Artificial intelligence is leading humanity towards what many see as a new renaissance for human creativity, augmented by almost unlimited computing power.  Others fear recursive AI could get out of control and behave in ways that are unpredictable and undesirable. Dr Ian Storey updates us on how far away we are from Kurtweil's Singularity, implications for information security and the potential for an AI super intelligence to come to being by 2045. Or earlier.

The Road To Singularity

A Vortex To Singularity

One of the boldest questions we could ask AI is this: “Design a causal AI system so intricate that human minds could never independently conceive it.”

Designing a Causal AI System Beyond Human Intuition

Paper 1901.03729 - Automated Rationale Generation_ A Technique for Explainable AI and its Effects on Human Perceptions

This research developed an algorithm to automatically detect meniscus tears in knee MRI scans, aiming to improve diagnostic access and radiologist workload efficiency. The study utilized a dataset of MRI scans with varying image qualities and employed several deep learning models (ResNet50, VGG16, and custom models based on AlexNet and LeCun's architecture), ultimately selecting a functional model for its precision and interpretability. The model's performance was evaluated using accuracy and precision metrics. Classification activation mapping (CAM) was used to enhance the model's explainability. The research concluded with recommendations for model refinement and external validation.

Automated Meniscus Tear Detection in Knee MRI Scans NotebookLM Podcast

Welcome to AI with Shaily! 🎙️ I’m Shailendra Kumar, your host, diving deep into one of the most pressing questions in the world of artificial intelligence: How can we truly trust and ensure the safety of Super AI? 🤖✨ As AI grows smarter every day, it’s not just about what it can do, but *how* it does it—and whether it behaves responsibly in the real world.

Let me share a personal story: A few years back, I worked with an enterprise implementing AI decision tools. Initially, everything seemed perfect, but soon customers noticed some strange recommendations from the system. That’s when I realized intelligence alone isn’t enough; we need transparency and explainability to understand *how* AI arrives at its conclusions. 🔍🧠

To confidently trust Super AI, it must answer some tough questions:

First, can it clearly explain its decision-making process? We need to trace outputs back to specific input data to detect errors or hidden biases. Without this, AI becomes a “black box,” which nobody wants—imagine trusting a doctor who won’t explain their diagnosis! 🏥❓

Next, continuous safeguards are crucial. AI models can “drift” over time, so constant monitoring is essential to catch bias or unintended harm early. Compliance with privacy laws like GDPR and HIPAA isn’t optional—it’s mandatory as regulations tighten worldwide. 🔐📜

Security is another key pillar. Super AI must defend against sneaky attacks like prompt injections or data leaks, using enterprise-grade protections to keep sensitive information safe. After all, if your AI gets hacked, what good is its intelligence? 🛡️⚠️

Failure mode analysis acts like a behind-the-scenes superhero, enabling AI to spot its own errors, explain them, and recover gracefully—think of it as the AI’s self-check to avoid catastrophic mistakes. 🦸‍♂️🔄

Moreover, as AI handles multi-turn conversations and integrates APIs and external tools, maintaining consistent and coherent responses becomes a complex challenge that must be flawlessly managed. 💬🔗

Legal and regulatory compliance is front and center now. New AI-specific laws coming by 2025—especially around automated decision-making and employment—require documented proof of bias testing and prevention. This isn’t just best practice; it’s the law. ⚖️📅

Finally, concrete metrics like Stanford’s CRFM Transparency Index help quantify how explainable and trustworthy a model really is, providing ongoing benchmarks for improvement. 📊✅

Here’s a bonus tip for all curious minds: When evaluating any AI system, ask for its transparency score or similar metrics. If it can’t provide one, treat it like a mystery novel missing its crucial chapters! 📚❌

Why does all this matter? Because AI is increasingly making critical decisions in healthcare, finance, and beyond. We owe it to ourselves and society to demand clarity, security, and fairness. It’s not just about smarter AI; it’s about *responsible* AI. 🌍🤝

Before we wrap up, remember this: “Trust is built not from what you say, but from what you show.” In AI, transparency is that proof. 👁️‍🗨️🔑

Thanks for tuning in to AI with Shaily! Don’t forget to connect with me on YouTube, Twitter, LinkedIn, and Medium for more insights. If you love deep dives like this, subscribe and share your thoughts—I’m eager to hear your take on the trust challenge facing Super AI! 🚀💬

Until next time, keep questioning, keep learning, and stay curious! 🌟🤓

Building Trust in Super AI: The Transparency and Safety Challenge

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Including features on cool gadgets, website reviews, the latest science and IT news, not to mention astronomy and space exploration.

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Special thanks to John Young, Paul Wattie, Steve Meyers, Brendon Telfer and the team at RPPfm.

Unlock the secrets of data mastery with Shailendra, a 27-year veteran in AI, ML, and Data Science, driving AU$ 6 Billion in transformative value. As the author of 'Making Money out of Data,' their strategies redefine digital success. With expertise honed at IBM and Accenture, they drive innovation and data monetization. Explore how loyalty programs like Flybuys and Everyday Rewards revolutionize business—connect with Shaily today.

I’m a curious explorer of the boundaries between humans and artificial intelligence. I'm fascinated by the questions AI raises about what it means to be intelligent, conscious, or creative. I believe technology is not just a tool but a mirror that reflects our own knowledge and limitations.

https://intelligencerai.substack.com/

I'm machine learning enthusiast with applied math background and a lot experience as full stack developer.
I'm from Ukraine but now I'm working remotely in Montenegro.

New tracks tagged #explainability