Lifeboat Foundation

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Can artificial intelligence, or AI, make it possible for us to live forever? Or at least, be preserved for posterity? What are the current developments in the fields of artificial intelligence and biotechnology?

Will humanity exist without biological bodies, in the near future? Could humans and AI merge into one being? This documentary explores these questions, and more.

Continue reading “AI and the quest for immortality — are we defeating death?” »

Anthropic cofounder and president Daniela Amodei sits down with Stripe head of corporate strategy Alex Komoroske at AI Day.

Large Language Models (LLMs) have gained a lot of attention for their human-imitating properties. These models are capable of answering questions, generating content, summarizing long textual paragraphs, and whatnot. Prompts are essential for improving the performance of LLMs like GPT-3.5 and GPT-4. The way that prompts are created can have a big impact on an LLM’s abilities in a variety of areas, including reasoning, multimodal processing, tool use, and more. These techniques, which researchers designed, have shown promise in tasks like model distillation and agent behavior simulation.

The manual engineering of prompt approaches raises the question of whether this procedure can be automated. By producing a set of prompts based on input-output instances from a dataset, Automatic Prompt Engineer (APE) made an attempt to address this, but APE had diminishing returns in terms of prompt quality. Researchers have suggested a method based on a diversity-maintaining evolutionary algorithm for self-referential self-improvement of prompts for LLMs to overcome decreasing returns in prompt creation.

LLMs can alter their prompts to improve their capabilities, just as a neural network can change its weight matrix to improve performance. According to this comparison, LLMs may be created to enhance both their own capabilities and the processes by which they enhance them, thereby enabling Artificial Intelligence to continue improving indefinitely. In response to these ideas, a team of researchers from Google DeepMind has introduced PromptBreeder (PB) in recent research, which is a technique for LLMs to better themselves in a self-referential manner.

Mo Gawdat openly discusses the current rate of advancement of AI and the expected technological innovation that will follow at the Nordic Business Forum 2023 in Helsinki on September 27, 2023.

Learning points:

Continue reading “Mo Gawdat: AI Today, Tomorrow and How You Can Save Our World (Nordic Business Forum 2023)” »

In this post you will learn about the transformer architecture, which is at the core of the architecture of nearly all cutting-edge large language models. We’ll start with a brief chronology of some relevant natural language processing concepts, then we’ll go through the transformer step by step and uncover how it works.

Who is this useful for? Anyone interested in natural language processing (NLP).

How advanced is this post? This is not a complex post, but there are a lot of concepts, so it might be daunting to less experienced data scientists.

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Continue reading “Artificial Intelligence Shaping the Future of Medicine” »

The Center for Artificial Intelligence at King Khalid University embraces AI-driven innovation by using and creating advanced digital technologies to help fulfill Vision 2030 objectives. With that as the main driver, Nada Saeed Al-Qahtani, student and inventor at the College of Computer Science at King Khalid University, developed a robot called “Eve”. Eve is tailored to students with Down Syndrome, being equipped with machine learning curriculum adaptations that make completing tasks easier.

A cyclist who was paralysed in an accident is able to walk again, thanks to a breakthrough in neurosurgery and artificial intelligence in Switzerland.
Al Jazeera’s Andrew Simmons reports.

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Continue reading “Technology helps paralysed man to walk” »