BENGALURU: Microsoft India on Wednesday announced to set up Artificial Intelligence (AI) labs in 10 universities and train five lakh youth across the country in disrupting technologies. The company also said it will upskill over 10,000 developers over the next three years. "We believe AI will enable Indian businesses and more for India's progress, especially in education, skilling, healthcare and agriculture," said Anant Maheshwari, President, Microsoft India. Microsoft AI today is fuelling digital transformation for over 700 customers and 60 per cent customers are large manufacturing and financial services enterprises. Over 700 partners have geared up to support the AI ecosystem, said the company.

I'll use a very interesting dataset presented in the book Machine Learning with R from Packt Publishing, written by Brett Lantz. My intention is to expand the analysis on this dataset by executing a full supervised machine learning workflow which I've been laying out for some time now in order to help me attack any similar problem with a systematic, methodical approach. If you are thinking this is nothing new, then you're absolutely right! I'm not coming up with anything new here, just making sure I have all the tools necessary to follow a full process without leaving behind any big detail. Hopefully some of you will find it useful too and be sure you are going to find some judgment errors from my part and/or things you would do differently. Feel free to leave me a comment and help me improve! Let's jump ahead and begin to understand what information we are going to work with: "In the field of engineering, it is crucial to have accurate estimates of the performance of building materials. These estimates are required in order to develop safety guidelines governing the materials used in the construction of building, bridges, and roadways. Estimating the strength of concrete is a challenge of particular interest. Although it is used in nearly every construction project, concrete performance varies greatly due to a wide variety of ingredients that interact in complex ways. As a result, it is difficult to accurately predict the strength of the final product. A model that could reliably predict concrete strength given a listing of the composition of the input materials could result in safer construction practices. For this analysis, we will utilize data on the compressive strength of concrete donated to the UCI Machine Learning Data Repository (http://archive.ics.uci.edu/ml) by I-Cheng Yeh. According to the website, the concrete dataset contains 1,030 examples of concrete with eight features describing the components used in the mixture. These features are thought to be related to the final compressive strength and they include the amount(in kilograms per cubic meter) of cement, slag, ash, water, superplasticizer, coarse aggregate, and fine aggregate used in the product in addition to the aging time (measured in days)."

This article was originally published by Ozy. In 2016, Houston's petrochemical industry had countless job positions that were unfilled. And at the same time, a number of the city's residents were looking for work. So, how was Houston going to fix this? In an effort to help match eligible candidates with open positions, private companies began to step in.

God might just as well have begun with a toaster oven. A few years ago at a yard sale, Nicholas Hud spotted a good candidate: A vintage General Electric model, chrome-plated with wood-grain panels, nestled in an old yellowed box, practically unused. The perfect appliance for cooking up the chemical precursors of life, he thought. He bought it for $5. At home in his basement, with the help of his college-age son, he cut a rectangular hole in the oven's backside, through which an automated sliding table (recycled from an old document scanner) could move a tray of experiments in and out. He then attached a syringe pump to some inkjet printer parts, and rigged the system to periodically drip water onto the tray.

The world's largest robot has been unveiled and it is a completely autonomous railway system. AutoHaul has been developed by a mining firm and is being used to transport iron ore from mines to shipping ports 500 miles away (800 km) in Western Australia. This journey can be completed in just 40 hours, including the loading and dumping of the ferrous cargo. Its deployment is the end result of a project which has so far cost $940 million (£740 million). Rio Tinto, the corporation that built the infrastructure and hardware for the locomotive, says this could be the first step in transforming the firm's 1,000-mile (1,700-kilometre) network connecting 16 iron ore mines and two ports.

Effects of the peptide were occluded in Syt3 knockout mice, implicating Syt3 in a GluA2-3Y–dependent mechanism of AMPA receptor internalization. Our data give rise to a model in which Syt3 at postsynaptic endocytic zones is bound to AP-2 and BRAG2 in the absence of calcium. GluA2 could then accumulate at endocytic zones by binding Syt3 in response to increased calcium during neuronal activity. This would potentially bring GluA2 into close proximity to BRAG2, where a transient interaction could activate BRAG2 and Arf6, and promote endocytosis of receptors via clathrin and AP-2 (10, 32). PICK1 is also important for AMPA receptor endocytosis, raising the question of the interplay of Syt3 and PICK1.

Artificial Intelligence (AI) has the power to transform how we live and work, providing businesses with powerful new tools to make their operations more efficient. However, academics and technologists have multiple concerns about the ethics of AI. Q - How are organisations currently using AI? KL: AI is being used to automate an increasing number of numerical, formulaic and repetitive processes. One of the most talked about applications for AI to-date is for self-driving or autonomous vehicles. Codelco, for example, is a Chilean copper mining company that has been a global pioneer in the use of autonomous trucks.

Mining corporation Rio Tinto says that an autonomous rail system called AutoHaul that it's been developing in the remote Pilbara region of Australia for several years is now entirely operational -- an accomplishment the company says makes the system the "world's largest robot." "It's been a challenging journey to automate a rail network of this size and scale in a remote location like the Pilbara," Rio Tinto's managing director Ivan Vella told the Sidney Morning Herald, "but early results indicate significant potential to improve productivity, providing increased system flexibility and reducing bottlenecks." The ore-hauling train is just one part of an ambitious automation project involving robotics and driverless vehicles that Rio Tinto wants to use to automate its mining operations. The company conducted its first test of the train without a human on board earlier this year, and it now claims that the system has completed more than a million kilometers (620,000 miles) of autonomous travel. In response to concerns from labor unions, Rio Tinto promised that the autonomous rail system will not eliminate any existing jobs in the coming year -- though it's difficult to imagine the project won't cut into human jobs in the long term.

IBM has teamed up with a Montreal-based Institute for Data Valorization (IVADO) to jointly develop artificial intelligence (AI) products, with plans to add another 100 technology professionals to its innovation center. The innovation center, launched in 2016 for providing digital transformation services, will henceforth focus on AI and Salesforce expertise, the American technology giant stated in a press release. "Research in AI is quickly expanding worldwide – and particularly in Montréal – but AI is far from having reached its full potential in delivering concrete results for businesses," said Claude Guay, General Manager, IBM Services, Canada. "IBM's CIC in Montréal will focus on delivering value to its clients through applied AI and bringing real solutions to real problems." The new recruits the company is looking for will work as machine learning engineers, data scientists, full stack developers, or data engineers.

Deep learning provides a powerful tool for machine perception when the observations resemble the training data. However, real-world robotic systems must react intelligently to their observations even in unexpected circumstances. This requires a system to reason about its own uncertainty given unfamiliar, out-of-distribution observations. Approximate Bayesian approaches are commonly used to estimate uncertainty for neural network predictions, but can struggle with out-of-distribution observations. Generative models can in principle detect out-of-distribution observations as those with a low estimated density. However, the mere presence of an out-of-distribution input does not by itself indicate an unsafe situation. In this paper, we present a method for uncertainty-aware robotic perception that combines generative modeling and model uncertainty to cope with uncertainty stemming from out-of-distribution states. Our method estimates an uncertainty measure about the model's prediction, taking into account an explicit (generative) model of the observation distribution to handle out-of-distribution inputs. This is accomplished by probabilistically projecting observations onto the training distribution, such that out-of-distribution inputs map to uncertain in-distribution observations, which in turn produce uncertain task-related predictions, but only if task-relevant parts of the image change. We evaluate our method on an action-conditioned collision prediction task with both simulated and real data, and demonstrate that our method of projecting out-of-distribution observations improves the performance of four standard Bayesian and non-Bayesian neural network approaches, offering more favorable trade-offs between the proportion of time a robot can remain autonomous and the proportion of impending crashes successfully avoided.