The need for a massive symbolic artificial intelligence project of this ilk was born in the early 1980s out of a large number of experiences early AI researchers had, in the previous 25 years, wherein their AI programs would generate encouraging early results but then fail to "scale up"--fail to cope with novel situations and problems outside the narrow area they were conceived and engineered to cope with. Douglas Lenat and Alan Kay publicized this need,[1][2][3] and organized a meeting at Stanford in 1983 to consider the problem; the back-of-the-envelope calculations by them and colleagues including Marvin Minsky, Allen Newell, Edward Feigenbaum, and John McCarthy indicated that that effort would require between 1000 and 3000 person-years of effort, hence not fit into the standard academic project model. Fortuitously, events within a year of that meeting enabled that Manhattan-Project-sized effort to get underway. The project was started in July,1984 as the flagship project of the 400-person Microelectronics and Computer Technology Corporation, a research consortium started by two dozen large United States based corporations "to counter a then ominous Japanese effort in AI, the so-called "fifth-generation" project."[4] The US Government reacted to the Fifth Generation threat by passing the National Cooperative Research Act of 1984, which for the first time allowed US companies to "collude" on long-term high-risk high-payoff research, and MCC and Sematech sprang up to take advantage of that ten-year opportunity.

This is a very important consideration that is often overlooked by many in the field of Artificial Intelligence (AI). I suspect there are very few academic researchers who understand this aspect. The work performed in academe is distinctly different from the work required to make a product that is sustainable and economically viable. It is the difference between computer code that is written to demonstrate a new discovery and code that is written to support the operations of a company. The former kind turns to be exploratory and throwaway while the the latter kind tends to be exploitive and requires sustainability.

The amount of personal data collected in our everyday interactions with connected devices offers great opportunities for innovative services fueled by machine learning, as well as raises serious concerns for the privacy of individuals. In this paper, we propose a massively distributed protocol for a large set of users to privately compute averages over their joint data, which can then be used to learn predictive models. Our protocol can find a solution of arbitrary accuracy, does not rely on a third party and preserves the privacy of users throughout the execution in both the honest-but-curious and malicious adversary models. Specifically, we prove that the information observed by the adversary (the set of maliciours users) does not significantly reduce the uncertainty in its prediction of private values compared to its prior belief. The level of privacy protection depends on a quantity related to the Laplacian matrix of the network graph and generally improves with the size of the graph. Furthermore, we design a verification procedure which offers protection against malicious users joining the service with the goal of manipulating the outcome of the algorithm.

It is a fundamental, but still elusive question whether methods based on quantum mechanics, in particular on quantum entanglement, can be used for classical information processing and machine learning. Even partial answer to this question would bring important insights to both fields of both machine learning and quantum mechanics. In this work, we implement simple numerical experiments, related to pattern/images classification, in which we represent the classifiers by quantum matrix product states (MPS). Classical machine learning algorithm is then applied to these quantum states. We explicitly show how quantum features (i.e., single-site and bipartite entanglement) can emerge in such represented images; entanglement characterizes here the importance of data, and this information can be practically used to improve the learning procedures. Thanks to the low demands on the dimensions and number of the unitary matrices, necessary to construct the MPS, we expect such numerical experiments could open new paths in classical machine learning, and shed at same time lights on generic quantum simulations/computations.

Long-term observation of changes in Activities of Daily Living (ADL) is important for assisting older people to stay active longer by preventing aging-associated diseases such as disuse syndrome. Previous studies have proposed a number of ways to detect the state of a person using a single type of sensor data. However, for recognizing more complicated state, properly integrating multiple sensor data is essential, but the technology remains a challenge. In addition, previous methods lack abilities to deal with misclassified data unknown at the training phase. In this paper, we propose an architecture for multimodal sensor-based ADL recognition which spontaneously acquires knowledge from data of unknown label type. Evaluation experiments are conducted to test the architecture's abilities to recognize ADL and construct data-driven reactive planning by integrating three types of dataflows, acquire new concepts, and expand existing concepts semi-autonomously and in real time. By adding extension plugins to Fluentd, we expended its functions and developed an extended model, Fluentd++. The results of the evaluation experiments indicate that the architecture is able to achieve the above required functions satisfactorily.

A dynamic network is a network that changes with time. Nature, society, and the modern communications landscape abound with examples. Molecular interactions, chemical reactions, social relationships and interactions in human and animal populations, transportation networks, mobile wireless devices, and robot collectives form only a small subset of the systems whose dynamics can be naturally modeled and analyzed by some sort of dynamic network. Though many of these systems have always existed, it was not until recently the need for a formal treatment that would consider time as an integral part of the network has been identified. Computer science is leading this major shift, mainly driven by the advent of low-cost wireless communication devices and the development of efficient wireless communication protocols. The early years of computing could be characterized as the era of staticity and of the relatively predictable; centralized algorithms for (combinatorial optimization) problems concerning static instances, as is that of finding a minimum cost traveling salesman tour in a complete weighted graph, computability questions in cellular automata, and protocols for distributed tasks in a static network. Even when changes were considered, as is the case in fault-tolerant distributed computing, the dynamics were usually sufficiently slow to be handled by conservative approaches, in principle too weak to be useful for highly dynamic systems. An exception is the area of online algorithms, where the input is not known in advance and is instead revealed to the algorithm during its course. Though the original motivation and context of online algorithms is not related to dynamic networks, the existing techniques and body of knowledge of the former may prove very useful in tackling the high unpredictability inherent in the latter. In contrast, we are rapidly approaching, if not already there, the era of dynamicity and of the highly unpredictable. According to some latest reports, the number of mobile-only Internet users has already exceeded the number of desktop-only Internet users and more than 75% of all digital consumers are now using both desktop and mobile platforms to access the Internet. The Internet of Things, envisioning a vast number of objects and devices equipped with a variety of sensors and being connected to the Internet, and smart cities37 are becoming a reality (an indicative example is the recent £40M investment of the U.K. government on these technologies).

Computer-based human activity recognition of daily living has recently attracted much interest due to its applicability to ambient assisted living. Such applications require the automatic recognition of high-level activities composed of multiple actions performed by human beings in a given environment. We propose a deep neural architecture for kitchen activity recognition, which uses an ensemble of machine learning models and hand-crafted features to extract more information of the data. Experiments show that our approach achieves the state-of-the-art for identifying cooking actions in a well-known kitchen dataset.

The interactive computation paradigm is reviewed and a particular example is extended to form the stochastic analog of a computational process via a transcription of a minimal Turing Machine into an equivalent asynchronous Cellular Automaton with an exponential waiting times distribution of effective transitions. Furthermore, a special toolbox for analytic derivation of recursive relations of important statistical and other quantities is introduced in the form of an Inductive Combinatorial Hierarchy.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)