45%) of China's healthcare professionals, and more than a third in Saudi Arabia (34%), using AI technology to improve the accuracy of their diagnoses. Additionally, the report also hints that apprehension amongst Singapore's healthcare professionals may be one of the barriers to wider adoption, with one in five (20%) admitting that they fear their long-term job security is threatened by new advancements in healthcare technology, such as AI and telehealth. AI aside, the report highlights that Singapore consistently outperforms its Asia Pacific neighbour Australia and holds its own amongst additional Asian countries that were part of the study in terms of digital technology usage, with 89% of Singapore's healthcare professionals using digital health records in their hospital/practice, compared to 81% in Australia and China, and 76% in India.