The new patented method increases video compression factor by about 3 times, not 40%, against the existing modern standards like H.265, VP9 and AV1, with the same video quality.

Deployment of the Educast™ service on the ARK Broadcast Internet Network of NextGen TV stations

Future Electronics hosted one of the industry's most Advanced hands-on and interactive learning events in Boston for Tech Day 2023.

Prateek Agarwal serves as a technical lead at Tata Consultancy Services Ltd

Sai Sharanya Nalla recognized as an AI expert with over a decade of experience, including key roles at Nike, AWS and Amex.

Robert Daly serves as a database solutions architect at Amazon Web Services.

NetCom Learning achieves this prestigious recognition for the first time, highlighting its commitment to providing innovative, high-quality online learning resources and its growing impact in the e-learning industry.

Fenix Commerce will expand its AI & ML capabilities to generate even more incremental revenue to its customers

Google's new Learn About tool offers an interactive approach to learning about topics, including heady questions like whether money buys happiness.

The landscape of AI is rapidly evolving, presenting both opportunities and challenges. From its historical roots to the current AI wars and the pursuit of Artificial General Intelligence (AGI), AI is a force to be reckoned with. Despite remarkable advancements, current AI systems face limitations in adaptive learning and memory, sparking a paradigm shift towards creating more human-like capabilities.

The post Game-Changing Paradigm Shift in Machine Learning! appeared first on WPCult.

BOSTON (October 22, 2024) -- The Free Software Foundation (FSF) has announced today that it is working on a statement of criteria for free machine learning applications, which will require the software, as well as the raw training data and associated scripts, to grant users the four freedoms.

ETSI releases a Technical Report on autonomic network management and control applying machine learning and other AI algorithms

Sophia Antipolis, 5 March 2020

The ETSI Technical Committee on Core Network and Interoperability Testing (TC INT) has just released a Technical Report, ETSI TR 103 626, providing a mapping of architectural components for autonomic networking, cognitive networking and self-management. This architecture will serve the self-managing Future Internet.

The ETSI TR 103 626 provides a mapping of architectural components developed in the European Commission (EC) WiSHFUL and ORCA Projects, using the ETSI Generic Autonomic Networking Architecture (GANA) model.

The objective is to illustrate how the ETSI GANA model specified in the ETSI specification TS 103 195-2 can be implemented when using the components developed in these two projects. The Report also shows how the WiSHFUL architecture augmented with virtualization and hardware acceleration techniques can implement the GANA model. This will guide implementers of autonomics components for autonomic networks in their optimization of their GANA implementations.

The TR addresses autonomic decision-making and associated control-loops in wireless network architectures and their associated management and control architectures. The mapping of the architecture also illustrates how to implement self-management functionality in the GANA model for wireless networks, taking into consideration another Report ETSI TR 103 495, where GANA cognitive algorithms for autonomics, such as machine learning and other AI algorithms, can be applied.

scPROTEIN: a versatile deep graph contrastive learning framework for single-cell proteomics embedding  Nature.com

A multi-species benchmark for training and validating mass spectrometry proteomics machine learning models  Nature.com

Sunnyvale, CA – Deep learning will transform medicine, but not in the way that many advocates think. Biological complexity, rare mutations and confounding factors work against us, so that even if we sequence 100,000 genomes, it won’t be enough. Brendan Frey is engineering the future of personalized medicine. A professor in the University of Toronto’s Faculty […]

Toronto, ON –  Machine learning and artificial intelligence are poised to revolutionize the way companies do business in the fields of healthcare, transportation, and materials research. With the launch of the new Vector Institute, Toronto is quickly becoming a hub for machine learning development. Following this momentum is a three-part limited edition CIFAR seminar series, […]

Traditional machine vision used rule-based programming for controlled environments but struggled in less controlled settings. Edge learning AI, operating directly on vision systems, enhances machine vision's power and usability, revolutionizing quality assurance and manufacturing processes.

The aerospace industry constantly seeks new technologies for a competitive edge and enhanced capability. AI and maching learning in additive manufacturing offer significant value for meeting industry needs.

We explore two critical applications of AI and ML in quality management: predictive quality analytics and automated quality inspections.

NanKyu JALT (NanKyu Chapter of the Japan Association for Language Teaching). January 25 (Sat), 9:00-18:00 in Kumamoto. Edward Rubesch.

NanKyu JALT (NanKyu Chapter of the Japan Association for Language Teaching). January 25 (Sat) 2025, at SILC Building, Sojo University, Kumamoto.

Katharine Jarmul of DropoutLabs discusses security and privacy concerns as they relate to Machine Learning. Host Justin Beyer spoke with Jarmul about attack types and privacy-protected ML techniques.

Luis Ceze of OctoML discusses Apache TVM, an open source machine learning model compiler for a variety of different hardware architectures with host Akshay Manchale. Luis talks about the challenges in deploying models on specialized hardware and how TVM.

Ram Sriharsha of Pinecone discusses the role of vectors in machine learning, a technique that lies at the heart of many of the machine learning applications we use every day. Host Philip Winston spoke with Sriharsha about the basics of vectors, vector...

José Valim, creator of the Elixir programming language, Chief Adoption Officer at Dashbit, and author of three programming books, speaks with SE Radio host Gavin Henry about what Elixir is today, what Livebook is, the five spearheads of the new machine learning ecosystem for Elixir, and how they all fit together. Valim describes why he created Elixir, what “the beam” is, and how he pitches it to new users. This episode examines things you can do with Livebook and how it is well-aligned with machine learning, as well as why immutability is important and how it works. They take a detailed look at a range of topics, including tensors with Nx, traditional machine learning with Scholar, data munging with Explorer, deep learning and neural networks with Axon, Bumblebee and Huggingface, and model creation basics. Brought to you by IEEE Computer Society and IEEE Software magazine.

Catherine Nelson, author of the new O’Reilly book, Software Engineering for Data Scientists, discusses the collaboration between data scientists and software engineers -- an increasingly common pairing on machine learning and AI projects. Host Philip Winston speaks with Nelson about the role of a data scientist, the difference between running experiments in notebooks and building an automated pipeline for production, machine learning vs. AI, the typical pipeline steps for machine learning, and the role of software engineering in data science. Brought to you by IEEE Computer Society and IEEE Software magazine.

Packaging Strategies’ recent reporting on PACK EXPO International 2024 – though extensive – represents a mere fraction of the networking and expertise sharing that will take place at the event.

Groundbreaker Podcast associate producer Javed Mohammed [@JavedMohammed] first encountered Dr. Hulme in January 2020 at Oracle OpenWorld Middle East in Dubai, where Dr. Hulme, a featured speaker, delivered a session on “AI and the Future of Business” as part of the Transformational Technologies track. ”I was so impressed with his vision and his unconventional thinking,” Javed says. This program, which features Javed’s conversation with Dr. Hulme, grew out of their meeting in Dubai.

In this conversation Oracle Community Manager Javed Mohammed speaks with three AI-ML experts.

Autonomous technologies such as artificial intelligence (AI) and machine learning (ML) are on the tip of every tongue in tech. But what is the difference between AI and ML? What are interesting use cases? What is “under the hood” of AI/ML and the algorithms that power ML models?

We have three Subject Matter Experts who share their insights into a fascinating and at times humorous conversation.

• Charlie Berger, Sr. Director of Product Management for Machine Learning, AI and Cognitive Analytics at Oracle.
• Heli Helskyaho, CEO Miracle Finland  Oracle ACE Director, Groundbreaker Ambassador. Author. Doctoral student, University of Helsinki. Also known as HeliFromFinland.
• Katharine Jarmul, Head of Product at Cape Privacy, she is a Privacy activist, AI dissenter, machine learning engineer. Author and teacher for O'Reilly.

Listen to learn about what makes AI and ML solutions powerful as well as the challenges we face from them. Charlie, Heli and Katharine explain some of the fundamentals about this revolutionary technology but also share personal stories which make for a memorable Podcast.

Read the complete show notes here.

Red Bull Racing Honda and Oracle Team up on a Series of Machine Learning HOLs — First Lab for Beginners on Wednesday August 11 at 8 AM PST

Jim Grisanzio and Chris Bensen from Oracle Developer Relations preview the first in a series of unique hands-on labs. Starting on August 11 at 8 AM PST developers will have the opportunity to team up with Red Bull Racing Honda and Oracle in a hands-on lab that uses race data to teach machine learning. Video. 

Register for the lab here! Same link for on demand!

Podcast Host: Jim Grisanzio, Oracle Developer Relations
https://twitter.com/jimgris
https://developer.oracle.com/team/ 

Management requirements for federated machine learning systems

Functional architecture of machine learning-based quality of service assurance for the IMT-2020 network

Framework for data handling to enable machine learning in future networks including IMT-2020

Architectural framework for machine learning model serving in future networks including IMT-2020

ITU-T Y.3800-series - Quantum key distribution networks - Applications of machine learning

FIGI - DFS - Big data machine learning consumer protection and privacy

TR.sgfdm - FHE-based data collaboration in machine learning

Technical framework for the shared machine learning system

What it is: Recently, the good people at SAM Labs sent me an Alpha Kit to play with and review. You guys, this is such a cool product! I love that as soon as students open it up, it puts them in charge of the learning. Best of all, it encourages the learning to happen...

How are AI/ML strategies evolving to meet tomorrow’s mission?

The post Federal Executive Forum Artificial Intelligence & Machine Learning Strategies in Government Progress and Best Practices 2024 first appeared on Federal News Network.

Location: Electronic Resource- 

Location: Electronic Resource- 

Location: Engineering Library- QA76.9.D343M34 2016

Copyright 2020 NPR. To see more, visit STEVE INSKEEP, HOST: Distance learning in the pandemic highlights a problem that experts have warned about for years - some students have good access to the Internet, and others do not. It's called the digital divide. Many districts are about to start the school year with more distance learning, so how can they narrow that divide? Rachel Martin spoke with Nicol Turner Lee, who studies it. RACHEL MARTIN, BYLINE: When you look back at those two, sometimes three, months that students in this country were doing distance learning, what worked and what didn't? NICOL TURNER LEE: You know, I think, generally, I am in agreement with some of the folks that have looked at this short period time as somewhat of an abject failure for our children. What worked was that, you know, schools had the attention of their households to figure out what to do during a time of crisis. What didn't work was that schools were not necessarily ready to move to an online

This is a paper produced as part of the PROP2 (Practitioner Research: Outcomes and Partnership) programme, a partnership between the Centre for Research on Families and Relationships (CRFR) at the University of Edinburgh and IRISS that was about health and social care in Scotland. This paper was written by Elaine Monteith from ENABLE Scotland who participated in the PROP2 programme. What this research paper explores: All women are asked to go to the doctor every few years to get a check for cancer but women who have a learning disability don’t go for these checks as often as other women. The paper explore what barriers there are for women attending for checks and also looks at what could be done to encourage women them to attend.

Report 68 published by the Social Care Institute for Excellence (SCIE) in November 2014. This report will help readers to understand the Learning Together methodology.

Come one, come all! nycdevops does its first virtual meetup! All are invited!

• Speaker: Adarsh Shah
• Topic: "Continuous Delivery for Machine Learning"
• Time: Thursday, September 17, 2020, 5-630 PM
• Link: https://www.meetup.com/nycdevops/events/272914068/

Hope to see you there!

Billions of dollars were funneled to school districts across the U.S. to help them make up for learning loss from the pandemic. But new research shows that even with that extra money, school districts are still struggling to close the gaps in reading, writing and math. Stephanie Sy discussed the findings with Karyn Lewis of the Center for School and Student Progress and a lead researcher at NWEA.

Moving Energy Initiative Learning Briefs Research paper sysadmin 29 March 2019

Drawing on experiences from Phase II of the MEI in Burkina Faso, Kenya and Jordan, these learning briefs highlight MEI’s approach to innovation, engagement with the private-sector and host communities, and gender-sensitive energy projects. The four learning papers are intended for practitioners and policymakers working in the humanitarian sector and host-country governments.

Findings from Phase I of the Moving Energy Initiative (MEI) in 2015, published in the Chatham House research paper Heat, Light and Power for Refugees: Saving Lives, Reducing Costs, highlight the negative impacts of limited sustainable energy provision on the security of displaced populations. The paper also identified some of the challenges for energy programmes in this sector, such as the lack of robust data on energy access and the priorities of refugee populations.

In Phase II of the MEI, Practical Action led detailed research into the energy needs of refugees in Burkina Faso and Kenya. Chatham House analysed data on global refugee energy use in displacement contexts and produced an interactive map. Energy 4 Impact explored sustainable funding options, private-sector contract models and non-wood cooking concessions. The market development and low-carbon energy initiatives in Burkina Faso, Jordan and Kenya were managed by Practical Action and Energy 4 Impact, with the support of local partners. These partners represented the MEI at multiple conferences and events to share findings and advocate for the inclusion of displaced people in the sustainable energy agenda.

Drawing on experiences from Phase II of the MEI in Burkina Faso, Kenya and Jordan, these learning briefs highlight MEI’s approach to innovation, engagement with the private-sector and host communities, and gender-sensitive energy projects. The four learning papers are intended for practitioners and policymakers working in the humanitarian sector and host-country governments.

A grassroots online movement has helped shift the way scientists think about asexuality. But much is still unknown.