Things, not Strings Entity-centric views on enterprise information and all kinds of data sources provide means to get a more meaningful picture about all sorts of business objects. This method of information processing is as relevant to customers, citizens, or patients as it is to knowledge workers like lawyers, doctors, or researchers. People actually do not search for documents, but rather for facts and other chunks of information to bundle them up to provide answers to concrete questions. Strings, or names for things are not the same as the things they refer to. Still, those two aspects of an entity get mixed up regularly to nurture the Babylonian language confusion.

When creating a new AI-based app, there are many generic problems that are already being solved by other companies, for example face and gesture detection. Unless this is the main business and focus of the company, they will prefer to look for an out-of-the-box AI-as-a-service solution which will save them time, expertise and money. This type of solutions are called AI platforms and give their users many out-of-the-box services, such as computer vision (feature/face and gesture detection), natural language processing (NLP), speech to text, and translations between different language. See also: Artificial intelligence: a force for good or bad? Many companies including Google and Amazon sell this kind of AI services.

WHAT: French grocery chain Monoprix creates an almost exact remake of Amazon's recent promo video for its tech-powered grocery store of the future. WHY WE CARE: Not long ago, Amazon unveiled its plans for a beta version of its new cashierless grocery shopping experience called Go. Here, Monoprix sidesteps all the "computer vision," "deep learning algorithms," and "sensor fusion much like you'd find in self-driving cars" that Amazon touted about Go, and instead trolls the tech giant with an almost exact remake of the Go promo--actor doppelgangers dressed in the same outfits, similarly framed shots--with a human solution to the whole cashier line-up problem. And they deliver your groceries in an hour. Is there an Amazon drone for that yet?

A fleet of self-driving Uber cars left for Arizona on Thursday after they were banned from California roads over safety concerns. The announcement came after Arizona Gov. Doug Ducey took to social media on Wednesday and Thursday touting Arizona as an alternative to California for the ride-hailing company to test out its self-driving cars. Ducey, a Republican, sent tweets advertising Arizona's friendly business environment, saying Uber should ditch California for the Grand Canyon state. Uber said in a statement that it had shipped its cars to Arizona and will be expanding its self-driving pilot program in the next few weeks. The company hasn't announced a date when the cars will be tested, nor did it provide details about how many cars were included. Uber previously had 16 self-driving cars registered in California.

One of my favorite deep learning papers is Learning to Generate Chairs, Tables, and Cars with Convolutional Networks. It's a very simple concept – you give the network the parameters of the thing you want to draw and it does it – but it yields an incredibly interesting result. The network seems like it is able to learn concepts about 3D space and the structure of the objects it's drawing, and because it's generating images rather than numbers it gives us a better sense about how the network "thinks" as well. I happened to stumble upon the Radboud Faces Database some time ago, and wondered if something like this could be used to generate and interpolate between faces as well. To implement this, I adapted a version of the "1s-S-deep" model from the chairs paper.

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.