Lifeboat Foundation

Large language models are computer programs that can analyze and create text. They are trained using massive amounts of text data, which helps them become better at tasks like generating text. Language models are the foundation for many natural language processing (NLP) activities, like speech-to-text and sentiment analysis. These models can look at a text and predict the next word. Examples of LLMs include ChatGPT, LaMDA, PaLM, etc.

Parameters in LLMs help the model to understand relationships in the text, which helps them to predict the likelihood of word sequences. As the number of parameters increases, the ability of the model to capture complex relationships and its flexibility in handling rare words also increases.

ChatGPT is an open-source chatbot powered by the GPT-3 language model. It is capable of engaging in natural language conversations with users. ChatGPT is trained on a wide array of topics and can assist with various tasks like answering questions, providing information, and generating creative content.

My last genAI experiment where I created a children’s (audio)book in more than 10 languages within a few days with the help of AI was quite a while ago. So now it was time for a new experiment. This time I created a fully synthetic podcast using generative AI and brought Steve Jobs to life as a synthetic AI character to have a conversation with him.

In this blog post I talk about my motivation, explain how I proceeded step by step and also share my learnings.

Wtf… How is this possible? Scientists have developed an AI system called ProGen that can generate artificial enzymes from scratch. The technology was developed by Salesforce Research and uses natural language processing and next-token prediction to assemble amino acid sequences into artificial proteins. In laboratory tests, some of these enzymes worked as well as those found in nature, even when their artificially generated amino acid sequences The new technology could become more powerful than directed evolution, a Nobel-prize-winning protein design technology, and will speed up the development of new proteins for use in various fields, including therapeutics and degrading plastic.

The high profile race to enhance their search products has underscored the importance of artificial intelligence to Google and Microsoft – and the rest of the economy, too. Two of the world’s largest tech companies announced plans for AI-enhanced search this month, ratcheting up a tussle for supremacy in the artificial intelligence space. However, the debut of Google’s new chatbot, Bard, was scuppered when an error appeared, knocking $163bn (£137bn) off the parent company Alphabet’s share price. The stock’s plunge showed how crucial investors think AI could be to Google’s future.

However, the increasing prominence of AI has implications for every corner of the economy. From retail to transport, here’s how AI promises to usher in a wave of change across industries.

In a new study, researchers at the Indian Institute of Science (IISc) show how a brain-inspired image sensor can go beyond the diffraction limit of light to detect miniscule objects such as cellular components or nanoparticles invisible to current microscopes. Their novel technique, which combines optical microscopy with a neuromorphic camera and machine learning algorithms, presents a major step forward in pinpointing objects smaller than 50 nanometers in size. The results are published in Nature Nanotechnology.

Since the invention of optical microscopes, scientists have strived to surpass a barrier called the diffraction limit, which means that the microscope cannot distinguish between two objects if they are smaller than a certain size (typically 200–300 nanometers).

Their efforts have largely focused on either modifying the molecules being imaged, or developing better illumination strategies—some of which led to the 2014 Nobel Prize in Chemistry. “But very few have actually tried to use the detector itself to try and surpass this detection limit,” says Deepak Nair, Associate Professor at the Center for Neuroscience (CNS), IISc, and corresponding author of the study.

Users of the Replika chatbot system can no longer engage in erotic or sexual dialogue with their digital counterparts, after years of being able to do so. This highlights a dilemma when people become emotionally attached to chatbots.

Under the name “AI Companion”, Replika is marketing a chatbot system that, like ChatGPT and the like, converses with users in natural language and is also embodied as a visual avatar. Replika will be “there to listen and talk” and “always on your side”, the company promises. With augmented reality, you can project the avatar chatbots life-size into your room.

Continue reading “Replika’s chatbot dilemma shows why people shouldn’t trust companies with their feelings” »

Benchmarks from German AI startup Aleph Alpha show that the startup’s latest AI models can keep up with OpenAI’s GPT-3. A success that should not lull Europe into a false sense of security.

ChatGPT has catapulted artificial intelligence into the public discussion like no other product before it. Behind the chatbot is the U.S. company OpenAI, which made headlines with the large-scale language model GPT-3 and later with the text-to-picture model DALL-E 2. The impact of systems like ChatGPT or Midjourney on education and work, which can be felt today, was foreseeable even then.

The underlying language models are often referred to in research as foundation models: a large AI model that, due to its generalist training with large datasets, can later take on many tasks for which it was not explicitly trained.

ChatGPT is a powerful Artificial Intelligence platform that will take over the world!

In this video, I’m sharing with you all about ChatGPT, its capabilities, and why Bill Gates and other world-renowned experts believe it will be a major force in the future!

Continue reading “ChatGPT Artificial Intelligence Will Take OVER The World!” »

New textiles developed at Aalto University change shape when they heat up, giving designers a wide range of new options. In addition to offering adjustable esthetics, responsive smart fabrics could also help monitor people’s health, improve thermal insulation, and provide new tools for managing room acoustics and interior design.

The new fabrics weave together old technology and a new approach. Liquid crystalline elastomers (LCEs) were developed in the 1980s. LCEs are a smart material that can respond to heat, light, or other stimuli, and they’ve been used as thin films in soft robotics. Although LCEs have been made into fibers, so far they haven’t been made into textiles.

Continue reading “Developing fabrics that change shape when they heat up” »