Archive 30.09.2024

Engineers developed a system that helps robots quickly map a scene and identify items they need to complete a set of tasks.

Imagine having to straighten up a messy kitchen, starting with a counter littered with sauce packets. If your goal is to wipe the counter clean, you might sweep up the packets as a group. If, however, you wanted to first pick out the mustard packets before throwing the rest away, you would sort more discriminately, by sauce type. And if, among the mustards, you had a hankering for Grey Poupon, finding this specific brand would entail a more careful search.

SenseRobot Chess has achieved a significant breakthrough, mastering complex engineering challenges that allow its intelligent robotic arms to precisely grasp and position 3D chess pieces — marking a first in household robotics.

AI Engineer Snags $2.7 Billion to Sign With Google

If you’re chatting-up your boss for a raise, you may want to reference the deal Noam Shazeer just cut with Google.

A former Google employee that the tech titan sorely missed, the AI wunderkind was happy to let bygones be bygones — for a mere $2.7 billion signing fee.

Shazeer is one of the early pioneers of what were to become AI chatbots — the tech that powers most of today’s auto-writers.

Technically speaking, Google also purchased Shazeer’s start-up company — Character.AI — as part of his rehire.

But “within Google, Shazeer’s return is widely viewed as the primary reason the company agreed to pay the multibillion-dollar licensing fee,” according to writer Miles Kruppa.

Brings new meaning to the song lyric, “Mammas, don’t let your babies grow up to be cowboys.”

In other news and analysis on AI writing:

*In-Depth Guide: Meeting Minutes, Minus the Misery: Zoom’s AI Meeting Note-Taker: Wired reviewer John Brandon offers an in-depth look at new AI for Zoom software — dubbed ‘AI Companion’ — that auto-generates meeting notes and summaries.

AI Companion can also tell you who talked the most during a Zoom meeting — and even gauge the meeting’s overall emotional tone, according to Brandon.

Brandon’s verdict: “Overall, the AI Companion saved time in an important way: No one had to take notes in any of my meetings, and the final summaries were all quite useful. No clicking or clacking!”

*Brain Transplant: Google Adds ‘Copilot’ Type Chatbot to Its Workspace Suite: In an effort to compete with ‘Copilot’ — an AI chatbot that Microsoft offers for use in its business productivity suite — Google has added a similar AI chatbot to its competing suite, Workplace.

Observes writer Emilia David: “Workspace users in the Business, Enterprise and Frontline plans will automatically get access to the Gemini app that’s now built into the platform.

“Workspace offers enterprises access to a large swath of Google products — Gmail, Docs and Calendars — but with the option of using their own domains and enterprise-level security. “

*Salesforce Promising ‘Copilot Killer:’ Deriding Microsoft’s Copilot as little more than an annoying time-waster, Salesforce is promising to roll-out a competing product for its own workplace productivity suite.

Dubbed ‘Agentforce,’ the AI system is designed to make it easier to use business software — and to integrate with hundreds of business applications.

Observes writer Sasha Rogelberg: “It’s part of a growing movement of implementing AI agents over copilots to take tech assistance one step further.”

*The End of I Never Said That?: Editors and writers looking for an easy way to record — and instantly transcribe — in-person interviews may want to check-out the Plaud-AI pin.

Powered by ChatGPT, the $169, soon-to-be-released tech comes with 300 free, monthly transcription minutes, according to writer Brian Heater.

Observes Heater: “The recordings are saved on your phone in real time. And from there you can decide whether to upload them for transcription — depending on how robust a monthly subscription you have.”

*Chatbots Gone Wild: A Funny Thing Happened on the Way to the Third Reich: The Wall Street Journal became the latest to find out the hard way that hosting an experimental AI chatbot can be an egg-on-face experience.

Specifically: Pranksters recently spoofed an experimental WSJ chatbot — designed to offer iPhone information — to instead spout Hitler talk.

Observes writer Joanna Stern: “Like my three-year-old, bots struggle to follow the rules.”

*AI a Hit Among UK Lawyers: Increasing numbers of UK legal pros are going all-in on AI, according to a new survey from LexisNexis.

Researchers found that 41% of 800+ legal pros surveyed are currently using AI for work.

Observes writer Caroline Hill: “Lawyers with plans to use AI for legal work in the near future also jumped from 28% to 41%, while those with no plans to adopt AI dropped from 61% to 15%.”

*ChatGPT CEO’s Crystal Ball on Our Future: Everyone Wins the Lottery?: Sam Altman — the AI wunderkind that made ChatGPT and AI household words the world over — predicts AI’s impact on the future will most likely be so overwhelmingly positive, it’s unimaginable.

Observes Altman: “How did we get to the doorstep of the next leap in prosperity?

“In three words: Deep learning worked.

“In 15 words: Deep learning worked, got predictably better with scale — and we dedicated increasing resources to it.”

*Free-for-All: Open-Source AI Gets Another Boost: Fans of open-source AI software — released free to the world on the theory that the real money is in the apps to be built atop it — have something new to cheer about.

Facebook’s parent Meta has released an upgrade to its AI software that competes directly with the AI engine undergirding ChatGPT.

Dubbed Llama 3.2, the AI engine is still a bit weaker than the one running ChatGPT.

But long-run, the competition is sure to help keep AI prices lower.

*AI Big Picture: Hungry for Cash, ChatGPT-Maker on the Hunt for Unthinkable Billions: Sam Altman, CEO, OpenAI, is currently on a world tour attempting to convince players in the computer industry to cooperatively build the next generation of AI data centers.

Altman insists the world will need a spate of these centers to fully realize AI’s potential.

The cost of each new center: A cool $100 billion, he says.

Observes writer Cade Metz: “OpenAI believes this kind of technology could be the future of its business. If it can get its hands on more computing power, its AI can learn to do more. At least, that is the theory.”

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–Joe Dysart is editor of RobotWritersAI.com and a tech journalist with 20+ years experience. His work has appeared in 150+ publications, including The New York Times and the Financial Times of London.

The post Now That’s a Big Payday appeared first on Robot Writers AI.

A battery works on the basic principle that electrons tend to move from where they are abundant, or in excess, to where they are scarce. This can happen, if there is a medium that will allow the movement of these electrons.

The part of battery with too many electrons is called the anode which means it is the negative electrode (remember, the charge of an electron is negative). The part of the battery where electrons are scarce is called the cathode, which means it is the positive electrode, which means, there is net positive charge there.

Let’s present Anode and Cathode characteristics as lists, to make things more understandable:

Anode:
• Abundance of electrons
• Negatively charged
• Chemical reaction during discharge: Oxidation
• Material example: Zinc

Cathode:
• Scarcity of electrons
• Positively charged
• Chemical reaction during discharge: Reduction
• Material example: Manganese Dioxide (MnO2)

Between anode and cathode is a barrier material which prevents their direct contact but still allows passing of ions. If this direct contact was not prevented, there would be a short circuit in the battery and it would deplete very rapidly and be useless.

And there is the electrolyte material, such as an alkaline Potassium Hydroxide (KOH) in an alkaline battery.

An ion is an atom where the number of electrons and protons are not equal, therefore it is an atom with a net electrical charge. If electrons are more than protons, it is a negative ion, and if protons are more than electrons it is a positive ion.

When the battery is not in use, there is no flow of electrons. But when an external circuit is connected to the battery, the electrons are released from anode (i.e. Zinc), which travel through the external circuit, does its work in the circuit (such as turning a motor, illuminating a lamp, making a microprocessor work and so on…), and then they arrive at the cathode, which is able to accept those electrons as it was positively charged. But this is not all. For current to keep flowing, the loop of flow must continue, in other words, be closed. This means, inside the battery, there must also be a path to complete this loop. This is where the electrolyte material comes in which serves as this path. It makes possible for ions (see description above) to travel between cathode and anode, in order to complete this loop and keep the current running. Through this electrolyte inside the battery, positive ions travel from anode to cathode (so that cathode can keep being positive and continue to accept electrons when they arrive from external circuit), and negative ions travel from cathode to anode (so that it can keep sending electrons to the external circuit).

The figure above illustrates this process. This is how a battery works. In the figure, electrons traveling inside the battery electrolyte actually represent the free electrons of the negative ions that move from cathode to anode. And for clarity of the figure, positive ions that go from anode to cathode were not shown.

According to a recent study in Scientific Reports, Turing patterns can be used to develop a new method for designing and producing fabric-based soft pneumatic actuators (FSPAs).

The robotic micro-factories developed by AUAR are a breakthrough technology for an industry struggling with a lack of skilled labour and low productivity; the two Micro-Factories shipped to the US can each produce up to 180 homes a year

Claire chatted to John Leonard from Massachusetts Institute of Technology about autonomous navigation for underwater vehicles and self-driving cars.

John Leonard is a Professor of Mechanical and Ocean Engineering at Massachusetts Institute of Technology (MIT) and a member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). His research addresses the problems of navigation and mapping for autonomous underwater vehicles, self-driving vehicles, and other types of mobile robots. He has a degree in Electrical Engineering and Science from the University of Pennsylvania and PhD in Engineering Science from the University of Oxford. He is a Technical Advisor at Toyota Research Institute.

Unmanned aerial vehicles (UAVs), commonly known as drones, have proved to be highly effective systems for monitoring and exploring environments. These autonomous flying robots could also be used to create detailed maps and three-dimensional (3D) visualizations of real-world environments.

Robotic grippers have become essential across many industries, including manufacturing, packaging, and logistics, mainly for pick-and-place tasks. Recently, the demand for robotic grippers has also expanded into agriculture, where they are used for harvesting and packaging tasks.

Conventional robotic grippers struggle to adapt to complex shapes and sizes, such as those found in crops. This has created a demand for more adaptable robotic grippers that can be utilized in agriculture. In a new study, researchers introduced an innovative soft robotic gripper named ROtation-based Squeezing grippEr (ROSE) and optimized its unique wrinkling-based grasping mechanism using simulations. ROSE's soft yet secure grasp can make it a vital tool for agriculture.

Our AI method has accelerated and optimized chip design, and its superhuman chip layouts are used in hardware around the world.

Our AI method has accelerated and optimized chip design, and its superhuman chip layouts are used in hardware around the world.

The aim of the MHP study was to demonstrate the advantages of process-integrated charging points using inductive point charging systems such as Wiferion over conventional charging zones using a real-life optimization scenario in a medium-sized production company.

How to apply classifier-free guidance (CFG) on your diffusion models without conditioning dropout? What are the newest alternatives to generative sampling with diffusion models? Find out in this article!