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As of August 2024, the global employment landscape is facing significant turbulence, with more than 130,000 employees laid off across nearly 400 companies. Tech giants like Google, IBM, Apple, Amazon, SAP, Meta, and Microsoft have contributed to these staggering figures, indicating a major recalibration within the job market.

According to industry experts, this trend is accelerating as the integration of artificial intelligence (AI) and automation prompts companies to streamline operations. Amidst this upheaval, Ramesh Alluri Reddy, CEO of TeamLease Degree Apprenticeship, sheds light on layoffs, workforce reshaping, and the potential for recovery.

New research from the University of Massachusetts Amherst shows that programming robots to create their own teams and voluntarily wait for their teammates results in faster task completion, with the potential to improve manufacturing, agriculture and warehouse automation. The study is published in 2024 IEEE International Conference on Robotics and Automation (ICRA).

This research was recognized as a finalist for Best Paper Award on Multi-Robot Systems at the IEEE International Conference on Robotics and Automation 2024.

“There’s a long history of debate on whether we want to build a single, powerful humanoid robot that can do all the jobs, or we have a team of robots that can collaborate,” says one of the study authors, Hao Zhang, associate professor at the UMass Amherst Manning College of Information and Computer Sciences and director of the Human-Centered Robotics Lab.

While AI has the potential to automate many tasks, there are certain jobs that require human skills and abilities that AI cannot replicate. These include jobs that require creativity, empathy, critical thinking, and human interaction. According to the World Economic Forum, AI is unlikely to be able to replace jobs requiring human skills such as judgement, creativity, physical dexterity and emotional intelligence. Some examples of jobs that AI cannot replace include psychologists, caregivers, most engineers, human resource managers, marketing strategists, and lawyers. In this video, Dr. Michio Kaku mentioned three specific types of jobs that AI cannot replace: blue-collar jobs that are not repetitive, emotional jobs, and jobs requiring imagination. These types of jobs require human skills and abilities that are difficult for AI to replicate. For example, blue-collar jobs that are not repetitive often require physical dexterity and mobility. Emotional jobs require empathy and the ability to connect with others on a personal level. Jobs requiring imagination involve creativity and innovation. In conclusion, while AI has the potential to automate many tasks and change the job landscape, there are certain jobs that require human skills and abilities that AI cannot replicate. These include blue-collar jobs that are not repetitive, emotional jobs, and jobs requiring imagination. It is important for individuals to develop these skills in order to thrive in the future job market. Fair Use Disclaimer : Copyright disclaimer under section 107 of the Copyright Act 1976, allowance is made for “fair use” for purposes such as criticism, commenting, news reporting, teaching, scholarship and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. Disclaimer: The video and audio content used in this video is for educational purposes only and does not belong to me. I have given credit to the respective owners and creators of the content. This video is intended to provide information and knowledge to its viewers, and no copyright infringement is intended. I have made every effort to ensure that the content used in this video is properly credited and used in accordance with fair use guidelines. If you are the owner of any content used in this video and have any concerns, please contact me. Legal Disclaimer : The video clips incorporated into this project are the sole property of their respective owners and creators. I do not claim ownership or rights to any of the content used. All credit is attributed to the original sources. No copyright infringement is intended. Clips Provided by Cuckoo for Kaku Watch : https://youtu.be/JANGUKLJkPQ #shorts #shortsfeed #shortvideos #shortvideo #shortsvideo #shortsyoutube #shortsviral #viralshortsvideo #viralshorts #viral #viralvideo #viralvideos #space #spaceflightsimulator #deepspace #spaceship #spacelovers #spacesuit #spaceexploration #spacecraft #telescope #spacex #spacestation #universe #cosmos #nasa #viral #viralvideo #viralvideos #science #technology #physics #astronomy #astrophysics #astrophotography #cosmology #cosmos #jwst #jameswebbspacetelescope #jameswebb #hubble #hubbletelescope #video #videos #interstellar

CEDAR PARK, Texas (KXAN) — Cedar Park is now home to a first-of-its-kind distinction in the state. The city is now hoping to cash in on the popularity of video games and virtual reality.

Cedar Park is now officially known as a “Digital Media Friendly Texas Certified Community.”

“This program is really designed to bring in that tech and creative talent,” Arthur Jackson, Chief Economic Development Officer for the city, said.

The North East Texas Regional Mobility Authority (NET RMA) announced a $15 million infrastructure project that will soon begin to revitalize the Henderson Overton Branch rail line.

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KETK/FOX51 News covers East Texas, bringing you the latest local stories, weather, sports and lifestyle coverage from the Piney Woods.

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Sam Altman, CEO of OpenAI,… said some kind of national payments would likely be needed as technology killed more jobs even as it generated massive wealth for others.


Many tech entrepreneurs have long suggested that guaranteed income could cushion job losses from AI and automation. The latest and largest study of the idea was spearheaded by the man behind ChatGPT.

Implementing error correction in a quantum computer requires putting together a lot of different things. Of course, you want to start with good physical qubits that have as low a physical error rate that you can achieve. You want to add in an error correction algorithm, like the surface code, color code, q-LDPC, or others that can be implemented in your architecture, and you need a fast real time error decoder that can look at the circuit output and very quickly determine what the error is so it can be corrected. The error decoder portion doesn’t get as much attention in the media as the other things, but it is a very critical portion of the solution. Riverlane is concentrating on providing products for this with a series of solutions they name Deltaflow which consists of both a classical ASIC chip along with software. The Deltaflow solution consists of a powerful error decoding layer for identifying errors and sending back corrective instructions, a universal interface that communicates with the computer;s control system, and a orchestration layer for coordinating activities.

Riverlane has released its Deltaflow Error Correction Stack Roadmap that show yearly updates to the technology to support an increase in the number of QuOps (error free Quantum Operations) by 10X every year. We reported last year on a chip called DD1 that is part of their Deltaflow 1 solution that is capable of supporting 1,000 QuOps using a surface code error correction algorithm. And now, Riverlane is defining solutions that will achieve 10,000 QuOps with Deltaflow 2 later this year, 100,000 QuOps with Deltaflow 3 in 2025, and 1,000,000 QuOps, also called MegaQuops in 2026, with their Deltaflow Mega solution.

One characteristic that Riverlane is emphasizing in these designs is to perform the decoding in real time in order to keep the latencies low. Although it is fine for an academic paper to send the ancilla data off to a classical computer and have it determine the error, it might take milliseconds for the operation to complete. That won’t cut it in a production environment running real jobs. With their Deltaflow chips, these operations can be performed at megahertz rates and Riverlane has implemented techniques such as a streaming, sliding window, and parallized decoding approaches to increase the throughput of the decoder chips as much as possible. In future chips they will be implementing “fast logic” capabilities for Clifford gates using approaches including lattice surgery and transversal CZ gates.

Where do we stand with artificial intelligence? Might machines take over our jobs? Can machines become conscious? Might we be harmed by robots? What is the future of humanity? Professor Giorgio Buttazzo of Scuola Superiore Sant’Anna is an expert in artificial intelligence and neural networks. In a recent publication, he provides considered insights into some of the most pressing questions surrounding artificial intelligence and humanity.

A Brief History of Neural Networks and Deep Learning

In artificial intelligence (AI), computers can be taught to process data using neuron-like computing systems inspired by the mechanisms used by the human brain. These so-called neural networks represent a type of machine learning (‘deep learning’) in which interconnected nodes or neurons are able to adapt and learn from data to recognise patterns and solve complex problems.