As industrial machines are becoming more connected and flexible, the process of building and commissioning the machine is also getting smarter. Machines are built now using artificial intelligence, digital twins, and augmented reality. We caught up with Rahul Garg, VP of industrial machinery and mid-market program at Siemens Digital Industries Software. Garg explained the process of creating smart industrial machines using advanced technology. Design News: Is artificial intelligence becoming a major factor in building industrial machines?

It is rare that acquired data contains values for all features of all instances. Values can go missing for a number of reasons -- for example, through a faulty sensor, software bug, mapping issues from the source system or being left intentionally blank in a survey. To be able to use such a data set for model training, since machine learning algorithms require a value to work with, a quick and easy solution is to delete either the entire instances (rows) with missing values or delete the feature (column). However, doing so negatively impacts model training as deleting instances not only decreases the amount of training data, but also creates an imbalance in the example training data. In addition, removing features altogether affects the predictive power of the resulting model (Figure 1).

Raja Koduri has been in the thick of the past two eras of computing, which were marked by – among other things – the ability to architect systems and software that helped to get more performance into the hands into increasing numbers of people. In two stints with AMD, Koduri was key in steering the development and use of the chip maker's Radeon GPUs, expanding their use from client and gaming systems into the datacenter and HPC fields. In the middle of his almost 13 years at AMD, he left for four years to run Apple graphics architecture business, returning in 2013. And three years ago, of course, Koduri came into Intel, where he now is the company's chief architect, vice president, and general manager of the Cores and Visual Computing and Edge Computing Solutions unit. The job gives him an unobstructed view of what the future of computing looks like, and for all of the rapid changes that are happening and the challenges they present, the goal in many ways the same as it was in the PC era and now the mobile and cloud era – make a lot of compute capability available to as many people as possible.

I assume you already know what regression is. "Regression is a statistical method used in finance, investing, and other disciplines that attempts to determine the strength and character of the relationship between one dependent variable (usually denoted by Y) and a series of other variables (known as independent variables)." In the most simple terms -- we want to fit a line (or hyperplane) through data points to obtain a line of best fit. The algorithm behind aims to find the line which minimizes the cost function -- typically MSE or RMSE. That's linear regression, but there are other types -- like polynomial regression.

Data scientists used the opportunity to learn advanced trends in artificial intelligence, machine learning and deep learning methods in genomic prediction models. A deeper understanding of advanced trends in artificial intelligence (AI), machine learning (ML) and deep learning methods in genomic prediction models is critical to the success of smallholder agriculture. AI and ML algorithms are now being used to reduce risks in agriculture while also making it possible to forecast pest and disease outbreaks and alert farmers in advance. The annual collaborative workshop for Bioinformatics & Biometrics Community of Practices (CoP) under Excellence in Breeding (EiB) Platform Module 5, held in July in Montpellier, France, discussed the untapped potential of deep learning methods to make a significant impact on farming. With the theme: "Artificial Intelligence & Machine Learning with Genomic Selection Use Cases", the workshop served as a platform for data scientists across CGIAR institutions to explore using advanced agricultural research ML algorithms for genomics including prediction of plant phenotype, image identification, disease identification, and annotation of DNA sequences.

A new U.K. self-driving car startup founded by Amar Shah and Alex Kendall, two machine learning PhDs from University of Cambridge, is de-cloaking today. Wayve -- backed by New York-based Compound, Europe's Fly Ventures, and Brent Hoberman's Firstminute Capital -- is building what it describes as "end-to-end machine learning algorithms" to make autonomous vehicles a reality, an approach it claims is different to much of the conventional thinking on self-driving cars. Specifically, as Wayve CEO Shah explained in a call last week, the young company believes that the key to making an autonomous vehicle that is truly just that (i.e. In other words, self-driving cars is an AI problem first and foremost, and one that he and co-founder Kendall argue requires a very specific machine-learning development skill set. "Wayve is building intelligent software to decide how to control a vehicle on all public roads," he tells me.

More psychologists are now coming the tech space because they're trying to teach machines to become more social and sociable, according to BT's head of customer insight and futures, Dr Nicola Millard. "I'm not a technologist, I'm a psychologist – and it makes a lot of sense having me on-board because innovation in itself won't work unless people adopt it," she told CMO. "A psychologist in the team prevents us from getting carried away exclusively by the technology which often tech companies do." Leading the third largest innovation hum in the UK, Millard is responsible for tapping into the research and innovations that BT does for its global services clients. "I used to have a silly job title as a futurologist, and I hated that job title because everyone assumes you have a crystal ball," she said. "But I'm in global services so my clients are typically retailers, airlines and banks – big global corporates – and we often bring them around to our showcases of the retail store of the future, or the bank of the future, so they can have a play with our proof of concepts." While Astral Park in the UK is BT's main research hub, the company also has other centres dotted across the world including Abu Dhabi, Singapore University in Beijing and MIT in the US.

Futurists say that our destiny will be shaped by the Singularity, the moment when artificial intelligence surpasses human intelligence. Scholars don't agree on the details, but they say it will happen between 30 and 1000 years from today, with most predicting it will emerge in the next century. It will almost certainly have profoundly scary -- and deeply exciting -- consequences. Everything is going to change. The term'Singularity' was first used in the technological sense (as opposed to its definition within physics) by Hungarian American mathematician John von Neumann.