Lifeboat Foundation

Let’s look at some examples of this software-defined momentum at the edge. In manufacturing, AI enables weld quality detection in real time on factory floors, improving production yields. In agriculture, farmers can use AI-driven systems to move from focusing on entire crops to looking at individual plants in a field to determine where to fertilize, irrigate or weed. Healthcare is transforming at every level—from the granularity of tracking nerve structures for anesthesia during surgery to the scale and scope of securing patient privacy and data across healthcare networks. An intelligent, software-defined edge aids in delivering resilience for evolving business needs.

AI tools and platforms are now widely available, allowing businesses to harness their power to build solutions faster and gain a competitive edge. This accessibility is crucial for scaling their usefulness, as it shifts solutions from being built solely by data scientists and software engineers to being used by domain experts with less coding experience. With simplified AI model toolkits and an open development platform, these users can stitch together their own solutions and deploy them anywhere.

Let’s take the example of a quick service restaurant (QSR). QSRs could improve their operations by monitoring orders and ingredient levels, then dynamically resupplying their inventories. Lowering barriers to AI means businesses like a QSR can tap into automation and intelligent software solutions on any device, such as a point-of-service system, laptop or mobile device. Customers are happier, food waste is reduced and process efficiencies help QSRs maintain operations even in our current labor shortage.

There are an estimated 30,000 instances of arc flash each year in the United States alone, and one to two fatalities occur daily in North America. Ontario Power Generation (OPG) has five Boston Dynamics’ Spot robots deployed throughout their Enterprise Innovation division. In 2022, the team sought to see if Spot’s dexterous arm could be used to assist in tripping and racking out a 600 volt breaker—an activity that is high risk for arc flash. Now, Boston Dynamics engineers have taken this application to the next level by fully automating the procedure. Spot can perform the entire operation autonomously, with a human issuing high level commands safely out of harm’s way.

#PowerGeneration #bostondynamics #Robotics #spot

Industrial condition monitoring sensors collect data that facilitates intelligent decisions via machine learning or AI to predict machine failures and increase productivity.

So why not break the AI apart?

In a new study published in PNAS, the team took a page from cognitive neuroscience and built a modular AI agent.

The idea is seemingly simple. Rather than a monolithic AI—a single network that encompasses the entire “self”—the team constructed a modular agent, each part with its own “motivation” and goals but commanding a single “body.” Like a democratic society, the AI system argues within itself to decide on the best response, where the action most likely to yield the largest winning outcome guides its next step.

Other commentators, though, were not convinced. Noam Chomsky, a professor of linguistics, dismissed ChatGPT as “hi-tech plagiarism”.

For years, I was relaxed about the prospect of AI’s impact on human existence and our environment. That’s because I always thought of it as a guide or adviser to humans. But the prospect of AIs taking decisions – exerting executive control – is another matter. And it’s one that is now being seriously entertained.

One of the key reasons we shouldn’t let AI have executive power is that it entirely lacks emotion, which is crucial for decision-making. Without emotion, empathy and a moral compass, you have created the perfect psychopath. The resulting system may be highly intelligent, but it will lack the human emotional core that enables it to measure the potentially devastating emotional consequences of an otherwise rational decision.

Technological advancements like autonomous driving and computer vision are driving a surge in demand for computational power. Optical computing, with its high throughput, energy efficiency, and low latency, has garnered considerable attention from academia and industry. However, current optical computing chips face limitations in power consumption and size, which hinders the scalability of optical computing networks.

Thanks to the rise of nonvolatile integrated photonics, optical computing devices can achieve in-memory computing while operating with zero static power consumption. Phase-change materials (PCMs) have emerged as promising candidates for achieving photonic memory and nonvolatile neuromorphic photonic chips. PCMs offer high refractive index contrast between different states and reversible transitions, making them ideal for large-scale nonvolatile optical computing chips.

While the promise of nonvolatile integrated optical computing chips is tantalizing, it comes with its share of challenges. The need for frequent and rapid switching, essential for online training, is a hurdle that researchers are determined to overcome. Forging a path towards quick and efficient training is a vital step on the journey to unleash the full potential of photonic computing chips.

Two days after AIM said that it’s time for OpenAI to launch GPT-5, the company filed a trademark application for “GPT-5” with the United States Patent and Trademark Office (USPTO) on July 18. This move suggests the potential development of a new version of their language model. The news was shared by trademark attorney Josh Gerben on Twitter on July 31.

The trademark application says that GPT-5 is related to computer software for generating human speech and text, as well as for natural language processing, generation, understanding, and analysis. It is speculated to be the next powerful version of OpenAI’s generative chatbot, following the previous release of GPT-4 in March.

Despite the trademark application, there is no confirmation of immediate development for GPT-5. While it is likely that OpenAI has plans for an advanced language model in the future, the primary purpose of the trademark filing might be to secure the name “GPT-5” and prevent unauthorised use by others.

Micron just unveiled specs for its new high-bandwidth memory for AI, which appears to one-up the fastest HBM on the market.

As ive said before we should at least attempt to reverse engineer brains of: mice, lab rats, crows, octupi, pigs, chimps, and end on the… human brain. it would be messy and expensive, and animal activsts would be runnin around it.

Lurking just below the surface of these concerns is the question of machine consciousness. Even if there is “nobody home” inside today’s AIs, some researchers wonder if they may one day exhibit a glimmer of consciousness—or more. If that happens, it will raise a slew of moral and ethical concerns, says Jonathan Birch, a professor of philosophy at the London School of Economics and Political Science.

As AI technology leaps forward, ethical questions sparked by human-AI interactions have taken on new urgency. “We don’t know whether to bring them into our moral circle, or exclude them,” said Birch. “We don’t know what the consequences will be. And I take that seriously as a genuine risk that we should start talking about. Not really because I think ChatGPT is in that category, but because I don’t know what’s going to happen in the next 10 or 20 years.”

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