Lifeboat Foundation

Using AI-generated datasets to train future generations of machine learning models may pollute their output, a concept known as model collapse, according to a new paper published in Nature. The research shows that within a few generations, original content is replaced by unrelated nonsense, demonstrating the importance of using reliable data to train AI models.

Generative AI tools such as large language models (LLMs) have grown in popularity and have been primarily trained using human-generated inputs. However, as these AI models continue to proliferate across the Internet, computer-generated content may be used to train other AI models—or themselves—in a recursive loop.

Ilia Shumailov and colleagues present mathematical models to illustrate how AI models may experience model collapse. The authors demonstrate that an AI may overlook certain outputs (for example, less common lines of text) in training data, causing it to train itself on only a portion of the dataset.

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We often try to distinguish between what is alive and what is a machine, and note that machines can’t reproduce or fix themselves, but that may soon change.

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Continue reading “Self-Repairing Machines” »

Wings aren’t the only things that make birds so successful. If it weren’t for their feet, how would pelicans skimming the tips of waves be able to suddenly land on a pier piling, or owls grab a mouse at 64 kilometers per hour without missing a beat? Robot birds must be able to do the same—something that has been a challenge, until now.

In a new study, researchers analyzed the anatomy and behavior of a tiny American parrot called a parrotlet and peregrine falcons, two species known for their expert footwork. They then designed “SNAG” (Stereotyped Nature-Inspired Aerial Grasper) — a pair of jointed legs attached to feet with jointed talons that automatically close around any object they encounter, be it a dowel, branch, or tennis ball.

We’re expanding access to Meta AI – the assistant in our apps and devices – and introducing new features to help you with answers, ideas and inspiration. Meta AI is now available in 22 countries, with the newest today in Argentina, Chile, Colombia, Ecuador, Mexico, Peru and Cameroon. You can also interact with Meta AI across WhatsApp, Instagram, Messenger and Facebook in new languages: Hindi Hindi-Romanised Script, French, German, Italian, Portuguese and Spanish with more to come.

With Meta AI across WhatsApp, Instagram, Messenger, Facebook and meta.ai, people are tapping into the power of AI to get more done in less time, bring creative ideas to life and expand their knowledge. From tackling how-to tasks and answering questions to providing inspiration and guidance, Meta AI has enhanced people’s daily routines and been a creative partner to lean on. This is just the start — we’re listening to your feedback, updating Meta AI every two weeks to enhance your experience and innovating quickly to bring new features to help you create, get inspired and get more done.

Machine learning (ML) is one of the most important subareas of AI used in building great AI systems.

In ML, deep learning is a narrow area focused solely on neural networks. Through the field of deep learning, systems like ChatGPT and many other AI models can be created. In other words, ChatGPT is just a giant system based on neural networks.

However, there is a big problem with deep learning: computational efficiency. Creating big and effective AI systems with neural networks often requires a lot of energy, which is expensive.

Neural networks biological and artificial.

Neural Networks have found applications across various domains due to their ability to learn from data and improve over time without human intervention. They can solve challenging problems that are hard or impossible to solve using traditional methods. Here are some of the examples of how neural networks and artificial neurons are used in real-world scenarios:

Voice assistants: Voice assistants like Siri and Alexa use neural networks to understand spoken language commands and questions. They use trained models based on artificial neurons processing vast datasets of speech and text data. They can also generate natural-sounding responses and perform various tasks, such as playing music, setting reminders, searching the web, etc.

Continue reading “Neural Networks: From Biological to Artificial” »

Learn more about neural networks and large language models on Brilliant! First 30 days are free and 20% off the annual premium subscription when you use our link ➜ https://brilliant.org/sabine.

A lot of big banks are banking on quantum computing because they think it’ll give them an edge in trading. Though I have on previous occasions noted my doubt that we’ll see any useful quantum computers within the next ten years, two new papers detailing new methods of scaling quantum computers have shifted my perspective. Let’s have a look.

Continue reading “Big News for Quantum Computing: First Scalable Platforms” »

A new AI coding tool enters the race: let’s take a closer look at Devin, a new AI coding assistant (currently in early access).

The Quantum Insider (TQI) is the leading online resource dedicated exclusively to Quantum Computing.