This post is authored by Jaya Mathew & Hong Lu, Data Scientists at Microsoft, in collaboration with the team at ServusNet. Governments worldwide are actively promoting the use of renewable energy because of the growing concern over climate change and carbon emissions. Among the available renewable energy sources, harnessing energy through wind turbines offers a compelling alternative. There are a growing number of wind farms throughout the world now, and, as the industry matures at a rapid pace, it is increasing its focus on operational efficiency and cost-competitiveness. Given the inherent variability in wind energy production, the ability to analyze the data generated by wind turbines to characterize historical behavior and create a framework to predict future performance can be a critical asset and business differentiator.

Elos Medtech Pinol in Gørløse, Denmark, is one of Europe's leading development and production partners for medical technology products and components. In recent years, increasing demands for quality, process documentation, and product consistency have forced customers to use fewer suppliers. Customers have selected Elos Medtech Pinol largely due to targeted optimization, standardization, and automation in the last decade. The automated factory – a mantra used within the company that impacts all employees – includes ambitious investments in user-friendly and flexible production equipment and the latest automation technology, including an MiR100 robot from Mobile Industrial Robots (MiR). In 2011, Danish robot scientist Niels Jul Jacobsen came up with a new product idea that a few years later was transformed into a product that handles internal transport logistics.

Training neural networks involves quite a few tricky bits. We try to make everything clear and easy to understand, to get you training your neural networks as quickly as possible. Theano allows us to write relatively concise code that follows the structure of the underlying maths. We will train a network to classify digits. More precisely, we want a network that when presented with an image of a hand-written digit will tell us what digit it is ('0', '1', ..., '9').

With great power comes the responsibility to discern when and how to use such power. As we enter an age where the stuff of speculative science fiction--specifically Artificial Intelligence (AI)--becomes reality, climbing the mountain "because it's there" is no longer a sufficient motivational and ethical framework for our future. In order to create a future where essential human values such as autonomy and personal privacy remain vital, we must act with awareness and forethought. Fortunately, the same tech giants who are dedicated to developing sophisticated AI recognize the potential for profound societal shifts the technology will produce. A recent article in The New York Times highlights how thought leaders from Alphabet, Amazon, Facebook, IBM, and Microsoft want "to ensure that A.I. research is focused on benefiting people, not hurting them."

Believe it or not, but a recent study conducted by researchers at the University of Texas and Cornell University has revealed that [PDF] the blurring technology is soon to become exploitable if not obsolete. We may not enjoy full confidentiality that we do now by blurring pictures or license plates for much long because now computer devices can decode images using Artificial Intelligence. This means it is quite easy to unblur pixelated or blurred pictures using various readily and easily available software tools that help in identifying faces or information. The team of researchers utilized a range of deep learning tools to unblur 71% of the blurred faces and numbers, while the percentage increased to 80% when they allowed the computer to guess for up to 5 times. It is true that their algorithm cannot create the original image but it can identify whatever can be seen in the blurred photograph according to the information that it has acquired.

Back in 2012, a team at Google built a state-of-the-art artificial intelligence network and fed it ten million randomly selected images from YouTube. The computer churned through them, and announced that it kept finding these strange things with furry faces. It had, in other words, discovered cats. Artificial intelligence has, all of a sudden, become the next big thing again. It is not so much sweeping across our world as seeping into it, with a combination of enormous computing power and the latest'deep learning' techniques promising to give us better medical diagnoses, better devices, better recipes and better lives.

Driven by a veritable tsunami of data and bolstered by powerful analytics tools, the artificial intelligence solution is on the rise in the enterprise space. AI innovations are driving new efficiencies and a wealth of other positive metrics across a range of industries. In its latest survey of 235 business executives, Narrative Science took a deep look at the adoption of artificial intelligence in the enterprise. The resulting report details the emergence of AI as a core business strategy, with 38 percent of organizations saying they already use AI technologies in the workplace, and 62 percent likely to be using the technology by 2018. The study found AI manifesting in a number of different forms in the enterprise, including deep learning, natural language generation and recommendation engines.

Drones and robots are getting computer vision with higher-resolution cameras and artificial intelligence to recognize objects and images. Many are made with developer boards like Nvidia's Jetson TX1, which provides the smarts for auto-navigation and collision avoidance. TX1 has the horsepower to process live image feeds, and software tools to instantly analyze and provide context to visuals. The TX1 is now a lot faster and better equipped to handle AI and image processing. Nvidia's new Jetpack 2.3 software tools for TX1, announced on Tuesday, are a major update that doubles the deep-learning performance of the board.

Analyze internal and external data through advanced analytics uncovering key relationships and trends from relevant information.Recommend solutions to improve the ability for the business to make optimized, real-time, and predictive based business decisions fostering a new level of intelligence and drive quantified business value.• Highly trained statistical / operations research analyst in analytical disciplines (linear, mixed linear, constraint programming, modeling, simulation, time series analysis, pattern recognition, queuing theory, multivariate analysis, and other predictive analytics techniques.Requirements• Candidates with Singapore working experience highly preferred• Master's degree in Statistics or Operations Research• 5 years experience in Natural Language Processing (NLP)• 5 years experience in data mining, statistical modeling or optimization projects using tools such as SPSS, R, SAS, iLog• 5 years experience in working on a variety of advanced analytics projects with good understanding of the different stages of an analytics project• 2 years experience in working with databases, data mapping, data validation and data transformation• Strong experience in Unstructured Information Management Architecture, Computational Linguistics, and Machine Learning.• Strong knowledge and hands-on experience in Big data analytics and related open-source or commercial platforms is preferred.•

We need the following manual steps to complete the data factory.