Tingguang Li 1, 2, Krishnan Srinivasan 2, Max Qing-Hu Meng 1, Wenzhen Y uan 3 and Jeannette Bohg 2 Abstract -- Robotic in-hand manipulation has been a longstanding challenge due to the complexity of modelling hand and object in contact and of coordinating finger motion for complex manipulation sequences. T o address these challenges, the majority of prior work has either focused on model-based, low-level controllers or on model-free deep reinforcement learning that each have their own limitations. We propose a hierarchical method that relies on traditional, model-based controllers on the low-level and learned policies on the mid-level. The low-level controllers can robustly execute different manipulation primitives (reposing, sliding, flipping). We extensively evaluate our approach in simulation with a 3-fingered hand that controls three degrees of freedom of elongated objects. We show that our approach can move objects between almost all the possible poses in the workspace while keeping them firmly grasped. We also show that our approach is robust to inaccuracies in the object models and to observation noise. Finally, we show how our approach generalizes to objects of other shapes. I NTRODUCTION Dexterous Manipulation refers to the ability of changing the pose of an object to any other pose within the workspace of a hand [1-3]. In this paper, we are particularly concerned with the ability of in-hand manipulation where the object is continuously moved within the hand without dropping. This ability is used frequently in human manipulation e.g. when grasping a tool and readjusting it within the hand, when inspecting an object, when assembling objects or when adjusting an unstable grasp. Y et, in-hand manipulation remains a longstanding challenge in robotics despite the availability of multi-fingered dexterous hands such as [4-6].

An entirely novel synthesis combines the applied cognitive psychology of a task analytic approach with a neural cell assembly perspective that models both brain and mind function during task perf ormance; similar cell assemblies could be implemented as an artificially intelligent neural network. A simplified cell assembly model is introduced and this leads to several new representational formats that, in combination, are demonstrated as suitable f or analysing tasks. The advantages of using neural models are exposed and compared with previous research that has used symbolic artificial intelligence production systems, which make no attempt to model neurophysiology. For cognitive scientists, the app roach provides an easy and practical introduction to thinking about brains, minds and artificial intelligence in terms of cell assemblies. In the future, subsequent developments have t he potential to lead to a new, general t heory of psychology and neurophysiology, supported by cell assembly based artificial intelligences.

This paper presents a novel approach to translating natural language questions to SQL queries for given tables, which meets three requirements as a real-world data analysis application: cross-domain, multilingualism and enabling quick-start. Our proposed approach consists of: (1) a novel data abstraction step before the parser to make parsing table-agnosticism; (2) a set of semantic rules for parsing abstracted data-analysis questions to intermediate logic forms as tree derivations to reduce the search space; (3) a neural-based model as a local scoring function on a span-based semantic parser for structured optimization and efficient inference. Experiments show that our approach outperforms state-of-the-art algorithms on a large open benchmark dataset WikiSQL. We also achieve promising results on a small dataset for more complex queries in both English and Chinese, which demonstrates our language expansion and quick-start ability.

Given a natural language phrase, relation linking aims to find a relation (predicate or property) from the underlying knowledge graph to match the phrase. It is very useful in many applications, such as natural language question answering, personalized recommendation and text summarization. However, the previous relation linking algorithms usually produce a single relation for the input phrase and pay little attention to a more general and challenging problem, i.e., combinational relation linking that extracts a subgraph pattern to match the compound phrase (e.g. In this paper, we focus on the task of combinational relation linking over knowledge graphs. To resolve the problem, we design a systematic method based on the data-driven relation assembly technique, which is performed under the guidance of meta patterns. We also introduce external knowledge to enhance the system understanding ability. Finally, we conduct extensive experiments over the real knowledge graph to study the performance of the proposed method. 1 Introduction Knowledge graphs have been important repositories to materialize a huge amount of structured information in the form of triples, where a triple consists of nullsubject, predicate, objectnull or null subject, property, value null. There have been many such knowledge graphs, e.g., DBpedia (Auer et al. 2007), Y ago (Suchanek, Kasneci, and Weikum 2007), and Freebase (Bollacker et al. 2008). In order to bridge the gap between unstructured text (including text documents and natural language questions) and structured knowledge, an important and interesting task is conducting relation linking over the knowledge graph, i.e., finding the specific predicates/properties from the knowledge graph that match the phrases detected in the sentence (also may be a question). Relation linking can power many downstream applications. As a friendly and intuitive approach to exploring knowledge graphs, using natural language questions to query the knowledge graph has attracted a lot of attentions in both academia and industrial communities (Berant et al. 2013; Bao et al. 2016; Das et al. 2017; Hu et al. 2018; Huang et al. 2019). Generally, the simple questions, e.g., who is the founder of Microsoft, are easy to answer since Figure 1: Example of combinational relations matching the compound phrase mother-in-law. it is straightforward to choose the predicate "founder" from the knowledge graph that matches the phrase "founder" in the input question.

Natural language question answering over knowledge graphs is an important and interesting task as it enables common users to gain accurate answers in an easy and intuitive manner. However, it remains a challenge to bridge the gap between unstructured questions and structured knowledge graphs. To address the problem, a natural discipline is building a structured query to represent the input question. Searching the structured query over the knowledge graph can produce answers to the question. Distinct from the existing methods that are based on semantic parsing or templates, we propose an effective approach powered by a novel notion, structural query pattern, in this paper. Given an input question, we first generate its query sketch that is compatible with the underlying structure of the knowledge graph. Then, we complete the query graph by labeling the nodes and edges under the guidance of the structural query pattern. Finally, answers can be retrieved by executing the constructed query graph over the knowledge graph. Evaluations on three question answering benchmarks show that our proposed approach outperforms state-of-the-art methods significantly.

The famous St Petersburg Paradox shows that the theory of expected value does not capture the real-world economics of decision-making problem. Over the years, many economic theories were developed to resolve the paradox and explain the subjective utility of the expected outcomes and risk aversion. In this paper, we use the concept of the net utility to resolve the St Petersburg paradox. The reason why the principle of absolute instead of net utility does not work is because it is a first order approximation of some unknown utility function. Because the net utility concept is able to explain both behavioral economics and the St Petersburg paradox it is deemed a universal approach to handling utility. Finally, this paper explored how artificial intelligent (AI) agent will make choices and observed that if AI agent uses the nominal utility approach it will see infinite reward while if it uses the net utility approach it will see the limited reward that human beings see.

Google said it has built a computer that's reached "quantum supremacy," performing a computation in 200 seconds that would take the fastest supercomputers about 10,000 years. The results of Google's tests, which were conducted using a quantum chip it developed in-house, were published Wednesday in the scientific journal Nature. "This achievement is the result of years of research and the dedication of many people," Google engineering director Hartmut Neven said in a blog post. "It's also the beginning of a new journey: figuring out how to put this technology to work. We're working with the research community and have open-sourced tools to enable others to work alongside us to identify new applications."

Deepfakes have come across as serious problems this year and big companies are now paying attention. Amazon announced today it's joining the DeepFake Detection challenge (DFDC) driven by major corporations such as Facebook and Microsoft to boost efforts to identify manipulated content. The company is going to contribute $1 million in AWS credits over the next two years to researchers. AWS is also working with DFDC partners to explore hosting complicated datasets for deepfake detection on the cloud service using its Amazon S3 scalable infrastructure. Find out at TNW's Hard Fork Summit Amazon said researchers have to apply for a grant of a minimum of $1,000 and a maximum of $10,000.

Think your plans to resign from your job are a well-kept secret? Think again, as artificial intelligence has been developed to figure out and tell bosses when employees are likely to quit, the Oxford Mail reports. Created by Oxford University spin-out Zegami, the tech takes into account factors like age, pay, benefits and work location to spot disgruntled employees. Research by the firm suggests losing a valued employee who could have been retained costs companies more than £105,000. This is based on the average salary for someone in full-time work, hiring costs and productivity losses over several months while a replacement hire becomes as effective as the employee they replace.

Researchers are proposing a framework that would allow users to understand the rationale behind artificial intelligence (AI) decisions. The work is significant, given the push to move away from "black box" AI systems – particularly in sectors, such as military and law enforcement, where there is a need to justify decisions. "One thing that sets our framework apart is that we make these interpretability elements part of the AI training process," says Tianfu Wu, first author of the paper and an assistant professor of computer engineering at North Carolina State University. "For example, under our framework, when an AI program is learning how to identify objects in images, it is also learning to localize the target object within an image, and to parse what it is about that locality that meets the target object criteria. This information is then presented alongside the result." In a proof-of-concept experiment, researchers incorporated the framework into the widely-used R-CNN AI object identification system.