Analytics Vidhya

OCTOBER 13, 2024

AI is becoming more accessible, and one of the most exciting advancements is the release of OpenAI o1 on ChatLLM Teams. This powerful AI model can significantly boost your productivity by offering a wide range of tools, from generating creative content to solving complex technical problems. Best of all, you don’t need a premium subscription […] The post How to Access OpenAI’s o1 Models Without a Premium Subscription?

OpenAI 191

OpenAI AI Modeling AI AI 191

Marktechpost

OCTOBER 13, 2024

Large language models (LLMs) have made significant progress in language generation, but their reasoning skills remain insufficient for complex problem-solving. Tasks such as mathematics, coding, and scientific questions continue to pose a significant challenge. Enhancing LLMs’ reasoning abilities is crucial for advancing their capabilities beyond simple text generation.

Large Language Models 130

Large Language Models LLM AI AI 130

TheSequence

OCTOBER 13, 2024

Created Using Ideogram Next Week in The Sequence: Edge 439: Our series about state space models continues with a review of Zamba, a model that combines SSMs and attention layers. We review the original Zamba paper published by the Zyphra team and LitServe framework for model serving. Edge 440: We discuss EUREKA, a recent foundation model evaluation framework published by Microsoft.

AI 96

AI AI Machine Learning ML Engineer 96

Marktechpost

OCTOBER 13, 2024

Mixture of Experts (MoE) models are becoming critical in advancing AI, particularly in natural language processing. MoE architectures differ from traditional dense models by selectively activating subsets of specialized expert networks for each input. This mechanism allows models to increase their capacity without proportionally increasing the computational resources required for training and inference.

Large Language Models 125

Large Language Models AI Research AI Researcher Natural Language Processing 125

Speaker: Carolyn Clark and Miriam Connaughton

The future of HR is here, and it's all about collaboration, innovation, and impact. Join us for a forward-thinking session where seasoned experts Miriam and Carolyn will share insights and practical strategies to help you stay ahead of evolving HR trends. Discover how to build strong partnerships with internal teams to craft a transparent, authentic, and connected workforce experience.

AI

Extreme Tech

OCTOBER 13, 2024

Doing this might void the warranty of your Raspberry Pi 5, but the results could be amazing.

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Artificial Lawyer

OCTOBER 13, 2024

Baker McKenzie’s well-known Innovation Product Manager, Cem Ucan, is joining Leya, the genAI startup, to help drive the change he wants to see happen in.

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Marktechpost

OCTOBER 13, 2024

One of the most pressing challenges in the evaluation of Vision-Language Models (VLMs) is related to not having comprehensive benchmarks that assess the full spectrum of model capabilities. This is because most existing evaluations are narrow in terms of focusing on only one aspect of the respective tasks, such as either visual perception or question answering, at the expense of critical aspects like fairness, multilingualism, bias, robustness, and safety.

Automation 119

Automation ML Computer Vision AI 119

Machine Learning Research at Apple

OCTOBER 13, 2024

Optimal transport (OT) has profoundly impacted machine learning by providing theoretical and computational tools to realign datasets. In this context, given two large point clouds of sizes nnn and mmm in Rdmathbb{R}^dRd, entropic OT (EOT) solvers have emerged as the most reliable tool to either solve the Kantorovich problem and output a n×mntimes mn×m coupling matrix, or to solve the Monge problem and learn a vector-valued push-forward map.

Machine Learning 52

Machine Learning 52

Marktechpost

OCTOBER 13, 2024

Large language models (LLMs) often fail to consistently and accurately perform multi-step reasoning, especially in complex tasks like mathematical problem-solving and code generation. Despite recent advancements, LLMs struggle to detect and learn from errors because they are predominantly trained on correct solutions. This limitation leads to difficulties in verifying and ranking outputs, particularly when subtle flaws are present.

AI Research 117

AI Research AI Researcher LLM Large Language Models 117

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

Pickl AI

OCTOBER 13, 2024

Summary: Selecting the right Data Science Bootcamp is crucial for your career. Evaluate curriculum quality, instructor expertise, job placement support, flexibility, and costs to make an informed decision that aligns with your professional goals. Introduction In today’s data-driven world, Data Science has emerged as a crucial field across various industries, enabling businesses to make informed decisions and drive innovation.

Data Science 52

Data Science Machine Learning Data Analysis Python 52

Marktechpost

OCTOBER 13, 2024

Automatic benchmarks like AlpacaEval 2.0, Arena-Hard-Auto, and MTBench have gained popularity for evaluating LLMs due to their affordability and scalability compared to human evaluation. These benchmarks use LLM-based auto-annotators, which align well with human preferences, to provide timely assessments of new models. However, high win rates on these benchmarks can be manipulated by altering output length or style, even though measures have been developed to control these factors.

LLM 116

LLM Automation ChatGPT ML 116

Pickl AI

OCTOBER 13, 2024

Summary: Types of Artifical Intelligence based on functionalities: Reactive Machines, which respond to current inputs; Limited Memory AI, which learns from past data; Theory of Mind AI, which understands human emotions; and Self-Aware AI, which possesses consciousness. Each type plays a unique role in AI applications. Introduction What if machines could think, learn, and adapt like humans?

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Categorization Chatbots 52

Marktechpost

OCTOBER 13, 2024

Large language models (LLMs) have demonstrated impressive capabilities in in-context learning (ICL), a form of supervised learning that doesn’t require parameter updates. However, researchers are now exploring whether this ability extends to reinforcement learning (RL), introducing the concept of in-context reinforcement learning (ICRL). The challenge lies in adapting the ICL approach, which relies on input-output pairs, to an RL framework that involves input-output-reward triplets.

Large Language Models 114

Large Language Models Algorithm LLM ML 114

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Eleuther.ai

OCTOBER 13, 2024

Interim report on ongoing work on mechanistic anomaly detection

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Marktechpost

OCTOBER 13, 2024

High latency in time-to-first-token (TTFT) is a significant challenge for retrieval-augmented generation (RAG) systems. Existing RAG systems, which concatenate and process multiple retrieved document chunks to create responses, require substantial computation, leading to delays. Repeated computation of key-value (KV) caches for retrieved documents further exacerbates this inefficiency.

Large Language Models 107

Large Language Models AI AI ML 107

Robot Writers AI

OCTOBER 13, 2024

Red Flag: Google’s CoHosted-Podcast Maker Not Always Accurate Google’s new NotebookLM — which has gone viral with its ability to auto-script and auto-produce a co-hosted podcast in minutes — is unfortunately also very good at making things up. The new AI research tool — which uses two, extremely natural-sounding robot voices to discuss text, audio or video that you input into NotebookLM — is currently wowing the Web.

AI Engineer 52

AI Engineer Robotics ChatGPT Generative AI 52

Marktechpost

OCTOBER 13, 2024

Robotic task execution in open-world environments presents significant challenges due to the vast state-action spaces and the dynamic nature of unstructured settings. Traditional robots struggle with unexpected objects, varying environments, and task ambiguities. Existing systems, often designed for controlled or pre-scanned environments, lack the adaptability required to respond effectively to real-time changes or unfamiliar tasks.

Robotics 107

Robotics LLM Categorization Automation 107

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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

ODSC - Open Data Science

OCTOBER 13, 2024

Editor’s note: Dr. Einat Orr is a speaker for ODSC West this October 29th-31st. Be sure to check out her talk, “ Don’t Go Over the Deep End: Building an Effective OSS Management Layer for Your Data Lake ,” there! Managing a data lake can often feel like being lost at sea — especially when dealing with both structured and unstructured data. Join Dr. Einat Orr, at 11:35 on October 30th at ODSC West where she will guide you through that storm, offering a high-level overview of tools and strategies

Metadata 40

Metadata Data Science Artificial Intelligence Artificial Intelligence 40

Marktechpost

OCTOBER 13, 2024

Model merging is an advanced technique in machine learning aimed at combining the strengths of multiple expert models into a single, more powerful model. This process allows the system to benefit from the knowledge of various models while reducing the need for large-scale individual model training. Merging models cuts down computational and storage costs and improves the model’s ability to generalize to different tasks.

Machine Learning 101

Machine Learning AI AI ML 101

Marktechpost

OCTOBER 13, 2024

The current challenges in text-to-speech (TTS) systems revolve around the inherent limitations of autoregressive models and their complexity in aligning text and speech accurately. Many conventional TTS models require complex elements such as duration modeling, phoneme alignment, and dedicated text encoders, which add significant overhead and complexity to the synthesis process.

ML 62

ML Artificial Intelligence Artificial Intelligence AI 62