It is this time of year again where most of us willingly and willfully make fools out of ourselves trying to predict the future of our industry. The momentous electoral events we have witnessed and those coming up in 2017 remind me that, even more so for the next 12 months, macro risks will rule and influence the state of financial services and fintech. I will limit myself to comments pertaining to the US and Europe. I have already attempted to decipher a Trump presidency in a previous post, see here. Suffice it to say there will be winners and losers in the five sectors of the industry – lending, capital markets, asset management, payments and insurance.

Is AI the Next CRM Battleground? Posted December 8, 2016 By Artificial intelligence is helping sales and marketing professionals do a better job of getting the right messages to the right prospects. Artificial Intelligence (AI) is getting a lot of attention of late. Salesforce unleashed a torrent of hype about Einstein, Microsoft is countering with AI features built into Dynamics 365, and of course IBM continues to hit prime time with Watson ads featuring the likes of Bob Dylan and Stephen Hawking. EnterpriseAppsToday.com recently delved into this area in Where is AI Headed in the Enterprise.

There have been several discussions lately around machine intelligence that are started to converge in how they may change competition and regulation in all markets but is likely to become a bigger issue in 2017. This is a bubble waiting to burst. The recent letter for Apple to the National Highway Traffic Safety Administration regarding policy changes to help promote automated self-driving vehicles stressed not just a level playing field to promote new technology and data sharing but also the impact of automated vehicles on the public good, including their consequences for employment and public spaces. Other issues around consumer product automation in the home with Amazon Echo Alexa and Google Home have raised the bar in interactive systems and questions over the type of privacy and data use issues these may bring. Other issues have been raised over the use by Facebook of algorithms for "editing" the social media sites for certain political issues or in the case of their entry to the Chinese market and creating a censoring app to comply with regulations.

My view of the world is shaped by where I stand, but from this spot the future of analytics for 2016 looks pretty exciting! Analytics has never been more needed or interesting. Machine learning dates back to at least 1950 but until recently has been the domain of elites and subject to "winters" of inattention. I predict that it is here to stay, because large enterprises are embracing it. In addition to researchers and digital natives, these days established companies are asking how to move machine learning into production.

Mellanox Technologies (NASDAQ:MLNX) is a leading supplier of end-to-end Ethernet and InfiniBand intelligent interconnect solutions and services for servers, storage, and hyperconverged infrastructure. Mellanox intelligent interconnect solutions increase data center efficiency by providing the highest throughput and lowest latency, delivering data faster to applications and unlocking system performance. Mellanox offers a choice of high performance solutions: network and multicore processors, network adapters, switches, cables, software and silicon, that accelerate application runtime and maximize business results for a wide range of markets including high performance computing, enterprise data centers, Web 2.0, cloud, storage, network security, telecom and financial services. More information is available at http://www.mellanox.com/.

Increased investor interest in AI startups – from around 10 deals in Q1'11 to over 120 in Q2'16 – can be attributed to recent advances in machine learning algorithms, particularly "deep learning" technology, a souped up version of AI. Just this week, Google integrated deep learning into its Google Translate tool; Baidu announced the launch of DeepBench, an "open source benchmarking tool for evaluating deep learning performance across different hardware platforms"; and NVIDIA introduced Xavier, a deep learning-based supercomputer for driverless cars. In the private market, Google put deep learning in the spotlight back in 2014 when it acquired 4 startups focused on this AI tech in quick succession: DeepMind, Vision Factory, Dark Blue Labs, and DNNresearch. Apple, which joined the race in 2015, most recently acquired Turi, which has developed a deep learning toolkit, among other AI-based solutions. Not to be outdone, Intel has acquired around 5 AI startups since January 2015, including deep learning startup Nervana Systems and, more recently, Movidius.

Digital-related investment in industrial production is growing fast. Through 2020, enterprises expect to pump $907 billion annually into digital technologies on the industrial floor, according to PwC's Industry 4.0 research. That investment is expected to increase revenues by $493 billion annually and reduce costs by $421 billion each year. But where and how those dividends will be unearthed is only now coming into view. PwC sees enterprises following a path that spans prediction, prescription, optimization, and new business models.

Every day, K–12 educators juggle a bevy of tasks: teaching, developing lesson plans, grading papers, writing tests. And often, they battle with technology to do all of these things. With its more streamlined G Suite for Education -- formerly known as Apps for Education -- Google has updated its commonly used applications, hoping to save teachers a bit of time and energy with their everyday tasks. As announced in a blog post by Jonathan Rochelle, the director of product management at Google, G Suite's goal was to harness the intelligence of computers to create a smarter, easier and more efficient technology experience for educators and students. "G Suite for Education is the same set of apps that you know and love -- Gmail, Docs, Drive, Calendar, Hangouts, and more -- but designed with new intelligent features that make work easier and bring teachers and students together," writes Rochelle.

In a previous lesson I showed you how to do a K-means cluster in R. You can visit that lesson here: R: K-Means Clustering. Now in that lesson I choose 3 clusters. I did that because I was the one who made up the data, so I knew 3 clusters would work well. Choosing the right number of clusters is one of the trickier parts of performing a k-means cluster.

UPDATE: Mar 20, 2016 - Added my new follow-up course on Deep Learning, which covers ways to speed up and improve vanilla backpropagation: momentum and Nesterov momentum, adaptive learning rate algorithms like AdaGrad and RMSProp, utilizing the GPU on AWS EC2, and stochastic batch gradient descent. We look at TensorFlow and Theano starting from the basics - variables, functions, expressions, and simple optimizations - from there, building a neural network seems simple! Deep learning is all the rage these days. What exactly is deep learning? Well, it all boils down to neural networks.