Convolutional neural networks (CNNs) are similar to "ordinary" neural networks in the sense that they are made up of hidden layers consisting of neurons with "learnable" parameters. These neurons receive inputs, performs a dot product, and then follows it with a non-linearity. The whole network expresses the mapping between raw image pixels and their class scores. Conventionally, the Softmax function is the classifier used at the last layer of this network. However, there have been studies (Alalshekmubarak and Smith, 2013; Agarap, 2017; Tang, 2013) conducted to challenge this norm. The cited studies introduce the usage of linear support vector machine (SVM) in an artificial neural network architecture. This project is yet another take on the subject, and is inspired by (Tang, 2013). Empirical data has shown that the CNN-SVM model was able to achieve a test accuracy of ~99.04% using the MNIST dataset (LeCun, Cortes, and Burges, 2010). On the other hand, the CNN-Softmax was able to achieve a test accuracy of ~99.23% using the same dataset. Both models were also tested on the recently-published Fashion-MNIST dataset (Xiao, Rasul, and Vollgraf, 2017), which is suppose to be a more difficult image classification dataset than MNIST (Zalandoresearch, 2017). This proved to be the case as CNN-SVM reached a test accuracy of ~90.72%, while the CNN-Softmax reached a test accuracy of ~91.86%. The said results may be improved if data preprocessing techniques were employed on the datasets, and if the base CNN model was a relatively more sophisticated than the one used in this study.

Kyoto Protocol (KP) like strategic agreements on energy resources reflects the need for long run forecasting of renewable energy time series fluctuations and mitigate the problems of environment degradation due to emission exhausts from nonrenewable resources [1]. Photovoltaic systems for industrial and domestic uses require the distribution of grid connected power systems with solar radiation as the main energy source. However direct conversion of solar to electrical energy is costly and has relatively low efficiency [2]. Coupled with grid stability issues concerning scheduling and assets optimization for short-term (monthly)and long-term (yearly) forecasting requires guaranteed knowledge of solar radiation instabilities at local weather stations. All this information is based on satellite observations and data from ground stations, with uncertainty in geographic and time availability of data, and data sampling rate posing significant forecast granularity. To assess the PV plant operation dependability on global solar radiation (GSR), good measurement of GSR using a high class radiometer and correct controlling of the instrument through correct maintenance policy is essential.

The main aim of this paper is the development of easily verifiable sufficient conditions for stability (almost sure boundedness) and convergence of stochastic approximation algorithms (SAAs) with set-valued mean-fields, a class of model-free algorithms that have become important in recent times. In this paper we provide a complete analysis of such algorithms under three different, yet related sets of sufficient conditions, based on the existence of an associated global/local Lyapunov function. Unlike previous Lyapunov function based approaches, we provide a simple recipe for explicitly constructing the Lyapunov function, needed for analysis. Our work builds on the works of Abounadi, Bertsekas and Borkar (2002), Munos (2005), and Ramaswamy and Bhatnagar (2016). An important motivation for the flavor of our assumptions comes from the need to understand dynamic programming and reinforcement learning algorithms, that use deep neural networks (DNNs) for function approximations and parameterizations. These algorithms are popularly known as deep learning algorithms. As an important application of our theory, we provide a complete analysis of the stochastic approximation counterpart of approximate value iteration (AVI), an important dynamic programming method designed to tackle Bellman's curse of dimensionality. Further, the assumptions involved are significantly weaker, easily verifiable and truly model-free. The theory presented in this paper is also used to develop and analyze the first SAA for finding fixed points of contractive set-valued maps.

Beijing is harnessing government and commercial entities in pursuit of a once-in-a-generation technological kingmaker. China has made no secret of its ambitions to lead the world in artificial intelligence, nor of the military and geopolitical advantage it hopes to gain from this rapidly advancing technology. A closer look at Beijing's whole-of-nation AI strategy shows the challenge to the United States -- and suggests what America must do lest it be eclipsed in this latest round of great-power competition.

This is part of CNET's "Dining Redefined" series about how technology is changing the way you eat. When someone says "robot restaurant," I first think of an LED and laser show at a Tokyo venue where remote-controlled robots dance with bikini-clad girls in a sensory show that accompanies dinner. But the reality of robot restaurants is generally way more pedestrian and low-key. One example is Eatsa, the San Francisco-based restaurant company that takes orders through iPads and dispenses meals through automated machines. Until now, Eatsa has been using this tech to serve up quinoa bowls to health-food fans in its own restaurants.

Indeed, I am quite a bit interested in management practices and their new concepts, which are happening all over the world. Artificial Intelligence (AI) has become the most fascinating subject matter in the world. For me, my bookish knowledge as well as practical knowledge is equally important because they make me identify problems, no matter what they are. From virtual life to normal life, everything is changing fast as we speak. Unsurprisingly, the role of management has also been changing day by day. That being said, what about artificial intelligence's future for tomorrow?

Warnings about artificial intelligence launching World War III--including a few flares sent up by Elon Musk--are an unfair scourge on an AI sector that sees itself making life easier and helping traditional companies survive. That's the view of Chris Boos, chief executive officer of Germany-based software firm Arago, who told MarketWatch in an interview that anything produced by a process can, should and will be run by AI, allowing human beings to be the creative thinkers and doers they were designed to be. Arago advises mostly non-tech, established-economy Fortune 500 businesses on their AI adoption. "Within the next 2-3 years AI will be able to run any business process, which makes AI one -- potentially the only one -- defensive measure the established economy has against intrusion from the high-tech world," said Boos. For now, the sci-fi hyperbole can wait.

Editor's note: As consumers eager to get their hands on Apple's 10th anniversary phone line up online to be among the first to buy one, a few questions remain. Known as the iPhone X, the device starts at about US$1,000 and only gets more expensive from there. Some have warned that the high price tag will limit demand. John Jordan, an expert in supply chain management and information systems, believes answering that and other lingering questions requires a closer look at information economics, supply chain theory and encryption. Apple stores have become like meccas of consumerism and among most valuable retail spaces in the world.

Honda is putting the focus on artificial intelligence after it announced a partnership with SenseTime, a Chinese startup valued at over $1 billion, that will power its autonomous cars of the future. SenseTime, which raised a $410 million funding round this past summer and counts Qualcomm among its investors, is particularly well known for its object recognition technology which has been used by both public and private entities in China. The startup has agreed to a five-year joint R&D project with Honda that will see its AI and deep learning smarts combined with Honda's automotive focused-AI tech to develop solutions that the car-maker hopes will enable self-driving cars to operate safely in urban areas. Honda's own AI falls into'scene understanding,' 'risk prediction,' and'action planning,' all of which relates to gauging urban motoring scenarios and responding to them in the right way. It said earlier this year that it plans to introduce cars with level 4 automation by 2020.

About three years ago, DeepMind, a company owned by Google that specializes in AI development, turned its attention to the ancient game of Go. Go had been the one game that had eluded all computer efforts to become world class, and even up until the announcement was deemed a goal that would not be attained for another decade! This was how large the difference was. When a public challenge and match was organized against the legendary player Lee Sedol, a South Korean whose track record had him in the ranks of the greatest ever, everyone thought it would be an interesting spectacle, but a certain win by the human. The question wasn't even whether the program AlphaGo would win or lose, but how much closer it was to the Holy Grail goal.