If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Google is paying tribute Tuesday to the computer scientist who created the mathematical framework "fuzzy logic." On this day in 1964, Zadeh submitted the paper "Fuzzy Sets," which laid out the concept of "fuzzy logic." The logo featured on Google.com "The theory he presented offered an alternative to the rigid'black and white' parameters of traditional logic and instead allowed for more ambiguous or'fuzzy' boundaries that more closely mimic the way humans see the world," reads a biography of Zadeh by Google. The theory has been used in various tech applications, including anti-skid algorithms for cars.
We revisit Zadeh's notion of "evidence of the second kind" and show that it provides the foundation for a general theory of epistemic random fuzzy sets, which generalizes both the Dempster-Shafer theory of belief functions and Possibility theory. In this perspective, Dempster-Shafer theory deals with belief functions generated by random sets, while Possibility theory deals with belief functions induced by fuzzy sets. The more general theory allows us to represent and combine evidence that is both uncertain and fuzzy. We demonstrate the application of this formalism to statistical inference, and show that it makes it possible to reconcile the possibilistic interpretation of likelihood with Bayesian inference.
ABSTRACT different granularities [3, 9] or use a cross interaction block that couple the features from different modalities [10, 6]. It is imperative that all modalities in multimodal interactions and 3. Fusion of unimodal and cross Therefore, to learn better cross modal information, we introduce 1.6% and 1.34% absolute improvement over current state-ofthe-art. Furthermore, to capture long term dependencies across 1. INTRODUCTION These are categorised into three types, 1. Methods that learn the modalities independently and fuse the In our proposed model, we aim to learn the interaction between [3, 4], and 3. Methods that explicitly learn contributions Personal use of this material is permitted. Multimodal sentiment analysis provides an opportunity to 2.1. M T V H T W H T V; W R d d (3) (U 1, U 2,..., U u) for a Text modality can be defined as: Cross attentive representations of Text (C V T R u d) and H T Bi-GRU(U 1, U 2,..., U u) (1) Video (C T V R u d) can be represented as: Subscript T denotes Text modality, A and V represent Audio As much as there is an opportunity to leverage cross modal interactions, representations is employed.
Shayan Zadeh, Founder & CEO When a couple of hundreds of healthcare providers comprising doctors, nurses, and technologists convened for Grand Hack in MIT, they not only shared their profound insights with the community but also discussed the major pain-points they often encounter. One of the challenges that stood out among the many was that while operating rooms are dynamic and unpredictable, the tools leveraged for managing and optimizing them are painstakingly static, rendering lesser productivity and consuming more time. With an aim to confront the challenge head-on, Shayan Zadeh--a technology entrepreneur--and a small group of anesthesiologists and OR nurses proposed an award-winning solution in the congregation that could successfully assist healthcare staff in the rapidly changing OR ecosystem, which also marked the genesis of Leap Rail. "Leap Rail offers the best-in-class solutions that simplify the complexity of operating rooms with artificial intelligence (AI) and machine learning, providing actionable and intelligent information to facilitate better decisions, improve operational efficiency, and drive staff satisfaction," states Zadeh, founder and CEO of Leap Rail. According to him, lack of predictability and visibility are two potent problems that affect the efficiency of operating room personnel.
Using a social network like Facebook is a two-way street, part-shrouded in shadow. The benefits of sharing banter and photos with friends and family--for free--are obvious and immediate. So are the financial rewards for Facebook; but you don't get to see all of the company's uses for your data. An artificial intelligence experiment of unprecedented scale disclosed by Facebook Wednesday offers a glimpse of one such use case. It shows how our social lives provide troves of valuable data for training machine-learning algorithms.
This survey was conducted for technical and historical reasons: First, I work in the commercial AI industry and was worried about missing significant intellectual contributions to my work. Second, this work was intended to test the thesis that there is a coherent body of study called cognitive science. If a new scientific discipline has emerged that is a fusion of psychology, computer science, linguistics, mathematics, philosophy, and neuroscience (as claimed by Gardner 1985), then there should be some evidence of this new discipline in the pattern of scientific publications and researcher biographies. For example, a paper about AI could cite a psychology paper, or a graduate in a mathematics department could migrate into the linguistics field. I have been informally conducting this survey for the last three years.
In this paper we deal with the problem of extending Zadeh's operators on fuzzy sets (FSs) to interval-valued (IVFSs), set-valued (SVFSs) and type-2 (T2FSs) fuzzy sets. Namely, it is known that seeing FSs as SVFSs, or T2FSs, whose membership degrees are singletons is not order-preserving. We then describe a family of lattice embeddings from FSs to SVFSs. Alternatively, if the former singleton viewpoint is required, we reformulate the intersection on hesitant fuzzy sets and introduce what we have called closed-valued fuzzy sets. This new type of fuzzy sets extends standard union and intersection on FSs. In addition, it allows handling together membership degrees of different nature as, for instance, closed intervals and finite sets. Finally, all these constructions are viewed as T2FSs forming a chain of lattices.
Following the wave of U.K. terror attacks in the spring of 2017, prime minister Theresa May called on technology companies like Facebook and YouTube to create better tools for screening out controversial content--especially digital video--that directly promotes terrorism. Meanwhile, in the U.S., major advertisers including AT&T, Verizon, and WalMart have pulled ad campaigns from YouTube after discovering their content had been appearing in proximity to videos espousing terrorism, anti-Semitism, and other forms of hate speech. In response to these controversies, Google expanded its advertising rules to take a more aggressive stance against hate speech, and released a suite of tools allowing advertisers to block their ads from appearing on certain sites. The company also deployed new teams of human monitors to review videos for objectionable content. In a similar vein, Facebook announced that it would add 3,000 new employees to screen videos for inappropriate content.
Apple on Monday introduced Core ML, a set of tools that developers can use to incorporate machine learning techniques into their apps. The news was little noticed amid all the new hardware and iOS updates. The capabilities of Core ML are a "dead giveaway" that Apple is preparing to introduce a new kind of processor, presumably just for iPhones at first, that could make trendy machine learning workloads run more efficiently. A company would only release something like Core ML "if there's a really intense piece of hardware that it was going do compile down into," said Zadeh, who built the machine learning algorithms for Twitter's Who to Follow feature before starting Matroid last year. "All those converters and everything, it's a dead giveaway there's going to be some intense [processor] available down the line."
Scientists in the US have accurately reconstructed images of human faces by monitoring the responses of monkey brain cells. The brains of primates can resolve different faces with remarkable speed and reliability, but the underlying mechanisms are not fully understood. The researchers showed pictures of human faces to macaques and then recorded patterns of brain activity. The work could inspire new facial recognition algorithms, they report. In earlier investigations, Professor Doris Tsao from the California Institute of Technology (Caltech) and colleagues had used functional magnetic resonance imaging (fMRI) in humans and other primates to work out which areas of the brain were responsible for identifying faces.