A number of web APIs enable developers to develop applications using IBM Watson, Watson Machine Learning infrastructure, and capabilities running on IBM Cloud Services to build analytical models and neural networks, deploy AI, and more. Watson Analytics is a natural language-based cognitive service from IBM Watson that can provide real-time analysis, machine learning, and artificial intelligence (AI) capabilities. Watson Analytics, which includes IBM Cloud Services, an IBM cloud-based service that runs on both desktop and mobile devices, is available in a range of languages including English, French, German, Spanish, English – as – a – Second – Language (EASL) and Mandarin Chinese (Mandarin), as well as English and French. Watson is an IBM supercomputer that combines the best of both worlds – a high-performance computing platform and artificial intelligence (AI) for the optimal performance of an answering machine. This expert guide(IBM Watson) is designed to help you better understand the design and maintenance considerations of your infrastructure machine that support your initiative.

Pretrained language models have significantly improved the performance of down-stream language understanding tasks, including extractive question answering, by providing high-quality contextualized word embeddings. However, learning question answering models still need large-scaled data annotation in specific domains. In this work, we propose a cooperative, self-play learning framework, REGEX, for question generation and answering. REGEX is built upon a masked answer extraction task with an interactive learning environment containing an answer entity REcognizer, a question Generator, and an answer EXtractor. Given a passage with a masked entity, the generator generates a question around the entity, and the extractor is trained to extract the masked entity with the generated question and raw texts. The framework allows the training of question generation and answering models on any text corpora without annotation. We further leverage a reinforcement learning technique to reward generating high-quality questions and to improve the answer extraction model's performance. Experiment results show that REGEX outperforms the state-of-the-art (SOTA) pretrained language models and zero-shot approaches on standard question-answering benchmarks, and yields the new SOTA performance under the zero-shot setting.

Perhaps the single most important use case for differential privacy is to privately answer numerical queries, which is usually achieved by adding noise to the answer vector. The central question, therefore, is to understand which noise distribution optimizes the privacy-accuracy trade-off, especially when the dimension of the answer vector is high. Accordingly, extensive literature has been dedicated to the question and the upper and lower bounds have been matched up to constant factors [BUV18, SU17]. In this paper, we take a novel approach to address this important optimality question. We first demonstrate an intriguing central limit theorem phenomenon in the high-dimensional regime. More precisely, we prove that a mechanism is approximately Gaussian Differentially Private [DRS21] if the added noise satisfies certain conditions. In particular, densities proportional to $\mathrm{e}^{-\|x\|_p^\alpha}$, where $\|x\|_p$ is the standard $\ell_p$-norm, satisfies the conditions. Taking this perspective, we make use of the Cramer--Rao inequality and show an "uncertainty principle"-style result: the product of the privacy parameter and the $\ell_2$-loss of the mechanism is lower bounded by the dimension. Furthermore, the Gaussian mechanism achieves the constant-sharp optimal privacy-accuracy trade-off among all such noises. Our findings are corroborated by numerical experiments.

Knowledge Graph Question Answering (KGQA) systems are based on machine learning algorithms, requiring thousands of question-answer pairs as training examples or natural language processing pipelines that need module fine-tuning. In this paper, we present a novel QA approach, dubbed TeBaQA. Our approach learns to answer questions based on graph isomorphisms from basic graph patterns of SPARQL queries. Learning basic graph patterns is efficient due to the small number of possible patterns. This novel paradigm reduces the amount of training data necessary to achieve state-of-the-art performance. TeBaQA also speeds up the domain adaption process by transforming the QA system development task into a much smaller and easier data compilation task. In our evaluation, TeBaQA achieves state-of-the-art performance on QALD-8 and delivers comparable results on QALD-9 and LC-QuAD v1. Additionally, we performed a fine-grained evaluation on complex queries that deal with aggregation and superlative questions as well as an ablation study, highlighting future research challenges.

UKRAINE - 2021/02/19: In this photo illustration an IBM logo is seen on a smartphone screen. A pivotal event for AI happened when IBM's Watson beat two all-time champions of Jeopardy! in 2011. This showed that the technology was far from being experimental. IBM would soon go on to make Watson the centerpiece of its AI strategy. And a big part of this was to focus on healthcare.

Many organizations struggle with both identifying and prioritizing what sales leads to pursue. Where do you start when you have a large stack of leads to go through? What do you when your leads have gone cold? For Product Leaders, it's often a challenge to get a broad spectrum of feedback from their customers. How do they know where to focus next?

Answering logical queries over incomplete knowledge bases is challenging because: 1) it calls for implicit link prediction, and 2) brute force answering of existential first-order logic queries is exponential in the number of existential variables. Recent work of query embeddings provides fast querying, but most approaches model set logic with closed regions, so lack negation. Query embeddings that do support negation use densities that suffer drawbacks: 1) only improvise logic, 2) use expensive distributions, and 3) poorly model answer uncertainty. In this paper, we propose Logic Embeddings, a new approach to embedding complex queries that uses Skolemisation to eliminate existential variables for efficient querying. It supports negation, but improves on density approaches: 1) integrates well-studied t-norm logic and directly evaluates satisfiability, 2) simplifies modeling with truth values, and 3) models uncertainty with truth bounds. Logic Embeddings are competitively fast and accurate in query answering over large, incomplete knowledge graphs, outperform on negation queries, and in particular, provide improved modeling of answer uncertainty as evidenced by a superior correlation between answer set size and embedding entropy.

When I heard the 60th annual Grammy Awards show was going to feature artificial intelligence, I immediately thought "this is a marketing ploy." But then I found out IBM's Watson was the AI in question. Watson, you see, doesn't have a problem rolling up its non-existent sleeves and doing some good old fashioned hard work. Don't expect a silly robot rolling around doing a human impersonation on the red carpet, IBM's machines show up to solve problems and optimize workflows. And while that isn't very sexy – hard work seldom is – it's incredibly important.

Even so, some experts found that it can be difficult to apply AI to treating complex medical conditions. Having access to data that represents patient populations broadly has been a challenge, experts told the Journal, and gaps in knowledge about complex diseases may not be fully captured in clinical databases. "I believe that we're many years away from AI products that really positively impact clinical care for many patients," said Bob Kocher, a partner at venture-capital firm Venrock who focuses on healthcare IT and services investments and who was a White House health adviser under President Barack Obama. Software that makes recommendations on personal medical treatments needs data on what actions have worked in the past. But data on medical histories and treatment outcomes aren't always complete, may be recorded in different formats, and may be sitting in various systems owned by insurance carriers, health providers and other organizations.

Large, heterogeneous datasets are characterized by missing or even erroneous information. This is more evident when they are the product of community effort or automatic fact extraction methods from external sources, such as text. A special case of the aforementioned phenomenon can be seen in knowledge graphs, where this mostly appears in the form of missing or incorrect edges and nodes. Structured querying on such incomplete graphs will result in incomplete sets of answers, even if the correct entities exist in the graph, since one or more edges needed to match the pattern are missing. To overcome this problem, several algorithms for approximate structured query answering have been proposed. Inspired by modern Information Retrieval metrics, these algorithms produce a ranking of all entities in the graph, and their performance is further evaluated based on how high in this ranking the correct answers appear. In this work we take a critical look at this way of evaluation. We argue that performing a ranking-based evaluation is not sufficient to assess methods for complex query answering. To solve this, we introduce Message Passing Query Boxes (MPQB), which takes binary classification metrics back into use and shows the effect this has on the recently proposed query embedding method MPQE.