A student from India studying in the Nanyang Technological University (NTU) in Singapore, has developed a machine learning software along with his team consisting of 4 other students from the same institution. This software is said to be sent up to the International Space Station (ISS). Archit Gupta's team won a competition recently which was on developing different ways to apply artificial intelligence on space applications. This victory gave his team an opportunity to test their software in the International Space Station (ISS). The software will be installed into a supercomputer which is an artificial intelligence box and after that it will be sent up to the ISS.

In first collaboration with INNOTECH, OSARO supplies machine learning software to create smart general-purpose piece picking station.

The latest version is 2.0.1. TensorFlow -- Machine Learning Software, is an open source software library for machine learning. It was developed by the Google team for sorts of perceptual tasks. Also, to conduct sophisticated research on machine learning and deep neural networks. TensorFlow performs numerical computations using data flow graphs. These elaborate the mathematical computations with a directed graph of nodes and edges. Edges describe the input/output relationships between nodes. Data edges carry dynamically-sized multi-dimensional data arrays or tensors.

LONDON, ENGLAND - DECEMBER 05: Co-founder of Google DeepMind Mustafa Suleyman attends a Q&A during day 1 of TechCrunch Disrupt London at the Copper Box on December 5, 2016 in London, England. Google has found another use for DeepMind's machine learning software after buying the London artificial intelligence lab for a reported £400 million in 2014. Later this year, the search giant will roll out two new DeepMind-built Android features that are designed to improve battery life and optimise screen brightness levels. The features will be available to people with devices running the Android P operating system. The features -- announced during the Google I/O developer conference -- were built by a unit called "DeepMind for Google," which focuses on applying DeepMind's technology to Google products.

Now, Google has declared that AutoML has defeated the human AI engineers at their own game by setting machine-learning software that's more effective and powerful than the best human-designed systems. An AutoML system recently broke a record for classifying perceptions by their content, scoring 82 percent. While that's a relatively simple task, AutoML also beat the single-built system at a more complex task key to autonomous robots and augmented reality: showing the location of multiple objects in an image. For that task, AutoML scored 43 percent versus the individual-built system's 39 percent. These results are important because even at Google, few people have the needed expertise to build next-generation AI systems.

Mike Brooks proposes the use of machine learning software to improve plant reliability and to reduce unplanned downtime. There is a significant need to carry out failure prevention using data-driven truths instead of guesstimates, evidenced by the fact that a combination of mechanical and process induced breakdowns account for up to 10% of the worldwide $1.4 trillion manufacturing market, according to a 2012 report from The McKinsey Global Institute. While companies have spent millions trying to address this issue and ultimately avoid unplanned downtime, only recently have they been able to address wear and age-based failures. Current techniques are not able to detect problems early enough and lack insight into the reasons behind the seemingly random failures that cause over 80% of unplanned downtime. This is where using machine learning software to cast a'wider net' around machines can capture process induced failures.

In 2017, McKinsey conducted a study on productivity gains driven by technology transformations, such as the steam engine, early robotic technology and advances in information technology. McKinsey sees manufacturing on the brink of the next generation of industrial automation revolution with unprecedented annual productivity growth of between 0.8 – 1.4% in the decades ahead. Advances in robotics, artificial intelligence and machine learning will match or outperform humans in a range of work activities involving fast, precise, repetitive action and cognitive capabilities. To remain competitive, complex industries need to deploy industrial automation more than ever, as intense global competition drives process industries to increase efficiency through reduced operating costs, increased production, higher quality and lower inventories. The highest priority should be to eliminate production losses caused by unplanned downtime and address a $20 billion a year problem for the process industries.

Machine Learning is where computers learn things that they were not specifically designed to do. Traditional definitions of data quality define it as data which is able to do what it was designed to do. Spotless Data has recognised that this in an out-of-date definition which fails when it comes to Machine Learning and data quality in general. This traditional definition also fails when it comes to future-proofing the data one has or are likely to have. To be able to do new things with big data without having to completely overhaul the platform that took such effort and resources to create; now that is data quality! It is also the essence of Machine Learning.

WIRE)--GNS Healthcare (GNS), a leading precision medicine company that applies causal machine learning technology to massive and diverse data streams to better match drugs and other health interventions to individual patients, today announced that Celgene Corporation has entered into a service and license arrangement for the rights to operate the GNS Healthcare REFS (Reverse Engineering and Forward Simulation) causal machine learning and simulation platform for applications across drug discovery, clinical development, and commercialization and market access. In addition, several GNS causal modeling experts will be brought in-house at Celgene sites to operate the platform. GNS also announced that Celgene has made a second equity investment in GNS. "Companies that embrace data-driven frameworks and culture such as Celgene are gaining a competitive advantage to rapidly generate insights that are simply not possible with any other analytics methodology." This service and license arrangement with embedded GNS employees is a linking of people, process and technology.