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Editors Pick

Rising Developments in Reinforcement Learning: Uses Outside of the Gaming Industry

Reinforcement Learning (RL) expands beyond its origins in gaming and finds innovative applications across various industries such as finance, healthcare, robotics, autonomous vehicles, and smart infrastructure. In finance, RL algorithms are reinventing investment strategies and risk management by making sequential decisions, observing market conditions, and adjusting strategies based on rewards. Despite their potential, these algorithms struggle…

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The Significance of Compact Language Models in Advancing Artificial Intelligence (AI) Technology: A Tiny Force to Reckon With

In recent years, Large Language Models (LLMs) have gained prominence due to their exceptional text generation, analysis, and classification capabilities. However, their size, need for high processing power and energy, pose barriers to smaller businesses. As the rush for bigger models increases, an interesting trend is gaining momentum: the rise of Small Language Models (SLMs),…

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Must-Read LangChain Books for 2024

LangChain is an open-source framework for developers to easily implement Large Language Models (LLMs) in applications. The increased connectivity with external sources enhances the capabilities of these models, leading to better results. Its popular use includes in creating chatbots, retrieval-augmented generation, and document summary apps. In light of its growing importance, here are some must-read…

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KIVI: An Easy-to-use 2-bit KV Cache Quantization Strategy that Requires No Adjustments

Large Language Models (LLMs) are valuable in many areas, especially when it comes to generating texts or responding to queries. However, they face a significant challenge - they consume vast amounts of memory for efficient functioning. This memory is utilized to store information on previously encountered words and phrases, which aids the model in generating…

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WizardLM-2: An Open-Source AI Model Allegedly Surpasses GPT-4 in MT-Bench Benchmark Performance

A team of AI researchers has developed a new series of open-source large language models (LLMs) called WizardLM-2, signaling a significant breakthrough in artificial intelligence. Consisting of three models, WizardLM-2 8x22B, WizardLM-2 70B, and WizardLM-2 7B, each model is designed to handle different complex tasks, aiming to enhance machine learning capabilities. The introduction of WizardLM-2…

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An AI paper by Peking University and ByteDance presents VAR, a superior model that outpaces diffusion models in speed and efficiency.

Artificial Intelligence's powerful autoregressive (AR) large language models (LLMs), like the GPT series, have made significant progress in achieving general artificial intelligence (AGI). These models use self-supervised learning to predict the next token in a sequence, allowing them to adapt to a diverse range of unseen tasks through zero-shot and few-shot learning. This adaptability makes…

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Investigating the Function of Machine Learning in Anticipating and Mitigating Climate Change

Climate change is an impending threat to planet earth and the life on it. Luckily, the integration of machine learning (ML) and artificial intelligence (AI) into related fields offers promising solutions to predict and deal with its impacts more efficiently. ML aids in countering climate challenges by enhancing data analysis, forecasting, system efficiency, and driving…

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UC Berkeley researchers have unveiled GOEX, a new runtime for Low-Level Machines (LLMs) that includes user-friendly undo and damage containment features. This would enhance the safe implementation of LLM agents in real-world applications.

Language model-based machine learning systems, or LLMs, are reaching beyond their previous role in dialogue systems and are now actively participating in real-world applications. There is an increasing belief that many web interactions will be facilitated by systems driven by these LLMs. However, due to the complexities involved, humans are presently needed to verify the…

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Researchers from Harvard reveal the methods of adjusting text sequences strategically to influence AI-powered search outcomes.

Large Language Models (LLMs) like those used in Microsoft Bing or Google Search are capable of providing natural language responses to user queries. Traditional search engines often struggle to provide cohesive responses, only offering relevant page results. LLMs improve upon this by compiling results into understandable answers. Yet, issues arise with keeping LLMs current with…

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Scientists at Stanford suggest a set of Representation Finetuning (ReFT) methods. These operate on a fixed base model and are trained to implement task-specific action on hidden representation.

Pretrained language models (LMs) are essential tools in the realm of machine learning, often used for a variety of tasks and domains. But, adapting these models, also known as finetuning, can be expensive and time-consuming, especially for larger models. Traditionally, the solution to this issue has been to use Parameter-efficient finetuning (PEFT) methods such as…

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Introducing Andesite AI: A sophisticated AI Security Analysis Startup that Fuels Cyber Professionals from both Private and Public Sectors.

Artificial Intelligence (AI) is becoming increasingly important in the world of cybersecurity as cyber threats become more sophisticated and complex. However, cybersecurity faces a significant challenge due to a shortage of skilled experts and the overwhelming volume of data that security analysts have to process to identify pressing threats. This scenario is expected to worsen…

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