It consists of 30k turns plus 10k annotated SQL queries, obtained from a Wizard-of-Oz (WOZ) collection of 3k dialogues querying 200 complex DBs spanning 138 domains. Each dialogue simulates a real-world DB query scenario with a crowd worker as a user exploring the DB and a SQL expert retrieving answers with SQL, clarifying ambiguous questions, or otherwise informing of unanswerable questions. When user questions are answerable by SQL, the expert describes the SQL and execution results to the user, hence maintaining a natural interaction flow. CoSQL introduces new challenges compared to existing task-oriented dialogue datasets: (1) the dialogue states are grounded in SQL, a domain-independent executable representation, instead of domain-specific slot-value pairs, and (2) because testing is done on unseen databases, success requires generalizing to new domains. CoSQL includes three tasks: SQL-grounded dialogue state tracking, response generation from query results, and user dialogue act prediction. We evaluate a set of strong baselines for each task and show that CoSQL presents significant challenges for future research. The dataset, baselines, and leaderboard will be released at https:// yale-lily.github.io/cosql .

We introduce a new class of end-to-end learnable models wherein data processing nodes (or network layers) are defined in terms of desired behavior rather than an explicit forward function. Specifically, the forward function is implicitly defined as the solution to a mathematical optimization problem. Consistent with nomenclature in the programming languages community, we name our models deep declarative networks. Importantly, we show that the class of deep declarative networks subsumes current deep learning models. Moreover, invoking the implicit function theorem, we show how gradients can be back-propagated through declaratively defined data processing nodes thereby enabling end-to-end learning. We show how these declarative processing nodes can be implemented in the popular PyTorch deep learning software library allowing declarative and imperative nodes to co-exist within the same network. We provide numerous insights and illustrative examples of declarative nodes and demonstrate their application for image and point cloud classification tasks.

Knowledge graphs are important resources for many artificial intelligence tasks but often suffer from incompleteness. In this work, we propose to use pre-trained language models for knowledge graph completion. We treat triples in knowledge graphs as textual sequences and propose a novel framework named Knowledge Graph Bidirectional Encoder Representations from Transformer (KG-BERT) to model these triples. Our method takes entity and relation descriptions of a triple as input and computes scoring function of the triple with the KG-BERT language model. Experimental results on multiple benchmark knowledge graphs show that our method can achieve state-of-the-art performance in triple classification, link prediction and relation prediction tasks.

Algorithms that parse data, learn from that data, and then apply what they've learned to make informed decisions. I'm sure you are asking yourself, how can a program or algorithm make decisions and learn from data, doesn't every program need to be programmed? Not if the program was trained to learn from and adapt to data. In the case of machine learning the algorithm is not explicitly programmed, rather the model is "trained" using historical and present data in order to make future decisions and prediction. The more data available for training, the more accurate the predictions are.

For most of the past sixty years, a rich critique of artificial intelligence was avidly pursued, mostly by insiders, people either practicing AI or interested onlookers who were in close proximity. Now the world finds itself in a strange state: Just as AI has gone mainstream, showing up everywhere from your Instagram feed to your smartphone voice assistant, many of those voices of criticism have been lost as a generation of thinkers passed away, people like MIT scientist Marvin Minsky and UC Berkeley professor of philosophy Herbert Dreyfus. But a small contingent of critics remains, and the world needs them to keep a balance in its view of AI as the use of AI becomes more entwined with everyday life. They include Judea Pearl, whose Book of Why reminds AI practitioners of the need for causal reasoning; and University of Toronto professor Hector Levesque, whose test for common sense, the Winograd Schema Challenge, sets a high bar for conventional AI. But none have been more prolific in the modern era in the critique of AI than NYU professor of psychology Gary Marcus. In five books and numerous articles in popular publications such as The New York Times and The New Yorker, Marcus has skewered the latest AI headlines, to remind people of the limits to present AI.

Artificial intelligence researchers have long dreamed of building a computer as knowledgeable and communicative as the one in Star Trek, which could interact with humans in natural (i.e., human) language. Last week, we seemed to boldly go toward that ideal. The New York Times reported that a team at the Allen Institute for Artificial Intelligence (AI2) had achieved "an artificial-intelligence milestone." AI2's program, Aristo, not only passed but also excelled on a standardized eighth-grade science test. The machine, the Times heralded, "is ready for high school science. Melanie Mitchell is professor of computer science at Portland State University and External Professor at the Santa Fe Institute. Her book Artificial Intelligence: A Guide for Thinking Humans will be published in October by Farrar, Straus, and Giroux. Aristo isn't the first AI system to shine on a test designed to gauge human knowledge and reasoning abilities. In 2015 one system matched a 4-year-old's performance on an ...

S. Kevin Zhou, Ph.D. is currently a Principal Key Expert Scientist at Siemens Healthcare Technology Center, leading a team of full time research scientists and students dedicated to researching and developing innovative solutions for medical and industrial imaging products. His research interests lie in computer vision and machine/deep learning and their applications to medical image analysis, face recognition and modeling, etc. He has published over 150 book chapters and peer-reviewed journal and conference papers, registered over 250 patents and inventions, written two research monographs, and edited three books. He has won multiple technology, patent and product awards, including R&D 100 Award and Siemens Inventor of the Year. He is an editorial board member for Medical Image Analysis journal and a fellow of American Institute of Medical and Biological Engineering (AIMBE).

In this talk I will show data engineers and architects how to run real-time TensorFlow Inception Image Recognition on images captured by remote sensors and images in tweets. In the same flow I will also demonstrate how to apply real-time sentiment analysis and intelligent routing of data to Phoenix, Email and Slack. I will elaborate on a number of different sentiment analysis frameworks available for use within Apache NiFi including Python NLTK, Stanford CoreNLP, Python SpaCy and Python TextBlob. This talk will be a deep dive into how to manage complex dataflow pipelines ingesting from multiple streaming sources including social, public open data feeds, logs, drones, RDBMS and IoT with transformations, deep learning, machine learning and business rules. Data engineers will be shown the power of Apache NiFi for loading diverse sources of data, applying transformations in-stream, routing based on attributes, adding sentiment data to workflows, running deep learning algorithms in stream and storing data into Apache Phoenix on HBase.

Here are the most popular posts in KDnuggets in August, based on the number of unique page views (UPV), and social share counts from Facebook, Twitter, and Addthis. Most Shareable (Viral) Blogs Among the top blogs, here are the blogs with the highest ratio of shares/unique views, which suggests that people who read it really liked it.

Last week, I spoke at the Swiss Mobile Association. The event was held at one of the oldest cross-functional research institutes Gottlieb Duttweiler Institute just outside Zurich. Prior to being involved in IoT and AI, I worked for many years in Telecoms. So, this was a nice time to catch up with a few ideas for AI for Telecoms I believe that from an innovation standpoint – we are living in a post-mobile world. Today, just as the Web itself, Mobile is a mature industry.