Lifeboat Foundation

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” »

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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.

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

“Compared with other traditional methods, the proposed has lower computational complexity, faster operation speed, weak influence of light, and strong ability to locate dirt,” the research group said. “The improved path planning algorithm used in this study greatly improves the efficiency of UAV inspection, saves time and resources, reduces operation and maintenance costs, and improves the corresponding operation and maintenance level of photovoltaic power generation.”

The novel approach uses mathematical morphologies for image processing, such as image enhancement, sharpening, filtering, and closing operations. It also uses image histogram equalization and edge detection, among other methods, to find the dusted spot. For path optimization, it uses an improved version of the A (A-star) algorithm.

Recent advances in the field of artificial intelligence (AI) and computing have enabled the development of new tools for creating highly realistic media, virtual reality (VR) environments and video games. Many of these tools are now widely used by graphics designers, animated film creators and videogame developers worldwide.

One aspect of virtual and digitally created environments that can be difficult to realistically reproduce is fabrics. While there are already various computational tools for digitally designing realistic fabric-based items (e.g., scarves, blankets, pillows, clothes, etc.), creating and editing realistic renderings of these fabrics in real-time can be challenging.

Researchers at Shandong University and Nanjing University recently introduced a new lightweight artificial neural network for the real-time rendering of woven fabrics. Their proposed network, introduced in a paper published as part of the Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers ‘24, works by encoding the patterns and parameters of fabrics as a small latent vector, which can later be interpreted by a decoder to produce realistic representations of various fabrics.

Carbon nanotube networks made with high purity and ultraclean interfaces can be used to make a tensor processing unit that contains 3,000 transistors in a systolic array architecture to improve energy efficiency in accelerating neural network tasks.