Pattern discovery, the process of discovering previously unrecognized patterns, is often performed as an ad-hoc process with little resulting certainty in the quality of the proposed patterns. Pattern validation, the process of validating the accuracy of proposed patterns, remains dominated by the simple heuristic of "the rule of three". This article shows how to use established scientific research methods for the purpose of pattern discovery and validation. We present a specific approach, called the handbook method, that uses the qualitative survey, action research, and case study research for pattern discovery and evaluation, and we discuss the underlying principle of using scientific methods in general. We evaluate the handbook method using three exploratory studies and demonstrate its usefulness.

Z Advanced Computing, Inc. (ZAC), the pioneer Cognitive Explainable-AI (Artificial Intelligence) (Cognitive XAI) software startup, has made AI and Machine Learning (ML) breakthroughs: ZAC has achieved 3D Image Recognition using only a few training samples, and using only an average laptop with low power CPU, for both training and recognition, for the US Air Force (USAF). This is in sharp contrast to the other algorithms in industry that require thousands to billions of samples, being trained on large GPU servers. "ZAC requires much less computing power and much less electrical power to run, which is great for mobile and edge computing, as well as environment, with less Carbon footprint," emphasized Dr. Saied Tadayon, CTO of ZAC. ZAC is the first to demonstrate the novel and superior algorithms Cognition-based Explainable-AI (XAI), where various attributes and details of 3D (three dimensional) objects are recognized from any view or angle. "You cannot do this task with the other algorithms, such as Deep Convolutional Neural Networks (CNN) or ResNets, even with an extremely large number of training samples, on GPU servers. That's basically hitting the limitations of CNNs or Neural Nets, which all other companies are using now," said Dr. Bijan Tadayon, CEO of ZAC.

Machine learning is a subset of artificial intelligence. The concept has evolved from computational learning and pattern recognition in artificial intelligence. It explores the construction and study of algorithms and carries out forecasts on data. Machine Learning Market research is an intelligence report with meticulous efforts undertaken to study the right and valuable information. The data which has been looked upon is done considering both, the existing top players and the upcoming competitors.

This stock forecast is part of the Stocks Under 5 Dollars Package, as one of I Know First's algorithmic trading tools. Package Name: Stocks Under $5 Recommended Positions: Long Forecast Length: 3 Months (3/28/21 – 6/28/21) I Know First Average: 27.44% In this 3 Months forecast for the Stocks Under $5 Package, there were many high performing trades and the algorithm correctly predicted 10 out 10 trades. SB was the highest-earning trade with a return of 50.38% in 3 Months. Other notable stocks were EDN and ENOB with a return of 41.71% and 35.0%.

MIT's Hockfield Court is bordered on the west by the ultramodern Stata Center, with its reflective, silver alcoves that jut off at odd angles, and on the east by Building 68, which is a simple, window-lined, cement rectangle. At first glance, Bonnie Berger's mathematics lab in the Stata Center and Joey Davis's biology lab in Building 68 are as different as the buildings that house them. And yet, a recent collaboration between these two labs shows how their disciplines complement each other. The partnership started when Ellen Zhong, a graduate student from the Computational and Systems Biology (CSB) Program, decided to use a computational pattern-recognition tool called a neural network to study the shapes of molecular machines. Three years later, Zhong's project is letting scientists see patterns that run beneath the surface of their data, and deepening their understanding of the molecules that shape life.

Gradual pattern mining allows for extraction of attribute correlations through gradual rules such as: "the more X, the more Y". Such correlations are useful in identifying and isolating relationships among the attributes that may not be obvious through quick scans on a data set. For instance, a researcher may apply gradual pattern mining to determine which attributes of a data set exhibit unfamiliar correlations in order to isolate them for deeper exploration or analysis. In this work, we propose an ant colony optimization technique which uses a popular probabilistic approach that mimics the behavior biological ants as they search for the shortest path to find food in order to solve combinatorial problems. In our second contribution, we extend an existing gradual pattern mining technique to allow for extraction of gradual patterns together with an approximated temporal lag between the affected gradual item sets. Such a pattern is referred to as a fuzzy-temporal gradual pattern and it may take the form: "the more X, the more Y, almost 3 months later". In our third contribution, we propose a data crossing model that allows for integration of mostly gradual pattern mining algorithm implementations into a Cloud platform. This contribution is motivated by the proliferation of IoT applications in almost every area of our society and this comes with provision of large-scale time-series data from different sources.

A lip-reading app from Irish startup Liopa is said to represent a breakthrough in the field of visual speech recognition (VSR), which trains AI to read lips without any audio input. Liopa's product, SRAVI (Speech Recognition App for the Voice Impaired) is a communication aid for speech-impaired patients. It is likely to be the first lip-reading AI app available for public purchase, according to an account from Vice/Motherboard. Researchers driven by a range of potential commercial applications including surveillance tools have been working for years to teach computers to lip-read, and it has proven a challenging task. Liopa is working to certify SRAVI as a Class I medical device in Europe, hoping to complete the certification by August.

It's hard to imagine the world without all the technology of today that have transformed the way we live, work and play. It wasn't long ago when we had to stop to look at a map or ask for directions. It used to take days to see a doctor, but with the power of technology, like wearable digital health meters, you can get information to and from your doctor in real time. Video games meant playing a simple game of "Pac-Man" to now being able to be virtually transported to be part of a game. These examples play a role in how digital experiences have enriched and changed the way we live our daily lives.

Handwritten digit recognition is one of the extensively studied area in machine learning. Apart from the wider research on handwritten digit recognition on MNIST dataset, there are many other research works on various script recognition. However, it is not very common for multi-script digit recognition which encourage the development of robust and multipurpose systems. Additionally working on multi-script digit recognition enables multi-task learning, considering the script classification as a related task for instance. It is evident that multi-task learning improves model performance through inductive transfer using the information contained in related tasks. Therefore, in this study multi-script handwritten digit recognition using multi-task learning will be investigated. As a specific case of demonstrating the solution to the problem, Amharic handwritten character recognition will also be experimented. The handwritten digits of three scripts including Latin, Arabic and Kannada are studied to show that multi-task models with reformulation of the individual tasks have shown promising results. In this study a novel way of using the individual tasks predictions was proposed to help classification performance and regularize the different loss for the purpose of the main task. This finding has outperformed the baseline and the conventional multi-task learning models. More importantly, it avoided the need for weighting the different losses of the tasks, which is one of the challenges in multi-task learning.

This is the consensus view of an MIT Technology Review Insights survey of 210 members of technology executives, conducted in March 2021. These respondents report that they need--and still often lack-- the ability to develop new digital channels and services quickly, and to optimize them in real time. Underpinning these waves of digital transformation are two fundamental drivers: the ability to serve and understand customers better, and the need to increase employees' ability to work more effectively toward those goals. Two-thirds of respondents indicated that more efficient customer experience delivery was the most critical objective. This was followed closely by the use of analytics and insight to improve products and services (60%).