Incorporating external knowledge into Named Entity Recognition (NER) systems has been widely studied in the generic domain. In this paper, we focus on clinical domain where only limited data is accessible and interpretability is important. Recent advancement in technology and the acceleration of clinical trials has resulted in the discovery of new drugs, procedures as well as medical conditions. These factors motivate towards building robust zero-shot NER systems which can quickly adapt to new medical terminology. We propose an auxiliary gazetteer model and fuse it with an NER system, which results in better robustness and interpretability across different clinical datasets. Our gazetteer based fusion model is data efficient, achieving +1.7 micro-F1 gains on the i2b2 dataset using 20% training data, and brings + 4.7 micro-F1 gains on novel entity mentions never presented during training. Moreover, our fusion model is able to quickly adapt to new mentions in gazetteers without re-training and the gains from the proposed fusion model are transferable to related datasets.

Sound event detection is an important facet of audio tagging that aims to identify sounds of interest and define both the sound category and time boundaries for each sound event in a continuous recording. With advances in deep neural networks, there has been tremendous improvement in the performance of sound event detection systems, although at the expense of costly data collection and labeling efforts. In fact, current state-of-the-art methods employ supervised training methods that leverage large amounts of data samples and corresponding labels in order to facilitate identification of sound category and time stamps of events. As an alternative, the current study proposes a semi-supervised method for generating pseudo-labels from unsupervised data using a student-teacher scheme that balances self-training and cross-training. Additionally, this paper explores post-processing which extracts sound intervals from network prediction, for further improvement in sound event detection performance. The proposed approach is evaluated on sound event detection task for the DCASE2020 challenge. The results of these methods on both "validation" and "public evaluation" sets of DESED database show significant improvement compared to the state-of-the art systems in semi-supervised learning.

The hard truth of AI is that methods that exploit deep, hard-won human knowledge about a particular domain are outperformed by methods that cleverly exploit the increasing power of computation. I have a modest proposal to distill this fundamental truth into a set of conditions which, when met, will revolutionise a given field. I do not claim this insight about the fundamental mechanism of progress in AI as original; I have been heavily influenced by Richard Sutton's powerful essay The Bitter Lesson. The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin. Sutton is not alone in this observation.

Microsoft is integrating AI technologies with its PowerFx low-code programming language. This integration will enable customers to use natural-language input and "programming by example" techniques when developing with PowerApps. Microsoft announced the coming new capabilities during the opening day, May 25, of its virtual Build 2021 developers conference. Officials said these new features will be in public preview in English throughout North America by the end of June. PowerFx is the low-code textual programming language Microsoft announced earlier this year.

AI and machine learning have gained a lot of popularity and acceptance in recent years. With the onset of the Covid-19 pandemic, the situation changed even more. During the crisis, we witnessed a rapid digital transformation and the adoption of disruptive technology across different industries. Healthcare was one of the potential sectors that gained many benefits from deploying disruptive technologies. AI, machine learning, and deep learning have become an imperative part of the sector.

The OpenAI Startup Fund is investing $100 million to help AI companies have a profound, positive impact on the world. We're looking to partner with a small number of early-stage startups in fields where artificial intelligence can have a transformative effect--like health care, climate change, and education--and where AI tools can empower people by helping them be more productive. The fund is managed by OpenAI, with investment from Microsoft and other OpenAI partners. In addition to capital, companies in the OpenAI Startup Fund will get early access to future OpenAI systems, support from our team, and credits on Azure. If your startup plans to push the boundaries of today's artificial intelligence by building with our API, we want to hear from you.

Have you ever thought about what would happen if you combined the power of machine learning and artificial intelligence with financial engineering? Today, you can stop imagining, and start doing. This course will teach you the core fundamentals of financial engineering, with a machine learning twist. We will learn about the greatest flub made in the past decade by marketers posing as "machine learning experts" who promise to teach unsuspecting students how to "predict stock prices with LSTMs". You will learn exactly why their methodology is fundamentally flawed and why their results are complete nonsense.

In recent years, researchers have used artificial intelligence to improve translation between programming languages or automatically fix problems. The AI system DrRepair, for example, has been shown to solve most issues that spawn error messages. But some researchers dream of the day when AI can write programs based on simple descriptions from non-experts. On Tuesday, Microsoft and OpenAI shared plans to bring GPT-3, one of the world's most advanced models for generating text, to programming based on natural language descriptions. This is the first commercial application of GPT-3 undertaken since Microsoft invested $1 billion in OpenAI last year and gained exclusive licensing rights to GPT-3.

OpenAI is launching a $100 million startup fund, which it calls the OpenAI Startup Fund, though which it and its partners will invest in early-stage AI companies tackling major problems (and productivity). Among those partners and investors in the fund is Microsoft, at whose Build conference OpenAI founder Sam Altman announced the news. In a prerecorded video, Altman explained that "this is not a typical corporate venture fund. We plan to make big early bets on a relatively small number of companies, probably not more than 10." It's not clear exactly how the $100M will be divided or disbursed, or on what timeline, or whether this is part of a longer program. But it seems to be a limited fund, not just the 2021 round. Altman did say that they will be looking for companies that are taking on serious issues, like healthcare, climate change, and education, where AI-powered applications or approaches could "benefit all of humanity," in keeping with OpenAI's mission statement.

OpenAI today launched the OpenAI Startup Fund, a $100 million fund to -- in the words of OpenAI -- "help AI companies have a profound, positive impact on the world." The fund is managed by OpenAI, with investment from Microsoft and other partners, and OpenAI says that companies selected for it will get early access to future OpenAI systems, support from OpenAI's team, and credits on Microsoft Azure. According to Sam Altman, CEO of OpenAI and the former president of Y Combinator, the OpenAI Startup Fund will make "big, early bets" on a relatively small number of companies, likely no more than 10. It'll look to partner with early-stage startups in fields where AI can have a "transformative" effect -- like health care, climate change, and education -- and where AI tools can empower people by helping them be more productive, like personal assistance and semantic search. "We think that helping people be more productive with new tools is a big deal. And we can imagine brand new interferences that weren't possible a year ago," Altman said.