Stay Ahead in AI with Weekly AI Roundup; read and listen on AITechCircle:
Welcome to the weekly AI Newsletter. This newsletter is a valuable resource for me and many others in the community. It provides practical and actionable ideas that can be immediately applied to our jobs and businesses.
Before we begin, please share this week’s updates with a friend or colleague:
Today at a Glance:
- Align Responsible AI maturity levels with your organization’s aims
- Generative AI Use case: On-the-Go Technician Support for Network Maintenance
- AI Weekly news and updates covering newly released LLMs
- Open Tech Talk Podcast, the latest episode on Building a Game-Changing AI Strategy: Step-by-Step Guide and Exercises for Your Organization
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Walking on the path of Responsible AI
Responsible AI is important because it ensures that AI systems are developed and used ethically, transparently, and fairly. Responsible AI protects individuals from harmful outcomes by prioritizing accountability and minimizing bias, maintains trust in technology, and promotes fairness in decision-making processes. It also ensures that AI complies with regulations, safeguarding privacy and security while fostering long-term, positive societal impacts.
This week, I have had a chance to read “The GSM Responsible AI Maturity Roadmap.”
The GSMA worked with a consortium of mobile operators, AI experts, and influencers to develop an actionable Responsible AI Maturity Roadmap. This roadmap is designed to guide organizations in implementing responsible AI practices, offering step-by-step instructions tailored to the organization’s maturity level. It addresses vital dimensions necessary for integrating responsible AI (RAI) across various aspects of an organization.
Four Responsible AI Maturity levels:
Foundational: Initial awareness for Responsible AI (RAI) is achieved by establishing AI principles, defining essential roles, setting up basic registries for tracking AI use cases, and implementing 3rd party RAI-specific criteria.
Evolving: Structured processes are in place, with early integration of Responsible AI (RAI)
Performing: Responsible AI (RAI) principles are embedded into well-defined processes and robust governance structures.
Advanced: Responsible AI practices are ingrained in the organizational culture and supported by proactive oversight and management.
Responsible AI Dimensions:
Responsible AI maturity is structured across five key dimensions, further divided into 20 sub-dimensions to capture all essential components for effective RAI implementation.
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Source: Navigating the Path of Responsible AI from GSMA |
You can download the following guides to start with your Responsible AI maturity preparation:
- Step by Step guide
- Best Practice tools
- RAI roadmap methodology
Link to download
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Weekly News & Updates…
Last week’s AI breakthroughs marked another leap forward in the tech revolution.
- Llama 3.2 is released with Lightweight 1B & 3B models for edge devices and 11B & 90B vision models, a collection of large language models (LLMs) pre-trained and fine-tuned in 1B and 3B sizes that are multilingual text only, and 11B and 90B sizes that take both text and image inputs and output text, link
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2 updated models released: Gemini-1.5-Pro-002 and Gemini-1.5-Flash-002, along with: link
- >50% reduced price on 1.5 Pro (both input and output for prompts <128K)
- 2x higher rate limits on 1.5 Flash and ~3x higher on 1.5 Pro
- Prithvi WxC, a new general-purpose AI model for weather and climate from NASA, IBM, link
- Now, you can upload YouTube video URLs and audio files directly to NotebookLM, Google Docs, PDFs, text files, Google Slides, and web pages. link
- Flux code is open-sourced, and you can run FLUX.1 on Replicate with an API or in the browser. Use this code as a template for customizations on Flux own hardware. link
- Molmo: a family of open, state-of-the-art multimodal AI models built by Ai2. According to the statistics, it is outperforming GPT-4o, Gemini 1.5 Pro & Claude 3.5 across 11 benchmarks. link
The Cloud: the backbone of the AI revolution
- Announcing General Availability of OCI Compute with AMD MI300X GPUs, link
- Decoding How AI Can Accelerate Data Science Workflows, an article from Nvidia
- Trustworthy AI: Unlocking human potential starts with trust, link
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Gen AI Use Case of the Week:
Generative AI use cases in the Telecom Industry:
On-the-Go Technician Support for Network Maintenance (Telecom Industry), this use case is derived from Deloitte.
Business Challenges
Telecommunications companies face challenges maintaining their complex network infrastructure, especially in promptly identifying and resolving network issues. Field technicians often need to sift through large volumes of documentation and data to diagnose problems, leading to downtime, inefficiency, and customer dissatisfaction.
AI Solution Description
Large Language Models (LLMs) can assist field technicians by simulating real-time network scenarios and guiding them through diagnostic processes. Using natural language processing, LLMs provide personalized, context-aware troubleshooting steps based on real-time data from the network. The LLM processes network logs, equipment details, and historical issue patterns to identify root causes and suggest optimal resolutions. Technicians can input questions or describe symptoms in natural language, and the LLM provides actionable insights and instructions in response. The LLM can be accessed via mobile devices, allowing technicians to get support directly in the field.
Expected Impact/Business Outcome
- Revenue: The faster resolution of network issues leads to less downtime, increasing customer satisfaction and retention
- User Experience: Technicians can resolve problems quickly with on-the-go support, improving operational efficiency and reducing frustration.
- Operations: Automation of diagnostic processes reduces manual intervention, streamlining network maintenance and repair workflows.
- Process: Technicians can access historical and real-time data analysis, minimizing the need for manual troubleshooting.
- Cost: Reduces operational costs by lowering the time and effort required for issue resolution and minimizing the likelihood of human errors.
Required Data Sources
- Real-time network performance data
- Historical maintenance records
- Network equipment specifications and configurations
- Technician reports and feedback
- System logs and troubleshooting documentation
Strategic Fit and Impact
This solution is a high-impact and strategic fit for telecom organizations aiming to optimize network reliability and minimize downtime. It drives efficiency, reduces costs, and improves customer satisfaction by enabling technicians to perform faster and more accurate network maintenance.
Rating: High Impact & strategic fit
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Favorite Tip Of The Week:
Here’s my favorite resource of the week.
- Step-by-step reference app to generate a multimodal AI agent with Llama 3.2 and build a Streamlit app that seamlessly processes text, images, PDFs, and PPTs. link
Potential of AI
- PDF3Audio, an open-source alternative to the podcast feature of Notebook LM with flexibility & tailored outputs. You can make podcasts, lectures, discussions, and short/long-form summaries. link
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Things to Know…
Quantum computing, with Qibo, is an open-source platform built for classical and quantum algorithms.
Qibo: an open-source middleware for quantum computing. link
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The Opportunity…
Podcast:
- This week’s Open Tech Talks episode 145 is “Building a Game-Changing AI Strategy: Step-by-Step Guide and Exercises for Your Organization,” I will guide you through actionable steps and hands-on exercises to help your team develop an AI strategy that creates real impact and sets you apart from the competition.
Apple | Spotify | Amazon Music
Courses to attend:
- CUDA Programming Course – High-Performance Computing with GPUs from Free Code Camp on Youtube, link
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Events:
Tech and Tools…
- Llama Stack: This repository consists of Llama Stack API specifications, API Providers and Llama Stack Distributions
- MLX Swift Examples: MLX is an array framework for machine learning research on Apple silicon. MLX Swift expands MLX to the Swift language, making research and experimentation easier on Apple silicon
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Data Sets…
- Multilingual Massive Multitask Language Understanding (MMMLU) dataset released by OpenAI. It is available in 14 languages and 57 categories from elementary to professional subjects. Link
Other Technology News
Want to stay updated on the latest information in the field of Information Technology? Here’s what you should know:
- AI identifies hundreds of mysterious Nazca drawings in Peruvian desert, reported by Science org
- OpenAI’s Strawberry program is reportedly capable of reasoning. It might be able to deceive humans published by The Conversation
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Earlier week’s Post:
That’s it!
As always, thanks for reading.
Hit reply and let me know what you found most helpful this week – I’d love to hear from you!
Until next week,
Kashif Manzoor
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The opinions expressed here are solely my conjecture based on experience, practice, and observation. They do not represent the thoughts, intentions, plans, or strategies of my current or previous employers or their clients/customers. The objective of this newsletter is to share and learn with the community.
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