…Digital Twins and Metaverse are both dynamic topics
…In a nutshell, digital twins can be seen as building blocks to the metaverse
In practice that means
1) Through an immersive mechanism, the metaverse provides a way to experience a digital twin
2) the metaverse provides a way to collaborate through a digital twin
3) Causal machine learning is an interesting area that I am interested in
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Daily Archives: September 22, 2022
Designing an Inclusive Metaverse
The metaverse is full of promise. People are hopeful that this shared, interactive, immersive, and hyper-realistic virtual space will revolutionize the internet. Goldman Sachs has estimated that the metaverse could ultimately be an $8 trillion opportunity.
One particular promise of the metaverse is that it offers an opportunity to remedy some of the mistakes of Web 2.0 — in particular the failure of social media platforms to safeguard and protect marginalized and underrepresented people from hateful behavior online. Read More
DeepMind’s new chatbot uses Google searches plus humans to give better answers
The lab trained a chatbot to learn from human feedback and search the internet for information to support its claims.
The trick to making a good AI-powered chatbot might be to have humans tell it how to behave—and force the model to back up its claims using the internet, according to a new paper by Alphabet-owned AI lab DeepMind.
In a new non-peer-reviewed paper out today, the team unveils Sparrow, an AI chatbot that is trained on DeepMind’s large language model Chinchilla. Read More
I don’t know how to solve prompt injection
Some extended thoughts about prompt injection attacks against software built on top of AI language models such a GPT-3. This post started as a Twitter thread but I’m promoting it to a full blog entry here.
The more I think about these prompt injection attacks against GPT-3, the more my amusement turns to genuine concern.
I know how to beat XSS, and SQL injection, and so many other exploits.
I have no idea how to reliably beat prompt injection! Read More
Introducing Whisper (from OpenAI)
We’ve trained and are open-sourcing a neural net called Whisper that approaches human level robustness and accuracy on English speech recognition.
Whisper is an automatic speech recognition (ASR) system trained on 680,000 hours of multilingual and multitask supervised data collected from the web. We show that the use of such a large and diverse dataset leads to improved robustness to accents, background noise and technical language. Moreover, it enables transcription in multiple languages, as well as translation from those languages into English. We are open-sourcing models and inference code to serve as a foundation for building useful applications and for further research on robust speech processing. Read More
State of Data Science 2022: Paving the Way for Innovation
Anaconda’s 2022 State of Data Science report is here! As with years prior, we conducted a survey to gather demographic information about our community, ascertain how that community works, and collect insights into big questions and trends that are top of mind within the community. As the impacts of COVID continue to linger and assimilate into our new normal, we decided to move away from covering COVID themes in our report and instead focus on more actionable issues within the data science, machine learning (ML), and artificial intelligence industries, like open-source security, the talent dilemma, ethics and bias, and more. Read More
Read the Report
PP-Matting: High-Accuracy Natural Image Matting
Natural image matting is a fundamental and challenging computer vision task. It has many applications in image editing and composition. Recently, deep learning-based approaches have achieved great improvements in image matting. However, most of them require a user-supplied trimap as an auxiliary input, which limits the matting applications in the real world. Although some trimap-free approaches have been proposed, the matting quality is still unsatisfactory compared to trimap-based ones. Without the trimap guidance, the matting models suffer from foreground-background ambiguity easily, and also generate blurry details in the transition area. In this work, we propose PP-Matting, a trimap-free architecture that can achieve high-accuracy natural image matting. Our method applies a high-resolution detail branch (HRDB) that extracts fine-grained details of the foreground with keeping feature resolution unchanged. Also, we propose a semantic context branch (SCB) that adopts a semantic segmentation subtask. It prevents the detail prediction from local ambiguity caused by semantic context missing. In addition, we conduct extensive experiments on two well-known benchmarks: Composition-1k and Distinctions-646. The results demonstrate the superiority of PP-Matting over previous methods. Furthermore, we provide a qualitative evaluation of our method on human matting which shows its outstanding performance in the practical application. Read More
Rick's Cafe AI 