Collaborate Disseminate
Large Language Models (LLMs ) are everywhere, but how exactly do they work under the hood? [Miguel Grinberg] provides a great explanation of the inner workings of LLMs in simple …read more Continue reading How AI Large Language Models Work, Explained Without Math
Back in the 1960s, if you played a 2,600Hz tone into an AT&T pay phone, you could make calls without paying. A phone hacker named John Draper noticed that the plastic whistle that came free in a box of Captain Crunch cereal worked to make the right sound. That became his hacker name, and everyone who knew the trick made free pay-phone calls.
There were all sorts of related hacks, such as faking the tones that signaled coins dropping into a pay phone and faking tones used by repair equipment. AT&T could sometimes change the signaling tones, make them more complicated, or try to keep them secret. But the general class of exploit was impossible to fix because the problem was general: Data and control used the same channel. That is, the commands that told the phone switch what to do were sent along the same path as voices…
There’s a new report on how criminals are using generative AI tools:
Key Takeaways:
Adoption rates of AI technologies among criminals lag behind the rates of their industry counterparts because of the evolving nature of cybercrime.
Compared to l… Continue reading How Criminals Are Using Generative AI
Breakthroughs in large language models (LLMs) are driving an arms race between cybersecurity and social engineering scammers. Here’s how it’s set to play out in 2024. For businesses, generative AI is both a curse and an opportunity. As enterprises race to adopt the technology, they also take on a whole new layer of cyber risk. […]
The post Social engineering in the era of generative AI: Predictions for 2024 appeared first on Security Intelligence.
Continue reading Social engineering in the era of generative AI: Predictions for 2024
Web scraping is usually only a first step towards extracting meaningful data. Once you’ve got everything pulled down, you’ve still got to process it into something useful. Here to assist …read more Continue reading AI Helps Make Web Scraping Faster and Easier
[Andrej Karpathy] recently released llm.c, a project that focuses on LLM training in pure C, once again showing that working with these tools isn’t necessarily reliant on sprawling development environments. …read more Continue reading Train a GPT-2 LLM, Using Only Pure C Code
Most AI foundation models are being developed in markets like the U.S. and China. Research from Australia asks whether other countries should be building sovereign AI models for citizens and businesses. Continue reading Time for APAC Region to Build Its Own AI Foundation Models, Says CSIRO
Canadian legislators proposed 19,600 amendments—almost certainly AI-generated—to a bill in an attempt to delay its adoption.
I wrote about many different legislative delaying tactics in A Hacker’s Mind, but this is a new one.
Continue reading Using AI-Generated Legislative Amendments as a Delaying Technique
Some LLMs (Large Language Models) can act as useful programming assistants when provided with a project’s source code, but experimenting with this can get a little tricky if the chatbot …read more Continue reading Dump a Code Repository as a Text File, For Easier Sharing with Chatbots
This mini-essay was my contribution to a round table on Power and Governance in the Age of AI. It’s nothing I haven’t said here before, but for anyone who hasn’t read my longer essays on the topic, it’s a shorter introduction.
The increasingly centralized control of AI is an ominous sign. When tech billionaires and corporations steer AI, we get AI that tends to reflect the interests of tech billionaires and corporations, instead of the public. Given how transformative this technology will be for the world, this is a problem.
To benefit society as a whole we need an …
Continue reading Public AI as an Alternative to Corporate AI