The dark data extraction or knowledge base construction (KBC) problem is to populate a relational database with information from unstructured data sources, such as emails, webpages, and PDFs. KBC is a long-standing problem in industry and research that encompasses problems of data extraction, cleaning, and integration. We describe DeepDive, a system that combines database and machine learning ideas to help to develop KBC systems. The key idea in DeepDive is to frame traditional extract-transform-load (ETL) style data management problems as a single large statistical inference task that is declaratively defined by the user. DeepDive leverages the effectiveness and efficiency of statistical inference and machine learning for difficult extraction tasks, whereas not requiring users to directly write any probabilistic inference algorithms.

Piracy has always been a problem for content like online video, but Facebook looks to have an unconventional pitch to content owners who've had their videos reuploaded: You'll still be able to make money on them. In a post Thursday, Facebook announced a tweak to how it processes videos that are pirated and reuploaded from one owner to another. When Facebook's rights management system detects a video that has been pirated, the original owner can now choose to receive ad earnings from the duplicated clip. With Rights Manager, rights owners can find matches of their video content on Facebook; these matches are surfaced on a dashboard. Previously, the rights owner would review these matches in the dashboard to take action.

Harnessing the statistical power of neural networks to perform language understanding and symbolic reasoning is difficult, when it requires executing efficient discrete operations against a large knowledge-base. In this work, we introduce a Neural Symbolic Machine, which contains (a) a neural "programmer", i.e., a sequence-to-sequence model that maps language utterances to programs and utilizes a key-variable memory to handle compositionality (b) a symbolic "computer", i.e., a Lisp interpreter that performs program execution, and helps find good programs by pruning the search space. We apply REINFORCE to directly optimize the task reward of this structured prediction problem. To train with weak supervision and improve the stability of REINFORCE, we augment it with an iterative maximum-likelihood training process. NSM outperforms the state-of-the-art on the WebQuestionsSP dataset when trained from question-answer pairs only, without requiring any feature engineering or domain-specific knowledge.

Artificial Intelligence (AI) is not one big, specific technology. Rather, it is comprised of one or more building block technologies. So, to understand AI, you have to understand each of these nine building block technologies. Now, you could argue that there are more technologies than the ones listed here, but any additional technology can fit under one of these building blocks. Knowledge engineering is a process to understand and then represent human knowledge in data structures, semantic models, and heuristics (rules).

Michael Witbrock received his Ph.D. in Computer Science from Carnegie Mellon University, and is currently a distinguished research staff member at IBM T J Watson Research center, leading work at the intersection of learning and reasoning. Previously he Founded and acted as CEO at Curious Cat Company, building assistants that actually understand you and what you do, and was Vice President for Research at Cycorp, and CEO at Cycorp Europe, where he lead research in automated knowledge acquisition from text and dialogue, automated reasoning, and intelligent human-computer interaction. Previously to that he was Principal Scientist at Terra Lycos, working on integrating statistical and knowledge-based approaches to understanding web user behavior, a research scientist at Just Systems Pittsburgh Research Center, working on statistical text summarization, and a systems scientist at Carnegie Mellon on the Informedia spoken and video document information retrieval project. He is the author of numerous publications in areas ranging from neural networks, parallel computer architecture, multimedia information retrieval, web browser design, genetic design, computational linguistics and speech recognition, and is an inventor on seven US patents.

Yes, I miss you all and sincere apologies for a long hiatus. You would get to understand better in my next post. Wow, I'm so inspired to write about my thoughts on LEARNING especially with a lot of questions that has popped up with how I started my journey to be an AI expert. I remembered vividly when I was assigned my final year project supervisor in my finals and discovered she has a doctorate degree in Artificial Intelligence, I was bent on picking a project topic related to her field because I wanted an A in my finals. After a lot of research for an AI topic, I settled with building a Dental Expert system.

In this paper, we present a robotic prediction agent including a darkforest Go engine, a fuzzy markup language (FML) assessment engine, an FML-based decision support engine, and a robot engine for game of Go application. The knowledge base and rule base of FML assessment engine are constructed by referring the information from the darkforest Go engine located in NUTN and OPU, for example, the number of MCTS simulations and winning rate prediction. The proposed robotic prediction agent first retrieves the database of Go competition website, and then the FML assessment engine infers the winning possibility based on the information generated by darkforest Go engine. The FML-based decision support engine computes the winning possibility based on the partial game situation inferred by FML assessment engine. Finally, the robot engine combines with the human-friendly robot partner PALRO, produced by Fujisoft incorporated, to report the game situation to human Go players. Experimental results show that the FML-based prediction agent can work effectively.

The state of AI today reminds us of the old observation about teenagers and sex: there's more curiosity than knowledge, more talk than action, more failed attempts than actual achievement. Also, it's been around a long time, but each generation has to learn it anew. As Forrester Research observes in its new report "Artificial Intelligence Technologies, Q1 2017," AI dates back to the 1950's and has gone through several rebirths over the decades, notably around "expert systems" in the 1980s (at which time an office wag was heard to say, "artificial intelligence is better than none at all"). Today, another AI rebirth is taking place and it seems the availability of low-cost, increasingly powerful compute infrastructures (processors, data storage, networking) will bring it to fruition this time, putting impressive AI capabilities with the grasp of the enterprise. One of his core points is that AI in the enterprise will not so much replace workers but "amplify human intelligence."

Anything you'd add to the list?) Regular readers will no doubt have noticed that these are the subject areas I most often cover on The Morning Paper. I've chosen today's paper as representative of a large body of work at Stanford on a system called DeepDive. DeepDive sits at a very interesting intersection of the above topics, and its goal is to build a knowledge base – stored in a relational database – from information in large volumes of semi-structured and unstructured data. Such data is sometimes called dark data, and creating a knowledge base from it is the task of knowledge base construction (KBC).

Essential Education: Could one-stop shopping for schools come to L.A. Unified? Welcome to Essential Education, our daily look at education in California and beyond. Several organizations are working together to encourage L.A. Unified to create a universal enrollment system. Students and professors nationwide have launched a campaign to push the Trump administration to enforce Title IX. Several organizations are working together to encourage L.A. Unified to create a universal enrollment system.