AGI = Iterative Learning

AGI = Current AI methods + RHLF Experiments + Human Applied Fine Tuning + Custom Experiments + Ton of Guardrails + Domain Specifics Pattern Ingestion

Ilya Sutskever had a TED talk this year and predicted the two most important concepts that we haven't discussed much:

• AGI will have the ability to improve itself
• AGI will work on the next generation of AGI pic.twitter.com/CMvmgTc0oz

— Haider. (@slow_developer) October 19, 2024

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AI Companion Market = Unethical misuse

A troubling case study emerges from the experience with Character.AI, a role-playing application that allows users to create and interact with AI personalities. What began as casual interaction for one student, Sewell, evolved into a concerning pattern of emotional dependency that exemplifies the risks of unmoderated AI engagement:

The student developed an intense emotional attachment to an AI character named Dany

• He maintained constant communication, updating the AI dozens of times daily
• Interactions escalated to include romantic and sexual content
• The situation remained hidden from parents and support systems
• Academic performance declined significantly
• Social isolation increased as he spent hours alone with the AI companion
• Behavioral issues emerged at school

AI Companion Market = Unethical misuse

The Dangerous Reality of AI Companionship Apps: Hidden Threats 🚨

• Predatory marketing targeting lonely individuals (Stats - Almost a Quarter of the World Feels Lonely)
• Deliberate exploitation of human psychology
• ZERO addiction prevention measures
• Dangerous normalization of human-AI relationships

AI Companion Market size was valued at USD 196.63 Billion in 2023 and is projected to reach USD 279.22 Billion by 2031, growing at a CAGR of 36.6% during the forecast period 2024-2031. (Stats)

Warning: Unregulated profits driving dangerous innovation

Without immediate, strict #regulatory action, we risk a global mental health crisis.

#AIRegulation #AIEthics #GenAI #AIRisks #TechPolicy #ResponsibleAI #EthicalAI

Ref - Link1, Link2, Link3, Link4

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Balanced Perspective of AI Adoption - Human + AI (OR) Humans vs AI

 

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🤖 From Consulting to GenAI Product Development: Key Learnings

After transitioning from consulting to GenAI projects, I've noticed some fascinating shifts in approach and mindset. Here's what I've learned:

🎓 Education is Key

• Founders / Teams need deep understanding of AI capabilities
• Critical to distinguish between data quality issues vs. AI limitations
• Fine-tuning ≠ behavior change; it's about control

🎯 Problem-First Approach

• Consulting: Platform sales → Problem solving
• Product: Problem solving → Platform selection
• Accuracy requires significant iteration
• 1 PRD line can spawn 100+ test cases
• Multiple design variations are common

⚙️ Building for Scale

• Focus on concrete solutions over platforms
• Balance data quality with model output
• Small teams enable rapid iteration
• Quick pivots back to fundamentals when needed

🚀 Product Development Reality

• Patience is crucial for monetization
• Innovation must be truly unique
• Products need time to get paid users. The first few cycles of the pilot 
• Success relies on understanding trade-offs

🔄 Blended Role Benefits

• These experiences enhance my current work:

Workshop facilitation

• Domain-specific use case brainstorming
• Core LLM training from practitioner's view
• Business-focused advisory

#GenAI #ProductDevelopment #AI #Innovation #StartupLife #TechTransition #Leadership

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The Evolving Landscape of ML Hiring: A Veteran's Perspective

 

Job interviews often miss true talent. They reward rehearsed responses over candidates who can persistently build practical, context-aware solutions beyond just technical know-how

As someone in the trenches of data science hiring for over 7 years, I've watched our field transform dramatically. Recently, a job description for an ML role caught my eye - and not necessarily in a good way. It got me thinking about how our industry's hiring practices often need to catch up to the reality of our work. Let me share some observations:

The Commodity of Code

• LLM can generate working solutions / provide ideas / get started on any topic as long as you have good basic skills and coding knowledge. Now, I ask interns hiring assignment tasks to focus on accuracy and bugs. Code has become a commodity. The real value lies in understanding models, and limitations, bridging the gap between visions and technical realities, and architecting solutions that solve real-world problems.

The Kitchen Sink JD

• This particular job description reads like a wish list for a tech superhero. Data structures, algorithms, AI/ML, coding, system design - oh, and don't forget a dash of product sense! While it's great to aim high, this scattergun approach often misses the mark. We need specialists with deep expertise, not generalists who've dabbled in everything.

The Interview Gauntlet

• The hiring process outlined was a marathon: write-ups, HackerEarth assessments, coding tests, multiple rounds with the ML team, and then more conversations. In a market where top talent is scarce and in high demand, do we really need to put candidates through such a lengthy ordeal?

The Missing Pieces

• What struck me most was what the JD and process didn't emphasize. Where was the assessment of a candidate's ability to translate business problems into technical solutions? How about evaluating their capacity to stay ahead of rapidly evolving trends in ML?

A Call for Pragmatism

• To my fellow hiring managers and HR teams: let's get practical. The perfect candidate who ticks every box on your mile-long list probably doesn't exist - and if they do, they're likely happily employed or running their own startup.

Instead, focus on core competencies that drive real value:

• The ability to understand and translate business needs
• A knack for architecting scalable, efficient solutions
• Adaptability and a passion for continuous learning
• Strong communication skills to bridge technical and non-technical stakeholders

The ML landscape is changing faster than ever. Our hiring practices need to keep pace. Let's move beyond the "code on a whiteboard" era and design processes that identify true innovators who can propel our field forward.

Another Good Read - Why We Don't Interview Product Managers Anymore

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The Human Animal is just a Machine - Warren McCulloch

 

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Ethical AI vs. Agentic Autonomous AI: Navigating the Complexities of Modern AI Systems

• Human Oversight vs. AI Independence: Ethical AI frameworks typically advocate for human-in-the-loop systems, ensuring human oversight. Agentic Autonomous AI aims to minimize human intervention, raising questions about responsibility and control.
• Short-term Gains vs. Long-term Consequences: The push for rapid AI advancement (often seen in Agentic Autonomous AI) may overlook long-term ethical implications. Ethical AI approaches tend to prioritize careful consideration of potential future impacts.
• The Reasoning Conundrum: While Large Language Models (LLMs) demonstrate language understanding and generation capabilities, they still lack true reasoning abilities. This limitation is crucial when considering the ethical implications of deploying AI systems in decision-making roles.
• Ethical Constraints vs. Autonomous Agency: The core tension between Ethical AI and Agentic Autonomous AI lies in balancing moral safeguards with the desire for increasingly independent AI systems. Ethical AI prioritizes human values and safety, while Agentic Autonomous AI pushes for greater AI self-direction.
• Transparency Trade-offs: Ethical AI often demands explainability and interpretability, potentially limiting model complexity. Conversely, highly autonomous AI systems may sacrifice transparency for increased capabilities, raising ethical concerns about accountability and trust.
• Data Ethics in AI Development: Ethical AI emphasizes the importance of unbiased, representative datasets. Agentic Autonomous AI, however, may prioritize data quantity over quality to enhance its learning capabilities, potentially perpetuating or amplifying societal biases.
• Continuous Learning and Ethical Drift: Agentic Autonomous AI systems that engage in continuous learning pose risks of ethical drift over time. Ethical AI frameworks must grapple with how to maintain moral constraints in evolving systems.
• Global Ethics vs. Local Autonomy: As AI systems become more autonomous, they may encounter scenarios where global ethical standards conflict with optimal local decisions. This tension between universal ethics and situational autonomy remains a critical challenge.
• Responsible AI Adoption in Practice: Implementing either Ethical AI or Agentic Autonomous AI requires a deep understanding of models, data, and their limitations. Superficial adoptions of either approach can lead to irresponsible and potentially harmful AI deployments.
• The Role of Human Values: Ethical AI explicitly encodes human values into AI systems, while Agentic Autonomous AI may develop its own set of values through learning. The alignment (or potential misalignment) of these values with human ethics is a crucial area of ongoing research and debate.

Technology will continue to change the world. A thoughtful approach is needed to prioritize use cases that offer broader positive impacts over those that primarily lead to monetization. This way of thinking can help align AI adoption with human values and ensure a more substantial positive impact on humanity.

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Prompt Caching Analysis

Prompt Caching Analysis

Caching is enabled automatically for prompts that are 1024 tokens or longer. 

Prompt Caching is enabled for the following models:

• gpt-4o (excludes gpt-4o-2024-05-13 and chatgpt-4o-latest)
• gpt-4o-mini
• o1-preview
• o1-mini

Usage Guidelines

1. Place static or frequently reused content at the beginning of prompts: This helps ensure better cache efficiency by keeping dynamic data towards the end of the prompt.

2. Maintain consistent usage patterns: Prompts that aren't used regularly are automatically removed from the cache. To prevent cache evictions, maintain consistent usage of prompts.

3. Monitor key metrics: Regularly track cache hit rates, latency, and the proportion of cached tokens. Use these insights to fine-tune your caching strategy and maximize performance.

Ref - Link1, Link2

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The Harsh Realities of GenAI Startups: AI Advisor Perspective

• 🔓 Open Source Paradox: There's a push to leverage open-source models and frameworks, yet expectations for state-of-the-art accuracy remain unrealistically high.
• 🧩 Holistic Product Development: Successful GenAI products require a synergy of innovative ideas, domain expertise, and high-quality training data - not just algorithms.
• 💰 Resource Constraints: Computational costs are a significant factor in GenAI development, often underestimated by founders.
• 🎈 Hype vs. Reality Gap: Many founders lack a deep understanding of the technical challenges and limitations in GenAI implementation.
• 🖥️ Infrastructure Costs: Even minimal GPU requirements for model training and inference can be daunting for bootstrapped startups.
• ⚖️ Resource Optimization Fallacy: Attempts to minimize costs across all aspects of development often lead to suboptimal results in model performance and product quality.
• 🏎️ Performance-Aesthetics Mismatch: Many startups focus on creating visually appealing UIs but struggle with the underlying AI engine's capabilities, resulting in a "sports car body with a scooter engine" scenario.
• 🚀 Democratization vs. Expertise: While AI tools are becoming more accessible, creating truly groundbreaking GenAI applications still requires deep technical expertise and innovation.
• 🌊 Depth vs. Breadth Trade-off: Founders who aren't willing to invest time and resources in deep technical development risk creating superficial, easily replicable products with limited longevity in the market.

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