Collaborating Authors

GitHub - ludwig-ai/ludwig: Data-centric declarative deep learning framework


Ludwig is a declarative machine learning framework that makes it easy to define machine learning pipelines using a simple and flexible data-driven configuration system. Ludwig is suitable for a wide variety of AI tasks, and is hosted by the Linux Foundation AI & Data. The configuration declares the input and output features, with their respective data types. Users can also specify additional parameters to preprocess, encode, and decode features, load from pre-trained models, compose the internal model architecture, set training parameters, or run hyperparameter optimization. Ludwig will build an end-to-end machine learning pipeline automatically, using whatever is explicitly specified in the configuration, while falling back to smart defaults for any parameters that are not.

Machine Teaching for Autonomous AI


Just as teachers help students gain new skills, the same is true of artificial intelligence (AI). Machine learning algorithms can adapt and change, much like the learning process itself. Using the machine teaching paradigm, a subject matter expert (SME) can teach AI to improve and optimize a variety of systems and processes. The result is an autonomous AI system. In this course, you'll learn how automated systems make decisions and how to approach building an AI system that will outperform current capabilities.

Optimizing Machine Learning Performance


This course synthesizes everything your have learned in the applied machine learning specialization. You will now walk through a complete machine learning project to prepare a machine learning maintenance roadmap. You will understand and analyze how to deal with changing data. You will also be able to identify and interpret potential unintended effects in your project. You will understand and define procedures to operationalize and maintain your applied machine learning model.

Understanding The Data Types For Machine Learning And Data Science - MarkTechPost


Machine learning (a subfield of AI) aims to program computers to learn and grow as people do. Machine learning may automate virtually any activity that can be solved using a pattern or set of data-developed rules. It's crucial to have a firm grasp of the various data kinds to clean and preprocess the data in preparation for use with ML algorithms. For machines to recognize patterns in data, it must first be translated into a numerical representation. This will allow us to pick the top-performing models that can quickly and accurately identify the underlying patterns.

Humanoid robots won't roam our streets any time soon - Verdict


Humanoid robots have long been a common staple of science fiction. Arnold Schwarzenegger killing machines and synthethics like Bishop from the Aliens movies have long been confined to realms of fantasy. However, a wave of innovative tech companies are actively trying to make those visions a reality. "There is a chance that one day life will imitate art and robots and people will look alike," according to a recent report from research firm GlobalData. "If and when that happens, societies will face an ethical conundrum: what rights to give to non-human creatures that look like us?"

The task of magnetic classification suddenly looks easier, thanks to machine learning


Knowing the magnetic structure of crystalline materials is critical to many applications, including data storage, high-resolution imaging, spintronics, superconductivity, and quantum computing. Information of this sort, however, is difficult to come by. Although magnetic structures can be obtained from neutron diffraction and scattering studies, the number of machines that can support these analyses--and the time available at these facilities--is severely limited. As a result, the magnetic structures of only about 1,500 materials worked out experimentally have been tabulated to date. Researchers have also predicted magnetic structures by numerical means, but lengthy calculations are required, even on large, state-of-the-art supercomputers.

Decoding An efficient IoT based smart farming system using machine learning algorithms


Agriculture has recently been identified as one of the main strengths of the global and national economies. Farming is one of the most important jobs in the world, and the product crucial is the diversity of crops. With the growth in population, agriculture has faced concerns that may endanger its future, such as drought, crop quality and productivity issues, and yield projection issues. Agriculture is undergoing a significant revolution in the collection and use of data to inform effective agricultural choices. Smart farming is the application of current Information and Communication Technology (ICT) in agriculture, such as machine learning algorithms, and the rationalization of natural resource usage, as a capital-based system, advanced technology in food growing in sustainable and clean methods.

Needed: More Worker Involvement In Artificial Intelligence Initiatives


Despite all the panicky warnings seen in the mainstream media, AI will not be taking over and automating peoples' jobs. AI will be replacing manual tasks, not job categories. However, something very important is missing from the picture: the involvement of the employees who will be charged with making AI and data-driven enterprises work. AI is only ramping up demand for the human talent needed to guide AI systems to engage in tasks relevant to the business, monitor and maintain the fairness and actionability of AI decisions, and to build, program, update, and ultimately retire these systems. That's one of the takeaways of Deloitte's latest research on the state of AI, which finds a lack of employee input into the ways AI will be deployed and what it will deliver.

After demons and death gods, 'Warcraft' gets sunny with 'Dragonflight'

Washington Post - Technology News

In an interview, two "World of Warcraft" developers talk fan expectations and controversial plot beats — and drop hints about the future of cross-faction play.

The Roomba j7 , a Black Friday best-seller, is even cheaper for Cyber Monday


Save $290: As of Nov. 28, the iRobot Roomba j7 (opens in a new tab) is available for $509.99 (typically $799.99) at Amazon thanks to a new Cyber Monday coupon applied at checkout on top of its previous Black Friday price. The best-selling Roomba on Black Friday just got a Cyber Monday second wind in the form of extra savings. Amazon has quietly tacked an extra $89.01 coupon to the Roomba j7 (opens in a new tab) on top of its previous Black Friday price of $599, where it has been chilling on and off throughout November. The coupon is reflected at checkout and brings the robot vac down to just $509.99 -- a $290 (35%-ish) total price cut. Mashable readers didn't hesitate to add the smartest Roomba to their cart when they saw it on sale for Black Friday.