Herein we define a measure of similarity between classification distributions that is both principled from the perspective of statistical pattern recognition and useful from the perspective of machine learning practitioners. In particular, we propose a novel similarity on classification distributions, dubbed task similarity, that quantifies how an optimally-transformed optimal representation for a source distribution performs when applied to inference related to a target distribution. The definition of task similarity allows for natural definitions of adversarial and orthogonal distributions. We highlight limiting properties of representations induced by (universally) consistent decision rules and demonstrate in simulation that an empirical estimate of task similarity is a function of the decision rule deployed for inference. We demonstrate that for a given target distribution, both transfer efficiency and semantic similarity of candidate source distributions correlate with empirical task similarity.

Artificial intelligence is continuously evolving and propagating across every industry. With much of the groundbreaking innovations moving the industry forward, the technology is continuously making headlines every day. AI refers to software or systems that perform intelligent tasks like those of human brains such as learning, reasoning, and judgment. Its applications range from automation and translation systems for natural languages that people use daily, to image recognition systems that help identify faces and letters from images. Today, AI is used in different forms include digital assistants, chatbots and machine learning, among others.

At any given moment in time, our brain is involved in various activities. For example, when typing on a keyboard, our brain not only dictates our finger movements but also how thirsty we feel at that time. As a result, brain signals contain dynamic neural patterns that reflect a combination of these activities simultaneously. A standing challenge has been isolating those patterns in brain signals that relate to a specific behavior, such as finger movements. Further, developing brain-machine interfaces (BMIs) that help people with neurological and mental disorders requires the translation of brain signals into a specific behavior, a problem called decoding.

If you are a non-technical person learning a technical subject or trying to understand a technical field such as Artificial Intelligence (AI), let me share a simple tip that will help you a lot. Don't let the complex-sounding technical terms confuse you. In due course, you will get comfortable with terminologies if you focus on first principles and try to get an intuitive understanding of the concepts. So, let's start with the basics and some working definitions. I've noticed the specific phrase "artificial intelligence and machine learning" and its shorthand AI/ML being used quite often.

The first three entries in my "2020: A consumer electronics forecast for the year(s) ahead" piece, published back in January, all had to do with deep learning. Why? Here's part of what I wrote back then: The ability to pattern-match and extrapolate from already-identified data ("training") to not-yet-identified data ("inference") has transformed the means by which many algorithms are developed nowadays, with impact on numerous applications. This transformation is already well underway, as even a casual perusal of the titles and coverage topics of content published at EDN, EE Times, and elsewhere will make clear. Don't panic: there's still time to "catch the wave," especially if your focus is on resource-constrained implementations. But you don't want to wait too long lest you end up stuck bobbing around in the water while more foresighted colleagues are already at the beach enjoying the AI "party."

Artificial intelligence is continuously evolving and propagating across every industry. With much of the groundbreaking innovations moving the industry forward, the technology is continuously making headlines every day. AI refers to software or systems that perform intelligent tasks like those of human brains such as learning, reasoning, and judgment. Its applications range from automation and translation systems for natural languages that people use daily, to image recognition systems that help identify faces and letters from images. Today, AI is used in different forms include digital assistants, chatbots and machine learning, among others.

One of the most common applications of machine learning is pattern recognition. Computers that use well-trained algorithms recognize animals in photos, anomalies in stock fluctuations, and signs of cancer in mammograms much better than humans. Let us find out what lies behind this complex process. Pattern recognition is the process of recognizing regularities in data by a machine that uses machine learning algorithms. In the heart of the process lies the classification of events based on statistical information, historical data, or the machine's memory.

Cloud computing is one of the sub-sectors in technology that should continue to thrive as a heavy reliance on tech continues amid the pandemic. When you throw artificial intelligence (AI) into the mix, the cloud computing space becomes even more of a disruptive force. "As far as cloud goes, AI is a key enabler of several ways in which we can expect technology to adapt to our needs throughout 2021," a Forbes article noted. "Cloud-based as-a-service platforms enable users on just about any budget and with any level of skill to access machine learning functions such as image recognition tools, language processing, and recommendation engines. Cloud will continue to allow these revolutionary toolsets to become more widely deployed by enterprises of all sizes and in all fields, leading to increased productivity and efficiency."

Software debugging has been shown to utilize upwards of 50% of developers' time. Machine programming, the field concerned with the automation of software (and hardware) development, has recently made progress in both research and production-quality automated debugging systems. In this paper, we present ControlFlag, a system that detects possible idiosyncratic violations in software control structures. ControlFlag also suggests possible corrections in the event a true error is detected. A novelty of ControlFlag is that it is entirely self-supervised; that is, it requires no labels to learn about the potential idiosyncratic programming pattern violations. In addition to presenting ControlFlag's design, we also provide an abbreviated experimental evaluation.

Pattern recognition is the process of recognizing regularities in data by a machine that uses machine learning algorithms. In the heart of the process lies the classification of events based on statistical information, historical data, or the machine's memory. A pattern is a regularity in the world or in abstract notions. If we talk about books or movies, a description of a genre would be a pattern. If a person keeps watching black comedies, Netflix wouldn't recommend them heartbreaking melodramas.