The Chinese packaging machinery manufacturer - ELITER Packaging Machinery - expects to launch its Wrap-Around Sleever by December 2022, to offer packaging automation solutions for ready meals market.

Mayor Joon-pyo Hong of Daegu said that, "This event is an expo that was held by combining robots, automation devices, and parts and materials. It is a place where you can see the endless potential for development in the relevant fields."

ÉLITER Packaging Machinery, manufacturer of packaging equipment, has completed a project with the nutrition drinks brand ENSURE in the United States to install a cartoner machine to wrap nutritional shakes bottles with cardboard sleeve in 6 packs.

Machine manufacturer in the USA has been providing equipment for trade schools for almost 50 years

Discovering Kiddleton, where Claw Machines and beloved anime characters join for captivating prizes!

Step Into the World of "KAWAI": Experience Japanese Claw Machines at Anime Expo LA!

Claw-machine business has been developing dramatically as well as a variety of prizes, simultaneously Kiddleton has expanded its prizes-selections along with exclusive character

Bringing Smiles with Every Step! Step into a world of amusement with our new, amusing mascot, poised to bring smiles and laughter wherever the game takes you!

Claw Machine Extravaganza: Play, Win, and Take Home Adorable Treasures!

Never-Ending releases of captivating prizes from Kiddleton

Kiddleton' momentum keeps rolling!

Kiddleton's Momentum Continues as It Reached 150 Stores Last Month!

Kiddleton continuously adds exciting new prizes to keep claw machines gamers engaged!

The Time Machine car is deemed one of the top three most iconic movie cars. The crowd-funding window will be open for 30 days only.

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

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.

The post Turn Your Hobby & Passion into a Profitable Money Making Machine appeared first on DailyBlogTips.com.

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.

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

We used to think some jobs were safe from automation. Though machines have transformed industries like agriculture and manufacturing, the conventional wisdom was that they could never perform what's called "knowledge work." That the robots could never replace lawyers or accountants — or journalists, like us.

Well, ever since the release of artificial intelligence tools like ChatGPT, it feels like no job is safe. AI can now write essays, generate computer code, and even pass the bar exam. Will work ever be the same again?

Here at Planet Money, we are launching a new three-part series to understand what this new AI-powered future looks like. Our goal: to get the machines to make an entire Planet Money show.

In this first episode, we try to teach the AI how to write a script for us from scratch. Can the AI do research for us, interview our sources, and then stitch everything together in a creative, entertaining way? We're going to find out just how much of our own jobs we can automate — and what work might soon look like for us all.

(And, in case you're wondering... this text was not written by an AI.)

This episode was produced by Emma Peaslee and Willa Rubin. It was edited by Keith Romer. Maggie Luthar engineered this episode. It was fact-checked by Sierra Juarez. Jess Jiang is Planet Money's acting executive producer.

Help support Planet Money and get bonus episodes by subscribing to Planet Money+ in Apple Podcasts or at plus.npr.org/planetmoney.

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In Part 1 of this series, AI proved that it could use real research and real interviews to write an original script for an episode of Planet Money. Our next task was to teach the computer how to sound like us. How to read that script aloud like a Planet Money host.

On today's show, we explore the world of AI-generated voices, which have become so lifelike in recent years that they can credibly imitate specific people. To test the limits of the technology, we attempt to create our own synthetic voice by training a computer on recordings of former Planet Money host Robert Smith. Then we introduce synthetic Robert to his very human namesake.

There are a lot of ethical, and economic, questions raised by a technology that can duplicate anyone's voice. To help us make sense of it all, we seek the advice of an artist who has embraced AI voice clones: the musician Grimes.

This episode was produced by Emma Peaslee and Willa Rubin, with help from Sam Yellowhorse Kesler. It was edited by Keith Romer and fact-checked by Sierra Juarez. Engineering by James Willetts. Jess Jiang is our acting executive producer.

We built a Planet Money AI chat bot. Help us test it out: Planetmoneybot.com.

Help support Planet Money and get bonus episodes by subscribing to Planet Money+ in Apple Podcasts or at plus.npr.org/planetmoney.

Learn more about sponsor message choices: podcastchoices.com/adchoices

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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, […]

How our billion-year-old cousin, baker’s yeast, can reveal — more reliably than leading algorithms — whether a genetic mutation is actually harmful.Toronto, ON – It’s easier than ever to sequence our DNA, but doctors still can’t exactly tell from our genomes which diseases might befall us. Professor Fritz Roth is setting out to change this by […]

For someone not in the education industry (word used on purpose), I spend a lot of time thinking about education. Just like work issues, the constant and daily reality of school tends to hit you over the head until you just have to pay attention. The thing on my mind today is how much we force kids to record about their lives. Our schools today encourage and often require kids to monitor and measure ...

For quality assurance purposes and to optimize the manufacturing workflow, machine vision is employed throughout the entire process to identify production errors, damage, or impurities early on.

We will explore the use of CMMs, advancements in technology, and their growing contribution to support leading developments in automation, artificial intelligence, and machine learning.

System integration strategies vary by industry and project scale. Success relies on thorough planning and execution, especially in machine vision technology. Here are key integration elements for success with vision technologies.

Labor shortages continue to pressure manufacturers, with some dedicating up to 20% of their workforce to manual inspection. Embracing Quality 4.0 with automated in-line inspections and AI process analytics could provide significant value.

In this article we survey key considerations when making a lens selection. Of course, your lensing professional will be happy to advise – but they’ll ask you some of these questions anyway, so it’s helpful to make notes relative to your planned application.

The famous quote "Adapt or die" applies to businesses like Kodak and Blockbuster, which missed out on opportunities by not embracing new technologies. This concept also applies to industries like manufacturing and logistics today.

Quality spoke with David Dechow of Machine Vision Source following his recent presentation at The Quality Show South about vision solutions for quality applications and integration that drives application success with current and emerging technologies.

In the fast-paced and dynamic world of manufacturing, one trend is clear: short-wave infrared (SWIR) imaging and sensing is revolutionizing the industry.

Industry solutions and products rely on common standards and interfaces, enabling seamless collaboration and increasing efficiency.

Machine vision has evolved significantly over the past forty years, and the demand for specialized solutions has increased in recent years. This article will explore current and future imaging requirements.

The growing use of AI in manufacturing has revolutionized quality control. Traditional inspection methods struggle to keep pace with complex production processes, but AI augments accuracy and efficiency, upholding high-quality standards.

Machine vision projects often face challenges such as slow progress, difficulty in getting quotes, cost overruns, and unreliable operation. These issues require recognizing and adapting to the unique nature of machine vision projects compared to other types of projects.

Machine vision can measure with greater precision and accuracy than human vision. This series starts by exploring techniques for high-precision measurements in vision systems. The next installments will examine challenges and solutions for maintaining this precision and accuracy. First, we'll clarify key terms related to measurement accuracy.

The first part of this three-part series covered the principles that allow machine vision to make high-accuracy measurements. This second part examines challenges that the measurement environment imposes on machine vision and gives approaches to mitigate the effects and retain much of the high-accuracy capability.

Initially seen as science fiction, machine vision in manufacturing faced hesitance due to high costs and lack of awareness. However, interest has surged, shifting the focus from "Can it be done?" to "How will we do it?" This reflects significant transformative changes in the industry.

The first part of this series covered the basic principles that make high-accuracy measurements possible for machine vision. The second part of the series looked at challenges to accurate measurements due to the application conditions. This third part looks at characteristics and components of the vision system that can limit the achievable accuracy.

As businesses increasingly rely on machine vision to enhance quality, improve productivity, and increase the bottom line, technology providers are relying more on industrial computing solutions that enable faster processing speeds and higher efficiency, or that support new tasks altogether. 

It is impossible to overestimate the critical nature of keen and constant visual inspection in any production process. That’s why an advanced vision system is vital for any of today’s advanced production lines – as well as for “yesterday’s” older and more issue-prone lines. 

Success of the industry again produces record numbers for VISION 2018.

When the materials testing application calls for up to 3,000 kN of hydraulic force applied by advanced digital control, Tinius Olsen offers its all-new SL series, replacing the company’s Super L series.

ABB has launched its new OmniVance™ Collaborative Machine Tending Cell, providing a complete integrated solution for automated machine tending that is easy to program, install and use.

Mahr Inc. announced a new solution for contour measurement.

Verisurf Software, Inc. demonstrated its new Machine Tool Probing (MTP) suite at IMTS 2024.