Jan Leike, co-leading Superalignment of GPT4o, posted on X, “I resigned,” just the day before the GPT4o launch.
OpenAI co-founder Ilya Sutskever and another executive leave the company as GPT-4o launches.
OpenAI Spring Update – streamed live on Monday, May 13, 2024. Introducing GPT-4o, updates to ChatGPT, and more.
On the day of the ChatGPT-4o announcement, Sam Altman sat down to share behind-the-scenes details of the launch and offer his predictions for the future of AI. Altman delves into OpenAI’s vision, discusses the timeline for achieving AGI, and explores the societal impact of humanoid robots. He also expresses his excitement and concerns about AI personal assistants, highlights the biggest opportunities and risks in the AI landscape today, and much more.
(00:00) Intro.
(00:50) The Personal Impact of Leading OpenAI
(01:44) Unveiling Multimodal AI: A Leap in Technology.
(02:47) The Surprising Use Cases and Benefits of Multimodal AI
(03:23) Behind the Scenes: Making Multimodal AI Possible.
(08:36) Envisioning the Future of AI in Communication and Creativity.
(10:21) The Business of AI: Monetization, Open Source, and Future Directions.
(16:42) AI’s Role in Shaping Future Jobs and Experiences.
(20:29) Debunking AGI: A Continuous Journey Towards Advanced AI
(24:04) Exploring the Pace of Scientific and Technological Progress.
(24:18) The Importance of Interpretability in AI
(25:11) Navigating AI Ethics and Regulation.
(27:26) The Safety Paradigm in AI and Beyond.
(28:55) Personal Reflections and the Impact of AI on Society.
(29:11) The Future of AI: Fast Takeoff Scenarios and Societal Changes.
(30:59) Navigating Personal and Professional Challenges.
(40:21) The Role of AI in Creative and Personal Identity.
(43:09) Educational System Adaptations for the AI Era.
(44:30) Contemplating the Future with Advanced AI
Continue reading “Sam Altman talks GPT-4o and Predicts the Future of AI” »
Yet another OpenAI executive has been caught lacking on camera when asked if the company’s new Sora video generator was trained using YouTube videos.
During a recent talk at Bloomberg’s Tech Summit in San Francisco, OpenAI chief operating officer Brad Lightcap went off on a word vomit-style monologue in the wrong direction in an attempt to deflect from questions about Sora’s training data.
Continue reading “Another OpenAI Executive Choked When Asked If Sora Was Trained on YouTube Data” »
Researchers have leveraged deep learning techniques to enhance the image quality of a metalens camera. The new approach uses artificial intelligence to turn low-quality images into high-quality ones, which could make these cameras viable for a multitude of imaging tasks including intricate microscopy applications and mobile devices.
“It is nonsensical to say that an LLM has feelings,” Hagendorff says. “It is nonsensical to say that it is self-aware or that it has intentions. But I don’t think it is nonsensical to say that these machines are able to learn or to deceive.”
Brain scans
Other researchers are taking tips from neuroscience to explore the inner workings of LLMs. To examine how chatbots deceive, Andy Zou, a computer scientist at Carnegie Mellon University in Pittsburgh, Pennsylvania, and his collaborators interrogated LLMs and looked at the activation of their ‘neurons’. “What we do here is similar to performing a neuroimaging scan for humans,” Zou says. It’s also a bit like designing a lie detector.
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.
