Continuous learning using DYNAMICAL machine learning is ready for implementation today; it adds the following FOUR benefits: 1. Learning in real-time: Just like children mature over time . . . 2. As machines age, ML adapts to NEW normal: Designed for long-term use. 3. Underlying system “states” provide a meta-model: A more stable description; less False Positives. 4. System “states” as “Digital Twin”: Leading to continuous “closed-loop” performance improvement

What changes in your analytics and Big Data stack would you have to make if you only had 10 milliseconds to make a decision? There’s an entire industry that has to live by that rule. This is a great story about the collision of ecommerce, digital advertising, predictive analytics, and AI in a new digital battleground, the automation and optimization of advertising targeting and spend. Like so many interesting opportunities this new market starts with a pain point and an unmet

If you’re a business executive (rather than a data scientist or machine learning expert), you’ve probably been exposed to the mainstream media coverage of artificial intelligence or AI. You’ve seen articles in The Economist and Vanity Fair, you’ve seen emotional stories about Tesla Autopilot and the threat of AI to mankind by such luminaries as Stephen Hawking, and you might even have seen Dilbert make jokes about Artificial Intelligence and Human Intelligence. So if you’re

Summary: The data science press is so dominated by articles on AI and Deep Learning that it has led some folks to wonder whether Deep Learning has made traditional machine learning irrelevant. Here we explore both sides of that argument. On Quora the other day I saw a question from an aspiring data scientist that asked – since all the competitions on Kaggle these days are being won by Deep Learning algorithms, does it even make sense to bother studying traditional machine l

As AI enters our homes through smart home devices or tries to conquer our streets through self-driving cars, one need not be a Luddite to contemplate the potentially heavy implications of AI upon our daily lives and livelihood. The key to answering the question and indeed to understand the ultimate limits of AI is to ask if machines can really think. In this article, I list three tests drawn from three different disciplines to address that query. Turing Test from Theoretical

If you haven’t started using artificial intelligence in your business, you’re falling behind on the curve. Many business owners today are leveraging AI, whether they are aware of it or not. This is done through everyday business software suites that integrate machine learning and automation to carry out such functions as email communications, voice recognition and response and predictive analysis. The extent to which businesses employ AI solutions needs to be increased if the

In this article, I cover the 12 types of AI problems i.e. I address the question : in which scenarios should you use Artificial Intelligence (AI)? Some background: Recently, I conducted a strategy workshop for a group of senior executives running a large multi national. In the workshop, one person asked the question: How many cats does it need to identify a Cat? This question is in reference to Andrew Ng’s famous paper on Deep Learning where he was correctly able to identify

The differences between Data Scientists, Data Engineers, and Software engineers can get a little confusing at times. Thus, here is a guest post provided by Jake Stein, CEO at Stitch formerly RJ Metrics, which aims to clear up some of that confusion based upon LinkedIn data. As data grows, so does the expertise needed to manage it. The past few years have seen an increasing distinction between the key roles tasked with managing data: software engineers, data engineers, and dat

Artificial General Intelligence (AGI) is the long sought after ‘brain’ that brings together all the branches of AI into a general purpose platform that can perform with human level intelligence in a broad variety of tasks. Will it free us or replace us? It’s closer than you think. Can you see the forest for the trees? That’s one of the questions an AI system might be asked to answer and probably not succeed. However, if you’re trying to follow the general trends and literat

Deep learning is sometimes referred to as the intersection between machine learning and artificial intelligence. It is about designing algorithms that can make robots intelligent, such a face recognition techniques used in drones to detect and target terrorists, or pattern recognition / computer vision algorithms to automatically pilot a plane, a train, a boat or a car. Many deep learning algorithms (clustering, pattern recognition, automated bidding, recommendation engine, a

Predictive analytics aren’t always correct—they’re simply a best guess based on previously established patterns—but they are often reliable, especially if there is a good amount of data to be analyzed. Before learning about predictive analytics, you may be asking what is analytics? Analytics at its core is simply a detailed examination of something, often data. That data is then interpreted. Predictive analytics simply focuses that interpretation on the future. If you want

In this interview (in Spanish) Miguel Molina-Cosculluela (@mikeaingeru), Managing Partner and Analytics Evangelist at Analytikus explains how companies can generate value through the use of Analytics. Gabriela Frias (@gfrias) reporter at CNN sets up the context by explaining how companies such as Ford, LinkedIn or Netflix are using Data to gain competitive advantages. One of the topics discussed was how Analytics have evolved, from Descriptive Analytics, or what we usually ca