Analytics Vidhya

MARCH 2, 2024

Introduction Real-time AI systems rely heavily on fast inference. Inference APIs from industry leaders like OpenAI, Google, and Azure enable rapid decision-making. Groq’s Language Processing Unit (LPU) technology is a standout solution, enhancing AI processing efficiency. This article delves into Groq’s innovative technology, its impact on AI inference speeds, and how to leverage it using […] The post Getting Started with Groq API: The Fastest Ever Inference Endpoint appeared f

OpenAI 289

OpenAI AI AI LLM 289

Marktechpost

MARCH 2, 2024

Very large language models (LLMs) continue to face major computational cost barriers, which prevents their broad deployment, even with inference optimization approaches that have advanced significantly. Sequentially producing tokens throughout the autoregressive generation process is a major cause of the high inference latency. Because ML accelerators (GPUs/TPUs) are designed for matrix-matrix multiplications and not the matrix-vector operations common in LLMs, this limitation prevents them from

Large Language Models 142

Large Language Models ML LLM AI 142

Extreme Tech

MARCH 2, 2024

The Falcon 9 was the first rocket to perfect propulsive landing, but to reach distant, exotic locales like Mars and the outer planets, you need something with a little more oomph. That's the SpaceX Starship.

Explainability 105

Explainability 105

Marktechpost

MARCH 2, 2024

Large Language Models (LLMs) have significantly evolved in recent times, especially in the areas of text understanding and generation. However, there have been certain difficulties in optimizing LLMs for more effective human instruction delivery. While LLMs have shown progress in tasks involving token prediction and task execution with a limited number of demonstrations, this does not necessarily transfer to better human instruction.

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Large Language Models Artificial Intelligence Artificial Intelligence AI Researcher 138

Speaker: David Warren and Kevin O'Neill Stoll

Transitioning to a usage-based business model offers powerful growth opportunities but comes with unique challenges. How do you validate strategies, reduce risks, and ensure alignment with customer value? Join us for a deep dive into designing effective pilots that test the waters and drive success in usage-based revenue. Discover how to develop a pilot that captures real customer feedback, aligns internal teams with usage metrics, and rethinks sales incentives to prioritize lasting customer eng

Towards AI

MARCH 2, 2024

Last Updated on March 4, 2024 by Editorial Team Author(s): PromptDervish Originally published on Towards AI. Explore the transformative power of Midjourney v6’s — sref with Ukiyo-e, blending traditional Japanese art with futuristic themes for stunning AI-generated visuals. In my earlier article on Ukiyo-e, I explored various prompts with the art style to play with both traditional styles, bring in more modern themes, and add the art style to them.

Robotics 68

Robotics AI AI Machine Learning 68

Mlearning.ai

MARCH 2, 2024

Coral reefs are like underwater rainforests — full of colorful life and important for the health of our oceans. But climate change… Continue reading on MLearning.

AI 52

AI AI ML Machine Learning 52

Marktechpost

MARCH 2, 2024

The growth of Artificial Intelligence (AI), with Transformers leading the charge, ranges from applications in conversational AI to image and video generation. Yet, traditional symbolic planners have held the upper hand in complex decision-making and planning tasks due to their structured, rule-based approach. The problem at hand revolves around the inherent limitations of current Transformer models in solving complex planning and reasoning tasks.

Neural Network 139

Neural Network Artificial Intelligence Artificial Intelligence Algorithm 139

Bugra Akyildiz

MARCH 2, 2024

Articles BAIR(Berkeley AI Research) wrote rather an interesting blog post. Their main argument is that; the LLMs are not the solution to our problems, but rather “compound systems” that bring models to other systems(guardrail, etc) to solve a particular user/product problem. Theory is that AlphaCode, ChatGPT+, Gemini are examples of Compound AI Systems that solve tasks through multiple calls to models and other components.

LLM 52

LLM AI AI Computer Vision 52

Marktechpost

MARCH 2, 2024

In machine learning, the effectiveness of tree ensembles, such as random forests, has long been acknowledged. These ensembles, which pool the predictive power of multiple decision trees, stand out for their remarkable accuracy across various applications. This work, from researchers at the University of Cambridge, explains the mechanisms behind this success, offering a nuanced perspective that transcends traditional explanations focused on variance reduction.

Machine Learning 137

Machine Learning Explainability ML AI 137

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Many software teams have migrated their testing and production workloads to the cloud, yet development environments often remain tied to outdated local setups, limiting efficiency and growth. This is where Coder comes in. In our 101 Coder webinar, you’ll explore how cloud-based development environments can unlock new levels of productivity. Discover how to transition from local setups to a secure, cloud-powered ecosystem with ease.

Marktechpost

MARCH 2, 2024

Integrating Large Language Models (LLMs) in autonomous agents promises to revolutionize how we approach complex tasks, from conversational AI to code generation. A significant challenge lies at the core of advancing independent agents: data’s vast and varied nature. Diverse sources bring forth a plethora of formats, complicating the task of training agents efficiently and effectively.

Large Language Models 136

Large Language Models Artificial Intelligence Artificial Intelligence Conversational AI 136

Marktechpost

MARCH 2, 2024

The advent of transformer architectures has marked a significant milestone, particularly in their application to in-context learning. These models can make predictions based solely on the information presented within the input sequence without explicit parameter updates. This ability to adapt and learn from the input context has been pivotal in pushing the boundaries of achievable across various domains, from natural language processing to image recognition.

Machine Learning 135

Machine Learning Natural Language Processing Algorithm ML 135

Marktechpost

MARCH 2, 2024

In the evolving landscape of psycholinguistics, language models (LMs) have carved out a pivotal role, serving as both the subject and tool of study. These models, leveraging vast datasets, attempt to mimic human language processing capabilities, offering invaluable insights into the cognitive mechanisms that underpin language understanding and production.

Artificial Intelligence 131

Artificial Intelligence Artificial Intelligence AI AI 131

Marktechpost

MARCH 2, 2024

Recent advancements in (self) supervised learning models have been driven by empirical scaling laws, where a model’s performance scales with its size. However, such scaling laws have been challenging to establish in reinforcement learning (RL). Unlike supervised learning, increasing the parameter count of an RL model often leads to decreased performance.

Machine Learning 124

Machine Learning Neural Network Deep Learning Robotics 124

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Large enterprises face unique challenges in optimizing their Business Intelligence (BI) output due to the sheer scale and complexity of their operations. Unlike smaller organizations, where basic BI features and simple dashboards might suffice, enterprises must manage vast amounts of data from diverse sources. What are the top modern BI use cases for enterprise businesses to help you get a leg up on the competition?

Business Intelligence

Marktechpost

MARCH 2, 2024

AI applications that translate textual instructions into 2D images or 3D models have expanded creative possibilities, yet the challenge persists in obtaining precise outputs. Existing tools often yield unexpected or “hallucinatory” results, lacking fidelity to input prompts. Stable Diffusion models faced issues with combining multiple concepts or distinguishing different attributes.

LLM 115

LLM Python Generative AI ML 115

Marktechpost

MARCH 2, 2024

The surge of advertisements across online platforms presents a formidable challenge in maintaining content integrity and adherence to advertising policies. While foundational, traditional mechanisms of content moderation grapple with the dual challenges of scale and efficiency, often becoming a bottleneck in the dynamic and voluminous environment of platforms such as Google Ads.

LLM 109

LLM Large Language Models ML AI 109

Marktechpost

MARCH 2, 2024

The ability to predict outcomes from a myriad of parameters has traditionally been anchored in specific, narrowly focused regression methods. While effective within its domain, this specialized approach often needs to be revised when confronted with the complexity and diversity inherent in real-world experiments. The challenge, therefore, lies not merely in prediction but in crafting a tool versatile enough to navigate across the broad spectrum of tasks, each with its distinct parameters and out

Machine Learning 109

Machine Learning Neural Network ML AI 109

Marktechpost

MARCH 2, 2024

Large Language Models (LLMs) have emerged as a powerful ally for developers, promising to revolutionize how coding tasks are approached. By serving as intelligent assistants, LLMs have the potential to streamline various aspects of the development process, from code generation to bug fixing, making the coder’s work not only faster but also more accurate.

Software Engineer 105

Software Engineer Large Language Models Software Development LLM 105

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Cloud Development Environments (CDEs) are changing how software teams work by moving development to the cloud. Our Cloud Development Environment Adoption Report gathers insights from 223 developers and business leaders, uncovering key trends in CDE adoption. With 66% of large organizations already using CDEs, these platforms are quickly becoming essential to modern development practices.

Marktechpost

MARCH 2, 2024

Developing middleware solutions for large language models (LLMs) represents an effort to bridge AI’s theoretical capabilities and its practical applications in real-world scenarios. The challenge of navigating and processing enormous quantities of data within complex environments, such as vast databases and intricate knowledge bases, has long been a bottleneck in harnessing the full potential of LLMs.

Large Language Models 105

Large Language Models LLM AI AI 105

Marktechpost

MARCH 2, 2024

Efficiently supporting LLMs is becoming more critical as large language models (LLMs) become widely used. Since getting a new token involves getting all of the LLM’s parameters, speeding up LLM inference is difficult. The hardware is underutilized throughout generation due to this I/O constraint. Offloading-based inference and small-batch inference settings worsen this problem because, on current GPUs, producing a single token takes as long as processing a prompt containing hundreds or tho

Algorithm 104

Algorithm Large Language Models LLM ML 104

Marktechpost

MARCH 2, 2024

Large Multimodal Models (LMMs), driven by AI advancements, revolutionize vision and language tasks but are mainly centered on English, neglecting non-English languages. This oversight excludes billions of speakers of languages like Chinese, Hindi, Spanish, French, Arabic, Bengali, Russian, Urdu, and Japanese. The lack of linguistic inclusivity underscores the need for broader representation in developing LMM to ensure effective communication across diverse global populations.

AI Developer 103

AI Developer AI Development Natural Language Processing AI 103