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- 2024-12-17
2024-12-17
What is an AI Agent?
Matthew McCann
December 17, 2024
AlphaWise
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TODAY’S SUMMARY
🎯 ARTICLES
What is an AI agent, AI benchmarking, and Google and Microsoft releases
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A few useful dev tools, some model releases, and a free AI Course
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Podcast and interview to keep you buzzing!
🎯 ARTICLES 🎯
What is an AI Agent?
Synopsis
AI agents are autonomous systems that interpret user input, make decisions, and take actions to complete tasks. Currently, AI agents are widely used in various applications like customer service (amazon), creative tools (canva), and virtual assistance (Siri, Alexa). Many are predicting a new wave of digital tools, streamlining workflows and enhancing creativity, to come that are all powered with AI agents.
Definition of AI Agents
AI agents are software entities capable of perceiving their environment, reasoning about it, and acting autonomously to achieve specific goals. They are used in applications ranging from customer support to creative tools.
AI agent - source: https://www.ori.co/
Core Observations
Definition of AI Agents: AI agents are systems designed to independently analyze information, make decisions, and execute actions. Unlike static tools, they adapt and learn from interactions, enabling dynamic solutions.
Autonomy and Adaptability: AI agents, are equipped with memory and adaptability, allowing them to evolve based on user interactions, making them essential for personalized user experiences.
Versatile Applications: AI agents are powering tools like automated scheduling assistants, intelligent customer support systems, and even creative aids in video and image generation.
Key Features: AI agents leverage natural language processing, machine learning, and data-driven insights to provide real-time, context-aware responses and actions.
Broader Context
AI agents are redefining the boundaries of automation and user interaction. They represent a shift from simple, rule-based programs to dynamic systems capable of complex reasoning and adaptation. For those who follow venture capital in start ups, one may have noticed a significant increase in investment in voice based start ups. Vision, language, and voice create the ability to do many things, but until recently AI models have been cut-off from the world. By incorporating features like memory, learning, and tooling, these agents may now do things like ask your name, what you need, and help you to find, or buy, or reserve, the right a ticket of your choice - as an example - and save those results.
AI Benchmarking - A Crucial Tool for Evaluating General AI Capabilities
Synopsis
AI benchmarking plays a pivotal role in assessing and standardising the capabilities of general AI systems. By establishing performance metrics across diverse tasks, benchmarking ensures the reliability, fairness, and safety of AI models. Platforms like MLCommons' AIluminate are driving these efforts, providing frameworks for testing AI capabilities comprehensively.
AI BenchMarks
Core Observations
Benchmarks evaluate AI performance on tasks such as natural language understanding, image recognition, and reasoning. They provide objective metrics to compare and improve models. Their key focus is to create:
Framework for General AI Evaluation: to simulate real-world scenarios and to measure how AI models adapt to diverse and complex tasks.
Standardisation for Safety and Fairness: to include safety protocols and fairness checks, ensuring AI systems meet ethical and equitable standards.
Ecosystem Collaboration: to unite researchers, developers, and organisations by providing a common ground for evaluation, fostering collaboration and rapid innovation in AI technologies.
Broader Context
AI benchmarking is critical for advancing general AI capabilities and ensuring these systems are trustworthy, scalable, and aligned with societal needs. Strengths and weaknesses are used to drive competition and innovation across a wide range of tasks. This standardisation is essential for ensuring fairness and safety, especially as AI technologies become deeply integrated into industries such as healthcare, finance, and transportation.
For support questions, contact: [email protected]
Google Whisk: An AI-Powered Image Remixing Tool
Synopsis
Google has unveiled Whisk, a groundbreaking AI tool that enables users to combine and remix three images into unique visual creations. It leverages advanced image generation capabilities to transform user-provided visuals into entirely new concepts. For AI enthusiasts, this development highlights the growing intersection of AI and creativity, aiding in quick visual design and ideation.
Core Observations
What is it?: input three images, get a new visual that incorporates elements from each source.
Utility: useful for brainstorming and exploring design concepts.
User Friendly?: TechCrunch gave a thumbs up
Restrictions: Only available in USA (if without VPN).
Broader Context
Google has released another tool useful helping creators create new concepts effortlessly. Its release aligns with broader trends in generative AI, where the focus is shifting toward empowering users with intuitive yet powerful creative capabilities. As AI tools like Whisk become more sophisticated and accessible, they are poised to redefine how we approach visual storytelling and artistic exploration.
Microsoft AI Research Unveils OLA-VLM
Synopsis
Microsoft AI Research has introduced OLA-VLM, a new vision-centric approach to optimising multimodal LLMs. By integrating advanced visual understanding with language processing, OLA-VLM bridges the gap between image and text data, pushing the boundaries of AI in areas like visual reasoning and complex multimodal applications. This innovation offers AI enthusiasts a glimpse into the future of cohesive and efficient multimodal AI systems.
Online demo
Core Observations
Vision-Centric Design: OLA-VLM emphasises visual understanding as a central component, enabling the model to interpret and reason across text and image inputs with enhanced accuracy.
Optimized Multimodal Architecture: The model uses a streamlined architecture to integrate visual and language data, reducing computational overhead while improving task-specific performance. (Hugging Face Paper)
Open-Source Availability: OLA-VLM code is now available on GitHub, with a getting-started guide and Hugging Face demo.
Applications in Visual Reasoning: The model excels in tasks like image captioning, visual question answering, and scene understanding - which is interesting for healthcare, and robotics.
Broader Context
OLA-VLM represents a wave in significant steps forward in the development of multimodal AI systems. By prioritising vision as a core element, Microsoft highlights the importance of integrating diverse data modalities to create cohesive, versatile AI tools. For AI enthusiasts, this release underscores the growing potential of multimodal models in bridging the gap between visual and textual understanding.
Want to try it quickly? Try the Hugging Face demo by uploading a picture.
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