This article originally appeared on TechRepublic. When it came to figuring out which computer scientist should help linguists decipher inscrutable alien texts, it was Stephen Wolfram who got the call. Sure, these extraterrestrials may only have existed in the sci-fi movie Arrival, but if ET ever does drop out of orbit, Wolfram might well still be on the short list of people to contact. Download this article as a PDF (free registration required). The British-born computer scientist's life is littered with exceptional achievements -- completing a PhD in theoretical physics at Caltech at age 20, winning a MacArthur Genius Grant at 21, and creating the technical computing platform Mathematica (which is used by millions of mathematicians, scientists, and engineers worldwide), plus the Wolfram Language, and the Wolfram Alpha knowledge engine.

We release unofficial pytorch code for "FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence,", which accepted in NeurIPS'20!! I found that some pytorch implementations are already released, but often fail to reproduce the results in the paper. We hope our implementation helps SSL researchers make their awesome projects! If you like our project, please use and make many issues, and mark the star!

I have a large data set of images with associated spatial coordinates. This data set represents a large raster scan (in the spatial coordinate space.) I do have a simple cost function that takes these images and returns a single, positive real number. Let's call a spatial position x, and the real scalar F(x). If I were to consider **only** the spatial coordinates and these responses, I have a fairly straightforward optimization problem.

Dialogue level quality estimation is vital for optimizing data driven dialogue management. Current automated methods to estimate turn and dialogue level user satisfaction employ hand-crafted features and rely on complex annotation schemes, which reduce the generalizability of the trained models. We propose a novel user satisfaction estimation approach which minimizes an adaptive multi-task loss function in order to jointly predict turn-level Response Quality labels provided by experts and explicit dialogue-level ratings provided by end users. The proposed BiLSTM based deep neural net model automatically weighs each turn's contribution towards the estimated dialogue-level rating, implicitly encodes temporal dependencies, and removes the need to hand-craft features. On dialogues sampled from 28 Alexa domains, two dialogue systems and three user groups, the joint dialogue-level satisfaction estimation model achieved up to an absolute 27% (0.43->0.70) and 7% (0.63->0.70) improvement in linear correlation performance over baseline deep neural net and benchmark Gradient boosting regression models, respectively.

Dictation in search uses server-based dictation in order to recognize terms you may be searching for from across the Internet.

The demand for AI-enabled applications that deliver increasingly refined results is driving the need for high-quality annotated data to train AI models. Many continuous intelligence (CI) applications need trained AI models to work. An autonomous vehicle relies on sample data sets that help it differentiate objects and identify road markings and traffic signs. Similarly, an automated video surveillance system needs a data set to learn how to distinguish between a raccoon and an intruder. If the quality of that training data is not right, the performance of the AI models will not be satisfactory.

Let's start by thinking about how vision can be. Most people rely on it to prepare food, walk around obstacles, read street signs, watch videos, and do hundreds of tasks. Vision is the highest bandwidth sense; it provides a fire house of information about the state of the world and how to act on it. For this reason, computer scientists have been trying to give computer vision for half a century, birthing the subfield of computer vision. It goals to give computers the ability to extract high-level understanding from digital images and videos.

IBM (NYSE: IBM) today announced three new products to add to its growing suite of AI solutions for brand and publishers. The new capabilities are privacy-forward and designed to allow brands to reach consumers while considering user privacy. IBM intends to work with industry leaders, including Xandr/AT&T, Magnite, Nielsen, MediaMath, LiveRamp and Beeswax to help scale the use of AI across the industry. The announcement was made this morning at Advertising Week's digital-first virtual event #AW2020. "While the advertising industry strives to re-emerge strong from the global economic and societal issues we faced this year, it's also deep in the throes of a major transformation with changes to mobile identity, certain elimination of third-party cookies, compliance and regulatory shifts and increased demand for trust and transparency," said Bob Lord, SVP, Cognitive Applications and Blockchain, IBM.

Underwater photography is a challenging task. It requires expensive cameras, lights, all sorts of equipment, and a lot of skills. Even then, there is still a blue-greenish tint from the water covering the objects, or animals in any pictures or video you will take. Fortunately for all the underwater enthusiasts, researchers from the University of Haifa were able to remove the water from these pictures and videos by using computer vision and machine learning algorithms. They called their new algorithm: Sea-thru.

Artificial Intelligence (AI) has rapidly emerged as a key disruptive technology in the 21st century. At the heart of modern AI lies Deep Learning (DL), an emerging class of algorithms that has enabled today's platforms and organizations to operate at unprecedented efficiency, effectiveness, and scale. Despite significant interest, IS contributions in DL have been limited, which we argue is in part due to issues with defining, positioning, and conducting DL research. Recognizing the tremendous opportunity here for the IS community, this work clarifies, streamlines, and presents approaches for IS scholars to make timely and high-impact contributions. Related to this broader goal, this paper makes five timely contributions. First, we systematically summarize the major components of DL in a novel Deep Learning for Information Systems Research (DL-ISR) schematic that illustrates how technical DL processes are driven by key factors from an application environment. Second, we present a novel Knowledge Contribution Framework (KCF) to help IS scholars position their DL contributions for maximum impact. Third, we provide ten guidelines to help IS scholars generate rigorous and relevant DL-ISR in a systematic, high-quality fashion. Fourth, we present a review of prevailing journal and conference venues to examine how IS scholars have leveraged DL for various research inquiries. Finally, we provide a unique perspective on how IS scholars can formulate DL-ISR inquiries by carefully considering the interplay of business function(s), application areas(s), and the KCF. This perspective intentionally emphasizes inter-disciplinary, intra-disciplinary, and cross-IS tradition perspectives. Taken together, these contributions provide IS scholars a timely framework to advance the scale, scope, and impact of deep learning research.