The brain is a generator of automatisms, which allow us to do things even though we can't explain how we do them. The goalkeeper who dives to clear that ball in the corner, the gymnast who throws the ribbon and catches it without looking after several somersaults, the tennis player who connects the passing shot on the run. None of them think about (or know), while executing these movements, the mathematical model or the laws of physics that determine these trajectories, yet nevertheless, based on some basic concepts and millions of repetitions, they are capable of doing them. But sometimes, something happens that truncates that ability. Like Simone Biles at the Tokyo Olympics, sometimes the brain loses its automatisms.
Data is the lifeblood of business – it drives innovation and enhances competitiveness. However, its importance was brought to the fore by the pandemic as lockdowns and social distancing drove digital transformation like never before. Forward-thinking businesses have started to grasp the importance of their data; they understand the consequences of not fully mobilizing it, but many are sat at the start of their journey. Even the best organizations are failing to extract the maximum benefits from their data while keeping it safe. This is where artificial intelligence (AI) comes into play – it can benefit enterprises with their data in three fundamental ways.
Using AI, IBM Watson and hybrid cloud, IBM is expanding the real-time data insights available to fans of the US Open Tennis tournament so they can get even deeper, match-by-match details on their favorite players and rounds as the event unfolds. IBM has been working with the US Open and its host, the United States Tennis Association (USTA), for 30 years to help bring the matches to tennis fans. For 2021 the company has enhanced its digital offerings with new IBM Power Rankings that fans can use to see what the data is saying about players ahead of upcoming matches. Among the newly available insights are "Likelihood to Win," "Ones to Watch," and "Upset Alerts." In addition, the first-time US Open Fantasy Tennis experience was also launched on the USOpen.org
IBM unveiled a slate of new AI-powered tools on Friday centered around the US Open, which kicks off in New York City on Monday. Tennis fans will have a bevy of new match and player information available to them thanks to IBM, which built out the tools on the US Open app and USOpen.org. The company created a new IBM Power Rankings with Watson as well as Match Insights with Watson, which is run on IBM Cloud. The Match Insights tool uses AI and natural language processing to provide fans with data on all of the tournament's 254 singles matches. The tools will also be incorporated into the television broadcasts of the tournament on ESPN and on the United States Tennis Association's (USTA), daily show, "The Changeover."
Content platforms thrive on suggesting related content to their users. The more relevant items the platform can provide, the longer the user will stay on the site, which often translates to increased ad revenue for the company. If you've ever visited a news website, online publication, or blogging platform, you've likely been exposed to a recommendation engine. Each of these takes input based on your reading history and then suggests more content you might like. As a simple solution, a platform might implement a tag-based recommendation engine -- you read a "Business" article, so here are five more articles tagged "Business."
In this paper, we explore the problem of developing personalized chatbots. A personalized chatbot is designed as a digital chatting assistant for a user. The key characteristic of a personalized chatbot is that it should have a consistent personality with the corresponding user. It can talk the same way as the user when it is delegated to respond to others' messages. We present a retrieval-based personalized chatbot model, namely IMPChat, to learn an implicit user profile from the user's dialogue history. We argue that the implicit user profile is superior to the explicit user profile regarding accessibility and flexibility. IMPChat aims to learn an implicit user profile through modeling user's personalized language style and personalized preferences separately. To learn a user's personalized language style, we elaborately build language models from shallow to deep using the user's historical responses; To model a user's personalized preferences, we explore the conditional relations underneath each post-response pair of the user. The personalized preferences are dynamic and context-aware: we assign higher weights to those historical pairs that are topically related to the current query when aggregating the personalized preferences. We match each response candidate with the personalized language style and personalized preference, respectively, and fuse the two matching signals to determine the final ranking score. Comprehensive experiments on two large datasets show that our method outperforms all baseline models.
Multimodal abstractive summarization with sentence output is to generate a textual summary given a multimodal triad -- sentence, image and audio, which has been proven to improve users satisfaction and convenient our life. Existing approaches mainly focus on the enhancement of multimodal fusion, while ignoring the unalignment among multiple inputs and the emphasis of different segments in feature, which has resulted in the superfluity of multimodal interaction. To alleviate these problems, we propose a Multimodal Hierarchical Selective Transformer (mhsf) model that considers reciprocal relationships among modalities (by low-level cross-modal interaction module) and respective characteristics within single fusion feature (by high-level selective routing module). In details, it firstly aligns the inputs from different sources and then adopts a divide and conquer strategy to highlight or de-emphasize multimodal fusion representation, which can be seen as a sparsely feed-forward model - different groups of parameters will be activated facing different segments in feature. We evaluate the generalism of proposed mhsf model with the pre-trained+fine-tuning and fresh training strategies. And Further experimental results on MSMO demonstrate that our model outperforms SOTA baselines in terms of ROUGE, relevance scores and human evaluation.
NotCo, a food technology company making plant-based milk and meat replacements, wrapped up another funding round this year, a $235 million Series D round that gives it a $1.5 billion valuation. Tiger Global led the round and was joined by new investors, including DFJ Growth Fund, the social impact foundation, ZOMA Lab; athletes Lewis Hamilton and Roger Federer; and musician and DJ Questlove. Follow-on investors included Bezos Expeditions, Enlightened Hospitality Investments, Future Positive, L Catterton, Kaszek Ventures, SOSV and Endeavour Catalyst. This funding round follows an undisclosed investment in June from Shake Shack founder Danny Meyer through his firm EHI. In total, NotCo, with roots in both Chile and New York, has raised more than $350 million, founder and CEO Matias Muchnick told TechCrunch.
Opsmatix, an innovative provider of AI-powered omnichannel operations automation solutions, announces a significant new hire to lead their platform development. Sateesh Pinnamaneni has joined the firm as Head of Engineering and will be working closely with Mark Barton, Opsmatix's Chief Technology Officer. Sateesh has over 20 years of experience in IT, having worked at some of the world's leading financial institutions, including; Nomura, Goldman Sachs and Credit Suisse. He is a self-proclaimed technology enthusiast and has joined Opsmatix to accelerate the platform development programme. Sateesh is also a keen tennis player and, in his spare time, coaches children.