Marktechpost

MARCH 15, 2024

Machine learning is witnessing rapid advancements, especially in the domain of large language models (LLMs). These models, which underpin various applications from language translation to content creation, require regular updates with new data to stay relevant and effective.

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Unite.AI

JANUARY 11, 2024

They serve as a core building block in many natural language processing (NLP) applications today, including information retrieval, question answering, semantic search and more. vector embedding Recent advances in large language models (LLMs) like GPT-3 have shown impressive capabilities in few-shot learning and natural language generation.

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Unite.AI

MARCH 9, 2025

What sets AI apart is its ability to continuously learn and refine its algorithms, leading to rapid improvements in efficiency and performance. Meanwhile, AI computing power rapidly increases, far outpacing Moore's Law.

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Unite.AI

OCTOBER 23, 2024

The rapid development of Large Language Models (LLMs) has brought about significant advancements in artificial intelligence (AI). However, as these models expand in use, so do concerns over privacy and data security. The post How LLM Unlearning Is Shaping the Future of AI Privacy appeared first on Unite.AI.

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Marktechpost

JANUARY 6, 2024

Recently, GPT-4 and other Large Language Models (LLMs) have demonstrated an impressive capacity for Natural Language Processing (NLP) to memorize extensive amounts of information, possibly even more so than humans. Join our 35k+ ML SubReddit , 41k+ Facebook Community, Discord Channel , and LinkedIn Gr oup.

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AI News

SEPTEMBER 27, 2024

One of Databricks’ notable achievements is the DBRX model, which set a new standard for open large language models (LLMs). “Upon release, DBRX outperformed all other leading open models on standard benchmarks and has up to 2x faster inference than models like Llama2-70B,” Everts explains. .”

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Unite.AI

MARCH 2, 2025

Artificial Intelligence (AI) is evolving at an unprecedented pace, with large-scale models reaching new levels of intelligence and capability. From early neural networks to todays advanced architectures like GPT-4 , LLaMA , and other Large Language Models (LLMs) , AI is transforming our interaction with technology.

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Marktechpost

OCTOBER 4, 2024

Multimodal large language models (MLLMs) represent a cutting-edge area in artificial intelligence, combining diverse data modalities like text, images, and even video to build a unified understanding across domains. In conclusion, the MM1.5 is poised to address key challenges in multimodal AI. Let’s collaborate!

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Unite.AI

FEBRUARY 19, 2025

Accessible Learning Advancements in large language models (LLMs) are empowering AI accessibility agents to deliver scalable, equitable educational content for differently-abled students.

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Marktechpost

MARCH 19, 2024

Large language models (LLMs) have taken center stage in artificial intelligence, fueling advancements in many applications, from enhancing conversational AI to powering complex analytical tasks. Join our Telegram Channel , Discord Channel , and LinkedIn Gr oup. If you like our work, you will love our newsletter.

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Marktechpost

NOVEMBER 16, 2024

Large language models (LLMs) have revolutionized natural language processing by offering sophisticated abilities for a range of applications. However, these models face significant challenges. Modulizing LLMs into functional bricks optimizes computational efficiency, scalability, and flexibility.

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ODSC - Open Data Science

NOVEMBER 30, 2023

Feature Store Architecture, the Year of Large Language Models, and the Top Virtual ODSC West 2023 Sessions to Watch Feature Store Architecture and How to Build One Learn about the Feature Store Architecture and dive deep into advanced concepts and best practices for building a feature store. Discover Dash Enterprise 5.2

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Marktechpost

OCTOBER 25, 2023

However, despite their remarkable zero-shot capabilities, these agents have faced limitations in continually refining their performance over time, especially across varied environments and tasks. If you like our work, you will love our newsletter. We are also on WhatsApp. Join our AI Channel on Whatsapp.

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Unite.AI

NOVEMBER 11, 2024

Our team maintains its technological edge through continuous learning and the participation in leading AI conferences. Our team continuously evolves how we leverage data, whether it is through more efficient mining of the data we have access to or augmenting the data with state-of-the-art generation technology.

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Unite.AI

DECEMBER 26, 2024

Though Large Language Models (LLMs) are incredibly impressive, they often struggle with staying accurate, especially when dealing with complex questions or retaining context. Once deployed, MoME continues to learn and improve through reinforcement mechanisms. How MoME Reduces AI Errors?

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AWS Machine Learning Blog

NOVEMBER 7, 2023

Large language models (LLMs) with their broad knowledge, can generate human-like text on almost any topic. Without continued learning, these models remain oblivious to new data and trends that emerge after their initial training.

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Unite.AI

FEBRUARY 6, 2025

Immersing oneself in the AI community can also greatly enhance the learning process and ensure that ethical AI application methods can be shared with those who are new to the field. Participating in meetups, joining online forums, and networking with fellow AI enthusiasts provide opportunities for continuous learning and motivation.

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Towards AI

JANUARY 21, 2025

This persistence would enable the continuous development of contextual awareness through memory, and thus the accumulated experience which is its outcome can inform and refine ongoing interactions. Persistence and continuous learning are obviously not requirements or even desirable features for all use cases.

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Unite.AI

OCTOBER 20, 2023

Using common terminology, holding regular discussions with stakeholders, and creating a culture of AI awareness and continuous learning can help achieve these goals. Ensure data privacy and security: AI models use mountains of data. Companies are leveraging first- and third-party data to feed models.

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The MLOps Blog

FEBRUARY 22, 2024

TL;DR: In many machine-learning projects, the model has to frequently be retrained to adapt to changing data or to personalize it. Continual learning is a set of approaches to train machine learning models incrementally, using data samples only once as they arrive. What is continual learning?

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Unite.AI

MARCH 25, 2025

It provides a complete toolkit to design, train, and deploy AI-driven support bots, harnessing the latest in large language models (LLMs). Top Features: AI Support Assistant Branded chatbot that answers FAQs, handles chats 24/7, and escalates to humans as needed (multilingual and continuously learning).

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NVIDIA

MARCH 18, 2025

To help address this challenge, NVIDIA today announced at the GTC global AI conference that its partners are developing new large telco models (LTMs) and AI agents custom-built for the telco industry using NVIDIA NIM and NeMo microservices within the NVIDIA AI Enterprise software platform.

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AWS Machine Learning Blog

MARCH 18, 2025

We were able to meet our large language model training requirements using Amazon SageMaker HyperPod, says John Duprey, Distinguished Engineer, Thomson Reuters Labs. To learn more, visit the SageMaker HyperPod product page and SageMaker pricing page. Trn2 and P5en instances are only in the US East (Ohio) Region.

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Flipboard

FEBRUARY 10, 2025

This emerging hybrid workforce has been made possible by advances in the natural language processing of large language models (LLMs) that enable humans to communicate with AI agents in the same way they would with a human team member.

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Marktechpost

MARCH 22, 2025

In R&D, two primary challenges must be addressed: enabling continuous learning and acquiring specialized knowledge. To overcome this, RD-Agent employs a dynamic learning framework that integrates real-world feedback, allowing it to refine hypotheses and accumulate domain knowledge over time.

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Flipboard

MARCH 28, 2023

“This is really a better together story,” Bell said, saying that Security Copilot is “not only an OpenAI large language model, but rather it contains a network effect, enabling organizations to truly defend at machine speed.” “AI-generated content can contain mistakes.

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BAIR

APRIL 6, 2023

Figure 1: “Interactive Fleet Learning” (IFL) refers to robot fleets in industry and academia that fall back on human teleoperators when necessary and continually learn from them over time. On-demand supervision enables effective allocation of limited human attention to large robot fleets. Continual learning.

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Marktechpost

MAY 4, 2024

The rapid evolution in AI demands models that can handle large-scale data and deliver accurate, actionable insights. Researchers in this field aim to create systems capable of continuous learning and adaptation, ensuring they remain relevant in dynamic environments.

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John Snow Labs

JUNE 27, 2023

Prepare to be amazed as we delve into the world of Large Language Models (LLMs) – the driving force behind NLP’s remarkable progress. In this comprehensive overview, we will explore the definition, significance, and real-world applications of these game-changing models. What are Large Language Models (LLMs)?

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Marktechpost

MARCH 3, 2025

LLMs face challenges in continual learning due to the limitations of parametric knowledge retention, leading to the widespread adoption of RAG as a solution. RAG enables models to access new information without modifying their internal parameters, making it a practical approach for real-time adaptation.

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Unite.AI

AUGUST 28, 2023

The diagram visualizes the architecture of an AI system powered by a Large Language Model and Agents. This approach ensures that even those without an extensive coding background can do task such as fully autonomous coding, text generation, language translation, and problem-solving.

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Marktechpost

APRIL 30, 2024

Multimodal large language models (MLLMs) integrate text and visual data processing to enhance how artificial intelligence understands and interacts with the world. This model incorporates three major improvements to close the performance gap between open-source and proprietary commercial models.

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Towards AI

APRIL 10, 2024

As you may know or not know, all standalone Large Language Models (LLMs), with prominent examples like ChatGPT, have a knowledge cutoff. What this means is that pre-training is a one-off exercise (unlike continual learning methods). In other words, LLMs have ‘seen’ data until a certain point in time.

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TheSequence

FEBRUARY 16, 2025

We discuss the potential and limitations of continuous learning in foundation models. The engineering section dives into another awesome framework and we discuss large action models in our research edition. You can subscribe to The Sequence below: TheSequence is a reader-supported publication.

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Unite.AI

JUNE 14, 2024

This initiative utilized a Large Language Model (LLM) to assist users in asking better questions and obtaining more accurate answers from the deeply technical content in the scholarly communications database. Implementing Large Language Models (LLMs) and Generative AI in enterprise solutions presents several emerging challenges.

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AWS Machine Learning Blog

MARCH 17, 2025

Large language models (LLMs) have revolutionized the field of natural language processing, enabling machines to understand and generate human-like text with remarkable accuracy. However, despite their impressive language capabilities, LLMs are inherently limited by the data they were trained on.

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Unite.AI

FEBRUARY 21, 2025

Large Language Models (LLMs) have significantly advanced natural language processing (NLP), excelling at text generation, translation, and summarization tasks. Future Directions: Toward Self-Improving AI The next phase of AI reasoning lies in continuous learning and self-improvement.

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