Thomson Reuters and Crowe LLP are showcasing an innovative collaboration to help tax professionals address the burdensome manual work related to Schedule K-1 forms at the 2019 SYNERGY users' conference. The significant increase in the number of alternative investments over the past decade has led to a growing number of K-1s. More than 40 million K-1 forms are produced annually, each one requiring manual data capture and aggregation into partner tax returns, resulting in countless hours of complex manual work for tax professionals. The K1 Analyzer tool uses artificial intelligence and machine learning to read, extract and analyze K-1 data from front to back, including statements and disclosures. This process virtually eliminates the manual data-entry work associated with K-1s and enables recipients to aggregate their stack of K-1s in minutes, rather than days.

Consider a forest: One notices the trunks, of course, and the canopy. If a few roots project artfully above the soil and fallen leaves, one notices those too, but with little thought for a matrix that may spread as deep and wide as the branches above. Fungi don't register at all except for a sprinkling of mushrooms; those are regarded in isolation, rather than as the fruiting tips of a vast underground lattice intertwined with those roots. The world beneath the earth is as rich as the one above. For the past two decades, Suzanne Simard, a professor in the Department of Forest & Conservation at the University of British Columbia, has studied that unappreciated underworld. Her specialty is mycorrhizae: the symbiotic unions of fungi and root long known to help plants absorb nutrients from soil. Beginning with landmark experiments describing how carbon flowed between paper birch and Douglas fir trees, Simard found that mycorrhizae didn't just connect trees to the earth, but to each other as well. Simard went on to show how mycorrhizae-linked trees form networks, with individuals she dubbed Mother Trees at the center of communities that are in turn linked to one another, exchanging nutrients and water in a literally pulsing web that includes not only trees but all of a forest's life.

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Technological change is the only constant in today's business world, disrupting everything from large organizations to small start-ups. Disruption affects everyone, but will you be the disruptor or the disrupted? You must pay close attention to the Hard Trends shaping the future of your industry, your business, and the outside world to identify opportunities used to innovate and grow rapidly, additionally using those Hard Trends to solve any problems your organization and customers might have before they occur. The shared definition and understanding of the words we use is an issue in business. While several companies are on course to use artificial intelligence (AI), machine learning (ML), and deep learning (DL), others hardly understand the fundamental differences between these powerful technologies. How can one be successful, much less disruptive, when they themselves do not differentiate between AI, ML, and DL? Recently, technology company Sage conducted surveys pertaining to AI and individuals' understanding of it.

Car manufacturers know: There's a huge amount of interest in AI-driven cars. Many people would love to automate the task of driving, because they find it tedious or at times impossible. A competent AI driver would have lightning-fast reflexes, would never weave or drift in its lane, and would never drive aggressively. An AI driver would never get tired and could take the wheel for endless hours while we humans nap or party. While AI does need huge volumes of data to program and guide it, that shouldn't be a problem.

Rapid advancements in the field of artificial intelligence (AI) are uniquely poised to transform entire occupations and industries, changing the way work will be done in the future. It is imperative to understand the extent and nature of the changes so that we can prepare today for the jobs of tomorrow. New empirical work from the MIT-IBM Watson AI Lab uncovers how jobs will transform as AI and new technologies continue to scale across business and industries. We created a novel dataset using machine learning techniques on 170 million U.S. job postings. The dataset and research, The Future of Work: How New Technologies Are Transforming Tasks, allow us to extract key insights into how AI is shaping the future of work.

The big battleground today is customer experience. And when new(ish) technologies such as AI and programmatic become part of their lives - creating new behaviours, building opinions, and even helping win elections - the creatives sit up and take note. Therefore, we asked The Drum Network members'what are the top trends in tech fuelling creativity?' Voice-enabled content, like Alexa skills and Flash briefings, will be central to creativity in the next five years. It's easy for brands to start using voice tech in creative ways today – for example by creating and sharing short, informative clips that are relevant to your company, industry and audience, covering anything from new offers and product launches to company or industry-wide announcements. We've recently helped Vodafone Business launch its 5G offering in the UK using a range of sound and audio tactics, including Alexa Flash briefings.

The Massachusetts Institute of Technology put on a spectacular show with a pack of mini cheetah robots the campus in Cambridge, Massachusetts. Researchers behind the small quadrupedal robots shared a video online of these mechanical animals running, jumping and even kicking around a soccer ball. The cheetahs are shown frolicking through an area of the college campus, while being controlled by a human. The machines perform a synchronized dance, where they show their gymnastic abilities and then they all join in a game of soccer. 'Eventually, I'm hoping we could have a robotic dog race through an obstacle course, where each team controls a mini cheetah with different algorithms, and we can see which strategy is more effective,' Kim said.

An increasing number of works in natural language processing have addressed the effect of bias on the predicted outcomes, introducing mitigation techniques that act on different parts of the standard NLP pipeline (data and models). However, these works have been conducted in isolation, without a unifying framework to organize efforts within the field. This leads to repetitive approaches, and puts an undue focus on the effects of bias, rather than on their origins. Research focused on bias symptoms rather than the underlying origins could limit the development of effective countermeasures. In this paper, we propose a unifying conceptualization: the predictive bias framework for NLP . We summarize the NLP literature and propose a general mathematical definition of predictive bias in NLP along with a conceptual framework, differentiating four main origins of biases: label bias, selection bias, model overamplification, and semantic bias . We discuss how past work has countered each bias origin. Our framework serves to guide an introductory overview of predictive bias in NLP, integrating existing work into a single structure and opening avenues for future research.

Attention-based models have shown significant improvement over traditional algorithms in several NLP tasks. The Transformer, for instance, is an illustrative example that generates abstract representations of tokens inputted to an encoder based on their relationships to all tokens in a sequence. Recent studies have shown that although such models are capable of learning syntactic features purely by seeing examples, explicitly feeding this information to deep learning models can significantly enhance their performance. Leveraging syntactic information like part of speech (POS) may be particularly beneficial in limited training data settings for complex models such as the Transformer. We show that the syntax-infused Transformer with multiple features achieves an improvement of 0.7 BLEU when trained on the full WMT '14 English to German translation dataset and a maximum improvement of 1.99 BLEU points when trained on a fraction of the dataset. In addition, we find that the incorporation of syntax into BERT fine-tuning outperforms baseline on a number of downstream tasks from the GLUE benchmark. Introduction Attention-based deep learning models for natural language processing (NLP) have shown promise for a variety of machine translation and natural language understanding tasks. For word-level, sequence-to-sequence tasks such as translation, paraphrasing, and text summarization, attention-based models allow a single token ( e.g., a word or subword) in a sequence to be represented as a combination of all tokens in the sequence (Luong, Pham, and Manning, 2015). The distributed context allows attention-based models to infer rich representations for tokens, leading to more robust performance.