The Rise of MLLMs and the Eagle Family of Models 🦅, GenRM: A New Approach to AI Verification 💡, AI powered Alexa 🎙️

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Generative Verifiers: Reward Modeling as Next-Token Prediction

Ensuring accuracy and reliability in AI-generated outputs is particularly challenging in fields like education, finance, and healthcare. Traditional methods, such as discriminative reward models (RMs) and the LLM-as-a-Judge approach, struggle with complex reasoning tasks.

To overcome these limitations, researchers from Google DeepMind and collaborators introduced Generative Reward Modeling (GenRM). This approach integrates text generation with verification through next-token prediction, leveraging the strengths of large language models (LLMs).

GenRM also uses Chain-of-Thought (CoT) reasoning, which enhances the accuracy of evaluations. In tests, GenRM significantly improved success rates from 73% to 92.8%, representing a major advancement in AI-driven problem-solving.

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NVIDIA Unveils NVEagle

Multimodal Large Language Models (MLLMs) represent the next frontier in AI by combining visual and linguistic information to interpret complex real-world scenarios. Central to these models are vision encoders that convert images into visual tokens, enabling the models to understand and interact with visual data. However, challenges like "hallucinations" and inaccuracies in tasks requiring high visual precision, such as OCR, remain significant.

To address these issues, researchers introduced the Eagle family of MLLMs (NVEagle), developed by NVIDIA, Georgia Tech, UMD, and HKPU. These models systematically explore different vision encoder designs and fusion strategies, finding that a simple concatenation of visual tokens from complementary encoders performs as well as more complex methods. The Eagle models also use a Pre-Alignment stage to align vision experts with the language model, boosting coherence and performance.

Eagle models, available in three versions (Eagle-X5-7B, Eagle-X5-13B, and Eagle-X5-13B-Chat), have shown exceptional performance across various benchmarks, including OCR and visual question-answering tasks, outperforming leading models. This advancement in MLLMs sets new standards for visual perception in AI, offering streamlined design and state-of-the-art results. As these models become more widely available, they are expected to drive further innovation in AI.

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Amazon to launch New AI-Powered Alexa using Anthropic's Claude

Amazon is set to revamp its Alexa voice assistant, which will now be powered by Anthropic's Claude AI model rather than Amazon's in-house AI technology. This decision follows performance issues with Amazon's internal software, which struggled with response times during testing. The upgraded Alexa, referred to as the "Remarkable" version, is expected to offer more advanced capabilities, such as more complex, context-aware conversations, shopping advice, and executing multi-step tasks.

Amazon plans to offer this enhanced Alexa as a paid subscription service, priced between $5 to $10 per month, while maintaining the existing "Classic" Alexa service for free. The move to partner with Anthropic marks a significant shift in Amazon's strategy, which typically relies on in-house developments. Amazon has invested $4 billion in Anthropic, further solidifying their partnership.

This new AI-powered Alexa is expected to launch in October 2024, and Amazon hopes it will generate significant revenue, particularly as Alexa has struggled to be a major revenue driver despite widespread adoption.

IBM Generative AI for Cybersecurity Professionals Specialization

The "Generative AI for Cybersecurity Professionals" course on Coursera equips you with essential skills in generative AI, focusing on its application in cybersecurity. You'll learn about prompt engineering, real-world AI use cases, and how to leverage AI for tasks like threat analysis and automated responses. The course includes hands-on projects and helps you build a portfolio to showcase your skills, culminating in a shareable certificate and badge.

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Big Data, Artificial Intelligence, and Ethics

The "Big Data, Artificial Intelligence, and Ethics" course on Coursera explores the impact of AI and big data on society, emphasizing ethical considerations. You'll gain hands-on experience with AI tools like IBM Watson and Google's teachable machines while learning to navigate the ethical challenges in computational social science.

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AI in Society

The "AI & Society" course offered by the University of Edinburgh is designed for learners interested in understanding the societal impacts of AI. It is suitable for individuals at various levels, including beginners who are new to the subject. The course covers key topics such as the ethical, social, and political implications of AI, helping students explore how AI influences society and decision-making. It aims to equip participants with a critical understanding of AI's role in shaping the future.

LIST OF TOOLS

💬 Dialogflow - Google’s platform for designing and integrating conversational interfaces into apps and devices.

🛠️ Microsoft Bot Framework - A comprehensive framework for building, testing, and deploying chatbots across multiple platforms.

🧠 Rasa - Open-source framework for building contextual chatbots with machine learning.

🤖 ChatGPT - OpenAI’s model for creating advanced, personalized conversational AI.

🏥 IBM Watson Assistant - AI-driven platform for creating chatbots and virtual agents that understand and interact naturally.

🗣️ Wit.ai - Facebook’s NLP tool for building voice and text-based bots with language understanding.

🎙️ Amazon Lex - AWS service for building conversational interfaces with voice and text capabilities.

🔧 Botpress - Open-source platform for building customizable and flexible chatbots.

AI Job Opportunities in the USA
The article highlights the growing job opportunities in the AI sector in the USA. It discusses the increasing demand for AI professionals across various industries, driven by advancements in AI technologies and their applications. Key roles include AI engineers, data scientists, machine learning specialists, and AI ethicists. The article also emphasizes the importance of relevant skills, such as programming, data analysis, and knowledge of AI frameworks, to succeed in these roles. The AI job market is expanding rapidly, offering significant career opportunities.

Generative AI creates playable version of Doom game with no code
The article discusses how generative AI has been used to create a fully playable version of the classic video game "Doom" without any traditional coding. This innovation showcases the potential of AI in game development, where AI can generate game environments, mechanics, and interactions autonomously. While this demonstrates the impressive capabilities of generative AI, it also raises questions about the future of game development and the role of human creativity in the process.

More than a quarter of employers use AI in recruitment
The article discusses concerns in Ireland about the growing use of AI in recruitment processes. There are fears that AI could perpetuate biases, lack transparency, and lead to unfair hiring practices. While AI can streamline recruitment and enhance efficiency, experts warn that it might reinforce existing inequalities if not properly managed. The article emphasizes the need for regulatory oversight and ethical guidelines to ensure that AI in recruitment is used fairly and responsibly.


‘Emotion AI’ may be the next trend for business software, and that could be problematic
The article discusses the rise of Emotion AI in business software, which aims to detect and respond to human emotions. While this technology has potential benefits, such as improving customer service or employee engagement, it also raises ethical concerns. These concerns include privacy issues, the accuracy of emotion detection, and the potential misuse of data. The article emphasizes the need for careful consideration and regulation to prevent negative consequences as this technology becomes more prevalent in business settings.


Introducing AI’s long-lost twin: Engineered intelligence
The article introduces "Engineered Intelligence" as a new discipline focused on applying AI research in practical, real-world scenarios, similar to how other scientific breakthroughs are translated into engineering solutions. Unlike traditional AI approaches, which often fail due to lack of application-focused expertise, engineered intelligence emphasizes leveraging domain expertise to create valuable, tangible outcomes. The article outlines steps for integrating this approach into organizations, aiming to unlock AI's full potential and prevent another AI winter by fostering collaboration between research and engineering.

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