Collaborating Authors


TikTok users will soon have an easier way to add popular GIFs


TikTok users will soon have even more ways to make their videos stand out from the crowd. The service has announced the TikTok Library, which will grant creators access to more entertainment-based content. You'll be able to find GIFs, clips from your favorite TV shows, memes and other content, which you can slot into your TikToks. Although there are already ways to insert GIFs from Giphy into TikTok videos, it should be easier to do that once you have access to the library. Until now, Giphy GIFs have been available as Stickers and via the Green Screen effect.

Japan to strengthen fertility treatment consultation system

The Japan Times

Japan will strengthen its consultation system for fertility treatment as its public health insurance program starts covering such treatment in April. The health ministry plans to integrate related public consultation windows under a single system. The new facilities will help people with specialist advice and provide emotional support to women who feel anxious. In the fiscal 2022 revision of official medical fees, the public insurance coverage will be extended to fertility treatment such as in vitro fertilization and artificial insemination as part of efforts to shore up the country's falling birthrate. Thanks to this, costs of fertility treatment that have been fully paid by patients will be limited to 30% in principle.

Netflix tests its TikTok-like comedy feed on TVs


You didn't think Netflix would leave its TikTok-style comedy feed on phones, did you? Sure enough, the company is launching a test that brings the Fast Laughs feature to TVs. Opt in and you'll get a flurry of hopefully funny clips from Netflix shows, movies and (of course) comedy specials. Find something you enjoy and you can watch the whole affair or add it to your watch list. The addition is "slowly" deploying to subscribers in English-speaking countries including the US, Canada, UK, Ireland, Australia and New Zealand.


AAAI Conferences

The Synthesis of ACT-R and Leabra (SAL) hybrid cognitive architecture is the integration of two theories of cognitive functioning, each itself a highly integrative theory of cognition, ACT-R being predominantly a symbolic production-rule based architecture and Leabra a neural modeling architecture. The combination of the two architectures allows for richer dynamics that take advantage of neural and symbolic aspects and provides mutual constraints that promote convergence towards models that are both neurophysiologically and psychologically valid. We present a hybrid model that makes use of multi-level and multi-system integration to allow an instructed assembly task to be carried out in way that is noise and error robust. Specifically, the model shows how higher-level error recovery routines can interface with lower-level sensory, motor, and error detection processes and result in a robustness to noise and noise-induced errors. Multiple systems and processes operating at multiple levels are recruited to provide a way around the limitations of simpler systems composed of isolated modules that do not allow information to be propagated as easily. The benefits of this approach provide motivation for the adoption of a generally integrated approach to cognitive systems.


AAAI Conferences

The Expressive Intelligence Studio is developing a new approach to freeform conversational interaction in playable media that combines dialogue management, natural language generation (NLG), and natural language understanding. In this paper, we present our method for dialogue generation, which has been fully implemented in a game we are developing called Talk of the Town. Eschewing a traditional NLG pipeline, we take up a novel approach that combines human language expertise with computer generativity. Specifically, this method utilizes a tool that we have developed for authoring context-free grammars (CFGs) whose productions come packaged with explicit metadata. Instead of terminally expanding top-level symbols -- the conventional way of generating from a CFG -- we employ an unusual middle-out procedure that targets mid-level symbols and traverses the grammar by both forward chaining and backward chaining, expanding symbols conditionally by testing against the current game state. In this paper, we present our method, discuss a series of associated authoring patterns, and situate our approach against the few earlier projects in this area.

Computing Rule-Based Explanations of Machine Learning Classifiers using Knowledge Graphs Artificial Intelligence

The use of symbolic knowledge representation and reasoning as a way to resolve the lack of transparency of machine learning classifiers is a research area that lately attracts many researchers. In this work, we use knowledge graphs as the underlying framework providing the terminology for representing explanations for the operation of a machine learning classifier. In particular, given a description of the application domain of the classifier in the form of a knowledge graph, we introduce a novel method for extracting and representing black-box explanations of its operation, in the form of first-order logic rules expressed in the terminology of the knowledge graph.

An Accelerator for Rule Induction in Fuzzy Rough Theory Artificial Intelligence

Rule-based classifier, that extract a subset of induced rules to efficiently learn/mine while preserving the discernibility information, plays a crucial role in human-explainable artificial intelligence. However, in this era of big data, rule induction on the whole datasets is computationally intensive. So far, to the best of our knowledge, no known method focusing on accelerating rule induction has been reported. This is first study to consider the acceleration technique to reduce the scale of computation in rule induction. We propose an accelerator for rule induction based on fuzzy rough theory; the accelerator can avoid redundant computation and accelerate the building of a rule classifier. First, a rule induction method based on consistence degree, called Consistence-based Value Reduction (CVR), is proposed and used as basis to accelerate. Second, we introduce a compacted search space termed Key Set, which only contains the key instances required to update the induced rule, to conduct value reduction. The monotonicity of Key Set ensures the feasibility of our accelerator. Third, a rule-induction accelerator is designed based on Key Set, and it is theoretically guaranteed to display the same results as the unaccelerated version. Specifically, the rank preservation property of Key Set ensures consistency between the rule induction achieved by the accelerator and the unaccelerated method. Finally, extensive experiments demonstrate that the proposed accelerator can perform remarkably faster than the unaccelerated rule-based classifier methods, especially on datasets with numerous instances.

Chinese tech companies must undergo government cyber review to list overseas


China on Tuesday evening confirmed it will increase oversight on how local tech companies operate their platforms both locally and overseas through two new sets of rules. The first set of rules, set to be enforced on February 15, is focused on cybersecurity reviews and will require local tech companies with personal information on over 1 million users to undergo a security review before being allowed to list onto overseas stock exchanges. Announced by the Cyberspace Administration of China (CAC), the rules did not specify whether cybersecurity reviews would be required for companies that list in Hong Kong. As part of a cybersecurity review process, the Chinese government can urge tech companies to make organisational changes to fulfil their commitments to the cybersecurity review. The CAC said the new listing requirement was established to address the risk of key infrastructure, data, and personal information being used maliciously by foreign actors.

Forecasting: theory and practice Machine Learning

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.

Challenges of Artificial Intelligence -- From Machine Learning and Computer Vision to Emotional Intelligence Artificial Intelligence

Artificial intelligence (AI) has become a part of everyday conversation and our lives. It is considered as the new electricity that is revolutionizing the world. AI is heavily invested in both industry and academy. However, there is also a lot of hype in the current AI debate. AI based on so-called deep learning has achieved impressive results in many problems, but its limits are already visible. AI has been under research since the 1940s, and the industry has seen many ups and downs due to over-expectations and related disappointments that have followed. The purpose of this book is to give a realistic picture of AI, its history, its potential and limitations. We believe that AI is a helper, not a ruler of humans. We begin by describing what AI is and how it has evolved over the decades. After fundamentals, we explain the importance of massive data for the current mainstream of artificial intelligence. The most common representations for AI, methods, and machine learning are covered. In addition, the main application areas are introduced. Computer vision has been central to the development of AI. The book provides a general introduction to computer vision, and includes an exposure to the results and applications of our own research. Emotions are central to human intelligence, but little use has been made in AI. We present the basics of emotional intelligence and our own research on the topic. We discuss super-intelligence that transcends human understanding, explaining why such achievement seems impossible on the basis of present knowledge,and how AI could be improved. Finally, a summary is made of the current state of AI and what to do in the future. In the appendix, we look at the development of AI education, especially from the perspective of contents at our own university.