Fox Sports has tapped into the potential of artificial intelligence and machine learning to deliver the innovative FIFA World Cup Highlight Machine, available through its Fox Sports App and FoxSports.com. A collaboration with IBM, the Highlight Machine is enabled by the IBM Watson computer technology. It analyzes video from the FIFA World Cup archive, as well as 2018 footage, and extracts data, allowing users to search for goals, red cards, players by name and the like. It is about creating a "compelling user experience around highlights, not just in 2018 but also previously, at leat 50 years," says David Mowrey, head of product and development at IBM Watson Media. More typically, gathering this sort of data would be a task done manually by employees, but considering the scope of the World Cup, that would be impractical, and arguably impossible, Mowrey explains, because of the enormous volume of video that is involved.

A track record of prior competency that is above and beyond the norm is what hiring managers look for when they recruit "top talent", as recruiters like to say. Usually "top talents" can command a premium in the market place because everybody wants to employ them. We can equate these "top talents" to top quality stocks. You often hear dividend investors talk about how top dividend growth stocks are "always too expensive". That comment usually refers to the yield for the stock being lower than average, which in the case of a quality stock just represents a greater anticipation of future growth.

Visual question answering (Visual QA) has attracted a lot of attention lately, seen essentially as a form of (visual) Turing test that artificial intelligence should strive to achieve. In this paper, we study a crucial component of this task: how can we design good datasets for the task? We focus on the design of multiple-choice based datasets where the learner has to select the right answer from a set of candidate ones including the target (\ie the correct one) and the decoys (\ie the incorrect ones). Through careful analysis of the results attained by state-of-the-art learning models and human annotators on existing datasets, we show that the design of the decoy answers has a significant impact on how and what the learning models learn from the datasets. In particular, the resulting learner can ignore the visual information, the question, or both while still doing well on the task. Inspired by this, we propose automatic procedures to remedy such design deficiencies. We apply the procedures to re-construct decoy answers for two popular Visual QA datasets as well as to create a new Visual QA dataset from the Visual Genome project, resulting in the largest dataset for this task. Extensive empirical studies show that the design deficiencies have been alleviated in the remedied datasets and the performance on them is likely a more faithful indicator of the difference among learning models. The datasets are released and publicly available via http://www.teds.usc.edu/website_vqa/.

And for soccer (er, football) fans in the US, Fox Sports will be the TV network responsible for bringing them all 64 games from Russia, at least if they want to watch them in English. But, beyond its broadcast offerings, Fox Sports wants to keep people engaged in the competition in different ways. Aside from its partnership with Twitter, which comes in the form of a show that'll stream live from Russia, Fox Sports has teamed up with IBM to build the ultimate World Cup highlight machine. Powered by Watson artificial intelligence, this video hub lets you create on-demand clips from every FIFA World Cup tournament dating back to 1958. Fox Sports says there are 300 archived matches that Watson is capable of analyzing, which you can filter out by World Cup year, team, player, game, play type or any combination of these.

One of the cornerstones of International Business Machines' (NYSE:IBM) ongoing transformation is cognitive computing, which encompasses artificial intelligence and other related technologies. IBM is a business that serves other businesses, and its approach to artificial intelligence (AI) stays true to its purpose. IBM Watson, the company's well-known AI system, is being used in industries like healthcare and financial services to augment the skills of professionals in those fields. The long-term potential of the technology is immense. This article originally appeared in the Motley Fool. IBM has made a bet that cognitive computing will be a big part of its future.

Attention is typically used to select informative sub-phrases that are used for prediction. This paper investigates the novel use of attention as a form of feature augmentation, i.e, casted attention. We propose Multi-Cast Attention Networks (MCAN), a new attention mechanism and general model architecture for a potpourri of ranking tasks in the conversational modeling and question answering domains. Our approach performs a series of soft attention operations, each time casting a scalar feature upon the inner word embeddings. The key idea is to provide a real-valued hint (feature) to a subsequent encoder layer and is targeted at improving the representation learning process. There are several advantages to this design, e.g., it allows an arbitrary number of attention mechanisms to be casted, allowing for multiple attention types (e.g., co-attention, intra-attention) and attention variants (e.g., alignment-pooling, max-pooling, mean-pooling) to be executed simultaneously. This not only eliminates the costly need to tune the nature of the co-attention layer, but also provides greater extents of explainability to practitioners. Via extensive experiments on four well-known benchmark datasets, we show that MCAN achieves state-of-the-art performance. On the Ubuntu Dialogue Corpus, MCAN outperforms existing state-of-the-art models by $9\%$. MCAN also achieves the best performing score to date on the well-studied TrecQA dataset.

A number of studies have found that today's Visual Question Answering (VQA) models are heavily driven by superficial correlations in the training data and lack sufficient image grounding. To encourage development of models geared towards the latter, we propose a new setting for VQA where for every question type, train and test sets have different prior distributions of answers. Specifically, we present new splits of the VQA v1 and VQA v2 datasets, which we call Visual Question Answering under Changing Priors (VQA-CP v1 and VQA-CP v2 respectively). First, we evaluate several existing VQA models under this new setting and show that their performance degrades significantly compared to the original VQA setting. Second, we propose a novel Grounded Visual Question Answering model (GVQA) that contains inductive biases and restrictions in the architecture specifically designed to prevent the model from 'cheating' by primarily relying on priors in the training data. Specifically, GVQA explicitly disentangles the recognition of visual concepts present in the image from the identification of plausible answer space for a given question, enabling the model to more robustly generalize across different distributions of answers. GVQA is built off an existing VQA model -- Stacked Attention Networks (SAN). Our experiments demonstrate that GVQA significantly outperforms SAN on both VQA-CP v1 and VQA-CP v2 datasets. Interestingly, it also outperforms more powerful VQA models such as Multimodal Compact Bilinear Pooling (MCB) in several cases. GVQA offers strengths complementary to SAN when trained and evaluated on the original VQA v1 and VQA v2 datasets. Finally, GVQA is more transparent and interpretable than existing VQA models.

IBM has laid off a number of employees in its Watson Health unit, but says initial reports that as much as 50 percent to 70 percent of the unit's workforce was furloughed are not accurate and that the reductions will not hurt its core cognitive computing business. A company representative, however, would not provide additional details or give the specific number of employees being let go. The company also refused to say how many people are employed in the Watson Health unit. "IBM is continuing to reposition our team to focus on the high-value segments of the IT market, and we continue to hire aggressively in critical new areas that deliver value for our clients and IBM," said the vendor in a written statement. "This activity affects a small percentage of our Watson Health workforce, as we move to more technology-intensive offerings, simplified processes and automation to drive speed."

IBM has laid off a number of employees in its Watson Health unit, but says initial reports that as much as 50 percent to 70 percent of the unit's workforce was furloughed are not accurate and that the reductions will not hurt its core cognitive computing business. A company representative, however, would not provide additional details or give the specific number of employees being let go. The company also refused to say how many people are employed in the Watson Health unit. "IBM is continuing to reposition our team to focus on the high-value segments of the IT market, and we continue to hire aggressively in critical new areas that deliver value for our clients and IBM," said the vendor in a written statement. "This activity affects a small percentage of our Watson Health workforce, as we move to more technology-intensive offerings, simplified processes and automation to drive speed."

Title: Did the Model Understand the Question? Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)