"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
"MLOps (a compound of Machine Learning and "information technology OPerationS") is [a] new discipline/focus/practice for collaboration and communication between data scientists and information technology (IT) professionals while automating and productizing machine learning algorithms." The understanding of the machine learning lifecycle is constantly evolving. When I first saw graphics illustrating this "cycle" years ago, the emphasis was on the usual suspects (data prep and cleaning, EDA, modeling etc…). Less notice was given to the more elusive and less tangible final state -- often termed "deployment", "delivery" or in some cases just "prediction". At the time, I don't think a lot of rising data scientists really considered the sheer scope of that last term (I sure as hell didn't).
One is a renowned German Corporate Performance Management (CPM) analyst, the other is the head of a global CPM company. We sat down with two influential figures in the international CPM market and asked them five questions about CPM, technology, and the future. As new technologies continue to unfold, the future of CPM is ever-changing. Many finance professionals and executives are left wondering: Which technologies are simply a fad and which have the potential to turn the world on its head? Why not let the experts speculate?
You know a technology has reached a tipping point when your kids ask about it. This happened recently when my eighth grade daughter asked, "What is Machine Learning and why is it so important?". Answering her question, I explained how Machine Learning is part of AI, where we teach machines to reason and learn like human beings. I used the example of fraud detection. In many ways catching fraud is like finding needles in a haystack – you must sort and make sense of massive amounts of data in order to find your "needles" or in this case, your fraudsters.
From autonomous vehicles, predictive analytics applications, facial recognition, to chatbots, virtual assistants, cognitive automation, and fraud detection, the use cases for AI are many. However, regardless of the application of AI, there is commonality to all these applications. Those who have implemented hundreds or even thousands of AI projects realize that despite all this diversity in application, AI use cases fall into one or more of seven common patterns. The seven patterns are: hyperpersonalization, autonomous systems, predictive analytics and decision support, conversational/human interactions, patterns and anomalies, recognition systems, and goal-driven systems. Any customized approach to AI is going to require its own programming and pattern, but no matter what combination these trends are used in, they all follow their own pretty standard set of rules.
The use of domain generation algorithms (DGAs) to spread malware is on the rise. A key reason why is because DGAs are notoriously hard to detect using conventional security methods. But just what makes them so elusive? And how can security professionals stay ahead of them? That's just some of what you'll learn when you download Using Artificial Intelligence/Machine Learning to Detect Domain Generation Algorithms.
This 2018 study found that realization rates average 81%, and collection rates average only 85%. This means law firms are missing out on 31.2% of hours expended. Let's say a firm has 1000 hours logged in its time tracking system for a given month. At $200 per hour the firm would be owed $200,000. But the average realization rate of 81% means that only 810 hours actually get billed.
With turnover rates in technological fields remaining around 10%, there is a clear need for tech companies to hire and retain talented machine learning engineers. While it is advantageous to find eager workers from home, there is an alternative that offers many benefits: hiring F1-OPT machine learning engineers. You may be asking why you should do this, and more importantly, what it means to be a F1-OPT machine learning engineer. Fortunately, we have the answers to these questions. In order to qualify as a F1-OPT engineer, an individual must be an international student who recently received their advanced engineering degree(s) in the United States.
There are two key aspects of artificial intelligence (AI) that you should be aware of. First, it's being designed into an increasing percentage of embedded systems at the deep edge of the network, from industrial controls to automotive applications to consumer/mass market devices. So there's a good chance you'll be needing a primer on how to work with these AI-related components. The second aspect is that designing around AI is potentially a complex endeavor. And that's where we come in.
Nvidia Corp. is upping its artificial intelligence game with the release of a new version of its TensorRT software platform for high-performance deep learning inference. TensorRT is a platform that combines a high-performance deep learning inference optimizer with a runtime that delivers low-latency, high-throughput inference for AI applications. Inference is an important aspect of AI. Whereas AI training relates to the development of an algorithm's ability to understand a data set, inference refers to its ability to act on that data to infer answers to specific queries. The latest version brings with it some dramatic improvements on the performance side.
Earth is enormous, and while humans have done a decent job of being able to map out the boundaries of countries and states, the roads in our cities and the location of geological sightseeing destinations, there remains a lot of the world that isn't precisely figured out. But a new project from Wenwen Li, associate professor in the School of Geographical Sciences and Urban Planning, aims to learn more about our world and its varying terrain by applying artificial intelligence. Artificial intelligence, or AI, has already made an indelible impact in daily life. From knowing our commutes or being able to suggest new shoes, what we divulge about ourselves and our habits has created a framework of information as it reveals hidden patterns in how we conduct our lives. The same can be true for our natural world as AI can help to reveal the patterns we haven't yet discovered.