The African spurred tortoise's recovery deserves a shell-ebration. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. After getting hit by a car twice, the tortoise's shell needed to be wired back together. Breakthroughs, discoveries, and DIY tips sent six days a week. Complex problems require creative solutions, and wildlife veterinarian Nielsen Donato is no stranger to what might seem like out-of-the-box problem solving.

More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Breakthroughs, discoveries, and DIY tips sent six days a week. Installing wheels on a tortoise might seem like a cruel joke--but a veterinary practice in the Philippines recently did so to help out an Aldabra giant tortoise () with troubled hind legs. As the name suggests, Aldabra giant tortoises are among the largest land tortoises. Also referred to as the Aldabra tortoise or giant tortoise, this reptile can weigh up to 550 pounds and can live over 150 years.

Large language models (LLMs) have been widely used in various applications but are known to suffer from issues related to untruthfulness and toxicity. While parameter-efficient modules (PEMs) have demonstrated their effectiveness in equipping models with new skills, leveraging PEMs for deficiency unlearning remains underexplored. In this work, we propose a PEMs operation approach, namely Extraction-before-Subtraction (Ext-Sub), to enhance the truthfulness and detoxification of LLMs through the integration of ``expert'' PEM and ``anti-expert'' PEM. Remarkably, even anti-expert PEM possess valuable capabilities due to their proficiency in generating fabricated content, which necessitates language modeling and logical narrative competence. Rather than merely negating the parameters, our approach involves extracting and eliminating solely the deficiency capability within anti-expert PEM while preserving the general capabilities. To evaluate the effectiveness of our approach in terms of truthfulness and detoxification, we conduct extensive experiments on LLMs, encompassing additional abilities such as language modeling and mathematical reasoning. Our empirical results demonstrate that our approach effectively improves truthfulness and detoxification, while largely preserving the fundamental abilities of LLMs.

Aiming to improve the Automatic Speech Recognition (ASR) outputs with a post-processing step, ASR error correction (EC) techniques have been widely developed due to their efficiency in using parallel text data. Previous works mainly focus on using text or/ and speech data, which hinders the performance gain when not only text and speech information, but other modalities, such as visual information are critical for EC. The challenges are mainly two folds: one is that previous work fails to emphasize visual information, thus rare exploration has been studied. The other is that the community lacks a high-quality benchmark where visual information matters for the EC models. Therefore, this paper provides 1) simple yet effective methods, namely gated fusion and image captions as prompts to incorporate visual information to help EC; 2) large-scale benchmark datasets, namely Visual-ASR-EC, where each item in the training data consists of visual, speech, and text information, and the test data are carefully selected by human annotators to ensure that even humans could make mistakes when visual information is missing. Experimental results show that using captions as prompts could effectively use the visual information and surpass state-of-the-art methods by upto 1.2% in Word Error Rate(WER), which also indicates that visual information is critical in our proposed Visual-ASR-EC dataset

In recent years, the field of image generation has been revolutionized by the application of autoregressive transformers and DDPMs. These approaches model the process of image generation as a step-wise probabilistic processes and leverage large amounts of compute and data to learn the image distribution. This methodology of improving performance need not be confined to images. This paper describes a way to apply advances in the image generative domain to speech synthesis. The result is TorToise -- an expressive, multi-voice text-to-speech system. All model code and trained weights have been open-sourced at https://github.com/neonbjb/tortoise-tts.

Tom Hanks didn't just call me to pitch me a part, but it sure sounds like it. Ever since PCWorld began covering the rise of various AI applications like AI art, I've been poking around in the code repositories in GitHub and links within Reddit, where people will post tweaks to their own AI models for various approaches. Some of these models actually end up on commercial sites, which either roll their own algorithms or adapt others that have published as open source. A great example of an existing AI audio site is Uberduck.ai, Enter the text in the text field and you can have a virtual Elon Musk, Bill Gates, Peggy Hill, Daffy Duck, Alex Trebek, Beavis, The Joker, or even Siri read out your pre-programmed lines.

I’ll demonstrate how to extract an audio clip from YouTube, implement speech recognition using OpenAI’s Whisper, and perform speech generation using Tortoise to clone a custom voice.

An image of babies doing parkour generated by DALL-E mini. An image of babies doing parkour generated by DALL-E mini. DALL-E mini is the AI bringing to life all of the goofy "what if" questions you never asked: What if Voldemort was a member of Green Day? What if there was a McDonald's in Mordor? What if scientists sent a Roomba to the bottom of the Mariana Trench?

An important feature of human intelligence is the ability to learn. The amazing learning abilities of the human brain enable babbling babies to grow into learned and easy-to-talk adults. For human beings, learning is an innate ability. The universal existence of this ability makes us ignore its strangeness and preciousness. As far as artificial intelligence research is concerned, how to make machines possess the most universal capabilities in the human world is a very challenging research direction. In different research paths, the subjects, contents and methods of learning are quite different.

Artificial intelligence is poised to transform the way we work, learn and live. Across the globe, businesses, governments and the public at large are already having to adapt to the rapid development of these technologies. The Global AI Index analyses how 54 countries are driving and adapting to AI's accelerating development through three pillars; investment, innovation and implementation. Here is the Index in full. Use the toggle to switch between our Index's ranks – where countries stand – or score – how far or close they are to each other.