Many recent studies have shown that for models trained on datasets for natural language inference (NLI), it is possible to make correct predictions by merely looking at the hypothesis while completely ignoring the premise. In this work, we manage to derive adversarial examples in terms of the hypothesis-only bias and explore eligible ways to mitigate such bias. Specifically, we extract various phrases from the hypotheses (artificial patterns) in the training sets, and show that they have been strong indicators to the specific labels. We then figure out `hard' and `easy' instances from the original test sets whose labels are opposite to or consistent with those indications. We also set up baselines including both pretrained models (BERT, RoBERTa, XLNet) and competitive non-pretrained models (InferSent, DAM, ESIM). Apart from the benchmark and baselines, we also investigate two debiasing approaches which exploit the artificial pattern modeling to mitigate such hypothesis-only bias: down-sampling and adversarial training. We believe those methods can be treated as competitive baselines in NLI debiasing tasks.

Various AI models are increasingly being considered as part of clinical decision-support tools. However, the trustworthiness of such models is rarely considered. Clinicians are more likely to use a model if they can understand and trust its predictions. Key to this is if its underlying reasoning can be explained. A Bayesian network (BN) model has the advantage that it is not a black-box and its reasoning can be explained. In this paper, we propose an incremental explanation of inference that can be applied to'hybrid' BNs, i.e. those that contain both discrete and continuous nodes. The key questions that we answer are: (1) which important evidence supports or contradicts the prediction, and (2) through which intermediate variables does the information flow. The explanation is illustrated using a real clinical case study. A small evaluation study is also conducted.

Knowledge Graphs are an emerging form of knowledge representation. While Google coined the term Knowledge Graph first and promoted it as a means to improve their search results, they are used in many applications today. In a knowledge graph, entities in the real world and/or a business domain (e.g., people, places, or events) are represented as nodes, which are connected by edges representing the relations between those entities. While companies such as Google, Microsoft, and Facebook have their own, non-public knowledge graphs, there is also a larger body of publicly available knowledge graphs, such as DBpedia or Wikidata. In this chapter, we provide an overview and comparison of those publicly available knowledge graphs, and give insights into their contents, size, coverage, and overlap.

"Hey Google, read this page." That's a new command for the Google Assistant that will see the robot reading web pages aloud. Use cases: catching on news that doesn't have a podcast component while driving, having pages translated to you in other languages (say if you're traveling) or just general help for people who are vision-impaired. Fine print: The feature is only available, starting today, on Google's Android smartphone platform. Whenever a web article is displayed on your browser in your Android phone, you can say, "Hey Google, read it" or "Hey Google, read this page" it will immediately read aloud the content of the web page," says Yossi Matias, a Google vice-president. "And to help people follow along at a convenient pace without having to touch the screen, your browser will highlight the words being read out and auto-scroll the page.

Tinder has posted a warning for its users telling them that coronavirus safety is'more important' than dating and urging them to wash their hands frequently. The dating app also encourages its singletons to carry hand sanitiser, avoid touching their face and'maintain social distance' when out in public. The warning says: 'Tinder is a great place to meet new people. While we want you to continue to have fun, protecting yourself from the coronavirus is more important'. It appears as a pop up while users are flipping between potential matches to warn of the dangers of the potentially deadly virus now called COVID-19. The pop-up warning also includes a link to the latest advice and information from the World Health Organisation (WHO) website.

It has to be said, the launch titles were not great. When the PlayStation 2 arrived in Japan on 4 March 2000, the first games early purchasers got to take home with them included a mahjong sim and a digital train set. The big-name titles, Street Fighter EX3 and Ridge Racer 5, were formulaic entries in tired legacy franchises. Meanwhile, Sega's Dreamcast machine, released a year earlier, was hosting innovative hits such as Shenmue, Crazy Taxi and Power Stone. Had Sony stumbled after its hugely successful and highly disruptive original PlayStation?

Viral particles are colorized purple in this color-enhanced transmission electron micrograph from a COVID-19 patient in the United States. Computer modeling can help epidemiologists predict how and where the illness will move next. Viral particles are colorized purple in this color-enhanced transmission electron micrograph from a COVID-19 patient in the United States. Computer modeling can help epidemiologists predict how and where the illness will move next. Scientists who use math and computers to simulate the course of epidemics are taking on the new coronavirus to try to predict how this global outbreak might evolve and how best to tackle it.

When Regina Barzilay had a routine mammogram in her early 40s, the image showed a complex array of white splotches in her breast tissue. The marks could be normal, or they could be cancerous--even the best radiologists often struggle to tell the difference. Her doctors decided the spots were not immediately worrisome. In hindsight, she says, "I already had cancer, and they didn't see it." Over the next two years Barzilay underwent a second mammogram, a breast MRI and a biopsy, all of which continued to yield ambiguous or conflicting findings. Ultimately she was diagnosed with breast cancer in 2014, but the path to that diagnosis had been unbelievably frustrating. "How do you do three tests and get three different results?" she wondered.

About Us: Adroit Market Research is a global business analytics and consulting company incorporated in 2018. Our target audience is a wide range of corporations, manufacturing companies, product/technology development institutions and industry associations that require understanding of a market's size, key trends, participants and future outlook of an industry. We intend to become our clients' knowledge partner and provide them with valuable market insights to help create opportunities that increase their revenues. We follow a code– Explore, Learn and Transform. At our core, we are curious people who love to identify and understand industry patterns, create an insightful study around our findings and churn out money-making roadmaps.

Machine learning is gaining popularity as a career choice among the young students of computer science and other quantitative fields. And of course, the study of machine learning does open up a few ways into the AI industry which is already quite big in spite of being at a nascent stage at best. It is time to gauge what the new year holds for us as far as these disruptive technologies are concerned. Machine learning adoption is at an all time high The concept of machine learning is pretty old. We can trace it back to the bloody days of the second world war where the legendary Alan Turing applied machine learning to break an impenetrable German code and ended up winning the war for England.