The Colossal-AI team has open-sourced Swiftlnfer, a TensorRT-based implementation of the StreamingLLM algorithm. The StreamingLLM algorithm addresses the challenge faced by Large Language Models (LLMs) in handling multi-round conversations. It focuses on the limitations posed by input length and GPU memory constraints. The existing attention mechanisms for text generation like dense attention, window attention, and sliding window attention with re-computation, struggle with maintaining generation quality during extended dialogues, especially with long input lengths.

StreamingLLM stabilizes text generation quality during multi-round conversations by employing a sliding-window-based attention module without requiring further fine-tuning. It analyses the output of the softmax operation in the attention module, identifying an attentional sink phenomenon where initial tokens receive unnecessary attention. 

One of the drawbacks in the initial implementation of StreamingLLM in native PyTorch is that it requires optimization to meet the low-cost, low-latency, and high-throughput requirements for LLM multi-round conversation applications.

The Colossal-AI’s SwiftInfer addresses this challenge by combining the strengths of StreamingLLM with TensorRT inference optimization, resulting in a 46% improvement in inference performance for large language models. In Swiftlnfer, the researchers re-imagined the KV Cache mechanism and attention module with position shift. It prevents unnecessary attention to initial tokens and focuses on attentional sink; the models ensure stable generation of high-quality texts during streaming., avoiding the collapse seen in other methods. It is important to note that StreamingLLM doesn’t directly increase the model’s context length but ensures reliable generation support for longer dialog text inputs.

Swiftlnfer successfully optimized StreamingLLM by overcoming the limitations of the algorithm. The integration of TensorRT-LLM’s API enables the construction of the model in a manner similar to PyTorch. Swiftlnfer supports longer dialog text inputs that shows speedup in both initial and optimized implementations. The Colossal-AI community’s commitment to open-source contribution further strengthens the impact of the research in enhancing the development and deployment of AI models.

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The existing 2D image editing methods face a substantial amount of limitations as they heavily rely on textual instructions, leading to ambiguity and restricted control. This confined nature of these methods within 2D spaces hinders the direct manipulation of object geometry, resulting in imprecise outcomes. The lack of tools for spatial interaction also limits the creative possibilities and fine-tuned adjustments that can be made, leaving a gap in image editing capabilities.

The research includes exploration into generative models like GANs, which have broadened the scope of image editing to encompass style transfer, image-to-image translation, latent manipulation, and text-based manipulation. However, text-based editing has limitations in precisely controlling object shapes and positions. ControlNet is one of the models that address this by incorporating additional conditional inputs for controllable generation. Single-view 3D reconstruction, a longstanding problem in computer vision, has seen advancements in algorithmic approaches and training data utilization.

The Image Sculpting method, developed by researchers at New York University, addresses these limitations in 2D image editing by integrating 3D geometry and graphics tools. This approach allows direct interaction with the 3D aspects of 2D objects, enabling precise editing such as pose adjustments, rotation, translation, 3D composition, carving, and serial addition. 

Using a coarse-to-fine enhancement process, the framework re-renders edited objects into 2D and seamlessly merges them into the original image, achieving high-fidelity results. This innovation harmonizes the creative freedom of generative models with the precision of graphics pipelines, significantly closing the controllability gap in image generation and computer graphics.

Figure 1. Overview of the coarse-to-fine generative enhancement model architecture. The red module denotes the one-shot DreamBooth, which requires tuning; the grey module is the SDXL Refiner, which is frozen in the experiments.

While Image Sculpting presents promising capabilities, it faces limitations in controllability and precision through textual prompts. Requests regarding detailed object manipulation remain challenging for current generative models. The method relies on the evolving quality of single-view 3D reconstruction, and manual efforts may be required for mesh deformation. Output resolution falls short of industrial rendering standards, and addressing background lighting adjustments is crucial for realism. Despite its innovative approach, Image Sculpting represents an initial step, and further research is essential to overcome these limitations and enhance its overall capabilities.

To summarize, the key highlights of this research include:

The proposed method of Image Sculpting integrates 3D geometry and graphics tools for 2D image editing.

It directly interacts with 3D aspects, enabling precise edits like pose adjustments and rotations.

Further re-renders edited objects into 2D, seamlessly merging for high-fidelity results.

Attempts to balance creative freedom of generative models with graphics precision.

Faces certain limitations in detailed object manipulation, resolution, and lighting adjustments, creating the need for further research and improvement.

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Creating visual content using AI algorithms has become a cornerstone of modern technology. AI-generated images (AIGIs), particularly those produced via Text-to-Image (T2I) models, have gained prominence in various sectors. These images are not just digital representations but carry significant value in advertising, entertainment, and scientific exploration. Their importance is magnified by the human inclination to perceive and understand the world visually, making AIGIs a key player in digital interactions.

Despite the advancements, the consistency of AIGIs poses a significant hurdle. The crux of the problem is the uniform refinement approach applied across different quality regions of an image. This one-size-fits-all methodology often degrades high-quality areas while attempting to enhance lower-quality regions, presenting a nuanced challenge in the quest for optimal image quality.

Previous methods that enhance the quality of AIGIs have approached them as natural images, relying on large-scale neural networks to restore or reprocess them through generative models. These methods, however, need to pay more attention to the diverse quality across various image areas, resulting in enhancements that are either insufficient or excessive and thus failing to improve image quality uniformly.

The introduction of Q-Refine by researchers from Shanghai Jiao Tong University, Shanghai AI Lab, and Nanyang Technological University marks a significant shift in this landscape. This innovative method employs Image Quality Assessment (IQA) metrics to guide the refinement process, a first in the field. It uniquely adapts to the quality of different image regions, utilizing three separate pipelines specifically designed for low, medium, and high-quality areas. This approach ensures that each part of the image receives the appropriate level of refinement, making the process more efficient and effective.

Q-Refine’s methodology combines human visual system preferences and technological innovation. It starts with a quality pre-processing module that assesses the quality of different image regions. Based on this assessment, the model applies one of three refining pipelines, each meticulously designed for specific quality areas. For low-quality regions, the model adds details to enhance clarity; for medium-quality areas, it improves clarity without altering the entire image; and for high-quality regions, it avoids unnecessary modifications that could degrade quality. This intelligent, quality-aware approach ensures optimal refinement across the whole image.

https://arxiv.org/abs/2401.01117

Q-Refine significantly elevates both the fidelity and aesthetic quality of AIGIs. This system has shown an exceptional ability to enhance images without compromising their high-quality areas, a feat that sets a new benchmark in AI image refinement. Its versatility across images of different qualities and its ability to enhance without degradation underscores its potential as a game-changer.

Conclusively, Q-Refine revolutionizes the AIGI refinement process with several key contributions:

It introduces a quality-aware approach to image refinement, using IQA metrics to guide the process.

The model’s adaptability to different image quality regions ensures targeted and efficient enhancement.

Q-Refine significantly improves the visual appeal and practical utility of AIGIs, promising a superior viewing experience in the digital age.

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The pursuit of fairness in Large Language Models (LLMs) is the primary concern addressed in recent research that recognizes the distinctive qualities associated with LLM deployment. At the core of the matter lies the imperative task of guaranteeing impartiality in providing services to every client while accounting for fluctuating demand, work patterns, unpredictable circumstances, and stochastic scenarios.

Current Large Language Model (LLM) serving systems predominantly prioritize enhancing performance through techniques such as sophisticated batching, memory optimization, and GPU kernel enhancements. Nevertheless, the fundamental aspect of fairness among clients has frequently been overlooked in these systems. Addressing this disparity, a team of researchers from UC Berkeley, Stanford University, and Duke University has introduced a groundbreaking fair scheduler (VTC) specifically designed for LLM serving. This approach functions at the level of individual tokens, providing a more precise and adaptable solution in contrast to conventional fairness methods.

https://arxiv.org/abs/2401.00588

The proposed fair scheduler uses a dynamic definition of fairness that considers both performance and GPU resource consumption. The system is meant to adapt to various fairness standards, allowing service metrics to be customized based on characteristics such as input and output token counts. The research team demonstrates the scheduler’s effectiveness under various workloads through rigorous evaluations. Real-world scenarios validate the approach, including traces from a live LLM serving platform. The study emphasizes the scheduler’s ability to deal with a wide range of client behaviors, workload patterns, and distribution shifts while ensuring equitable resource allocation.

The ability of the scheduler to adjust to various fairness criteria is the fundamental source of its flexibility. The algorithm’s flexibility is demonstrated by its ability to update counters in response to different definitions of the service function. For example, the algorithm seamlessly modifies its counter updates if fairness is defined with a service measurement function represented as h(nin, not), where nin and not represent the number of processed input tokens and generated tokens, respectively. This flexibility covers a range of situations, such as when output tokens are thought to be more costly than input tokens.

The study includes evaluations comparing the proposed fair scheduler, VTC, with alternative scheduling methods. Baseline methods like First Come, First Serve (FCFS), Request per Minute (RPM), and Least Counter First (LCF) are used as benchmarks to emphasize the advantages of VTC. Synthetic and real-world workloads are utilized to assess various aspects of fairness, and the results consistently confirm the fairness capabilities introduced by VTC. Remarkably, the proposed scheduler excels when clients demonstrate diverse request rates, workloads, and distribution patterns, demonstrating its strength and versatility.

In conclusion, the fair scheduler developed by the research team is a breakthrough in tackling the complex issues of fairness in Large Language Model (LLM) serving. This method stands out due to its ability to allocate resources at the level of individual tokens, its flexibility in accommodating various fairness criteria, and its successful implementation and validation in real-life situations. As a result, it offers a viable and efficient solution for ensuring equitable distribution of resources among clients in LLM serving systems.

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In the advancing field of Artificial Intelligence (AI) and Natural Language Processing (NLP), understanding how language models adapt, learn, and retain essential concepts is significant. In recent research, a team of researchers has discussed neuroplasticity and the remapping ability of Large Language Models (LLMs).

The ability of models to adjust and restore conceptual representations even after significant neuronal pruning is referred to as neuroplasticity. After pruning both significant and random neurons, models can achieve high performance again. This contradicts the conventional idea that eliminating important neurons would result in permanent performance deterioration.

A new study has emphasized the importance of neuroplasticity in relation to model editing. Although model editing aims to eliminate unwanted conceptions, neuroplasticity implies that these concepts can resurface after retraining. Creating models that are safer, more equitable, and more in line requires an understanding of how ideas are represented, redistributed, and reclaimed. Understanding the process of recovering removed concepts can also improve language models’ resilience.

The study has shown that models can swiftly recover from pruning by moving sophisticated concepts back to previous layers and redistributing trimmed concepts to neurons that share comparable semantics. This implies that LLMs have the ability to integrate both new and old concepts within a single neuron, which is a phenomenon known as polysemantic capabilities. Though neuron pruning improves the interpretability of model concepts, the findings have highlighted the difficulties in permanently eliminating concepts to increase model safety.

The team has also emphasized the significance of tracking the reemergence of concepts and creating strategies to prevent the relearning of risky notions. This becomes essential to guarantee stronger model editing. The study has highlighted how idea representations in LLMs remain flexible and resilient even if certain concepts are eliminated. Gaining this understanding is essential to improving language models’ safety and dependability as well as the field of model editing.

The team has summarized their primary contributions as follows.

Quick Neuroplasticity: After a few retraining epochs, the model quickly demonstrates neuroplasticity and resumes performance.

Concept Remapping: Neurons in previous layers are effectively remapped to concepts excised from later layers.

Priming for Relearning: After first capturing similar concepts, neurons that recovered pruned concepts may have been primed for relearning.

Polysemantic Neurons: Relearning neurons demonstrate polysemantic qualities by combining old and new ideas, demonstrating the model’s capacity to represent a variety of meanings.

In conclusion, the study has mainly focused on LLMs that have been optimized for named entity recognition. The team has retrained the model, induced neuroplasticity, and pruned significant concept neurons to get the model to function again. The study has looked at how the distribution of concepts shifts and studies the connection between previously linked concepts to a pruned neuron and the concepts that it retrains to learn.

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New Paper 🎉: https://t.co/pgrdha94swCan language models relearn removed concepts?Model editing aims to eliminate unwanted concepts through neuron pruning. LLMs demonstrate a remarkable capacity to adapt and regain conceptual representations which have been removed🧵1/8 pic.twitter.com/Bbek0bFPFm— Fazl Barez (@FazlBarez) January 6, 2024

Generative Artificial Intelligence, characterized by its focus on creating AI systems capable of human-like responses, innovation, and problem-solving, is undergoing a significant transformation. The field has been revolutionized by innovations like the Gemini model and OpenAI’s Q* project, which emphasize the integration of Mixture of Experts (MoE), multimodal learning, and the anticipated progression towards Artificial General Intelligence. This evolution symbolizes a significant shift from conventional AI techniques to more integrated, dynamic systems.

https://arxiv.org/abs/2312.10868

The central challenge in generative AI is developing models that can effectively mimic complex human cognitive abilities and handle diverse data types, including language, images, and sound. Ensuring these technologies align with ethical standards and societal norms further complicates this challenge. AI research’s complexity and volume necessitate efficient methods for synthesizing and evaluating the expanding knowledge landscape.

A team of researchers from Academies Australasia Polytechnic, Massey University, Auckland, Cyberstronomy Pty Ltd, and RMIT University did a comprehensive survey discussing advancements in key model architectures, including Transformer Models, Recurrent Neural Networks, MoE models, and Multimodal Models. The study also addresses challenges related to AI-themed preprints, examining their impact on peer-review processes and scholarly communication. Emphasizing ethical considerations, the study outlines a strategy for future AI research that advocates for a balanced and conscientious approach to MoE, multimodality, and Artificial General Intelligence in generative AI.

Central to many AI architectures, transformer models are now being complemented and sometimes replaced by more dynamic and specialized systems. While Recurrent Neural Networks have been effective for sequence processing, they are increasingly overshadowed by newer models due to their limitations in handling long-range dependencies and efficiency. Many researchers have introduced advanced models like MoE and multimodal learning methodologies to address these evolving needs. MoE models are pivotal for taking diverse data types, particularly in multimodal contexts, by integrating various data types like text, images, and audio for specialized tasks. This trend directly impacts field enhancement, with increased investment in research involving complex data processing and autonomous systems.

The detailed methodology of MoE models and multimodal learning is intricate and nuanced. MoE models are known for their efficiency and task-specific performance, leveraging multiple expert modules. These models are essential in understanding and leveraging complex structures often inherent in unstructured datasets. Their role in AI’s creative capabilities is particularly notable, as they enable the technology to engage in and contribute to creative endeavors, thereby redefining the intersection of technology and art.

The Gemini model has showcased state-of-the-art performance in various multimodal tasks, such as natural image, audio, video understanding, and mathematical reasoning. These advancements herald a future where AI systems could significantly extend their logic, contextual knowledge, and creative problem-solving capabilities, consequently altering the landscape of AI research and applications.

In summary, the ongoing advancements in AI are characterized by the following:

Generative AI, particularly through MoE and multimodal learning, is transforming and reshaping the technological and research landscapes.

The challenge of developing AI models that mimic human cognitive abilities while aligning with ethical standards remains significant.

Current methodologies, including MoE and multimodal learning, are pivotal in handling diverse data types and enhancing AI’s creative and problem-solving capabilities.

The performance of technologies like the Gemini model highlights the potential of AI in various multimodal tasks, signaling a future of extended AI capabilities.

Future research must align these advancements with ethical and societal norms, a critical area for continued development and integration.

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In 2023, the field of artificial intelligence witnessed significant advancements, particularly in the field of large language models. The landscape showcased progress, marking an intermediate stage between prior breakthroughs and the anticipation of more powerful advances in the future. Notably, generative AI tools gained mainstream awareness, becoming the center of discussions in the IT industry.

 Major tech companies invested billions in AI technologies, contributing to the transformative impact of AI across various sectors. The year emphasized the widespread adoption of generative AI, with predictions that a significant majority of enterprises would utilize GenAI APIs and models. This article will delve into the noteworthy stories and launches in the AI sector during 2023, shedding light on the impact and trends that shape the future of this industry.

We have categorized them to make it easier to cover maximum tools.

Text Generation

Gemini: Google’s Gemini is a powerful AI model positioned as a close competitor to OpenAI’s ChatGPT. Released as an advancement over Google’s PaLM 2, Gemini integrates natural language processing for effective understanding and processing of language in input queries and data. Additionally, it boasts image understanding and recognition capabilities, eliminating the need for external optical character recognition. 

Bard:  Google’s Bard is an AI-powered chatbot created by Google that utilizes natural language processing and machine learning to mimic human-like conversation. Trained on a diverse dataset encompassing text, code, and images, Bard also accesses real-time information from the web. This allows Bard to serve as a personal AI assistant, aiding in tasks like email responses, content creation, document translation, and meeting note summarization. 

Mistral 7B: It is a powerful language model, boasting 7.3 billion parameters, making it a significant advancement in large language model capabilities. It has innovative features like Grouped-query Attention for faster inference times and Sliding Window Attention for handling longer text sequences efficiently. The model is freely available for download, contributing to the open-source AI community.

GPT-4:  OpenAI has launched GPT-4, its latest large language model that accepts both image and text inputs and generates text outputs. GPT-4 focuses on improved alignment, following user intentions while minimizing offensive content. It excels in handling complex prompts and adapting to tones, emotions, and various genres. Capable of processing images, generating code, and understanding 26 languages.

Grok: Grok is Elon Musk’s AI chatbot developed by xAI, designed to respond with humor and sarcasm to user text prompts. Using large language model technology, Grok is trained on extensive web data to provide accurate and useful responses to user queries.

OverflowAI: It is a new tool from Stack Overflow that combines the platform’s expertise with artificial intelligence, including Natural Language Processing and Generative AI. Overflow.ai uses AI to deliver accurate answers and supports collaboration, making it easier for developers to solve problems and work together effectively. 

Llama 2: Llama 2 is Meta AI’s latest large language model, designed to offer enhanced efficiency and safety. Utilizing reinforcement learning and reward modeling, Llama 2 improves decision-making to generate helpful and secure outputs. It is suitable for tasks such as text generation, summarization, and question-answering.

Image Generation

Midjourney V.5: Midjourney’s V.5 model is an advanced AI art generator, improving efficiency and resolution. It turns text prompts into images on Discord. Users can also modify uploaded images. Accessible to all, Midjourney is free and open on Discord, allowing users to create, upscale, and share AI-generated art effortlessly.

Adobe Firefly: It is a new addition to Adobe’s suite of products, introducing generative AI models for visual content creation. Firefly is designed to generate content brushes, create variations of existing images, and potentially transform photos and videos based on user prompts. The first model, launching as a public beta, focuses on generating images and text effects.

Shutterstock: Shutterstock has unveiled its AI image generation platform, utilizing text-to-image technology to transform prompts into licensable imagery. The platform is designed to offer a seamless creative experience. This initiative is a result of Shutterstock’s collaboration with OpenAI.

DALL.E 3: OpenAI has introduced DALL·E 3, the latest iteration of its image-generative AI model. Built on ChatGPT, this version enhances user-friendliness by eliminating the need for complex prompt engineering. Operating based on natural language inputs or prompts, the model generates accurate images corresponding to the provided descriptions.

Google Imagen 2: Google has launched Imagen 2, an advanced image-generation technology, as part of its Vertex AI suite. This tool transforms text into images using Google DeepMind technology, resulting in improved image quality and introducing new features. Imagen 2 offers capabilities like inpainting, outpainting, and the ability to use a reference image. Individuals can try Imagen 2 by signing up for a free Google Cloud account and accessing it through the Vertex AI suite.

Video Generation

Stable Video Diffusion: Stability AI has introduced Stable Video Diffusion, a generative video model, with open-source access on GitHub. This model, designed for sectors like advertising, marketing, TV, film, and gaming, is available through Stability AI’s Developer Platform API. The Stable Video Diffusion focuses on both performance and safety, offering frame interpolation for 24fps video output, along with safety measures and watermarking.

Pika: Pika 1.0, developed by Pika Labs, has gained significant popularity. This upgraded AI model empowers users to create and edit videos in diverse styles, including 3D animation, anime, cartoon, and cinematic. Pika 1.0 offers features like text-to-video, image-to-video, and video-to-video conversions, making video creation more accessible and user-friendly for both amateur and professional creators.

HeyGen: HeyGen, an innovative AI video generation platform, has been introduced by a startup. It simplifies the video creation process, allowing users to produce high-quality and engaging videos effortlessly. It has features such as AI-assisted voiceovers, customizable avatars, including the option to use one’s own face, and templates for content creation.

Runaway Gen-2: Runway has introduced the Gen-2 model, a generative AI that empowers users to effortlessly generate full-fledged videos using text prompts, images, or existing videos. Gen-2 offers eight modes, including Text-to-video, Image-to-video, Stylization, Storyboard, Mask, Render, and Customization. The Storyboard mode transforms mockups into fully stylized and animated renders, providing versatile options for creative video synthesis.

VideoPoet: Google’s VideoPoet is an AI model in video creation that offers diverse multimodal features. It excels in text-to-video conversion, image-to-video transformation, video stylization, video inpainting and outpainting, and video-to-audio capabilities. Notably, VideoPoet integrates various video-making functions into one system, using methods like MAGVIT V2 for handling video and images and SoundStream for audio.

Miscellaneous

Evodiff: Microsoft’s EvoDiff is an innovative AI framework designed for protein generation, representing a departure from traditional methods. Unlike conventional approaches, EvoDiff doesn’t rely on structural information, making the process faster and cost-effective. Released as open source, EvoDiff has the potential to create enzymes for therapeutics, drug delivery, and industrial chemical reactions without the need for detailed structural data.

Segment Anything Model: Meta AI introduces SAM, a powerful segmentation model. It showcases remarkable adaptability by efficiently cutting out objects in images without requiring additional training. The model’s strength lies in its extensive training on a diverse dataset, demonstrating robust performance in object segmentation. 

Direct Preference Optimization: Direct Preference Optimization has emerged as a stable and efficient method for fine-tuning large-scale unsupervised language models and teaching text-to-image models. Unlike its counterpart, Reinforcement Learning from Human Feedback (RLHF), DPO eliminates the need for a reward model, offering a direct alternative.

Stable audio:  Stability AI’s audio research lab has introduced Stable Audio, a diffusion model for text-controlled audio generation. Users can specify the desired output length in seconds, allowing the model to generate sounds ranging from single instruments to full ensembles or ambient noise like crowd sounds. Stability Audio offers versatility for music production and other audio projects, leveraging the power of diffusion models trained on audio data.

In conclusion, the launch of Large Language Models in 2023 has marked a significant stride in the ever-evolving landscape of artificial intelligence. The unveiling of powerful models like those stated above reflects the continuous efforts to enhance language understanding, generation, and overall AI capabilities. These advancements pave the way for innovative applications across diverse sectors, from natural language processing to code generation and image synthesis. As AI continues to progress, the year 2023 stands as a testament to the ongoing pursuit of refining existing technologies, opening avenues for practical applications, and setting the stage for the next wave of breakthroughs in the field of artificial intelligence.

Understanding how convolutional neural networks (CNNs) operate is essential in deep learning. However, implementing these networks, especially convolutions and gradient calculations, can be challenging. Many popular frameworks like TensorFlow and PyTorch exist, but their complex codebases make it difficult for newcomers to grasp the inner workings.

Meet neograd, a newly released deep learning framework developed from scratch using Python and NumPy. This framework aims to simplify the understanding of core concepts in deep learning, such as automatic differentiation, by providing a more intuitive and readable codebase. It addresses the complexity barrier often associated with existing frameworks, making it easier for learners to comprehend how these powerful tools function under the hood.

One key aspect of neograd is its automatic differentiation capability, a crucial feature for computing gradients in neural networks. This capability allows users to effortlessly compute gradients for a wide array of operations involving vectors of any dimension, offering an accessible means to understand how gradient propagation works.

Moreover, neograd introduces a range of functionalities like gradient checking, enabling users to verify the accuracy of their gradient calculations. This feature helps in debugging models, ensuring that gradients are correctly propagated throughout the network.

The framework also boasts a PyTorch-like API, enhancing users’ familiarity with PyTorch and enabling a smoother transition between the two. It provides tools for creating custom layers, optimizers, and loss functions, offering a high level of customization and flexibility in model design.

Neograd’s versatility extends to its ability to save and load trained models and weights and even set checkpoints during training. These checkpoints help prevent loss of progress by periodically saving model weights, ensuring continuity in case of interruptions like power outages or hardware failures.

Compared to similar projects, neograd distinguishes itself by supporting computations with scalars, vectors, and matrices compatible with NumPy broadcasting. Its emphasis on readability sets it apart from other compact implementations, making the code more understandable. Unlike larger frameworks like PyTorch or TensorFlow, neograd’s pure Python implementation makes it more approachable for beginners, providing a clear understanding of the underlying processes.

In conclusion, neograd emerges as a valuable educational tool in deep learning, offering simplicity, clarity, and ease of understanding for those seeking to comprehend the intricate workings of neural networks. Its user-friendly interface and powerful functionalities pave the way for a more accessible learning experience in deep learning.

Since it enables humans to teach robots any skill, imitation learning via human-provided demonstrations is a promising approach for creating generalist robots. Lane-following in mobile robots, basic pick-and-place manipulation, and more delicate manipulations like spreading pizza sauce or inserting a battery may all be taught to robots through direct behavior cloning. However, rather than merely requiring individual mobility or manipulation behaviors, many activities in realistic, everyday situations need whole-body coordination of mobility and dexterous manipulation. 

Research by Standford University investigates whether imitation learning may be used for tasks where bimanual mobile robots need to be controlled with their entire body. Two key issues hamper the widespread use of imitation learning for bimanuaneeds to be improved. (1) Plug-and-play, readily available hardware for whole-body teleoperation needs to be improved. (2) Buying off-the-shelf bimanual mobile manipulators can be expensive. Robots such as the PR2 and the TIAGo are too expensive for typical research labs, costing over USD 200k. More gear and calibration are also required to enable teleoperation on these platforms.

This study addresses the difficulties in implementing imitation learning for bimanual mobile manipulation. Regarding hardware, the researchers introduce Mobile ALOHA, a whole-body teleoperation system that is inexpensive and designed to gather data on bimanual mobile manipulation. By placing it on a wheeled base, Mobile ALOHA expands the possibilities of the original ALOHA, the inexpensive and skillful bimanual puppeteering apparatus.

To permit base movement, the user back drives the wheels while physically attached to the system. This enables the base to move independently while the user controls ALOHA with both hands. They create a whole-body teleoperation system by recording arm puppeteering and base velocity data simultaneously.

The team notes that excellent performance in imitation learning can be obtained by simply concatenating the base and arm actions and then training by direct imitation learning. In particular, they create a 16-dimensional action vector by joining the mobile base’s linear and angular velocity with the 14-DoF joint positions of ALOHA. With nearly no implementation change, this formulation enables Mobile ALOHA to benefit directly from earlier deep imitation learning methods.

They highlight that there are few to no accessible bimanual mobile manipulation datasets. However, they were motivated by the recent success of pre-training and co-training on varied robot datasets to increase imitation learning performance further. As a result, they started using data from static bimanual datasets, which are easier to obtain and more plentiful. In particular, they use the static ALOHA datasets through the introduction of RT-X. It has 825 episodes with activities unrelated to the Mobile ALOHA tasks and has the two arms mounted separately. 

Despite the disparities in tasks and morphology, the study demonstrates positive transfer in almost all mobile manipulation tasks, achieving comparable or greater performance and data efficiency than policies taught using only Mobile ALOHA data. Additionally, this observation holds for other classes of cutting-edge imitation learning techniques, such as Diffusion Policy and ACT.

This imitation learning outcome is also robust to many complex activities, including pulling in chairs, contacting an elevator, opening a two-door wall cabinet to store heavy cooking pots, and cleaning up spilled wine. With just 50 human examples for each task, co-training allows us to obtain over 80% performance, with an absolute improvement of 34% on average compared to no co-training.

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Language modeling is important for natural language processing tasks like machine translation and text summarization. The core of this development revolves around constructing LLMs that can process and generate human-like text which transforms how we interact with technology.

A significant challenge in language modeling is the ‘feature collapse’ problem. This issue arises in the model’s architecture, where the expressive power of the model becomes limited which leads to a reduction in the generation quality and diversity of language models. This problem needs to be tackled as it is crucial for enhancing the performance and efficiency of LLMs.

Language models that already exist often focus on scaling up the size of models and datasets to improve performance. However, this approach generates massive computational costs which makes practical applications challenging. Recent studies in enhancing model architecture have explored modifications and particularly in the multi-head self-attention and feed-forward network components of the Transformer model.

The Huawei Noah’s Ark Lab research team addresses current LLMs’ limitations by introducing a model architecture named PanGu-π. This model aims to mitigate the feature collapse problem by enhancing the nonlinearity in the model’s architecture. The innovation lies in introducing series-based activation functions and augmented shortcuts within the Transformer framework. The PanGu-π architecture demonstrates improved nonlinearity. 

PanGu-π enhances the nonlinearity of language models through two main innovations. The first is the implementation of series-based activation functions in the Feed-Forward Network that adds more complexity and expressiveness to the model. The second is the introduction of augmented shortcuts in the Multi-Head Self-Attention modules which diversifies the model’s feature representation and improves its learning capability.

https://arxiv.org/abs/2312.17276

The PanGu-π architecture, including its PanGu-π-1B variant, offers a nonlinear and efficient design with a 10% speed improvement.  The YunShan model which is based on PanGu-π-7B excels in the financial sector and outperforms others in specialized areas like Economics and Banking. The FinEval benchmark shines in Certificate and Accounting tasks and shows remarkable adaptability and suitability for finance-related applications.

In Conclusion, PanGu-π is a new large language model architecture that enhances nonlinearity in its design and addresses feature collapse issues. This is achieved without significantly increasing complexity, as evident in the Feed-Forward Network and Multi-Head Self-Attention modules. The model matches the current top LLMs’ performance with a 10% faster inference. PanGu-π-1B excels in accuracy and efficiency which is the variant of PanGu-π. YunShan outshines in finance and law, particularly in financial sub-domains and benchmarks and it is built on PanGu-π-1B.

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