Lifeboat Foundation

Tony Czarnecki, Sustensis

London 1/12/2023

Image generated by DALL-E

Continue reading “The road to Superintelligence may have shortened” »

If electronics tried selling themselves by speaking to you, would you have a greater urge to buy them? This is what a recent study published in Decision Support Systems hopes to address as a research duo investigated how artificial intelligence (AI) could be used as a productive marketing and retail tool for selling their products. This study holds the potential to help researchers, businesses, and consumers better understand how AI can be sold using anthromorphism (possessing human attributes).

“Companies have long used cartoon-like characters to sell products. We are familiar with the ‘M&M spokescandies’, for example,” said Dr. Alan Dennis, who is a Professor of Information Systems in the Kelley School of Business at Indiana University and co-author on the study. “But adding human features to a product can be a powerful way to influence consumers’ perceptions and decision making, because it can trigger anthromorphism.”

For the study, the researchers enlisted approximately 50 undergraduate students and asked them to pretend they were new master’s degree students who needed a new television, camera, or laptop for their studies. Using an eBay-style auction website, the students then bid on the products after watching a two-minute video exhibiting a speaker with human attributes which described the product. The goal of the study was to ascertain how much the students were willing to bid on the products with the video compared to products without, all while using an Emotiv EPOC EEG headset to gather data on their brain activity.

You might recognize him from the spicy feuds on X with AI heavyweights such as Yann LeCun and Geoffrey Hinton. I went along for the ride.

Just a few weeks back, I wrote that we are probably still some way from being able to create a movie from a natural language prompt.

OpenAI’s Sora, a groundbreaking text-to-video model, has catapulted the AI community years ahead, offering near-photorealistic videos from text prompts.

Gemma 2B and Gemma 7B are smaller open-source AI models for language tasks in English.

Google has released Gemma 2B and 7B, a pair of open-source AI models that let developers use the research that went into its flagship Gemini more freely.

Gemma is a family of lightweight, state-of-the art open models built from the same research and technology used to create the Gemini models.

The company plans to release a subscription plan for the tool after it is out of beta.

Adobe’s new AI Assistant instantly generates summaries and insights from long documents, answers questions and formats information for sharing in emails, reports and presentations.

Fusion powers the Sun, and, by extension, makes life on Earth possible.

Researchers use AI to predict and prevent plasma instabilities in fusion reactors, averting reaction disruptions. Experiments show AI forecasts issues 300 milliseconds early, allowing real-time adjustments for stability.

MIT researchers unveil a revolutionary smart glove integrating tactile feedback for enhanced learning, robotics, and virtual reality interactions.

Discover how MIT’s new smart glove is transforming education, robotics, and virtual reality experiences with personalized tactile feedback.

W/ Andrej Karpathy

The Tokenizer is a necessary and pervasive component of Large Language Models (LLMs), where it translates between strings and tokens (text chunks). Tokenizers are a completely separate stage of the LLM pipeline: they have their own training sets, training algorithms (Byte Pair Encoding), and after training implement two fundamental functions: encode() from strings to tokens, and decode() back from tokens to strings. In this lecture we build from scratch the Tokenizer used in the GPT series from OpenAI. In the process, we will see that a lot of weird behaviors and problems of LLMs actually trace back to tokenization. We’ll go through a number of these issues, discuss why tokenization is at fault, and why someone out there ideally finds a way to delete this stage entirely.

Continue reading “Let’s build the GPT Tokenizer” »