You wouldn't think a 9th century Persian mathematician would be relevant to modern business. But the term algorithm stems from his name, Muhammed Al Khwarizmi (along with the Greek word arithmos), and the algorithmic business is sweeping across the business landscape with its autonomous, rules-based, lightning-fast operations--augmenting, and in some cases supplanting, human decision making. An algorithm is a step-by-step process or set of rules for calculating and solving problems. "Algorithmic business is the industrialized use of complex mathematical algorithms pivotal to driving improved business decisions or process automation for competitive differentiation," Gartner explains. The algorithmic business is based upon capturing knowledge in software, which then takes automated actions that speed business processes and perform decision making. Supply chain uses algorithms to forecast demand, optimize inventory, schedule production, and route transportation.

In the previous article, we presented two ways of categorizing conversational agents -- more widely known as chatbots -- along with their advantages, limitations, and use case scenarios. In this article, we are focused on emphasizing how chatbots infer knowledge from human conversations through a basic rule-based system, machine learning, and natural language processing, which all play a crucial role in facilitating the automation of a request-handling process. A few months ago, we took up the challenge to build a chatbot that could seamlessly integrate into a customer support platform, applicable to various industries. At Tremend, we developed a chatbot prototype for a major telecom client. The primary scope of the project was to create a quality-consistent chatbot that could minimize human labor with respect to tedious or repetitive tasks, usually time-consuming and hard to scale across brief periods of time and on an ad hoc basis.

Developers know a lot about the machine learning (ML) systems they create and manage, that's a given. However, there is a need for non-developers to have a high level understanding of the types of systems. Artificial neural networks and expert systems are the classical two key classes. With the advanced in computing performance, software capabilities and algorithm complexity, analytical algorithm can arguably be said to have joined the other two. This article is an overview of the three types.

Developers know a lot about the machine learning (ML) systems they create and manage, that's a given. However, there is a need for non-developers to have a high level understanding of the types of systems. Artificial neural networks and expert systems are the classical two key classes. With the advanced in computing performance, software capabilities and algorithm complexity, analytical algorithm can arguably be said to have joined the other two. This article is an overview of the three types.

The effective use and adoption of Machine Learning requires algorithms that are not only accurate, but also understandable. To address this need, BigML now includes functionality that allows for Prediction Explanation, model-independent explanations of classification and regression predictions. In this post, we will summarize what it means for a prediction to be "explainable," why this is important, and share a use case in which prediction explanation plays a key role. Rather than being hard-programmed with an exhaustive set of "if-then" rules, Machine Learning algorithms "learn" rules based on large datasets of examples. Understanding what these rules are, and how they are applied to new data, is generally referred to as the interpretability or explanation of the model.

We introduce MeSys, a meaning-based approach, for solving English math word problems (MWPs) via understanding and reasoning in this paper. It first analyzes the text, transforms both body and question parts into their corresponding logic forms, and then performs inference on them. The associated context of each quantity is represented with proposed role-tags (e.g., nsubj, verb, etc.), which provides the flexibility for annotating an extracted math quantity with its associated context information (i.e., the physical meaning of this quantity). Statistical models are proposed to select the operator and operands. A noisy dataset is designed to assess if a solver solves MWPs mainly via understanding or mechanical pattern matching. Experimental results show that our approach outperforms existing systems on both benchmark datasets and the noisy dataset, which demonstrates that the proposed approach understands the meaning of each quantity in the text more.

GENEVA – Major U.S. trading partners including the European Union, China and Japan voiced deep concern at the World Trade Organization (WTO) on Tuesday about possible U.S. measures imposing additional duties on imported autos and parts. Japan, which along with Russia had initiated the discussion at the WTO Council on Trade in Goods, warned that such measures could trigger a spiral of countermeasures and result in the collapse of the rules-based multilateral trading system, an official who attended the meeting said. Over 40 WTO members, including the 28 countries of the European Union -- warned that the U.S. action could seriously disrupt the world market and threaten the WTO system, given the importance of cars to world trade. The United States has imposed tariffs on European steel and aluminum imports and is conducting another national security study that could lead to tariffs on imports of cars and car parts. Both sets of tariffs would be based on concerns about U.S. national security. U.S. President Donald Trump said on June 29 that the probe would be completed in three to four weeks.

Monitoring transactions for suspicious ones can be more efficient. All it takes is doing it intelligently. Up to $2 trillion dollars representing 5% of global GDP – that's the estimated amount of money laundered worldwide each year according to the United Nations Office on Drugs and Crime. The fight against money laundering is one of top priorities of financial institutions – but it also poses a significant challenge for them. To combat the phenomenon, one needs to have a large number of human and technology resources at hand.

Under dynamic conditions on bridges, we need a real-time management. To this end, this paper presents a rule-based decision support system in which the necessary rules are extracted from simulation results made by Aimsun traffic micro-simulation software. Then, these rules are generalized by the aid of fuzzy rule generation algorithms. Then, they are trained by a set of supervised and the unsupervised learning algorithms to get an ability to make decision in real cases. As a pilot case study, Nasr Bridge in Tehran is simulated in Aimsun and WEKA data mining software is used to execute the learning algorithms. Based on this experiment, the accuracy of the supervised algorithms to generalize the rules is greater than 80%. In addition, CART decision tree and sequential minimal optimization (SMO) provides 100% accuracy for normal data and these algorithms are so reliable for crisis management on bridge. This means that, it is possible to use such machine learning methods to manage bridges in the real-time conditions.

The Herald-Times reports that the City Council voted Wednesday to solidify policies developed and reviewed by Bloomington Police Chief Mike Diekhoff and the city's Board of Public Safety. Bloomington Municipal Code now also prohibits the armored truck from carrying affixed firearms, water cannons and devices that can fire a projectile.