When we go grocery shopping, we often have a standard list of things to buy. Each shopper has a distinctive list, depending on one's needs and preferences. A housewife might buy healthy ingredients for a family dinner, while a bachelor might buy beer and chips. Understanding these buying patterns can help to increase sales in several ways. While we may know that certain items are frequently bought together, the question is, how do we uncover these associations?

Current plan checking process has been a tedious and time consuming process for developers and local authorities with many errors and problems. This may result to a late of building development progress and can cost a big number amount of money. The aim of this research is to build a software information system that integrates development plan using computer-aided design (CAD) and to evaluate the development plan according to the standards. The compliance plan will be stored into knowledge-based repository for use by other local authorities for checking purposes. This building plan checking system (BPCS) will also acts as a repository for the development plan configuration standards in the data processing system.

The roots of modern Artificial Intelligence, or AI, can be traced back to the classical philosophers of Greece, and their efforts to model human thinking as a system of symbols. More recently, in the 1940s, a school of thought called "Connectionism" was developed to study the process of thinking. In 1950, a man named Alan Turing wrote a paper suggesting how to test a "thinking" machine. He believed if a machine could carry on a conversation by way of a teleprinter, imitating a human with no noticeable differences, the machine could be described as thinking. His paper was followed in 1952 by the Hodgkin-Huxley model of the brain as neurons forming an electrical network, with individual neurons firing in all-or-nothing (on/off) pulses.

A few years ago, driven by my curiosity, I took my first steps into the world of Forex trading algorithms by creating a demo account and playing out simulations (with fake money) on the Meta Trader 4 trading platform. After a week of'trading', I'd almost doubled my money. Spurred on by my own success, I dug deeper and eventually signed up for a number of forums. Soon, I was spending hours reading about algorithmic trading systems (rule sets that determine whether you should buy or sell), custom indicators, market moods, and more. Around this time, coincidentally, I heard that someone was trying to find a software developer to automate a simple trading system.

Social media app Instagram announced this week it would start rolling out longer videos -- up to 60 seconds as opposed to just 15, the current limit. "This is one step of many you'll see this year," the tech giant promised in a Tuesday blog post. "In the last six months, the time people spent watching video increased by more than 40 percent. And longer videos mean more diverse stories from the accounts you love, whether it's Selena Gomez (@selenagomez) hanging out with friends or beauty star Bretman Rock's (@bretmanrock) latest makeup tutorial." So what are you going to do with all that freedom?

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

DEDHAM, MA--(Marketwired - Jan 28, 2016) - Rage Frameworks, a provider of knowledge-based automation technology and services, today announced major additions to its pioneering RAGE AI platform, adding two new powerful frameworks to its suite. RAGE AI significantly extends the frontier of deep learning and machine intelligence technology from "natural language processing" to "natural language understanding." RAGE AI incorporates deep linguistic parsing and proprietary innovations to understand meaning in context, which makes its solutions completely transparent, auditable and flexible. The platform facilitates unsupervised to supervised learning and contains several innovations to support automated knowledge acquisition including pragmatic knowledge. RAGE AI is not a black box and does not rely on statistical patterns present in training data.

This paper tackles the problem of endogenous link prediction for knowledge base completion. Knowledge bases can be represented as directed graphs whose nodes correspond to entities and edges to relationships. Previous attempts either consist of powerful systems with high capacity to model complex connectivity patterns, which unfortunately usually end up overfitting on rare relationships, or in approaches that trade capacity for simplicity in order to fairly model all relationships, frequent or not. In this paper, we propose Tatec, a happy medium obtained by complementing a high-capacity model with a simpler one, both pre-trained separately and then combined. We present several variants of this model with different kinds of regularization and combination strategies and show that this approach outperforms existing methods on different types of relationships by achieving state-of-the-art results on four benchmarks of the literature.

We represent knowledge as integrity constraints in a formalization of probabilistic spatio-temporal knowledge bases. We start by defining the syntax and semantics of a formalization called PST knowledge bases. This definition generalizes an earlier version, called SPOT, which is a declarative framework for the representation and processing of probabilistic spatio-temporal data where probability is represented as an interval because the exact value is unknown. We augment the previous definition by adding a type of non-atomic formula that expresses integrity constraints. The result is a highly expressive formalism for knowledge representation dealing with probabilistic spatio-temporal data. We obtain complexity results both for checking the consistency of PST knowledge bases and for answering queries in PST knowledge bases, and also specify tractable cases. All the domains in the PST framework are finite, but we extend our results also to arbitrarily large finite domains.

I am a practicing Expert Witness, with report, deposition, and court testimony experience. And I've used that system to automate forensic analysis in the course of my Expert Witness work.An expert system has two main objectives, after capturing a body of expertise in the form of rules: o Provide that expertise in the absence of the expert. However, nothing prevents the creation of expert system rules that do embody such learning behavior, and in fact I often create such rules in the course of my work. Once that happens, I do regard it as AI.A key feature of applying expert systems to legal work is the leverage they provide. If a case involves a large body of code, data, or text, and a judge (who often doesn't understand the technical implications of discovery in such a case) mandates a short deadline for discovery completion, automation is the only way to achieve the needed results. And it's expert systems such as mine that provide that automation.