Speaking at his mid-term semiconductor industry forecast seminar in London this week, Malcolm Penn, chairman and CEO of industry analyst Future Horizons, assured attendees that industry fundamentals were sound, and after a fall of around 15% in 2019, the industry will rebound with around 4% revenue growth to $414 billion in 2020. He said, "The fundamentals are sound. In terms of IC unit growth, fab capacity and average selling price, they are all in in good shape. It's the timing of the upswing in the economy that puts it into doubt." He added, "Rebound is a certainty, but its timing is not."

A Class 6 student, Samaira Mehta already seems to be on top of her game. Of Indian origin and based in California, this 11-year-old girl in tech is an inventor, and has invented CoderBunnyz, a STEM coding board game to teach coding to kids between the age group of four and 10. Samaira has taken Silicon Valley by storm and has been a part of more than 50 conferences. She has held 60 workshops that spotlight her board game, and taught over 2,000 kids, including over 50 "Google kids" at Googleplex, Google's headquarters in Mountain View. The young girl also received a letter from the White House, from then First Lady Michelle Obama, for her work.

The dreaded El Niño strikes the globe every 2 to 7 years. As warm waters in the tropical Pacific Ocean shift eastward and trade winds weaken, the weather pattern ripples through the atmosphere, causing drought in southern Africa, wildfires in South America, and flooding on North America's Pacific coast. Climate scientists have struggled to predict El Niño events more than 1 year in advance, but artificial intelligence (AI) can now extend forecasts to 18 months, according to a new study. The work could help people in threatened regions better prepare for droughts and floods, for example by choosing which crops to plant, says William Hsieh, a retired climate scientist in Victoria, Canada, who worked on early El Niño forecasts but who was not involved in the current study. Longer forecasts could have "large economic benefits," he says.

Wipro signed a strategic partnership with the Industrie 4.0 Maturity Center (I4.0MC), Based in Aachen, Germany, I4.0MC is a part of RWTH Aachen Campus With the help of this strategic partnership, Wipro consultants use the i4.0MC's program to support their client's digital transformation processes, the announcement notes. The acatech Industrie 4.0 Maturity Index, applied by the Industrie 4.0 Maturity Center, works as a methodical guideline to individually design the path to an agile company and to derive the necessary steps. The partnership will also promote the collaboration between the industry and academic experts across the industries such as industrial manufacturing, consumer goods, automotive, oil & gas, and life science. Christian Hocken, MBA, Managing Partner said, "We are looking forward to the cooperation with a leading technology company. Our competences complement each other in an ideal way. We are providing the management frameworks and tools while Wipro will be realizing the digital transformation. Together we will be able to serve our customers with tailor-made transformation projects to become a data-driven, agile company."

In 2002, a couple of Japanese visitors to Australia swapped passports with each other before walking through an automatic biometric border control gate being tested at Sydney airport. The facial recognition algorithm falsely matched each of them to the others' passport photo. These gentlemen were in fact part of an international aviation industry study group and were in the habit of trying to fool biometric systems then being trialed round the world. When I heard about this successful prank, I quipped that the algorithms were probably written by white people - because we think all Asians look the same. Colleagues thought I was making a typical sick joke, but actually I was half-serious.

Wine, spirits and craft beer retailer Fine Wine Delivery is using IBM's Watson artificial intelligence (AI) to help customers choose products from its range. It says, through IBM Watson, consumers are now able to access a level of expert product knowledge to "assist them in their discovery of the complex world of wine, craft beer and spirits" from their smartphone or tablet. Fine Wine Delivery operates an independent, expert tasting panel that creates tasting notes on all of its more than 2000 products. The company says it wanted to make that knowledge more accessible to customers and create an online experience that was as informative as chatting to their expert team in-store. To achieve this it worked with Auckland-based AI specialist, Spacetime to create a natural language search by ingesting the original tasting notes and using IBM Watson Virtual Assistant to provide customised advice online.

We introduce "extreme summarization," a new single-document summarization task which aims at creating a short, one-sentence news summary answering the question "What is the article about?". We argue that extreme summarization, by nature, is not amenable to extractive strategies and requires an abstractive modeling approach. In the hope of driving research on this task further: (a) we collect a real-world, large scale dataset by harvesting online articles from the British Broadcasting Corporation (BBC); and (b) propose a novel abstractive model which is conditioned on the article's topics and based entirely on convolutional neural networks. We demonstrate experimentally that this architecture captures long-range dependencies in a document and recognizes pertinent content, outperforming an oracle extractive system and state-of-the-art abstractive approaches when evaluated automatically and by humans on the extreme summarization dataset.

Question semantic similarity is a challenging and active research problem that is very useful in many NLP applications, such as detecting duplicate questions in community question answering platforms such as Quora. Arabic is considered to be an under-resourced language, has many dialects, and rich in morphology. Combined together, these challenges make identifying semantically similar questions in Arabic even more difficult. In this paper, we introduce a novel approach to tackle this problem, and test it on two benchmarks; one for Modern Standard Arabic (MSA), and another for the 24 major Arabic dialects. We are able to show that our new system outperforms state-of-the-art approaches by achieving 93% F1-score on the MSA benchmark and 82% on the dialectical one. This is achieved by utilizing contextualized word representations (ELMo embeddings) trained on a text corpus containing MSA and dialectic sentences. This in combination with a pairwise fine-grained similarity layer, helps our question-to-question similarity model to generalize predictions on different dialects while being trained only on question-to-question MSA data.

InterpretML is an open-source Python package which exposes machine learning interpretability algorithms to practitioners and researchers. InterpretML exposes two types of interpretability - glassbox models, which are machine learning models designed for interpretability (ex: linear models, rule lists, generalized additive models), and blackbox explainability techniques for explaining existing systems (ex: Partial Dependence, LIME). The package enables practitioners to easily compare interpretability algorithms by exposing multiple methods under a unified API, and by having a built-in, extensible visualization platform. InterpretML also includes the first implementation of the Explainable Boosting Machine, a powerful, interpretable, glassbox model that can be as accurate as many blackbox models. The MIT licensed source code can be downloaded from github.com/microsoft/interpret.

There has been a rapid progress in the task of Visual Question Answering with improved model architectures. Unfortunately, these models are usually computationally intensive due to their sheer size which poses a serious challenge for deployment. We aim to tackle this issue for the specific task of Visual Question Answering (VQA). A Convolutional Neural Network (CNN) is an integral part of the visual processing pipeline of a VQA model (assuming the CNN is trained along with entire VQA model). In this project, we propose an efficient and modular neural architecture for the VQA task with focus on the CNN module. Our experiments demonstrate that a sparsely activated CNN based VQA model achieves comparable performance to a standard CNN based VQA model architecture.