Marktechpost

JANUARY 24, 2024

This shift is driven by neural networks that learn through self-supervision, bolstered by specialized hardware. However, the dawn of deep learning brought about a paradigm shift in data representation, introducing complex neural networks that generate more sophisticated data representations known as embeddings.

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Unite.AI

APRIL 26, 2024

In this comprehensive guide, we will explore the inner workings of decoder-based LLMs, delving into the fundamental building blocks, architectural innovations, and implementation details that have propelled these models to the forefront of NLP research and applications. LLMs based on prefix decoders include GLM130B and U-PaLM.

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Marktechpost

JANUARY 11, 2024

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.

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Marktechpost

DECEMBER 17, 2023

Finally, a scalable training goal should be able to efficiently deal with an increasing number of modalities without causing the computational costs to skyrocket. New research by the Swiss Federal Institute of Technology Lausanne (EPFL) and Apple aims for scalability in all three areas while being compatible with different input types.

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Marktechpost

OCTOBER 20, 2023

Multimodal graph learning is a multidisciplinary field combining concepts from machine learning, graph theory, and data fusion to tackle complex problems involving diverse data sources and their interconnections. Multimodal graph learning can generate descriptive captions for images by combining visual data with textual information.

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Unite.AI

MAY 15, 2024

The latest in this lineage, GPT-4o , signifies years of research and development. It utilizes multimodal AI to comprehend and generate content across various data input forms. In this context, multimodal AI refers to systems capable of processing and understanding more than one type of data input, such as text, images, and audio.

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AI Weekly

JULY 27, 2023

This exclusive deal gets you access to eight of the web’s highest-rated apps, including Word, Excel, Publisher, and Access for enhanced efficiency in the ever-changing digital workplace. Supercharge Your Productivity for Life Step into the future of productivity with a lifetime license to Microsoft Office. Now through 7/31, just pay $39.99

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Marktechpost

MAY 24, 2024

These models leverage large datasets and intricate neural networks to deliver previously unattainable results with traditional algorithms. Researchers face the problem of seamlessly integrating these powerful models into everyday workflows. Due to the local model sizes, at least 16 GB of RAM is preferred for optimal performance.

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Marktechpost

OCTOBER 8, 2023

In an era marked by an insatiable appetite for artificial intelligence (AI) capabilities, BrainChip, a pioneer in neural network processors, has taken a significant stride towards empowering edge devices with unprecedented processing power. All Credit For This Research Goes To the Researchers on This Project.

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Unite.AI

NOVEMBER 28, 2023

However, current research is increasingly focused on enhancing large language models with the capability to perceive multimodal content. The LoRA framework is a common method used for parameter-efficient fine-tuning, and its inclusion in the SALMONN framework to value weight matrices and adapt the query in the self-attention layers.

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Marktechpost

APRIL 22, 2024

Recently, researchers have been exploring using neural networks to learn the mappings between input conditions and output solutions of PDEs. While the current work focuses on one-dimensional time-dependent PDEs, the researchers envision extending PROSE-PDE to multi-dimensional and non-time-dependent equations.

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Marktechpost

MARCH 19, 2024

Although GPT-4 and models like SORA have set impressive benchmarks, the evolving landscape of multimodal computer vision offers vast potential for innovation and refinement in generating 3D visual components from 2D images. It generates optimal results using diverse multimodal inputs such as text prompts. Check out the Paper.

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Marktechpost

JULY 23, 2023

The human brain, regarded as the paradigm for neural network theories, concurrently processes information from various sensory inputs, such as visual, auditory, and tactile signals. However, due to the huge modality gap in deep learning, constructing a unified network capable of processing various input forms takes a lot of work.

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Marktechpost

JANUARY 8, 2024

In Large Language Models (LLMs), models like ChatGPT represent a significant shift towards more cost-efficient training and deployment methods, evolving considerably from traditional statistical language models to sophisticated neural network-based models.

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IBM Journey to AI blog

FEBRUARY 9, 2024

2024 thus stands to be a pivotal year for the future of AI, as researchers and enterprises seek to establish how this evolutionary leap in technology can be most practically integrated into our everyday lives. Multimodal AI (and video) That being said, the ambition of state-of-the-art generative AI is growing.

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Marktechpost

FEBRUARY 27, 2024

Processing long video and text sequences together could enable a model to develop a broader multimodal understanding, making it a potentially powerful tool for many tasks. Fortunately, RingAttention allows us to scale to longer context sizes without overheads, enabling efficient training on long sequences.

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Unite.AI

APRIL 26, 2024

These extend far beyond the traditional text-based processing of LLMs to include multimodal interactions. More recent frameworks like LLaMA and BLIP leverage tailored instruction data to devise efficient strategies that demonstrate the potent capabilities of the model.

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Unite.AI

JANUARY 23, 2024

Word2Vec pioneered the use of shallow neural networks to learn embeddings by predicting neighboring words. Recent research has evolved embeddings to capture more semantic relationships. Recent variants like sparse attention improve efficiency by reducing redundant attention computations.

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AssemblyAI

MAY 3, 2023

Effective methods allowing for better control, or steerability , of large-scale AI systems are currently in extremely high demand in the world of AI research. Artificial neural networks consist of interconnected layers of nodes, or “neurons” which work together to process and learn from data.

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Google Research AI blog

FEBRUARY 7, 2023

Posted by Sanjiv Kumar, VP and Google Fellow, Google Research (This is Part 4 in our series of posts covering different topical areas of research at Google. Beyond efficiency, there are a number of other challenges around factuality, security, privacy and freshness in these models. You can find other posts in the series here.)

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Topbots

DECEMBER 5, 2023

From Generative Agents research paper In this article, we delve into ten transformative research papers from diverse domains, spanning language models, image processing, image generation, and video editing. Top 10 AI Research Papers 2023 1. Where to learn more about this research? Where to learn more about this research?

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NYU Center for Data Science

FEBRUARY 1, 2024

The efficiency of human language acquisition, achieved with relatively minimal data, starkly contrasts with AI systems, which often require vast corpuses of information. The team’s research, focusing on the stark contrast between the data requirements of human learners and AI systems, employs a unique approach. Emin Orhan.

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Heartbeat

FEBRUARY 8, 2024

Photo in pexel.com With technological advancements, many multimedia data requests efficient ways to search for and obtain information across several methodologies. Cross-modal retrieval frameworks have been developed through research using AI and CV. This function is pivotal in preparing images for various neural network architectures.

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Unite.AI

AUGUST 8, 2023

The Technologies Behind Generative Models Generative models owe their existence to deep neural networks, sophisticated structures designed to mimic the human brain's functionality. By capturing and processing multifaceted variations in data, these networks serve as the backbone of numerous generative models. How Are LLMs Used?

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Google Research AI blog

APRIL 26, 2023

Posted by Shekoofeh Azizi, Senior Research Scientist, and Laura Culp, Senior Research Engineer, Google Research Despite recent progress in the field of medical artificial intelligence (AI), most existing models are narrow , single-task systems that require large quantities of labeled data to train.

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Bugra Akyildiz

FEBRUARY 10, 2024

Articles Google wrote an article on announcing Graph Neural Networks(GNN)s on Tensorflows. They talk about following problems to solve: Data complexity: How to efficiently process and learn from the intricate relationships within graph-structured data. Their website has a number of really good examples.

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Marktechpost

DECEMBER 10, 2023

These models draw influence from traditional state space models and may be seen as a hybrid of convolutional and recurrent neural networks. The research team demonstrates that Mamba outperforms many baselines, including highly powerful contemporary Transformer training recipes based on LLaMa, with scaling laws up to 1B parameters.

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Viso.ai

MARCH 20, 2024

Applications in Multimodal Learning Models like CLIP Emerging Trends and Future Advancement in Foundation Model Research About Us: Viso Suite is the end-to-end computer vision infrastructure. Recent models like CLIP, Stable Diffusion , and Gato are multimodal and can handle data types like images, audio files, and videos.

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Google Research AI blog

FEBRUARY 10, 2023

Posted by Vahab Mirrokni, VP and Google Fellow, Google Research (This is Part 5 in our series of posts covering different topical areas of research at Google. Hence, developing algorithms with improved efficiency, performance and speed remains a high priority as it empowers services ranging from Search and Ads to Maps and YouTube.

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Marktechpost

MARCH 29, 2024

From the transformative potential of Multimodal AI, which goes beyond traditional single-mode data processing to encompass multiple input types like text, images, and sound, to the emergence of Quantum AI, the trends shaping the future of AI are both exciting and daunting. with substantially fewer parameters.

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Viso.ai

JANUARY 16, 2024

In this article, we will discuss Types of N-shot learning paradigms Different frameworks and approaches Applications Challenges, and Future Research About us: Viso.ai Our software helps several leading organizations start with computer vision and implement deep learning models efficiently with minimal overhead for various downstream tasks.

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Viso.ai

APRIL 26, 2024

Object detection systems typically use frameworks like Convolutional Neural Networks (CNNs) and Region-based CNNs (R-CNNs). Concept of Convolutional Neural Networks (CNN) However, in prompt object detection systems, users dynamically direct the model with many tasks it may not have encountered before.

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ODSC - Open Data Science

MARCH 8, 2024

To address these limitations, researchers have turned to Retrieval-Augmented Generation (RAG) as a promising solution. Researchers can analyze the retrieved information and understand how it influences the generated output. Introducing Multimodality Multimodality refers to the integration of information from different modalities (e.g.,

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Unite.AI

AUGUST 2, 2023

With the introduction of the data2vec algorithm, Meta AI hopes that it will make multimodal learning effective, and much more simpler. The data2vec algorithm combines the learnings of latent target representations with masked prediction, although it uses multiple network layers as targets to generalize the latent representations.

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Google Research AI blog

APRIL 30, 2023

We are proud to be a Diamond Sponsor of ICLR 2023, a premier conference on deep learning, where Google researchers contribute at all levels. Continue below to find the many ways in which Google researchers are engaged at ICLR 2023, including workshops, papers, posters and talks (Google affiliations in bold ).

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Marktechpost

JUNE 9, 2023

Now, researchers from MIT, the MIT-IBM Watson AI Lab, IBM Research, and other institutions have developed a groundbreaking technique that can efficiently solve these issues by analyzing unlabeled audio and visual data. Easier said than done; companies need help gathering and creating gold-standard data points every year.

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Towards AI

FEBRUARY 6, 2024

Gemini Ultra will include multimodal capabilities, enhanced coding features, and the ability to analyze documents, files, and data. All editions are available for free for research and commercial applications. This is a foundational article on the evolution of machine learning, covering everything from neural networks to transformers.

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