AI News Bytes: Meta AI introduces SAM (Segment Anything Model); UC Berkeley Researchers Introduce Koala; Meet OpenFlamingo.....

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Meta AI introduces SAM (Segment Anything Model): A Foundation model for image segmentation.?Meta AI team released both their general?Segment Anything Model (SAM)?and?Segment Anything 1-Billion mask dataset (SA-1B), the largest ever segmentation dataset, to enable a broad set of applications and foster further research into foundation models for computer vision. They are making the SA-1B dataset available for research purposes, and the Segment Anything Model is available under a permissive open license (Apache 2.0). Check out the?demo to try SAM?with your own images.

UC Berkeley Researchers Introduce Koala:?A Dialogue Model for Academic Research. Koala is a chatbot trained by fine-tuning Meta’s LLaMA on dialogue data gathered from the web. The researchers describe the dataset curation and training process of their model, and also present the results of a user study that compares our model to ChatGPT and Stanford’s Alpaca. The results show that Koala can effectively respond to a variety of user queries, generating responses that are often preferred over Alpaca, and at least tied with ChatGPT in over half of the cases.

What is AI Hallucination? What Goes Wrong with AI Chatbots? How to Spot a Hallucinating Artificial Intelligence?:?The phenomenon known as artificial intelligence hallucination happens when an AI model produces results that are not what was anticipated. Be aware that some AI models have been taught to purposefully make outputs without connection to real-world input (data). Hallucinations may occur in big language-based models like ChatGPT and its equivalents due to improper transformer decoding (machine learning model). Using an encoder-decoder (input-output) sequence, a transformer in AI is a deep learning model that employs self-attention (semantic connections between words in a sentence) to create text that resembles what a human would write.

Forget plugins. ChatGPT can solve general computer tasks using a keyboard and mouse!!-?The trick? Recursively criticizing and improving the output (RCI):?The RCI approach significantly outperforms existing LLM methods for automating computer tasks and surpasses supervised learning (SL) and reinforcement learning (RL) approaches on the MiniWoB++ benchmark. RCI is competitive with the state-of-the-art SL+RL method, using only a handful of demonstrations per task rather than tens of thousands, and without a task-specific reward function. Furthermore, we demonstrate RCI prompting's effectiveness in enhancing LLMs' reasoning abilities on a suite of natural language reasoning tasks, outperforming chain of thought (CoT) prompting. We find that RCI combined with CoT performs better than either separately.

Meet SwissBERT:?Researchers from the University of Zurich propose a multilingual language model for Switzerland. SwissBERT is a masked language model created specifically for processing Switzerland-related text. SwissBERT is a pre-trained model that is adapted to news articles written in the national languages of Switzerland -- German, French, Italian, and Romansh. The research team evaluated SwissBERT on natural language understanding tasks related to Switzerland and find that it tends to outperform previous models on these tasks, especially when processing contemporary news and/or Romansh Grischun. Since SwissBERT uses language adapters, it may be extended to Swiss German dialects in future work. The model and our open-source code are publicly released.

Meet OpenFlamingo:?A framework for training and evaluating large multimodal models (LMMs) capable of processing images and text.?OpenFlamingo?is an open-source framework that aims to democratize access to state-of-the-art Large Multimodal Models (LMMs) by providing a system capable of handling various vision-language tasks. Developed as a reproduction of DeepMind’s Flamingo model, OpenFlamingo offers a Python framework to train Flamingo-style LMMs, a large-scale multimodal dataset, an in-context learning evaluation benchmark, and the first version of OpenFlamingo-9B model based on LLaMA.

Databricks Open-Sources Dolly:?A ChatGPT like Generative AI Model that is Easier and Faster to Train.?Dolly?is a low-cost large language model (LLM) that demonstrates surprisingly high levels of the instruction-following abilities seen in ChatGPT. This work indicates that anyone with access to high-quality training data and an out-of-date open-source large language model (LLM) can train it to perform like ChatGPT in under 30 minutes on a single machine. Dolly uses data from Alpaca to make minor adjustments to an existing, open-source 6 billion parameter model from EleutherAI to elicit instruction following capabilities such as brainstorming and text production.

LLMs Can Outperform Humans on Data Annotation:?The University of Zurich researchers used 2,382 tweets to compare the performance of ChatGPT with crowd-workers and trained annotators for various annotation tasks. ChatGPT was found to outperform crowd-workers in relevance, stance, topics, and frames detection, with its zero-shot accuracy being higher than that of crowd-workers for four out of five tasks. The intercoder agreement of ChatGPT was also higher than that of both crowd-workers and trained annotators for all tasks. Additionally, the cost per annotation with ChatGPT was less than $0.003, making it twenty times cheaper than using MTurk. These findings demonstrate the potential of large language models in significantly improving the efficiency of text classification.

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