The most valuable part of machine learning is predictive modeling. This is the development of models that are trained on historical data and make predictions on new data. This cheat sheet contains my best advice distilled from years of my own application and studying top machine learning practitioners and competition winners. With this guide, you will not only get unstuck and lift performance, you might even achieve world-class results on your prediction problems. Note, the structure of this guide is based on an early guide that you might fine useful on improving performance for deep learning titled: How To Improve Deep Learning Performance. Machine Learning Performance Improvement Cheat Sheet Photo by NASA, some rights reserved. This cheat sheet is designed to give you ideas to lift performance on your machine learning problem. All it takes is one good idea to get a breakthrough. Find that one idea, then come back and find another.

That joke is at least as old as Deep Blue's 1997 victory over then world chess champion Garry Kasparov, but even with the great strides made in the field of artificial intelligence over that time, we're still not much closer to having to worry about computers' feelings. Computers can analyze the sentiments we express in social media, and project expressions on the face of robots to make us believe they are happy or angry, but no one seriously believes, yet, that they "have" feelings, that they can experience them. Other areas of AI, on the other hand, have seen some impressive advances in both hardware and software in just the last 12 months. Deep Blue was a world-class chess opponent -- and also one that didn't gloat when it won, or go off in a huff if it lost. Until this year, though, computers were no match for a human at another board game, Go.

Imagine the holy grail: getting the right message to the right customer at exactly the right time -- every time. And what if you could deliver hyper-relevant cross-channel customer experiences that amplify loyalty and result in increased Average Order Value, reduced churn, and increased conversions faster? As hyper-scalable programmatic technology shifts from the adtech space and into the martech world, organizations are, for the first time, able to leverage predictive scoring on an incredible scale built upon a real-time view of every customer. Leading companies have already implemented user-centric strategies that place an emphasis on marrying systems of record, systems of intelligence, and systems of action to create what is now being called Programmatic CRM. Join master marketers in our latest VB Live executive event, where you'll learn how to turn martech innovation into user-centric, budget-stretching, personalized marketing that works.

Businesses are beginning to see the benefits of using chatbots for their consumer-facing products, according to a survey by Oracle. The survey included responses from 800 decision makers including chief marketing officers, chief strategy officers, senior marketers, and senior sales executives from France, the Netherlands, South Africa, and the UK. When asked which emerging technologies they are already using and which they intended to implement, 80% of respondents said they already used or planned to use chatbots by 2020. Chatbots are interactive software platforms that reside in apps, live chat, email, and SMS and can behave in a human-like manner. Additionally, the survey shows that business leaders and decision makers are turning to the broader umbrella of automation technologies, which includes chatbots, for things like sales, marketing, and customer service.

Today our focus has been on KPIs, ERP, WMS, TMS, YMS, EDI, The Cloud, S and OP, 3 D Printing, IoT, IoE, Drones: Same Hour/Day/Time Delivery to Customers, Cyber Security, Theft, Government Regulations, E-Commerce, Omni-Channel, Modeling/Simulation, Risk Management, Tracking, Traceability, Re-shoring, Robotics, et al, but…what about Artificial Intelligence or AI technologies? AI is a controversy of deep, lasting dimensions. Will machines learn to think like humans…and then outthink us? If AI Technologies Can Think & Act Like "Us" Where do "We" Go? The application of AI technologies has created the ability to understand, store and use product information in an entirely new way.

Artificial intelligence--the mere words bring on a myriad of conflicting emotions. Images of The Matrix swim before your eyes. That is until you remember how helpful Siri is when you can't put your finger on the name of a song on the radio. "You seem to be listening to'Come On Eileen' by Dexy's Midnight Runners, but don't ask me to sing it." Aside from the fact that you should probably know this song, Siri is both useful and playful, no doubt.

The term'Artificial Intelligence' was originally coined in the 1950s by the computer scientist John McCarthy. Human-style intelligence, is the desire for people to create human-like consciousness in a machine, enabling it to apply common sense, work out varied problems and even have emotional intelligence, sometimes referred to as'general' or'strong' AI, and Task-orientated intelligence, is the ability to do a limited range of tasks very well, such as the ability to drive a car, answer questions or to make health diagnoses, referred to as'narrow' or'weak' AI. Human-style intelligence, is the desire for people to create human-like consciousness in a machine, enabling it to apply common sense, work out varied problems and even have emotional intelligence, sometimes referred to as'general' or'strong' AI, and Task-orientated intelligence, is the ability to do a limited range of tasks very well, such as the ability to drive a car, answer questions or to make health diagnoses, referred to as'narrow' or'weak' AI. Today, the hype around artificial intelligence (AI) is ramping up, especially as big tech companies like Apple, Amazon, Google, Facebook, IBM and Microsoft attempt to commercialize its use. Digital Ad Agencies are also starting to figure out how they can leverage Artificial Intelligence techniques to make their clients' marketing and advertising efforts more effective. So far in 2016, Artificial Intelligence technology has grabbed headlines as the focus of Apple's first acquisition--in the form of Emotient Inc--and Facebook CEO Mark Zuckerberg has resolved to build an AI assistant to run his home and help him at work. Google has also gone down in the history books after its DeepMind team developed an AI program capable of defeating human world champions of complex Chinese board game Go. It's an achievement reminiscent of IBM's milestone moment when its cognitive system IBM's Watson thrashed human contestants in the U.S. game show Jeopardy in 2011. Watson was custom-built to process natural language and reason its way through information.

Working in the field of big data and AI means that I see the leading edge advances that come with it. It also means routinely getting freaked out when you think too closely about the possibilities and implications of those advances and where they might be taking us. Robots and AIs Will Take Our Jobs This isn't just science fiction, it's happening now. Manufacturing are the first places we see robots and automation eliminating human jobs, but it's hard to think of an industry that will be left unaffected as robots and AI become more affordable and widespread. It's estimated that between 35 and 50 percent of jobs that exist today are at risk of being lost to automation.

One of my favorite small projects, EmailFiler, was motivated by a school assignment for Georgia Tech's Intro to Machine Learning class. Basically, the assignment was to pick some datasets, throw a bunch of supervised learning algorithms at them, and analyze the results. But here's the thing: we could make our own datasets if we so chose. And so choose I did - to export my gmail data and explore the feasibility of machine-learned email categorization. See, I learned long ago that it's often best to keep emails around in case there is randomly some need to refer back to them in the future.

My last interview for this year is with Steve Lohr. Steve Lohr has covered technology, business, and economics for the New York Times for more than twenty years. In 2013 he was part of the team awarded the Pulitzer Prize for Explanatory Reporting. We discussed Big Data and how it influences the new Artificial Intelligence awakening. Steve Lohr: Both Google and Microsoft are contributing their tools to expand and enlarge the AI community, which is good for the world and good for their businesses.