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With code interfaces regularly changing and the limitation of in-context learning, there is a strong need to be able to update program synthesis performance without expensive data collection and model retraining. This work suggests that using documentation can improve the generated code of CodeT5. The results are good with ~3% improvement Pass@1. This isn’t a foundational change, but may be a useful trick for practitioners.
KOSMOS-12 is a Multimodal Large Language Model that can learn in context, follow instructions, and perceive general modalities. It achieves impressive performance on a range of tasks, including language understanding, perception-language, and vision tasks. The model was trained on web-scale multimodal corpora and benefits from cross-modal transfer. The authors also introduce a dataset for diagnosing the nonverbal reasoning capability of MLLMs.
Text-guided diffusion models struggle to compose scenes with multiple objects in specific positions, which is crucial in storytelling. To address this, the authors propose Directed Diffusion, a method that provides positional control over multiple objects by injecting "activation" at desired positions in cross-attention maps while attenuating the rest. This method can be used with an existing pre-trained model and requires only a few lines of implementation.
The authors propose voxformer, a framework proposed to enable AI systems to imagine the complete 3D geometry of occluded objects and scenes from 2D images. VoxFormer uses a two-stage design where a sparse set of visible and occupied voxel queries from depth estimation is followed by a densification stage that generates dense 3D voxels. The framework adopts a masked autoencoder design to propagate the information to all the voxels by self-attention. Experiments on SemanticKITTI show that VoxFormer outperforms the state of the art with a relative improvement of 20.0% in geometry and 18.1% in semantics and reduces GPU memory during training by ~45% to less than 16GB.
A nice compilation of various trends across the years in AI. Required computation doubles every 9 months while the number of parameters doubles every 18 months. Hardware improvements may improve through 2031. It doesn’t make sense to train models longer than 15 months. This is not exclusive to language but also includes vision and RL. Interactive charts included.
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Apple is renowned for its secrecy, but the company's Exploratory Design Group (XDG) is even more mysterious. The XDG is a startup-like operation within Apple made up of only a few hundred engineers and academics. It is well-funded so that staff are able to explore countless ideas. The group is currently researching glucose monitoring, new display technology, artificial intelligence, and AR/VR headset features to help people with eye diseases. This article explores XDG's structure and operations and takes a look at the technologies it is developing.
Computer Science classes often cover topics such as operating systems and machine learning, but they rarely teach proficiency with tools. This course gives students the opportunity to become proficient in the command line, text editor, version control systems, and more. The mastery of tools not only saves time but also opens up possibilities to solve complex problems. By investing in their tool proficiency, students can make the most of their education and career.
Konsta UI is a library of pixel-perfect mobile UI components built with Tailwind CSS. It includes iOS and Material Design components for React, Vue, and Svelte. All components are created using official design guidelines to ensure a native-like look and feel. Konsta UI is designed to be used with parent frameworks such as Ionic and Framework7.
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Skip the pip-install and dependencies. We added the Whisper model to the Deepgram API so you can try out the latest in speech recognition quickly. Try it free.
A repository that demonstrates the consequences of giving large language models interfaces to other applications.
Liquidators are capitalizing on the wave of tech companies that are shrinking their physical footprints and shifting to remote work.
There are good reasons why people feel younger than their actual age.
In this work we see additional evidence that alignment improves capabilities. Previous research seems to have mostly been focused on capabilities that are weakly aligned to human preference (e.g., improving FID or SOTA on a benchmark). Now, however, we find that if we train a reward model on human feedback data, our models become significantly more useful. They outline a “reward weighted likelihood maximization” scheme. Their experiments are constrained to categories of count, color, and background.
The goal of Civitai is to create a platform where people can share their stable diffusion models (textual inversions, hypernetworks, aesthetic gradients, VAEs, and any other crazy stuff people do to customize their AI generations), collaborate with others to improve them, and learn from each other's work. The platform allows users to create an account, upload their models, and browse models that have been shared by others. Users can also leave comments and feedback on each other's models to facilitate collaboration and knowledge sharing.
Composer is a large (5 billion parameters) controllable diffusion model trained on billions of (text, image) pairs. It can exponentially expand the control space through composition, leading to an enormous number of ways to generate and manipulate images, i.e., making the infinite use of finite means.
The generative AI chatbot can be spectacularly wrong, executives say, and is mostly good for rote tasks at present. Nonetheless, they say CIOs who fail to explore the technology’s possibility risk their companies falling behind.
Prompt engineers are being paid a healthy wage for their skill in getting AI systems to produce exactly what they want.
Of the 1,000 business leaders who use or plan to use ChatGPT surveyed by Resumebuilder.com, 25% have used ChatGPT to replace some employees.