In 2008, engineering student Samay Kohli wanted to build a humanoid robot, but his professor told him it would not be possible. Along with his fellow student Abhay Singhal, not only did they achieve that task, but they have also built GreyOrange, a multi-national robotics company based in India and operating across Asia. "We've done some stuff that India was not supposed to do," Mr Kohli told the BBC. "People are not supposed to build hardware, robot products, out of India and we've been able to do that." So how did GreyOrange grow from an engineering classroom to an international robotics player?

SANAA/ADEN – Suspected U.S. drone strikes have killed three alleged al-Qaida operatives in Yemen's southwestern Bayda province, security and tribal officials said, the first such killings reported in the country since Donald Trump assumed the U.S. presidency Friday. The two Saturday strikes killed Abu Anis al-Abi, an area field commander, and two others, the officials said, speaking on condition of anonymity as they were not authorized to release the information to journalists. U.S. drone strikes against suspected al-Qaida targets have been commonplace in the years since the Sept. 11, 2001, attacks on New York and Washington, as a retaliatory measure against the group. The use of unmanned aircraft as well as airstrikes in the Arab world's poorest country rose dramatically under President Barack Obama, with data from the Britain-based Bureau of Investigative Journalism showing spikes in attacks, especially in 2012 and 2016. On Thursday, U.S. intelligence officials said as many as 117 civilians had been killed in drone and other counterterror attacks in Pakistan, Yemen and elsewhere during Obama's presidency.

The trend and seasonality can be accounted for in a linear model by including sinusoidal components with a given frequency. However, finding the appropriate frequency for each sinusoidal component requires a little more digging. This post shows how to use fast Fourier transforms to find these frequencies. For the purposes of this post, we will only focus on the T(t) and S(t) components. The actual model fitting will be done in a separate post.

This text provides R tutorials on statistics including hypothesis testing, ANOVA and linear regressions. It fulfills popular demands by users of r-tutor.com for exercise solutions and offline access. Part III of the text is about Bayesian statistics. It begins with closed analytic solutions and basic BUGS models for simple examples. Then it covers OpenBUGS for Bayesian ANOVA and regression analysis.

AI can improve customer personalization, identify patterns and connections that humans can't, and answer questions about banking issues in real-time. Financial institutions are already finding success with AI. However, what may be'amazing' today will be table stakes in the near future. Artificial intelligence (AI) is not new to banking. If we consider that the definition of AI is the ability for machines to interact and learn to do tasks previously done by humans, the history of AI goes back to the 50s in the banking industry.

There is a certain allure to the deep learning space in that the very inspiration is based on biomimicry. Deep learning is a subset of artificial intelligence (AI) with an architecture that roughly mirrors the human brain: information is processed through multiple layers to compute an outcome. Unlike other machine learning algorithms, which only have one or two layers, deep learning is "deep" because it has multiple layers – typically between 10 and 100 layers. Computations at each level build upon previous levels, allowing the network to learn more nuanced and abstract characteristics. Each layer is responsible for the detection of one characteristic, basing assumptions on earlier layers.

The main point of this discussion is that Prolog, and logic in general, is much more powerful than the basic sort of expert system using IF..THEN rules and backward chaining. If you want to get involved in reasoning with facts then you really do have to go to the extra trouble of learning Prolog and developing a program. It is worth saying that while Prolog isn't an impossibly difficult language, people vary in their ability to absorb it and use it naturally. That is you might spend a lot of time learning Prolog never to get very far. If you are planning an expert system project it is worth considering a simple expert system shell because most of the really cost effective applications are simple enough not to need first-order predicate calculus.

The unique combination of Amazon Web Services and Cloud Raxak, a Gartner Cool Vendor in IT Automation, provides a seamless and cost-effective way of securely moving on-premise IT workloads to Amazon Web Services. Any enterprise can now leverage the cloud, manage risk, and maintain continuous security compliance. Forrester's analysis shows that enterprises need automated security to lower security risk and decrease IT operational costs. Through the seamless integration into Amazon Web Services, Raxak Protect automates security for any workload running on traditional IT, private clouds, and public clouds. A line of business user with no security experience can now provision a VM on Amazon Web Services with a CISO (Chief Information Security Officer) defined security profile (e.g., PCI-DSS, HIPAA, FFIEC, FISMA).

You do not have to model your time series forecast problem as-is. There are many ways to reframe your forecast problem that can both simplify the prediction problem and potentially expose more or different information to be modeled. A reframing can ultimately result in better and/or more robust forecasts. In this tutorial, you will discover how to reframe your time series forecast problem with Python. How to Reframe Your Time Series Forecasting Problem Photo by Sean MacEntee, some rights reserved.

As business opportunities for artificial intelligence multiply, how can industry leaders design the principles and technical standards into their products that benefit society as a whole?