Lifeboat Foundation

I will begin with the first point and make my way gradually to the tenth point.

I’ve already mentioned to you that the AI of the 1970s was toy-like in comparison to the more involved and expansive AI of today. Modern-day generative AI, for example, makes use of vast amounts of data as scanned across the Internet to pattern-match the nature of human writing. This requires a massive amount of computing resources (something far beyond the depth readily employable in the 1970s). The large-scale modeling or pattern matching is what makes contemporary generative AI seem highly fluent.

A common phrase is to say that generative AI is mimicking or parroting human writing.

Exploring the design of efficient quantum emitters using defects in wide-bandgap semiconductors, specifically silicon carbide (SiC) and diamond.

It highlights how these defects can be engineered to emit single photons, which are crucial for quantum technologies like secure communication and quantum…

Computers benefit greatly from being connected to the internet, so we might ask: What good is a quantum computer without a quantum internet?

This was originally posted on our Voronoi app. Download the app for free on iOS or Android and discover incredible data-driven charts from a variety of trusted sources.

Emerging technologies of today have the power to reshape industries, achieve significant scale, and shift the economic landscape.

From AI-driven advancements in disease detection to carbon-capturing microbes, these technologies stand to improve future society. Meanwhile, greater efficiencies in wireless connectivity allow networks to drive higher data rates and enhance robust communications across 6G networks and the industrial internet-of-things.

The internet is going through a shift. There used to be a thing called a “follower.” It allowed publishers, creators, and anyone online to build a community around their work. But this piece of foundational architecture for human creativity and communication – the “follow” – has been threatened. In this talk, I put this internet-wide shift in historical context from the perspective of a creator, outline where the web will go in the future, and offer thoughts on what to do about it all as a modern creator on the internet.

Google Chrome now warns when downloading risky password-protected files and provides improved alerts with more information about potentially malicious downloaded files.

These new, more detailed warning messages help users quickly learn the nature of the danger presented by each file downloaded from the Internet.

For this, Google introduced a two-tier download warning system that uses AI-powered malware verdicts sourced from its Safe Browsing service to help evaluate the actual risk quickly.

Using AI-generated datasets to train future generations of machine learning models may pollute their output, a concept known as model collapse, according to a new paper published in Nature. The research shows that within a few generations, original content is replaced by unrelated nonsense, demonstrating the importance of using reliable data to train AI models.

Generative AI tools such as large language models (LLMs) have grown in popularity and have been primarily trained using human-generated inputs. However, as these AI models continue to proliferate across the Internet, computer-generated content may be used to train other AI models—or themselves—in a recursive loop.

Ilia Shumailov and colleagues present mathematical models to illustrate how AI models may experience model collapse. The authors demonstrate that an AI may overlook certain outputs (for example, less common lines of text) in training data, causing it to train itself on only a portion of the dataset.

They also developed non-noble metal catalysts, including molybdenum-doped nickel-cobalt phosphide and plasma-induced iron composite cobalt oxide bifunctional electrodes, which showed high durability and activity. These electrodes enabled hydrogen and oxygen production at different times and locations by switching the current direction, resulting in low cell voltages, high decoupling efficiency, and high energy conversion efficiency.

To improve layered double hydroxide (LDH) electrodes, which suffer from limited capacity and poor conductivity/stability, the researchers used non-thermal plasma technology to fabricate nitrogen-doped nickel-cobalt LDH and nitrogen-doped reduced graphene oxide/nickel-cobalt LDH electrodes, which significantly improved capacity and conductivity.

Two-step water electrolysis shows promise for large-scale hydrogen storage and applications such as 5G base stations and data centers. “Our performance indicators for two-step water electrolysis for hydrogen production are synchronized with advanced indicators globally, marking an important step towards industrial operation,” said Prof. Chen Changlun.

An AT&T outage map shows a reported issue covering much of San Antonio on Wednesday evening, July 24. The outage could last into Thursday, AT&T says.

Neural networks biological and artificial.

Neural Networks have found applications across various domains due to their ability to learn from data and improve over time without human intervention. They can solve challenging problems that are hard or impossible to solve using traditional methods. Here are some of the examples of how neural networks and artificial neurons are used in real-world scenarios:

Voice assistants: Voice assistants like Siri and Alexa use neural networks to understand spoken language commands and questions. They use trained models based on artificial neurons processing vast datasets of speech and text data. They can also generate natural-sounding responses and perform various tasks, such as playing music, setting reminders, searching the web, etc.

Continue reading “Neural Networks: From Biological to Artificial” »