Revolutionizing AI Efficiency: UC Berkeley’s SqueezeLLM Debuts Dense-and-Sparse Quantization, Marrying Quality and Speed in Large Language Model Serving

Recent developments in Large Language Models (LLMs) have demonstrated their impressive problem-solving ability across several fields. LLMs can include hundreds of billions of parameters and are trained on enormous text corpora. 

Studies show that in LLM inference, memory bandwidth, not CPU, is the key performance limitation for generative tasks. This indicates that the rate at which parameters can be loaded and stored for memory-bound situations, rather than arithmetic operations, becomes the key latency barrier. However, progress in memory bandwidth technology has lagged far behind computation, giving rise to a phenomenon known as the Memory Wall.

Quantization is a promising method that involves storing model parameters with less accuracy than the usual 16 or 32 bits used during training. Despite recent advancements like LLaMA and its instruction-following variations, it is still difficult to achieve good quantization performance, especially with lower bit precision and relatively modest models (e.g., 50B parameters).
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A new study from UC Berkeley investigates low-bit precision quantization in depth to reveal the shortcomings of current methods. Based on these findings, the researchers introduce SqueezeLLM, a post-training quantization framework that combines a Dense-and-Sparse decomposition technique with a unique sensitivity-based non-uniform quantization strategy. These methods permit quantization with ultra-low-bit precision while preserving competitive model performance, drastically cutting down on model sizes and inference time costs. Their method reduces the LLaMA-7B model’s perplexity at 3-bit precision from 28.26 with uniform quantization to 7.75 on the C4 dataset, which is a considerable improvement.

Through comprehensive testing on the C4 and WikiText2 benchmarks, the researchers discovered that SqueezeLLM consistently outperforms existing quantization approaches by a wide margin across different bit precisions when applied to LLaMA-7B, 13B, and 30B for language modeling tasks.

According to the team, the low-bit precision quantization of many LLMs is particularly difficult due to substantial outliers in the weight matrices. These outliers likewise impact their non-uniform quantization approach since they bias the allocation of bits toward extremely high or low values. To eliminate the outlier values, they provide a straightforward method that splits the model weights into dense and sparse components. By isolating the extreme values, the central region displays a narrower range of up to 10, resulting in better quantization precision. With efficient sparse storage methods like Compressed Sparse Rows (CSR), the sparse data can be kept in full precision. This method incurs low overhead by using efficient sparse kernels for the sparse half and parallelizing the computation alongside the dense part. 

The team demonstrates their framework’s potential quantizing IF models by applying SqueezeLLM to the Vicuna-7B and 13B models. They compare two systems in their tests. To begin, they use the MMLU dataset, a multi-task benchmark that measures a model’s knowledge and problem-solving abilities, to gauge the quality of the generated output. They also use GPT-4 to rank the generation quality of the quantized models relative to the FP16 baseline, using the evaluation methodology presented in Vicuna. In both benchmarks, SqueezeLLM regularly outperforms GPTQ and AWQ, two current state-of-the-art approaches. Notably, in both assessments, the 4-bit quantized model performs just as well as the baseline.

The work shows considerable latency reductions and advances in quantization performance with their models running on A6000 GPUs. The researchers demonstrate speedups of up to 2.3 compared to the baseline FP16 inference for LLaMA-7B and 13B. Additionally, the proposed method achieves up to 4x quicker latency than GPTQ, demonstrating its efficacy in quantization performance and inference efficiency. 

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Meta AI Shatters Barriers with Voicebox: An Unprecedented Generative AI Model-Revolutionizing the Field of Speech Synthesis

Meta-AI Researchers have recently achieved a significant breakthrough in generative AI for speech. They have developed Voicebox, an innovative AI model that showcases the state-of-the-art performance and the ability to generalize to speech-generation tasks without specific training.

Unlike previous speech-generation models, Voicebox utilizes a novel approach called Flow Matching, which surpasses diffusion models in terms of performance. Voicebox has proven to outperform existing models in both intelligibility and audio similarity while also being up to 20 times faster. Furthermore, it can synthesize speech in six languages and perform noise removal, content editing, style conversion, and diverse sample generation.

Traditionally, generative AI for speech required thorough training for each specific task using carefully curated data. However, Voicebox breaks this barrier by learning from raw audio and its accompanying transcription. This breakthrough allows the model to modify any part of a given sample rather than being limited to changing only the end of an audio clip.
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The researchers trained Voicebox using over 50,000 hours of recorded speech and transcripts from public-domain audiobooks in English, French, Spanish, German, Polish, and Portuguese. The model was trained to predict speech segments based on surrounding speech and corresponding transcripts. By learning to infill speech from context, Voicebox can generate speech portions in the middle of an audio recording without recreating the entire input.

Voicebox’s versatility enables it to excel in various speech-generation tasks. It can perform in-context text-to-speech synthesis, cross-lingual style transfer, speech denoising and editing, and diverse speech sampling. For instance, with a two-second input audio sample, Voicebox can match the audio style and use it for text-to-speech generation. This capability has potential applications in helping individuals unable to speak or customizing voices for virtual assistants and nonplayer characters.

Another impressive feature of Voicebox is its ability to perform cross-lingual style transfer. Given a speech sample and a text passage in one of the supported languages, Voicebox can generate a reading of the text in the corresponding language. This breakthrough could facilitate natural and authentic communication among individuals who speak different languages.

Additionally, Voicebox’s in-context learning makes it proficient in seamlessly editing segments within audio recordings. It can resynthesize speech segments corrupted by short-duration noise or replace misspoken words without re-recording the entire speech. This capability simplifies the process of cleaning up and editing audio, potentially revolutionizing audio editing tools.

Moreover, Voicebox’s training on diverse real-world data enables it to generate speech that better represents how people naturally talk across different languages. This ability could be employed to generate synthetic data for training speech assistant models. Remarkably, speech recognition models trained on Voicebox-generated synthetic speech achieve near-parity with models trained on real speech, resulting in minimal accuracy degradation.

While the researchers acknowledge the importance of openness and sharing research with the AI community, they are withholding public access to the Voicebox model and code due to potential risks of misuse. In their research paper, they outline the development of a highly effective classifier to distinguish between authentic speech and audio generated with Voicebox, aiming to mitigate possible future risks.

Voicebox represents a significant advancement in generative AI for speech, offering a versatile and efficient model that exhibits task generalization capabilities. With the potential for numerous applications, Voicebox opens up new possibilities for speech synthesis, cross-lingual communication, audio editing, and training speech recognition models. As the research community builds upon this breakthrough, the field of generative AI for speech is poised for exciting advancements and discoveries.

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Microsoft Researchers Propose BioViL-T: A Novel Self-Supervised Framework Ushering in Enhanced Predictive Performance and Data Efficiency in Biomedical Applications

Artificial Intelligence (AI) has emerged as a significant disruptive force across numerous industries, from how technological businesses operate to how innovation is unlocked in different subdomains in the healthcare sector. In particular, the biomedical field has witnessed significant advancements and transformation with the introduction of AI. One such noteworthy progress can be boiled down to using self-supervised vision-language models in radiology. Radiologists rely heavily on radiology reports to convey imaging observations and provide clinical diagnoses. It is noteworthy that prior imaging studies frequently play a key role in this decision-making process because they provide crucial context for assessing the course of illnesses and establishing suitable medication choices. However, current AI solutions in the mark cannot successfully align images with report data due to limited access to previous scans. Furthermore, these methods frequently do not consider the chronological development of illnesses or imaging findings typically present in biological datasets. This lack of contextual information poses risks in downstream applications like automated report generation, where models may generate inaccurate temporal content without access to past medical scans.

With the introduction of vision-language models, researchers aim to generate informative training signals by utilizing image-text pairs, thus, eliminating the need for manual labels. This approach enables the models to learn how to precisely identify and pinpoint discoveries in the images and establish connections with the information presented in radiology reports. Microsoft Research has continually worked to improve AI for reporting and radiography. Their prior research on multimodal self-supervised learning of radiology reports and images has produced encouraging results in identifying medical problems and localizing these findings within the images. As a contribution to this wave of research, Microsoft released BioViL-T, a self-supervised training framework that considers earlier images and reports when available during training and fine-tuning. BioViL-T achieves breakthrough results on various downstream benchmarks, such as progression classification and report creation, by utilizing the existing temporal structure present in datasets. The study will be presented at the prestigious Computer Vision and Pattern Recognition Conference (CVPR) in 2023.

The distinguishing characteristic of BioViL-T lies in its explicit consideration of previous images and reports throughout the training and fine-tuning processes rather than treating each image-report pair as a separate entity. The researchers’ rationale behind incorporating prior images and reports was primarily to maximize the utilization of available data, resulting in more comprehensive representations and enhanced performance across a broader range of tasks. BioViL-T introduces a unique CNN-Transformer multi-image encoder that is jointly trained with a text model. This novel multi-image encoder serves as the fundamental building block of the pre-training framework, addressing challenges such as the absence of previous images and pose variations in images over time.
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A CNN and a transformer model were chosen to create the hybrid multi-image encoder to extract spatiotemporal features from image sequences. When previous images are available, the transformer is in charge of capturing patch embedding interactions across time. On the other hand, CNN is in order of giving visual token properties of individual images. This hybrid image encoder improves data efficiency, making it suitable for datasets of even smaller sizes. It efficiently captures static and temporal image characteristics, which is essential for applications like report decoding that call for dense-level visual reasoning over time. The pre-training procedure of the BioViL-T model can be divided into two main components: a multi-image encoder for extracting spatiotemporal features and a text encoder incorporating optional cross-attention with image features. These models are jointly trained using cross-modal global and local contrastive objectives. The model also utilizes multimodal fused representations obtained through cross-attention for image-guided masked language modeling., thereby effectively harnessing visual and textual information. This plays a central role in resolving ambiguities and enhancing language comprehension, which is of utmost importance for a wide range of downstream tasks.

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The success of the Microsoft researchers’ strategy was aided by a variety of experimental evaluations that they conducted. The model achieves state-of-the-art performance for a variety of downstream tasks like progression categorization, phrase grounding, and report generation in single- and multi-image configurations. Additionally, it improves over previous models and yields appreciable results on tasks like disease classification and sentence similarity. Microsoft Research has made the model and source code available to the public to encourage the community to investigate their work further. A brand-new multimodal temporal benchmark dataset dubbed MS-CXR-T is also being made public by the researchers to stimulate additional research into quantifying how well vision-language representations can capture temporal semantics. 

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Stanford and Cornell Researchers Introduce Tart: An Innovative Plug-and-Play Transformer Module Enhancing AI Reasoning Capabilities in a Task-Agnostic Manner

Without changing the model parameters, large language models have in-context learning skills that allow them to complete a job given only a small number of instances. One model may be used for various tasks because of its task-agnostic nature. In contrast, conventional techniques for task adaptation, including fine-tuning, modify the model parameters for each task. Even though task-independent, in-context learning is rarely the practitioner’s method of choice because it routinely performs worse than task-specific adaption techniques. Most previous studies blame this performance disparity on the LLMs’ constrained context window, which can only accommodate a small number of task cases. 

However, they demonstrate that the gap between in-context learning and fine-tuning techniques remains even when given identical task examples. This discovery begs whether the performance difference is a general constraint of task-agnostic strategies for adaptation or if it is unique to in-context learning. Can they specifically create adaption strategies that meet the requirements listed below: 

• Task-agnostic: The same model applies universally to various activities. 
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• Quality: Across these several tasks, achieves accuracy competitive with task-specific approaches. 

• Data-scalable: Learning efficiency increases as the number of task instances increases. They start by looking at the causes of the quality discrepancy. 

They divide an LLM’s capacity for in-context learning into two components: the acquisition of effective task representations and the execution of probabilistic inference, or reasoning, over these representations. Is the gap caused by a lack of information in the representations or by the LLMs’ inability to analyze them? By evaluating the reasoning and representational gaps across a range of LLM families throughout several binary classification tasks, they test this notion empirically. They conclude that LLMs have strong representations and that the majority of the quality disparity is caused by weak reasoning on their part.

They also discover that fine-tuning enhances the basic model on both axes but predominantly enhances task-specific reasoning, responsible for 72% of the performance improvement. Surprisingly, most methods for narrowing the performance gap, such as prompt engineering and active example selection, only target the LLM’s learned representations. In contrast, their research examines an alternative strategy for enhancing LLM reasoning skills. They refine LLMs using artificially created probabilistic inference challenges as a first step to improving their reasoning skills. While this method enhances the model’s baseline in-context learning performance, it also necessitates individually fine-tuning each LLM. 

They go a step further and speculate on the prospect of developing reasoning skills in a way that is independent of tasks and models. They demonstrate that an entirely agnostic approach may be taken to enhance reasoning skills. Researchers from Standford University and Cornell University in this study suggest Tart, which uses a synthetically taught reasoning module to improve an LLM’s reasoning capabilities. Only synthetically produced logistic regression problems, regardless of the downstream task or the base LLM, are used by Tart to train a Transformer-based reasoning module. Without further training, this inference module may be constructed using an LLM’s embeddings to enhance its deductive capabilities. 

In particular, Tart achieves the necessary goals: 

• Task-neutral: Tart’s inference module must be trained once with fictitious data. 

• Quality: Performs better than basic LLM across the board and closes the gap using task-specific fine-tuning techniques. 

• Data-scalable: Handling 10 times as many instances as in-context learning. 

Tart is independent of task, model, and domain. They demonstrate that Tart generalizes across three model families over 14 NLP classification tasks and even across distinct domains, using a single inference module trained on synthetic data. They demonstrate that Tart’s performance is superior in terms of quality to in-context learning by 18.4%, task-specific adapters by 3.4%, and complete task-specific fine-tuning by 3.1% across various NLP tasks. 

On the RAFT Benchmark, Tart raises GPT-Neo’s performance to the point where it equals GPT-3 and Bloom while outperforming the latter by 4%. Tart solves the inconveniently short context duration barrier of in-context learning and is data-scalable. In an LLM, each example can take up several tokens, often hundreds, whereas Tart’s reasoning module only uses two tokens per case—one for the context and one for the label. The benefits that can result from this data scalability can reach 6.8%. Theoretically, they demonstrate that Tart’s generalization skills mostly depend on the distribution shift between the synthetic data distribution and the natural text embedding distribution, as evaluated by the Wasserstein-1 metric. 

The following is a summary of their principal contributions: 

• Using a representation-reasoning decomposition, investigate why task-specific fine-tuning outperforms in-context learning while having access to the same information. 

• Present Tart, a novel task-agnostic approach that outperforms task-specific approaches and requires no real data for training. 

• Prove that Tart is effective for various model families across NLP tasks. The same inference module also applies to voice and visual domains.

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Meet TARDIS: An AI Framework that Identifies Singularities in Complex Spaces and Captures Singular Structures and Local Geometric Complexity in Image Data

We are deluged with enormous volumes of data from all the different domains, including scientific, medical, social media, and educational data. Analyzing such data is a crucial requirement. With the increasing amount of data, it is important to have approaches for extracting simple and meaningful representations from complex data. The previous methods work on the same assumption that the data lies close to a small-dimensional manifold despite having a large ambient dimension and seek the lowest-dimensional manifold that best characterizes the data.

Manifold learning methods are used in representation learning, where high-dimensional data is transformed into a lower-dimensional space while keeping crucial data features intact. Though the manifold hypothesis work for most types of data, it doesn’t work well in data with singularities. Singularities are the regions where the manifold assumption breaks down and can contain important information. These regions violate the smoothness or regularity properties of a manifold.

Researchers have proposed a topological framework called TARDIS (Topological Algorithm for Robust DIscovery of Singularities) to address the challenge of identifying and characterizing singularities in data. This unsupervised representation learning framework detects singular regions in point cloud data and has been designed to be agnostic to the geometric or stochastic properties of the data, only requiring a notion of the intrinsic dimension of neighborhoods. It aims to tackle two key aspects – quantifying the local intrinsic dimension and assessing the manifoldness of a point across multiple scales. 
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The authors have mentioned that quantifying the local intrinsic dimension measures the effective dimensionality of a data point’s neighborhood. The framework has achieved this by using topological methods, particularly persistent homology, which is a mathematical tool used to study the shape and structure of data across different scales. It estimates the intrinsic dimension of a point’s neighborhood by applying persistent homology, which gives information on the local geometric complexity. This local intrinsic dimension measures the degree to which the data point is manifold and indicates whether it conforms to the low-dimensional manifold assumption or behaves differently.

The Euclidicity Score, which evaluates a point’s manifoldness on different scales, quantifies a point’s departure from Euclidean behavior, revealing the existence of singularities or non-manifold structures. The framework captures differences in a point’s manifoldness by taking Euclidicity into account at various scales, making it possible to spot singularities and comprehend local geometric complexity.

The team has provided theoretical guarantees on the approximation quality of this framework for certain classes of spaces, including manifolds. They have run experiments on a variety of datasets, from high-dimensional image collections to spaces with known singularities, to validate their theory. These findings showed how well the approach identifies and processes non-manifold portions in data, shedding light on the limitations of the manifold hypothesis and exposing important data hidden in singular regions.

In conclusion, this approach effectively questions the manifold hypothesis and is efficient in detecting singularities which are the points that violate the manifoldness assumption.

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MetaVERTU Revolutionizes Smartphone Market with ChatGPT Integration, Redefining Conversational Capabilities and Pioneering AI-Driven Luxury

In an amazing set of events, Vetu, a luxury smartphone brand, dropped their new project integrating ChatGPT into their new upcoming devices, MetaVertu. This news made headlines on April 24 and was reported by an authoritative Chinese media outlet Jinsefinance. This turn of events and a breakthrough development made it into the market just before Apple’s highly anticipated launch of ChatGPT on the App Store on May 19.

Vertu is a premium smartphone manufacturing company that Nokia formerly owned. They claim to provide the best class experience and service to the users. Although they don’t offer any major technological advancement in terms of hardware, they claim to have best-in-class encryption, global GSM sim coverage, a best-in-class camera, and other general features, along with one unique feature that comes along with a concierge service entitlement. Driven by the philosophy of “If you can spend $20,000 on a watch, then why not on your smartphone,” they aim to cater to the elite class better regarding their cellular needs.

MetaVertu is now integrated with ChatGPT, which will introduce an unwitnessed and unparalleled user experience, offering a wide range of features and benefits that puts it ahead of all the competition. Unlike Apple’s app store, where you have to pay a subscription fee of $19.9, MetVertu has decided to give out free access to chatgpt and applications based on that. The company is claiming the affordability factor as its unique selling point for users searching for an exceptional AI-powered conversational experience.
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When a user access ChatGPT on the MetaSpace platform, that user can gain access to a comprehensive set of functionalities. The ChatGPT app, known as V-GPT, enables seamless one-click login and unrestricted conversations at no cost (as opposed to all the other paid models on different platforms), and it also supports voice input for user queries. Not only that, but users can also engage in dialogues with various AI personalities such as AI Buddha, a comic, or even a dream interpreter, which supports how versatile and entertaining, conversational experience they are set to deliver.

MetaVertu has laid out some ambitious plans for what they aim and visualize for the future post the chatgpt integration. They are counting on the new release of ChatGPT4, which will introduce new custom AI roles and lead to the creation of personal AI gigs tailored to suit every user on a personal level. The company plans to integrate voice chat capabilities and deploy various tools for various scenarios. These tools will include an emotional assistant for managing emotional intelligence, conflict resolution, and blame shifting; an efficiency expert offering reporting, OKR (Objectives and Key Results) composition, and translation tools; and a copywriting genius specializing in marketing and everyday written content.

What is worth noting is that Vertu disclosed all of this information related to ChatGPT integration on April 24, significantly before May 19, demonstrating a significant and visionary commitment to pioneer AI integration and redefine the smartphone landscape.

In conclusion, Vertu’s integration of ChatGPT into their latest MetaVERTU smartphone series has initiated a new age of conversational capabilities. The affordability, versatility, and customization offered by MetaVERTU make it unique. Since it is leading the race of AI integration with smartphones, this move has positioned Vertu as a pioneering force driving the AI-integrated smartphone market. With its ambitious plans for future updates and tools, it would be interesting to see how it evolves.

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Meta AI Unveils Revolutionary I-JEPA: A Groundbreaking Leap in Computer Vision That Emulates Human and Animal Learning and Reasoning

Humans pick up a tremendous quantity of background information about the world just by watching it. The Meta team has been working on developing computers that can learn internal models of how the world functions to let them learn much more quickly, plan out how to do challenging jobs, and quickly adapt to novel conditions since last year. For the system to be effective, these representations must be learned directly from unlabeled input, such as images or sounds, rather than manually assembled labeled datasets. This learning process is known as self-supervised learning.

Generative architectures are trained by obscuring or erasing parts of the data used to train the model. This could be done with an image or text. They then make educated guesses about what pixels or words are missing or distorted. However, a major drawback of generative approaches is that the model attempts to fill in any gaps in knowledge, notwithstanding the inherent uncertainty of the real world. 

Researchers at Meta have just unveiled their first artificial intelligence model. By comparing abstract representations of images (rather than comparing the pixels themselves), their Image Joint Embedding Predictive Architecture (I-JEPA) can learn and improve over time.
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According to the researchers, the JEPA will be free of the biases and problems that plague invariance-based pretraining because it does not involve collapsing representations from numerous views/augmentations of an image to a single point.

The goal of I-JEPA is to fill in knowledge gaps using a representation closer to how individuals think. The proposed multi-block masking method is another important design option that helps direct I-JEPA toward developing semantic representations. 

I-JEPA’s predictor can be considered a limited, primitive world model that can describe spatial uncertainty in a still image based on limited contextual information. In addition, the semantic nature of this world model allows it to make inferences about previously unknown parts of the image rather than relying solely on pixel-level information.

To see the model’s outputs when asked to forecast within the blue box, the researchers trained a stochastic decoder that transfers the I-JEPA predicted representations back into pixel space. This qualitative analysis demonstrates that the model can learn global representations of visual objects without losing track of where those objects are in the frame.

Pre-training with I-JEPA uses few computing resources. It doesn’t require the overhead of applying more complex data augmentations to provide different perspectives. The findings suggest that I-JEPA can learn robust, pre-built semantic representations without custom view enhancements. A linear probing and semi-supervised evaluation on ImageNet-1K also beats pixel and token-reconstruction techniques.

Compared to other pretraining methods for semantic tasks, I-JEPA holds its own despite relying on manually produced data augmentations. I-JEPA outperforms these approaches on basic vision tasks like object counting and depth prediction. I-JEPA is adaptable to more scenarios since it uses a less complex model with a more flexible inductive bias.

The team believes that JEPA models have the potential to be used in creative ways in areas like video interpretation is quite promising. Using and scaling up such self-supervised approaches for developing a broad model of the world is a huge step forward.

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How Should We Store AI Images? Google Researchers Propose an Image Compression Method Using Score-based Generative Models

A year ago, generating realistic images with AI was a dream. We were impressed by seeing generated faces that resemble real ones, despite the majority of outputs having three eyes, two noses, etc. However, things changed quite rapidly with the release of diffusion models. Nowadays, it is difficult to distinguish an AI-generated image from a real one.

The ability to generate high-quality images is one part of the equation. If we were to utilize them properly, efficiently compressing them plays an essential role in tasks such as content generation, data storage, transmission, and bandwidth optimization. However, image compression has predominantly relied on traditional methods like transform coding and quantization techniques, with limited exploration of generative models.

Despite their success in image generation, diffusion models and score-based generative models have not yet emerged as the leading approaches for image compression, lagging behind GAN-based methods. They often perform worse or on par with GAN-based approaches like HiFiC on high-resolution images. Even attempts to repurpose text-to-image models for image compression have yielded unsatisfactory results, producing reconstructions that deviate from the original input or contain undesirable artifacts.
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The gap between the performance of score-based generative models in image generation tasks and their limited success in image compression raises intriguing questions and motivates further investigation. It is surprising that models capable of generating high-quality images have not been able to surpass GANs in the specific task of image compression. This discrepancy suggests that there may be unique challenges and considerations when applying score-based generative models to compression tasks, necessitating specialized approaches to harness their full potential. 

So we know there is a potential for using score-based generative models in image compression. The question is, how can it be done? Let us jump into the answer.

Google researchers proposed a method that combines a standard autoencoder, optimized for mean squared error (MSE), with a diffusion process to recover and add fine details discarded by the autoencoder. The bit rate for encoding an image is solely determined by the autoencoder, as the diffusion process does not require additional bits. By fine-tuning diffusion models specifically for image compression, it is shown that they can outperform several recent generative approaches in terms of image quality. 

The proposed method can preserve details much better compared to the state-of-the-art approaches. Source: https://arxiv.org/pdf/2305.18231.pdf

The method explores two closely related approaches: diffusion models, which exhibit impressive performance but require a large number of sampling steps, and rectified flows, which perform better when fewer sampling steps are allowed. 

The two-step approach consists of first encoding the input image using the MSE-optimized autoencoder and then applying either the diffusion process or rectified flows to enhance the realism of the reconstruction. The diffusion model employs a noise schedule that is shifted in the opposite direction compared to text-to-image models, prioritizing detail over global structure. On the other hand, the rectified flow model leverages the pairing provided by the autoencoder to directly map autoencoder outputs to uncompressed images.

Overview of proposed HFD model. Source: https://arxiv.org/pdf/2305.18231.pdf

Moreover, the study revealed specific details that can be useful for future research in this domain. For example, it is shown that the noise schedule and the amount of noise injected during image generation significantly impact the results. Interestingly, while text-to-image models benefit from increased noise levels when training on high-resolution images, it is found that reducing the overall noise of the diffusion process is advantageous for compression. This adjustment allows the model to focus more on fine details, as the coarse details are already adequately captured by the autoencoder reconstruction.

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AMD Unveils Advanced CPU and AI Accelerators, Taking Aim at Nvidia’s Dominance

The American semiconductor company, Advanced Micro Devices (AMD), made significant strides in the chip-making market as it unveiled its highly anticipated CPU and AI accelerator solutions at the “Data Center and AI Technology Premiere” event. To compete directly with Nvidia, AMD showcased its AI Platform strategy, including introducing the AMD Instinct™ MI300 Series accelerator family, touted as “the world’s most advanced accelerator for generative AI.”

The reveal of the AMD Instinct MI300X accelerator, a part of the MI300 Series, marked a notable milestone for AMD’s ambitions. Positioned as a potential rival to Nvidia’s powerful H100 chipset and the GH200 Grace Hopper Superchip currently in production, the MI300X boasts impressive specifications. A staggering 192 GB of HBM3 memory offers the computational and memory efficiency required for large language model training and inference in generative AI workloads. The MI300X’s extensive memory capacity enables it to accommodate massive language models such as Falcon-40, and a 40B parameter model, all within a single accelerator.

Nvidia has long dominated the GPU market, commanding over 80% market share. The H100 GPU stands as Nvidia’s flagship product for AI, high-performance computing (HPC), and data analytics workloads. Its fourth-generation Tensor Cores significantly enhance AI training and inference speeds, outperforming the previous generation by up to 7 times for GPT-3 models. The H100 also features Nvidia’s revolutionary Hopper Memory, a high-bandwidth, low-latency memory system that accelerates data-intensive tasks and offers twice the speed of the previous generation. Furthermore, the H100 is the first GPU to support Nvidia’s Grace CPU architecture, which, when combined with the GPU, delivers up to 10 times the performance of previous-generation systems. With a memory capacity of 188 GB, the H100 boasts the highest memory capacity of any GPU currently available.
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The impending release of AMD’s Instinct MI300X later this year could disrupt Nvidia’s dominance in the market. A Reuters report suggested that Amazon Web Services (AWS) is contemplating the adoption of AMD’s new chips. While AMD has yet to disclose the pricing for its new accelerators, Nvidia’s H100 chipset typically carries a price tag of approximately $10,000, with resellers listing it for as much as $40,000.

In addition to its hardware advancements, AMD introduced the ROCm software ecosystem—a comprehensive collection of software tools and resources designed for data center accelerators. Notably, AMD highlighted collaborations with industry leaders during the event. PyTorch, a popular AI framework, partnered with AMD and the PyTorch Foundation to integrate the ROCm software stack, ensuring immediate support for PyTorch 2.0 on all AMD Instinct accelerators. This integration empowers developers to utilize a wide range of AI models powered by PyTorch on AMD accelerators. Furthermore, Hugging Face, an open platform for AI builders, announced plans to optimize thousands of their models for AMD platforms.

The announcement of AMD’s AI strategy has garnered attention from investors and market analysts alike. In May, the company reported revenue of $5.4 billion for the first quarter of 2023, experiencing a 9% year-over-year decline. However, AMD’s stock surged more than 2% following the event, currently trading at $127. Prominent financial institutions, including Barclays, Jefferies, and Wells Fargo, have raised AMD’s target price to $140-$150.

AMD’s foray into the CPU and AI accelerator market signals its commitment to becoming a formidable competitor to Nvidia. With the introduction of the AMD Instinct MI300X and its promising specifications, combined with strategic software partnerships, the company aims to accelerate the deployment of its AI platforms at scale in the data center. As the battle for dominance in the chip-making market intensifies, all eyes will be on AMD and Nvidia as they strive to shape the future of computing with their innovative solutions.

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Best AI Tools For E-commerce Startups (2023)

AI is assisting companies in streamlining their operations. It’s efficient, long-lasting, and extensible. The eCommerce industry has benefited greatly from AI. An online store’s full customer service and stock-keeping processes can now be automated.

This article reviews the best artificial intelligence tools for an online store in 2023. Retailers have varying requirements, though. You get to choose which processes are automated.

Pixelcut is a robust AI platform that any company can utilize. The best part is that it’s costless for your mobile device. This is a nice option if you need to become more experienced with photo editing programs like Photoshop. Background eraser, object removal magic eraser, image upscaler, and more are just some of the free tools available from Pixelcut. However, the app’s AI-generated product images are among its most impressive features. With Pixelcut’s product photo maker, you can ditch those boring stock images once and for all. Is there a Christmas sale going on? Sure enough, you’ve nailed it! Is there more of a summer vibe at your store? Easy! Using Pixelcut’s AI-driven tools is a no-brainer if you want creative product photographs that reflect the character of your brand.
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tinyEinstein is an AI Marketing manager that helps you grow your Shopify store 10x faster with almost zero time investment from you. TinyEinstein can Automate key marketing activities, Create on-brand emails, and Send automated emails; tinyEinstein AI can help you increase sales by sending targeted and timely emails to your customers, and many more benefits. If you’re looking for an AI-powered marketing tool that can help you grow your Shopify store, then you can try tinyEinstein AI. It’s a powerful tool that can help you increase sales. Try it today!

One of the most nerve-wracking aspects of managing an online shop is coming up with content. However, with Writerly, this is no longer the case. Writerly is a piece of AI software that can generate in-depth articles based on the owners’ honest thoughts. Flawless in terms of grammar and presentation. A few details regarding the material, including a headline, topic, keywords, and phrases, are required. It can be used to revise and refresh previously published material so that it is more interesting and informative. Its algorithm carefully studies your business to produce content that appeals to customers and search engines alike. With Writely, you can translate your work into more than 25 languages, making it accessible to an international audience.

Frase is another AI tool for improved writing. It makes fast, accurate content creation easy. This eliminates the need to wait days or hours to upload content to the web. Frase also allows social media posting. Frase handles research, writing, and SEO so that you may focus on business growth. Frase can create engaging blog openers, headlines, and FAQs for your business. Use the app’s statistics and insights to identify content that needs editing. It also has a dashboard where you can track how well your content is performing, determine which pages need upgrading, and find high-volume keywords to use in your content.

Engagement and conversion rates are increased by visual content. Your visual, then, should always be of the highest possible quality. Neural Love is an available artificial intelligence photo editor. It resizes and reformats photos for user usage. It creates aviators and artistically reworks your photographs. Uploading images gives you a fresh appearance. It also quadruples photo size without compromising quality. It enhances image quality, so you don’t have to take pixelated photos. AI creates high-definition images that seem like the originals, reviving photography.

Another AI-powered image generator, Deep Dream, lets you whip up gorgeous works of art in a flash. It can transform your otherwise boring photo into a fascinating piece of art. Any image you feed into this program will look better for it. The straightforward design makes it accessible to users of all experience levels. In addition, the AI system may generate an image depending on the phrases, words, or sentences you input into a text prompt. Deep Dream is straightforward to employ. Some examples of the images it generated based on user input are provided below.

Descript is an effective artificial intelligence application for any online shop that aims to boost revenue and customer engagement. Its purpose is to help you make interesting videos on your website. Also, this is one of the greatest AI video editing tools if you have an online business. Intriguingly, it doesn’t only concentrate on video but on the complete process, allowing you to record, edit, and distribute screencasts. You may also use the premade video themes to transform your content into entertaining vignettes. Descript is also used for podcast transcriptions and audio editing. Descript’s embeddable player also allows you to host and distribute your material.

Salesforce Einstein is loaded with cutting-edge AI tools to streamline your processes and boost productivity. It forecasts future events based on historical information. Furthermore, it has been pre-programmed to avoid and deal with potential confrontations. The use of preventative measures avoids conflicts. Using this tool improves the efficiency of your business processes. Intriguingly, it can anticipate the next sales opportunity to serve customers’ wants better. Since customer relationship management is its main concern, it tailors its services to each client.

E-commerce sites must prioritize client needs and provide satisfaction throughout the buying process. Google Cloud is an AI technology that every online store must employ. It tailors suggestions to the buyer’s past purchases. Because their services are automated, there is no need to preprocess orders or manually deploy infrastructure to accommodate traffic peaks. Easily integrates data, manages models, makes recommendations, and monitors performance. Adding unstructured metadata like product names and descriptions will make your data collection more predictive. It also remembers first-time visitors and provides helpful suggestions from the top page to the cart and confirmation page.

Our next artificial intelligence e-commerce tool is Prisync. You may count on this software to increase your earnings and sales. This tool makes it simple to keep tabs on the pricing strategies of your rivals in a certain market. You may monitor this information from the control panel as well. In addition, the system’s flexibility makes it simple to evaluate and track shifts in pricing and stock levels. Prisync is an interesting tool since it can automate managing product pricing to maximize profitability. It offers hassle-free services that solve any potential website technical issues.

The Visense artificial intelligence software streamlines finding new products for online retailers. This software is ideal for online retailers looking to boost sales through product discovery. Displaying things your clients are most likely to buy thanks to intelligent recommendations and machine intelligence. Customers can more quickly find what they need, reducing their shopping time. Additionally, this app facilitates the importation of product catalogs from external websites. Amazingly precise artificial intelligence technology drives Visenze.

Customers have come to expect instantaneous responses to any inquiries they may have while out shopping. Do you think they will stick around if they need help to acquire the answers to their questions about buying right away? As a result, Chatbot implementation is crucial, especially for businesses with limited customer service teams. Using artificial intelligence and machine learning, Liveperson enables your business to implement both efficient and complementary chatbots to your human support staff. Customers’ anxieties can be alleviated, and the possibility of a purchase can be increased with the help of a high-quality chatbot.

While effective, creating compelling body copy, subject lines, and calls to action for email marketing may be time-consuming. Phrasee solves this problem by using an innovative AI algorithm to predict which of your writings will impact your target readers the most. Machine learning trains the AI to produce material that reads like a person who authored it and ensures that voice stays true to your brand throughout. Allowing AI to generate more interesting email content can boost retailers’ bottom lines.

If you’re writing content for an online store, Jasper AI can help you save time. Reading huge blocks of text can be tedious. Nonetheless, it improves a site’s visibility in search engines like Google. Using Jasper AI, you can make dull or dry content more interesting and visually appealing. The efficiency of your content creators may also increase as a result. The site features a fantastic community that provides users with guides written by other members. There are also tutorials on how to increase blog traffic on various e-commerce sites. Several “skills” templates are also included. You can use them for Facebook ads, email subject lines, and captions, among other things. Enter your company name or product name, a short product description, and the voice you wish to use, and the tool will generate text for you.

To completely automate the post-production of photographs for e-commerce enterprises, SolidGrids is an AI-powered image processing platform. Users only need to adjust their settings once, and then the platform will do all the image editing, saving them time and effort. SolidGrids allows customers to quickly and cheaply tailor the look and feel of their grids to fit their company’s branding needs. It’s possible to generate photos in a matter of seconds for a fraction of the cost of conventional approaches, and the platform provides several integration choices to cater to individual requirements.

Maverick is a marketing tool for e-commerce businesses that use videos generated by artificial intelligence to facilitate one-on-one communication with each customer. It enables companies to make only one video and have it tailored to each customer. It works with Shopify and WooCommerce, two of the most prominent e-commerce systems, allowing you to broadcast welcome greetings, post-purchase videos, and abandoned cart films. It has a library of pre-made scripts and templates that can be used immediately after installation. Both consumers and business owners have praised the technology, with many hailing the program’s ability to send personalized messages to their customers. It has been reported that thanks to Maverick, email interaction, repeat purchases, and refund requests have all increased. The San Francisco-based team behind Maverick specializes in artificial intelligence and tailored video marketing. They provide a trial version without charge and are open to inquiries and requests for help.

Kili is a custom AI assistant that lets non-technical users make their own AI helpers. Kili uses a data source and infrastructure to instruct the assistant on responding to queries. It’s a cost-effective tool for making bespoke experiences for one’s target demographic. There are various options available on Kili, from the free tier, which has a training limit of 20,000 words and 1,000 questions per month, to the enterprise tier, which has a training limit of 100,000 comments or more and 10,000 queries per month. The AI assistant learns from the user’s input and then uses that information to respond to inquiries. Kili accepts data via CSV file import and integrates with newsletter provider Substack for automatic content retrieval.

Enterprise and e-commerce companies can use Copysmith, an AI-powered content production platform. It has many tools to increase a company’s earnings from its internet presence. The Chrome add-on and API simplify integration, while the tool’s bulk product description and content production features save time. In addition to the text editor, it also features a campaign builder and an artificial intelligence image generator. In addition to these examples of applications, Copysmith also provides content enhancement, advertising, social media, blog templates, and creative thinking prompts. Finally, it includes a price list, a client list, and a blog. These options streamline the content creation process for organizations so that more time can be spent on other matters.

Don’t forget to join our 24k+ ML SubReddit, Discord Channel, and Email Newsletter, where we share the latest AI research news, cool AI projects, and more. If you have any questions regarding the above article or if we missed anything, feel free to email us at [email protected]

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