Lifeboat Foundation

For decades, philosopher Nick Bostrom (director of the Future of Humanity Institute at Oxford) has led the conversation around technology and human experience (and grabbed the attention of the tech titans who are developing AI – Bill Gates, Elon Musk, and Sam Altman).

Now, a decade after his NY Times bestseller S uperintelligence warned us of what could go wrong with AI development, he flips the script in his new book Deep Utopia: Life and Meaning in a Solved World (March 27), asking us to instead consider “What could go well?”

Ronan recently spoke to Professor Nick Bostrom.

The desktop-sized LPU100 eschews traditional electronics and qubits in favor of lasers, and it can reportedly perform complex AI calculations in nanoseconds.

The advent of large language models (LLMs) like GPT-4 has sparked excitement around enhancing them with multimodal capabilities to understand visual data alongside text. However, previous efforts to create powerful multimodal LLMs have faced challenges in scaling up efficiently while maintaining performance. To mitigate these issues, the researchers took inspiration from the mixture-of-experts (MoE) architecture, widely used to scale up LLMs by replacing dense layers with sparse expert modules.

In the MoE approach, instead of passing inputs through a single large model, there are many smaller expert sub-models that each specialize on a subset of the data. A routing network determines which expert(s) should process each input example. It allows scaling up total model capacity in a more parameter-efficient way.

In their approach (shown in Figure 2), CuMo, the researchers integrated sparse MoE blocks into the vision encoder and the vision-language connector of a multimodal LLM. This allows different expert modules to process different parts of the visual and text inputs in parallel rather than relying on a monolithic model to analyze everything.

🤖 🏛️ Have you ever wondered about the connection between AI and Ancient Greek Philosophy?

🧔 📜 The ancient Greek philosophers, such as Aristotle, Plato, Socrates, Democritus, Epicurus and Heraclitus explored the nature of intelligence and consciousness thousands of years ago, and their ideas are still relevant today in the age of AI.

🧠 📚 Aristotle believed that there are different levels of intelligence, ranging from inanimate objects to human beings, with each level having a distinct form of intelligence. In the context of AI, this idea raises questions about the nature of machine intelligence and where it falls in the spectrum of intelligence. Meanwhile, Plato believed that knowledge is innate and can be discovered through reason and contemplation. This view has implications for AI, as it suggests that a machine could potentially have access to all knowledge, but it may not necessarily understand it in the same way that a human would.

From Stanford, Albert Einstein, & Johns Hopkins U: a multimodal agent benchmark to evaluate AI in simulated clinical environments.

From stanford, albert einstein, & johns hopkins U

AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environments abs: https://arxiv.org/abs/2405.07960 project page: https://agentclinic.github.io code: https://github.com/samuelschmidgall/agentclinic.

Continue reading “SamuelSchmidgall/AgentClinic: Agent benchmark for medical diagnosis” »

At Google I/O, Google previewed a generative AI model that can create videos at 1080p around a minute in length.

Cheap data and the absence of coincidences make maths an ideal testing ground for AI-assisted discovery — but only humans will be able to tell good conjectures from bad ones.

Did you hear the news? OpenAI’s newest model can reason across audio, vision, and text in real time.

How does GPT-4o do with math tutoring? 🤔

Sal and his son test it out on a Khan Academy math problem.

Continue reading “AI-powered tutor Khanmigo by Khan Academy: Your 24/7 homework helper” »

Jeff Bezos emailed Amazon execs to ask why more AI firms aren’t using its cloud services, CNBC reported.