For Lou Carvalheira, advanced analytics manager in IT vendor Cisco Systems Inc.'s customer intelligence unit, which is also based in San Jose, machine learning has underpinned analytics for many years. That notion is so familiar that "it is not something we speak about anymore," he said. What is new in the quest to identify potential buyers, he continued, is that "we are finding ways to scale processing. Machine learning is empowered by the fact that you can now process much more data. You use a tremendous amount of computation power, not just one computer."

A'chat bot' is a computer programme designed to simulate an intelligent conversation with human users by way of text or telephone. RBS will use IBM's'Watson' - a platform that analyses unstructured data - to power its service, which will aim to be able to answer specific customer questions, such as'how do I authorise my card to be used overseas?'. In the case of more complex questions, the chat bot will direct customers to a human who can answer them. The bank said it had already tested the servicce among 1,200 RBS and NatWest staff over a two-month trial and now expects the AI bot - if it is successful in its customer pilot - to be rolled out to customers of both brands. In March, RBS announce it would let go 220 investment advisers as part of a cost-cutting drive that would see large parts of its face-to-face service replaced with telephone and online solutions.

You might not be campaigning to be America's next president, or have any desire to hold such a demanding office (bless you, Hillary), but wouldn't it still be nice to be treated like POTUS when you travel? Or, at least spend a few days in the presidential suite feeling like one of the world's most … Election jokes are i Saturday Night Live' /i s bread and butter, so it should come as no surprise that the cast took aim at Donald Trump's hot mic scandal. But host Lin-Manuel Miranda also got a chance to shine in his opening monologue. Below, we've rounded up the must-see moments from last night's /b … Humans may live longer and longer, but eventually we all grow old and die. This leads to a simple question: Is there an intrinsic maximum limit to human lifespan or not?

New York-based Calrifai -- backed by investors including Google Ventures, Lux Capital, and NVidia -- entered the R/GA accelerator this year, after raising 10M in Series A in Q2'15. BI, Sales & CRM: Applications here include voice analytics to extract information from calls, automated customer response solutions, business data analytics, and sales targeting. To name a few, Palo Alto-based Mariana raised 2M in seed money from investors including Blumberg Capital; London-based True AI, previously seed funded by Entrepreneur First, entered the Microsoft Ventures Accelerator in Q3'16; another UK-based startup, Ripjar, raised funds from Winton Ventures in Q2'16. Three startups in the private sector using AI in e-commerce raised funding rounds this year: Reflektion raised 18M in Q1'16 from investors including Intel Capital, Battery Ventures, and Marc Benioff; ViSenze raised 10.5M in Series B from investors including Rakuten Ventures, Enspire Capital, and Phillip Private Equity; India-based Staqu raised angel funds in Q2'16.

Perspica added a new root cause analysis feature to its software-as-a-service (SaaS) visibility platform. The company says its root cause analysis detects issues within layers of the application stack and recommends potential fixes. Powered by machine learning, Perspica's root cause analysis feature is able to understand what is supposed to be happening in an application's performance, based on times of the day and days of the week, says JF Huard founder and CTO of Perspica. It is then able to analyze these metrics, detect anomalies, and determine the root cause. "Not only are we eliminating false positives, but we are also able to catch relevant problems by comparing an application's topology from day-to-day," Huard says.

Since PipelineDB released the developer preview of its realtime analytics API, Stride, one is tempted to ask whether APIs in connection with machine learning may replace the growing role of human data scientists in the data analytics space. To further explore this concept, ProgrammableWeb caught up with PipelineDB's President and Co-Founder, Jeff Ferguson. Ferguson and his team believe that machine learning can easily replace the role humans play in a number of data analytics tasks, and this shift has already begun to take place. "Machines can learn all sorts of things from streaming and siloed datasets, all with orders of magnitude more accuracy, speed, and scale than human beings," Ferguson told ProgrammableWeb. "A simple example is realtime a/b testing in advertising, where self-healing software systems can create, test, and iterate on multiple versions of ad copy, continuously weighting campaign spend toward ads with the highest click-through rates."

Predictive analytics / machine learning / artificial intelligence is a hot topic – what's it about? Using algorithms to help make better decisions has been the "next big thing in analytics" for over 25 years. It has been used in key areas such as fraud the entire time. But it's now become a full-throated mainstream business meme that features in every enterprise software keynote -- although the industry is battling with what to call it. It appears that terms like Data Mining, Predictive Analytics, and Advanced Analytics are considered too geeky or old for industry marketers and headline writers.

Starting with this article, I will write a series of articles on deep learning covering the popular Deep Learning libraries and their hands-on implementation. Fast forward to 2012, a deep neural network architecture won the ImageNet challenge, a prestigious challenge to recognise objects from natural scenes. Nodes in the graph represents mathematical operations, while graph edges represent multi-dimensional data arrays (aka tensors) communicated between them. Here we solve our deep learning practice problem – Identify the Digits.

At last year's Spark Summit conference, Patrick Wendell, a software engineer at Databricks Inc. and a contributor to the Apache Spark open source project, said the technology's data processing capabilities are impressive but its real power lies in the Spark library components that sit on top of the core engine. "The future of Spark is the libraries," he said. "That's what the community has invested in and where the innovation is coming from." Sure enough, this month's Spark Summit 2015 event prominently featured case studies in which users explained how they're putting the libraries to work in Spark analytics applications. The Spark platform comes with four distinct libraries -- Spark SQL, Spark Streaming, a graph processing library called GraphX and a machine learning one known as MLlib -- that include pre-built algorithms and programming capabilities designed to streamline data preparation, exploration and analysis tasks. The libraries enable users to automate certain tasks and eliminate some of the coding that typically would be required.