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The 2026 Timeline: AGI Arrival, Safety Concerns, Robotaxi Fleets & Hyperscaler Timelines | 221

The 2026 Timeline: AGI Arrival, Safety Concerns, Robotaxi Fleets & Hyperscaler Timelines ## The rapid advancement of AI and related technologies is expected to bring about a transformative turning point in human history by 2026, making traditional measures of economic growth, such as GDP, obsolete and requiring new metrics to track progress ## ## Questions to inspire discussion.

Measuring and Defining AGI

đŸ€– Q: How should we rigorously define and measure AGI capabilities? A: Use benchmarks to quantify specific capabilities rather than debating terminology, enabling clear communication about what AGI can actually do across multiple domains like marine biology, accounting, and art simultaneously.

🧠 Q: What makes AGI fundamentally different from human intelligence? A: AGI represents a complementary, orthogonal form of intelligence to human intelligence, not replicative, with potential to find cross-domain insights by combining expertise across fields humans typically can’t master simultaneously.

📊 Q: How can we measure AI self-awareness and moral status? A: Apply personhood benchmarks that quantify AI models’ self-awareness and requirements for moral treatment, with Opus 4.5 currently being state-of-the-art on these metrics for rigorous comparison across models.

AI Capabilities and Risks.

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The 6 Steps to Reach the Singularity. Ep #114

The 6 steps to reach the singularity.

## The technological singularity, a point where AI surpasses human intelligence, is predicted to occur by 2045 and will profoundly transform humanity, requiring proactive adaptation and integration of AI into daily life ## ## Questions to inspire discussion.

Advancing AI and Machine Learning.

🧠 Q: How can we progress towards autonomous machine learning? A: Shift from supervised to unsupervised learning, enabling AI to identify patterns and make predictions without labeled data, thus advancing towards independent learning and improvement.

đŸ€– Q: What is the significance of achieving Artificial General Intelligence (AGI)? A: AGI represents the pinnacle of AI development, capable of matching or surpassing human-level intelligence across various domains, potentially leading to an unprecedented technological growth boom.

🧬 Q: What are initial steps towards neural augmentation? A: Develop brain-interfacing technologies to enhance specific aspects of human cognition, such as implants or non-invasive devices for improving memory, processing speed, or sensory perception.

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AGI Just Arrived, And We Didn’t Notice!

Artificial General Intelligence (AGI) may have been achieved with recent AI models, marking a significant shift in AI capabilities that could revolutionize industries and potentially make human cognitive labor obsolete ## Questions to inspire discussion.

Getting Started with AI Agents.

đŸ€– Q: How can I start using Claude Opus 4.5 for autonomous coding overnight?

A: Use the Ralph Wigum harness (open-source scaffolding tool) that wraps around Claude Opus 4.5, requiring only basic setup knowledge to enable the AI to autonomously develop code while you sleep, with a simplified user interface expected to launch soon that will make setup even easier.

Understanding Current AGI Capabilities.

🎯 Q: What does AGI arrival actually mean for my work bottlenecks?

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Navigating the Deep Tech Industrial Revolution

Podcast with Chuck Brooks, Adjunct Professor at Georgetown University and President of Brooks Consulting International — Quantum Computing Report.

Overview In this episode of The Quantum Spin by HKA, host Veronica Combs discusses the intersections of quantum technology and cybersecurity with Chuck Brooks, an adjunct professor at Georgetown University and the president of Brooks Consulting International. Chuck discusses how the evolution of technology, particularly AI and quantum computing, has dramatically transformed cybersecurity. The conversation also touches on the role of CISOs, the integration of new technologies, and the importance of ongoing education and adaptation in the face of rapidly changing technologies. 00:00 Introduction to Quantum Spin Podcast00:34 Guest Introduction: Chuck Brooks00:46 Chuck Brooks’ Career [
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AI Model for Imaging-Based Extranodal Extension Detection in Human HPV-Positive Oropharyngeal Cancer

An AI-powered pipeline accurately classified imaging-based extranodal extension from CT scans in HPV-positive oropharyngeal carcinoma and predicted worse oncologic outcomes, outperforming expert radiologist assessment and offering a promising prognostic tool for clinical decision-making.

Question Can an artificial intelligence (AI)−driven model predict imaging-based extranodal extension (iENE) and oncologic outcomes from pretreatment computed tomography scans of patients with human papillomavirus (HPV)−positive oropharyngeal squamous cell carcinoma (OPSCC)?

Findings In this single-center cohort study of 397 patients with HPV-positive cN+ OPSCC, an automated pipeline integrating lymph node segmentation and iENE classification achieved an area under the receiver operating characteristic curve of 0.81. AI-predicted iENE was significantly associated with worse distant failure, recurrence-free survival, and overall survival, and outperformed expert radiologist assessment.

Meaning These findings suggest that automated iENE detection using AI models may offer a powerful prognostic tool to complement clinical decision-making in HPV-positive OPSCC and extend iENE interpretation capabilities to centers that lack specialized radiologists.

Discrimination of normal from slow-aging mice by plasma metabolomic and proteomic features

Tests that can predict whether a drug is likely to extend mouse lifespan could speed up the search for anti-aging drugs. We have applied a machine learning algorithm, XGBoost regression, to seek sets of plasma metabolites (n = 12,000) and peptides (n = 17,000) that can discriminate control mice from mice treated with one of five anti-aging interventions (n = 278 mice). When the model is trained on any four of these five interventions, it predicts significantly higher lifespan extension in mice exposed to the intervention which was not included in the training set. Plasma peptide data sets also succeed at this task. Models trained on drug-treated normal mice also discriminate long-lived mutant mice from their respective controls, and models trained on males can discriminate drug-treated from control females.

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