Machine Learning is a great way to use your existing data to identify trends going forward. In this webinar, Gregory Deckler will show us step-by-step how to get started using this very exciting Azure Service, using Power BI and R script. Based on your requests, this webinar will show you how to bring together Azure ML, Power BI, and R in a single solution! Power BI MVP Gregory Deckler will demonstrate how to use R to bridge the gap between Azure ML and Power BI in order to build predictive analytics directly into your data model and reports. Greg is a Director at Fusion Alliance and the Solution Director of Cloud Services.

ELEANOR HALL: Welcome to Techtopia, our segment on the emerging technologies set to disrupt our future; and some of the unexpected questions we may need to ask about them. Joining me in Sydney, as he does every week for Techtopia, is entrepreneur and technology author Steve Sammartino. And also here today in the Sydney studio is AI specialist at the University of Technology Sydney, Professor Mary-Anne Williams. Professor Williams is the director of the Innovation and Enterprise Research Laboratory at UTS, which is also known as The Magic Lab.

Each artificial intelligence (AI) technology is developed to solve a particular problem. Once it solves a problem, however, it's no longer considered AI anymore. Everything from simple calculators and computers, to innovations such as voice recognition on the phone, are just everyday tools that would hardly be considered all that "intelligent". It's almost as if the term AI refers to the science and engineering of making computers do things they can't do yet. Or so suggests Prof Peter Stone from the University of Texas: chair of a panel of academic and industrial thinkers who co-authored the first offering from the 100 Year Study on Artificial Intelligence, an ongoing project hosted by Stanford University to help inform society on how best to manage and respond to developments in smart software, sensors and machines.

Last week, we discussed Multi-class SVM loss; specifically, the hinge loss and squared hinge loss functions. In reality, these values would not be randomly generated -- they would instead be the output of your scoring function f. Let's exponentiate the output of the scoring function, yielding our unnormalized probabilities: Figure 2: Exponentiating the output values from the scoring function gives us our unnormalized probabilities. Figure 4: Taking the negative log of the probability for the correct ground-truth class yields the final loss for the data point. To examine some actual probabilities, let's loop over a few randomly sampled training examples and examine the output probabilities returned by the classifier: Note: I'm randomly sampling from the training data rather than the testing data to demonstrate that there should be a noticeably large gap in between the probabilities for each class label.

Last week, we discussed Multi-class SVM loss; specifically, the hinge loss and squared hinge loss functions. A loss function, in the context of Machine Learning and Deep Learning, allows us to quantify how "good" or "bad" a given classification function (also called a "scoring function") is at correctly classifying data points in our dataset. In fact, if you have done previous work in Deep Learning, you have likely heard of this function before -- do the terms Softmax classifier and cross-entropy loss sound familiar? I'll go as far to say that if you do any work in Deep Learning (especially Convolutional Neural Networks) that you'll run into the term "Softmax": it's the final layer at the end of the network that yields your actual probability scores for each class label. To learn more about Softmax classifiers and the cross-entropy loss function, keep reading.

Artificial intelligence can research and write routine documents. A Chicago company called Narrative Science was among the first to give computers the power of the pen -- or keyboard. And, like anyone with a creative streak, automated writing software likes to stretch its artistic muscles. Best of all, this virtual reality and machine learning revolution will spark the investing trend of the decade ahead.

Benevolent.ai, a British artificial intelligence company, has appointed IBM Watson's vce president of Watson platform; Jerome Pesenti as CEO of its technology division, benevolent technology. Jerome will also join the main board of the company. Jerome has been focused on big data and machine learning for the past 16 years. At IBM he also held the role of chief scientist for the core IBM big data product portfolio. Prior to joining IBM, Jerome co-founded search and text analytics company Vivisimo that was acquired by IBM in 2012.

Leveraging data to predict customer satisfaction is more important than ever––it can help your business engage with customers proactively, improve operations, reduce customer churn, and improve customer relationships over the long-term. Think better support experiences for everyone. Join us for a unique opportunity to learn from guest speaker Ken Landoline, Principal Analyst at Ovum, a market-leading research and consulting business. Adrian McDermott, Senior Vice President of Product Development at Zendesk, will join Landoline to discuss how your business can use machine learning to provide a better customer experience. This webinar is complimentary, so feel free to spread the word and share with colleagues who you think might benefit from this live, interactive session.

Notes platform Evernote has announced plans to migrate its complete data infrastructure from its own servers and networks onto Google's Cloud. With an existing relationship with Google, through a previous integration with Google Drive, Evernote has made swift progress on finalising the infrastructure plans. The data migration is expected to begin from next month, finishing towards the end of 2016. It is these machine learning and artificial intelligence capabilities which Google plans to leverage in attracting large enterprises to its cloud, over rivals Amazon Web Services (AWS) and Microsoft Azure.

Artificial Intelligence (AI)-powered bots will become the next interface, shaping our interactions with the applications and devices we rely on and Microsoft's latest solutions are set to change the way HP interacts with its customers and partners, Indian-born Microsoft CEO Satya Nadella has said. "Bots are now learning in human context and the relevant thing for us is to make them intelligent as we learn from customers' experience. Our solutions are going to give HP a 360-degree view of its customer services," Nadella announced at HP's Global Partner Conference (GPC) 2016 at the Boston Conference and Exhibition Centre (BCEC) here. HP announced a six-year agreement to deploy Microsoft Dynamics customer relation management (CRM) online in order to enhance collaboration across marketing, sales and service operations. With Dynamics, as well as Azure, Office 365 and other Microsoft Cloud solutions, HP has invested in the sales and service collaboration platform to deliver a seamless sales experience for customers and partners.