Monitoring home appliances from power readings with ML Google Cloud Blog

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As the popularity of home automation and the cost of electricity grow around the world, energy conservation has become a higher priority for many consumers. With a number of smart meter devices available for your home, you can now measure and record overall household power draw, and then with the output of a machine learning model, accurately predict individual appliance behavior simply by analyzing meter data. For example, your electric utility provider might send you a message if it can reasonably assess that you left your refrigerator door open, or if the irrigation system suddenly came on at an odd time of day. In this post, you'll learn how to accurately identify home appliances' (e.g. Once the algorithm identifies an appliance's operating status, we can then build out a few more applications.


What Does Machine Learning Really Mean?

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Surely we've heard the word float around in recent years as one of the hottest new buzzwords to hit the technosphere, but does anyone know what machine learning is? Well, from the title alone we can deduce that it has something to do with machines learning, which is undoubtedly true. However, let's look at a more formal definition: "Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to'learn' with data, without being explicitly programmed." The magic of this field of study comes in the very last segment of that definition: "Without being explicitly programmed." Traditionally in computer science, whenever we needed to have the machine do something, we'd write out a clear-cut instruction set in the form of code.


The 29-Year-Old Who Rocked Facebook Has Big Data Plans for H&M

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Fashion retailer Hennes & Mauritz AB wants to get a better sense of what makes its customers tick. To help it, the Swedish company just hired a man best known for revealing a data privacy scandal that rocked Facebook Inc. and raised serious questions around how some corners of technology are shaping human existence. At H&M, 29-year-old Christopher Wylie will help the company use big data and artificial intelligence to make sure it actually designs things shoppers want. If successful, the Cambridge Analytica whistle-blower might be able to help fix some of H&M's most pressing issues, including getting its inventory under control and ultimately making the company more profitable. "If you better understand what people like to wear, and how they like to wear it, and how they want to feel when they're wearing it, you'll naturally start to create insights as to modernizing and updating your collection," Wylie said in an interview at H&M's Stockholm headquarters on Thursday.


An Ex-Marine Wants to Print Autonomous Vehicles for Your City

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On an isolated stretch of industrial flatland outside Knoxville, Tenn., a minibus is taking shape in a car factory unlike any other. The space is small, the size of a supermarket, and all but tool-free. Instead, perched in the center is the world's largest 3D printer, a gangly 10-by-40-foot behemoth with a steel-gray exterior, thick columnar footings, and derrick-like roof beams to true its frame. When the print heads are in motion, the equipment emits little more than a whisper, dexterously cutting sharp angles and rounded edges. Programmers on laptops and quality-control experts with tablets mill around, inputting design changes and fine-tuning the minibus's sensor instructions. Beyond the assembly room lies a kind of alchemist's playground, where young staffers with advanced degrees in materials science and mechanical engineering synthesize nanopolymers or test exotic particles for strength or thermal and electrical conductivity.


Researchers improve AI emotion classification by combining speech and facial expression data

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Systems that can classify a person's emotion from their voice and facial tics alone are a longstanding goal of some AI researchers. Firms like Affectiva, which recently launched a product that scans drivers' faces and voices to monitor their mood, are moving the needle in the right direction. But considerable challenges remain, owing to nuances in speech and muscle movements. Researchers at the University of Science and Technology of China in Hefei claim to have made progress, though. In a paper published on the preprint server Arxiv.org this week ("Deep Fusion: An Attention Guided Factorized Bilinear Pooling for Audio video Emotion Recognition"), they describe an AI system that can recognize a person's emotional state with state-of-the-art accuracy on a popular benchmark.


Predicting the accuracy of a neural network prior to training

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Constructing a neural network model for each new dataset is the ultimate nightmare for every data scientist. What if you could forecast the accuracy of the neural network earlier thanks to accumulated experience and approximation? This was the goal of a recent project at IBM Research and the result is TAPAS or Train-less Accuracy Predictor for Architecture Search (click for demo). Its trick is that it can estimate, in fractions of a second, classification performance for unseen input datasets, without training for both image and text classification. In contrast to previously proposed approaches, TAPAS is not only calibrated on the topological network information, but also on the characterization of the dataset difficulty, which allows us to re-tune the prediction without any training.


52 Week Low Stocks Based on Artificial Intelligence: Returns up to 102.29% in 3 Months

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The 52 Week Low Stocks Package is designed for investors and analysts who need predictions for stocks currently at their 52-week low price level, offering the best market opportunities based on algo-trading. Package Name: 52 Week Low Stocks Recommended Positions: Long Forecast Length: 3 Months (11/13/2018 – 02/13/2019) I Know First Average: 18.61% In this 3 Months forecast for the 52 Week Low Stocks Package, there were many high performing trades and the algorithm correctly predicted 9 out 10 trades. The top-performing prediction in this forecast was FNMA, which registered a return of 102.29%. Other notable stocks were LRCX and CGNX with a return of 26.66% and 17.63%. The package had an overall average return of 18.61%, providing investors with a premium of 17.63% over the S&P 500's return of 0.98% during the same period.


"The first step is to accept that AI is the future" with Dr. Halleli Sharir and Tyler Gallagher

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Thank you so much for doing this with us! Can you share with us the'backstory" of how you decided to pursue this career path? My education and the different roles I have held throughout my scientific career, have led me to my current position. It perfectly combines the skills I have acquired in both drug discovery and cannabinoid research. I consider the implementation of AI in my research as another feature of scientific growth, which is a MUST due to the continued advancement of technology. What lessons can others learn from your story? Each role I have held in my past has contributed to my current position. My advice for fellow scientists is to be persistent. Know what your heart desires and keep going till you get there. Thinking outside the box is another important element. You must always realize that in order to succeed, you have to be open and engage in technology advancements. You have to be open to learning collaborating and developing.


Automated Machine Learning: is it the Holy Grail? - AnalyticsWeek

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Machine learning is in the ascendancy. Particularly when it comes to pattern recognition, machine learning is the method of choice. Tangible examples of its applications include fraud detection, image recognition, predictive maintenance, and train delay prediction systems. In day-to-day machine learning (ML) and the quest to deploy the knowledge gained, we typically encounter these three main problems (but not the only ones). Data Quality – Data from multiple sources across multiple time frames can be difficult to collate into clean and coherent data sets that will yield the maximum benefit from machine learning.


Machines are not our masters – but the sinister side of AI demands a smart response Will Hutton

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You are not about to surrender your life and understanding of the world to machines. That head of yours with its conscious mind, reading this column, remains in the driving seat and always will. It's true that the capacity of machines to supplement human intelligence, monitor us, mimic us and replace routine jobs and tasks is exploding and in the wrong hands could represent a step change in creating dark forms of economic and social control. But that is the battle for democracy, with the confrontation of the worst of capitalism taking on a new dimension. It does not mean that the end of human life is nigh – it means we have to be cleverer in fashioning responses.