We've all been there--sitting and staring at row after row of Netflix categories, trying to find something new to watch. I've browsed Netflix so much, I feel like I've scrolled for shows more than I've actually streamed them. Netflix has offered many solutions to try and help solve the video browsing blues, from the streaming's famous algorithm to the "play something" button. Now, Netflix has a new tool to help you find the perfect movie or show--and yes, AI has entered the chat, quite literally. Coming soon as a "small" opt-in only beta for the Netflix iOS app, Netflix's new AI chatbot will help you search for videos using "natural, conversational phrases" rather than just sifting through rows and rows of categories. For example, you'll be able to type "Something funny and upbeat" or "I want something scary, but not too scary" into the chat box, and Netflix's AI bot will serve up a list of suggestions, and with a comment like "Good vibes only: These comedies will leave you smiling, laughing or both."

We have developed a framework to reliably build agents capable of UI navigation. The state space is simplified from raw-pixels to a set of UI elements extracted from screen understanding, such as OCR and icon detection. The action space is restricted to the UI elements plus a few global actions. Actions can be customized for tasks and each action is a sequence of basic operations conditioned on status checks. With such a design, we are able to train DQfD and BC agents with a small number of demonstration episodes. We propose demo augmentation that significantly reduces the required number of human demonstrations. We made a customization of DQfD to allow demos collected on screenshots to facilitate the demo coverage of rare cases. Demos are only collected for the failed cases during the evaluation of the previous version of the agent. With 10s of iterations looping over evaluation, demo collection, and training, the agent reaches a 98.7\% success rate on the search task in an environment of 80+ apps and websites where initial states and viewing parameters are randomized.

Condition-based monitoring (CBM) has been widely utilised in the wind industry for monitoring operational inconsistencies and failures in turbines, with techniques ranging from signal processing and vibration analysis to artificial intelligence (AI) models using Supervisory Control & Acquisition (SCADA) data. However, existing studies do not present a concrete basis to facilitate explainable decision support in operations and maintenance (O&M), particularly for automated decision support through recommendation of appropriate maintenance action reports corresponding to failures predicted by CBM techniques. Knowledge graph databases (KGs) model a collection of domain-specific information and have played an intrinsic role for real-world decision support in domains such as healthcare and finance, but have seen very limited attention in the wind industry. We propose XAI4Wind, a multimodal knowledge graph for explainable decision support in real-world operational turbines and demonstrate through experiments several use-cases of the proposed KG towards O&M planning through interactive query and reasoning and providing novel insights using graph data science algorithms. The proposed KG combines multimodal knowledge like SCADA parameters and alarms with natural language maintenance actions, images etc. By integrating our KG with an Explainable AI model for anomaly prediction, we show that it can provide effective human-intelligible O&M strategies for predicted operational inconsistencies in various turbine sub-components. This can help instil better trust and confidence in conventionally black-box AI models. We make our KG publicly available and envisage that it can serve as the building ground for providing autonomous decision support in the wind industry.

Google has perfected its ability to execute web search results for its users all over the world. In the early days of the Internet, the search engine was primarily suited for displaying search results for English users. Non-English-speaking users have complained that search results are often displayed in the wrong language entirely. However, Google is becoming more proficient at providing search results in other languages as well. A lot of factors can play a role, but one of the biggest is its use of deep learning to understand semantic references--enter semantic indexing. This can now be accomplished in any language that Google serves.

The concept of artificial intelligence, or AI, has existed for centuries, even if the phrase itself wasn't coined until 1956. The idea that humans could create something capable of thought processes similar to, or even superior to, their own, existed with the ancient Greeks and has extended through the millennia. The concept ramped up significantly in the 1950s, though computer memory and construction limitations prevented significant breakthroughs from occurring. Science fiction novels and films began foreseeing an ominous future, and recently, AI applications have started infiltrating our world. More recent years have seen some interest spikes: Deep Blue, a chess-playing supercomputer, defeated Garry Kasparov in 1997, and IBM's Watson destroyed its human competition in Jeopardy! in 2011.

When coming across the term "text search", one usually thinks of a large body of text, which is indexed in a way that makes it possible to quickly look up one or more search terms when they are entered by a user. This is a classic problem for computer scientists, to which many solutions exist. What if what's available for indexing beforehand is a group of search phrases, and only at runtime is a large body of text presented for searching? These questions are what this trie data structure tutorial seeks to address. A real world application for this scenario is matching a number of medical theses against a list of medical conditions and finding out which theses discuss which conditions.

The concept of artificial intelligence, or AI, has existed for centuries, even if the phrase itself wasn't coined until 1956. The idea that humans could create something capable of thought processes similar to, or even superior to, their own, existed with the ancient Greeks and has extended through the millennia. The concept ramped up significantly in the 1950s, though computer memory and construction limitations prevented significant breakthroughs from occurring. Science fiction novels and films began foreseeing an ominous future, and recently, AI applications have started infiltrating our world. More recent years have seen some interest spikes: Deep Blue, a chess-playing supercomputer, defeated Garry Kasparov in 1997, and IBM's Watson destroyed its human competition in Jeopardy! in 2011.