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 318

OpenAI AI AI LLM 318

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

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 AI AI Artificial Intelligence 139

Speaker: John Mansour

If you're looking to advance your career in product management, there are more options than just climbing the management ladder. Join our upcoming webinar to learn about highly rewarding career paths that don't involve management responsibilities. We'll cover both career tracks and provide tips on how to position yourself for success in the one that's right for you.

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 81

Robotics AI AI Machine Learning 81

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 emergence of Large Language Models (LLMs) has notably enhanced the domain of computational linguistics, particularly in multi-agent systems. Despite the significant advancements, developing multi-agent applications remains a complex endeavor. This complexity arises from the challenge of effectively coordinating multiple agents’ actions and navigating the unpredictable nature of LLMs.

Computational Linguistics 137

Computational Linguistics Large Language Models AI AI 137

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

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 135

Large Language Models Artificial Intelligence Artificial Intelligence Conversational AI 135

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Generative AI is upending the way product developers & end-users alike are interacting with data. Despite the potential of AI, many are left with questions about the future of product development: How will AI impact my business and contribute to its success? What can product managers and developers expect in the future with the widespread adoption of AI?

Generative AI

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 134

Machine Learning Natural Language Processing Algorithm ML 134

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 130

Artificial Intelligence Artificial Intelligence AI AI 130

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 110

LLM Python Generative AI ML 110

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 105

LLM Large Language Models ML AI 105

Speaker: Scott Sehlhorst

We know we want to create products which our customers find to be valuable. Whether we label it as customer-centric or product-led depends on how long we've been doing product management. There are three challenges we face when doing this. The obvious challenge is figuring out what our users need; the non-obvious challenges are in creating a shared understanding of those needs and in sensing if what we're doing is meeting those needs.

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 105

Machine Learning Neural Network ML AI 105

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 103

Software Engineer Large Language Models Software Development LLM 103

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 103

Large Language Models LLM AI AI 103

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 101

AI Developer AI Development Natural Language Processing AI 101

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Start-ups & SMBs launching products quickly must bundle dashboards, reports, & self-service analytics into apps. Customers expect rapid value from your product (time-to-value), data security, and access to advanced capabilities. Traditional Business Intelligence (BI) tools can provide valuable data analysis capabilities, but they have a barrier to entry that can stop small and midsize businesses from capitalizing on them.

Data Analysis

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 120

Machine Learning Neural Network Deep Learning Robotics 120

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 135

Machine Learning Explainability ML AI 135

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 101

Algorithm LLM Large Language Models ML 101