IESE kicked off its new Artificial Intelligence and the Future of Management Initiative, strengthening its commitment to educating managers on what …

Applying trained models to new challenges requires an immense amount of new data training, and time.

What's New: Today at the Intel Industrial Summit 2020, Intel announced new enhanced internet of things (IoT) capabilities. The 11th Gen Intel Core processors, Intel Atom x6000E series, and Intel Pentium and Celeron N and J series bring new artificial intelligence (AI), security, functional safety and real-time capabilities to edge customers. With a robust hardware and software portfolio, an unparalleled ecosystem and 15,000 customer deployments globally, Intel is providing robust solutions for the $65 billion edge silicon market opportunity by 2024. "By 2023, up to 70% of all enterprises will process data at the edge.1 11th Gen Intel Core processors, Intel Atom x6000E series, and Intel Pentium and Celeron N and J series processors represent our most significant step forward yet in enhancements for IoT, bringing features that address our customers' current needs, while setting the foundation for capabilities with advancements in AI and 5G." –John Healy, Intel vice president of the Internet of Things Group and general manager of Platform Management and Customer Engineering Why It's Important: Intel works closely with customers to build proofs of concept, optimize solutions and collect feedback along the way. Innovations delivered with 11th Gen Intel Core processors, Intel Atom x6000E series, and Intel Pentium and Celeron N and J series processors are a response to challenges felt across the IoT industry: edge complexity, total cost of ownership and a range of environmental conditions.

Beyond becoming friends with Mike over time, from the values perspective there are numerous examples in my career developing games and overseeing development teams where we came down to these critical decisions we had to make,

Contextual bandits provide an effective way to model the dynamic data problem in ML by leveraging online (incremental) learning to continuously adjust the predictions based on changing environment. We explore details on contextual bandits, an extension to the traditional reinforcement learning (RL) problem and build a novel algorithm to solve this problem using an array of action-based learners. We apply this approach to model an article recommendation system using an array of stochastic gradient descent (SGD) learners to make predictions on rewards based on actions taken. We then extend the approach to a publicly available MovieLens dataset and explore the findings. First, we make available a simplified simulated dataset showing varying user preferences over time and how this can be evaluated with static and dynamic learning algorithms. This dataset made available as part of this research is intentionally simulated with limited number of features and can be used to evaluate different problem-solving strategies. We will build a classifier using static dataset and evaluate its performance on this dataset. We show limitations of static learner due to fixed context at a point of time and how changing that context brings down the accuracy. Next we develop a novel algorithm for solving the contextual bandit problem. Similar to the linear bandits, this algorithm maps the reward as a function of context vector but uses an array of learners to capture variation between actions/arms. We develop a bandit algorithm using an array of stochastic gradient descent (SGD) learners, with separate learner per arm. Finally, we will apply this contextual bandit algorithm to predicting movie ratings over time by different users from the standard Movie Lens dataset and demonstrate the results.

Online social networks provide a platform for sharing information and free expression. However, these networks are also used for malicious purposes, such as distributing misinformation and hate speech, selling illegal drugs, and coordinating sex trafficking or child exploitation. This paper surveys the state of the art in keeping online platforms and their users safe from such harm, also known as the problem of preserving integrity. This survey comes from the perspective of having to combat a broad spectrum of integrity violations at Facebook. We highlight the techniques that have been proven useful in practice and that deserve additional attention from the academic community. Instead of discussing the many individual violation types, we identify key aspects of the social-media eco-system, each of which is common to a wide variety violation types. Furthermore, each of these components represents an area for research and development, and the innovations that are found can be applied widely.

When collecting item ratings from human judges, it can be difficult to measure and enforce data quality due to task subjectivity and lack of transparency into how judges make each rating decision. To address this, we investigate asking judges to provide a specific form of rationale supporting each rating decision. We evaluate this approach on an information retrieval task in which human judges rate the relevance of Web pages for different search topics. Cost-benefit analysis over 10,000 judgments collected on Amazon's Mechanical Turk suggests a win-win. Firstly, rationales yield a multitude of benefits: more reliable judgments, greater transparency for evaluating both human raters and their judgments, reduced need for expert gold, the opportunity for dual-supervision from ratings and rationales, and added value from the rationales themselves. Secondly, once experienced in the task, crowd workers provide rationales with almost no increase in task completion time. Consequently, we can realize the above benefits with minimal additional cost.

You can now use Google Assistant voice controls to navigate Disney content on smart displays like Nest Hub and Nest Hub Max. To use the feature, you'll have to link your Disney subscription to your Google Home or Assistant app. Then, just say something like "Hey Google, play The Mandalorian," to stream content. From the start, Disney has been available on Google Assistant smart displays like Nest Hub. You can already use Assistant to play Netflix, Hulu, CBS All Access and HBO content, so it only makes sense that the same feature would be available for Disney .

The British startup Photogram AI has announced a new camera called the Alice Camera. It's an "AI-accelerated computational camera" that aims to deliver better connectivity than a DSLR and better quality than a smartphone. Smartphones have been making huge advances in the area of computational photography in recent years while traditional camera companies have largely been left in the dust. Alice is trying to bring the worlds of standalone cameras and computational photography together. Alice is an interchangeable lens camera that features a dedicated AI chip "that elevates machine learning and pushes the boundaries of what a camera can do."

Dive into the data science & machine learning behind Netflix's recommendation algorithms.