Tesla will unveil something on Monday October 17, according CEO and founder Elon Musk. Musk previously teased the October 28 event, saying it would include the unveiling of joint products from both the electric car and the solar energy companies. SAN FRANCISCO – On Monday, Elon Musk is promising a surprise announcement about Tesla. The reveal will be "unexpected by most," Musk tweeted last Sunday. Anyone familiar with the electric automaker and its CEO's cryptic ways will know that this passes for specificity in Teslaworld.

For those who hate mowing the lawn, a new robot could prove to be the answer. Described by its makers as the'world's first fully autonomous all-season garden robot', Kobi can cut grass, pick up leaves and even plough snow. The autonomous machine sports a rechargeable battery and will automatically dock itself in its charging station when it needs a power boost. Described by its makers as the'world's first fully autonomous all-season garden robot', Kobi can cut grass, pick up leaves and even plough snow Using GPS and built-in sensors, the robot helper can navigate around on its own. It can cut grass over a lawn measuring up to 7 acres (28,328 square metres) pick up leaves over an area of 3 acres (12,141 square metres) and shovel show over 0.37 acres (1,497 square metres).

Products using artificial intelligence (AI) are creeping into our lives: in the home, online, at work, in the marketplace, in the doctor's office. What if AI gets carried away, if it hasn't already? Plenty of movies and books that contemplate this. While those scenarios may be easy to dismiss, the consequences of what could happen are not. Unless it's fully grasped for its benefits, companies that use AI are putting their brands at risk if society doesn't adequately understand how it benefits from the technology.

For many people, Google is simply the gateway to a vast archive of facts and memories. For those who pay closer attention to its business dealings, the company also invests billions to find new ways to use the power of computers: it's developing robots, virtual reality gear and self-driving cars. Remember all the hubbub about Google Glass? Google has been using the same approach in sustainability – spreading its wealth in a variety of projects to cut its waste and carbon footprint, initiatives which may one day generate profits. During the SXSW Eco conference this week, I caught up with Google's sustainability officer, Kate Brandt, to find out more.

Navigant Research estimates that utility companies will spend almost 50 billion on asset management and grid monitoring technology by 2023. Today many organizations are facing budgetary challenges in order to increase reliability, uptime and safety within their facilities. The industry is adapting to new technologies including utilization of advanced sensors and sensor fusion, edge devices, artificial intelligence, and machine learning to create the maintenance center of the future. Bernie Cook, former Director of Maintenance and Diagnostics at Duke Energy and now VP of Woyshner Service consulting, will join us to provide practical guidance and examples of how utilities can begin adapting these next generation technologies within their facilities to drive significant reduction in maintenance costs. Following Bernie, Stuart Gillen, Director of Business Development at SparkCognition, will give examples of how machine learning technologies are augmenting current practices that make maintenance engineers more efficient at predicting critical asset failure.

Mapping forest aboveground biomass (AGB) has become an important task, particularly for the reporting of carbon stocks and changes. AGB can be mapped using synthetic aperture radar data (SAR) or passive optical data. However, these data are insensitive to high AGB levels (\textgreater{}150 Mg/ha, and \textgreater{}300 Mg/ha for P-band), which are commonly found in tropical forests. Studies have mapped the rough variations in AGB by combining optical and environmental data at regional and global scales. Nevertheless, these maps cannot represent local variations in AGB in tropical forests. In this paper, we hypothesize that the problem of misrepresenting local variations in AGB and AGB estimation with good precision occurs because of both methodological limits (signal saturation or dilution bias) and a lack of adequate calibration data in this range of AGB values. We test this hypothesis by developing a calibrated regression model to predict variations in high AGB values (mean \textgreater{}300 Mg/ha) in French Guiana by a methodological approach for spatial extrapolation with data from the optical geoscience laser altimeter system (GLAS), forest inventories, radar, optics, and environmental variables for spatial inter-and extrapolation. Given their higher point count, GLAS data allow a wider coverage of AGB values. We find that the metrics from GLAS footprints are correlated with field AGB estimations (R 2 =0.54, RMSE=48.3 Mg/ha) with no bias for high values. First, predictive models, including remote-sensing, environmental variables and spatial correlation functions, allow us to obtain "wall-to-wall" AGB maps over French Guiana with an RMSE for the in situ AGB estimates of ~51 Mg/ha and R${}^2$=0.48 at a 1-km grid size. We conclude that a calibrated regression model based on GLAS with dependent environmental data can produce good AGB predictions even for high AGB values if the calibration data fit the AGB range. We also demonstrate that small temporal and spatial mismatches between field data and GLAS footprints are not a problem for regional and global calibrated regression models because field data aim to predict large and deep tendencies in AGB variations from environmental gradients and do not aim to represent high but stochastic and temporally limited variations from forest dynamics. Thus, we advocate including a greater variety of data, even if less precise and shifted, to better represent high AGB values in global models and to improve the fitting of these models for high values.

A startup from California is using GPUs and big data to predict what homes are likely to buy solar panels. PowerScout is using GPUs on the Amazon cloud and cuDNN to train their deep learning models on a mix of data from commercial databases and LIDAR to detect solar panels from satellite images, and to also detect the presence of trees near homes that could cast shade onto roofs. The tools the startup developed can also help estimate how much energy could be harvested from a home's rooftop without needing to take measurements in person with a decent degree of accuracy. From the information, they can target direct mail and online marketing to the most promising customers and quickly give them online estimates. Then, those who are interested in rooftop solar can choose a financing plan and get connected to a local installation partner to have it installed.

A couple of weeks ago I wrote an article on simple linear regression, which I would recommend reading before proceeding to read this one. Machine learning is a very interesting topic and I have been studying it on my free time. I hope this article sparks your interest in the subject or helps continue fuel it. In simple linear regression there is a one-to-one relationship between the input variable and the output variable. But in multiple linear regression, as the name implies there is a many-to-one relationship, instead of just using one input variable, you use several.

Data is everywhere – masses of it. And it's helping businesses to make better decisions across departments. Marketing can utilise data to discover the effectiveness of email campaigns, Finance can analyse past trends to make predictions and projections for the future, and Sales can target their follow-up with detailed information on prospective customers. But data is only useful when business tools transform it into valuable information. Data intelligence through algorithms and analytics make business data relatable. The most advanced solutions require enormous amounts of data to be able to offer accurate insight to users.

Billions of connected devices, zetabytes of data, power and brand loyalty now in the hands of the consumer, businesses having to market and sell to each and every one of us. How can any business make sense of it all? How can they learn and avoid making the same mistakes – and become smarter. Oh – and did I mention much of this needs to happen in real time? That's where Machine Leaning as part of a cognitive strategy comes in to its own.