Artificial Intelligence (AI) is solving problems that seemed well beyond our reach just a few years back. Using deep learning, the fastest growing segment of AI, computers are now able to learn and recognize patterns from data that were considered too complex for expert written software. Today, deep learning is transforming every industry, including automotive, healthcare, retail and financial services. Enterprises, and their leaders, looking to get started should first get familiar with the fundamentals of deep learning, and as well as understand the current challenges and how to address them. This crash course provides a starting point, as well as practical guidance on next steps.

As the tail end of the Millennial generation enters the workforce, considers having children, and cements itself in the world at large, it is without question that Millennials are going to experience a unique future compared to Generation X or any other generation that came before them for that matter. The world itself has evolved tremendously in recent years, with one of the biggest changes being the introduction of more advanced technology and the accessibility of Artificial Intelligence (AI) to consumers and businesses. The generation that people love to hate is known for being early adopters of new technologies; however, it remains to be seen exactly how AI will shape the post-adolescent lives of 80 million Millennials which has become the largest age grouping in American history. Unlike Robotic Process Automation (RPA), AI is self-learning, meaning it adapts and learns as it goes. RPA, however, performs the tasks it was programmed to do and does not adapt to changes by itself.

A common problem for any scaling business is bringing on the right help at the right time. Small-business owners often find that the people they can afford don't have the skills or experience to do the job well, and the people they want cost too much relative to the business's current cash flow. Most of the work is labor intensive, repetitive, boring and must be done correctly. As luck would have it, that's exactly the kind of tasks we build computers to do. In the past 15 or so years, outsourcing to international assistants and part-time workers has been the go-to solution for entrepreneurs looking to grow on a budget.

You've probably been told that the singularity is coming. It is that long-awaited point in time -- likely, a point in our very near future -- when advances in artificial intelligence lead to the creation of a machine (a technological form of life?) smarter than humans. If Ray Kurzweil is to be believed, the singularity will happen in 2045. If we throw our hats in with Louis Rosenberg, then the day will be arriving a little sooner, likely sometime in 2030. MIT's Patrick Winston would have you believe that it will likely be a little closer to Kurzweil's prediction, though he puts the date at 2040, specifically.

To counter the threats posed by small drones, the U.S. military may have to rapidly step up its R&D timeframes, according to a new report commissioned by the U.S. Army. Small unmanned aircraft systems (sUASs) have become increasingly affordable and sophisticated. With millions of these drones now available worldwide, "It's become very easy for an adversary to use them in nefarious ways," says Albert Sciarretta, chair of the committee behind the new study and president of CNS Technologies in Springfield, Virginia. The U.S. Army asked for a detailed report from the National Academies of Sciences, Engineering, and Medicine that analyzes potential risks from these devices, especially to dismounted infantry (that is, foot soldiers) and lightly armored vehicles. For example, hobby drones could be fitted with lethal weapons such as explosive, chemical, biological, or radiological payloads--or modified to jam military radio signals, Sciarretta says.

The majority of Uber and Lyft drivers make less than minimum wage according to study published by MIT. They may actually end up losing money. Uber and Lyft both are making in-roads with heathcare providers that offer rides to those in need of transportation to doctors appointments. SAN FRANCISCO -- It's no good having health care if you can't make the appointment. For house-bound patients, the answer may increasingly come in the form of an Uber or Lyft -- rather than a medical-transport van or taxi.

The logarithm is an interesting character. Let us only understand its personality applicable for regression models. The fundamental property of a logarithm is its base. The typical base of the logarithm is 2, 10 or e. There is another common base for logarithms.

The difference between the two pictures is that the one on the right has been tweaked a bit by an algorithm to make it difficult for a type of computer model called a convolutional neural network (CNN) to be able to tell what it really is. In this case, the CNN think it's looking at a dog rather than a cat, but what's remarkable is that most people think the same thing. This is an example of what's called an adversarial image: an image specifically designed to fool neural networks into making an incorrect determination about what they're looking at. Researchers at Google Brain decided to try and figure out whether the same techniques that fool artificial neural networks can also fool the biological neural networks inside of our heads, by developing adversarial images capable of making both computers and humans think that they're looking at something they aren't. Visual classification algorithms powered by convolutional neural networks are commonly used to recognize objects in images.

In marketing we are often confronted with a continuous stream of responses to marketing messages. Such streaming data provide invaluable information regarding message effectiveness and segmentation. However, streaming data are hard to analyze using conventional methods: their high volume and the fact that they are continuously augmented means that it takes considerable time to analyze them. We propose a method for estimating a finite mixture of logistic regression models which can be used to cluster customers based on a continuous stream of responses. This method, which we coin oFMLR, allows segments to be identified in data streams or extremely large static datasets. Contrary to black box algorithms, oFMLR provides model estimates that are directly interpretable. We first introduce oFMLR, explaining in passing general topics such as online estimation and the EM algorithm, making this paper a high level overview of possible methods of dealing with large data streams in marketing practice. Next, we discuss model convergence, identifiability, and relations to alternative, Bayesian, methods; we also identify more general issues that arise from dealing with continuously augmented data sets. Finally, we introduce the oFMLR [R] package and evaluate the method by numerical simulation and by analyzing a large customer clickstream dataset.