How to Implement the Use Case Correctly
- Field of View
- Stable Infrastructure
- Minimal Occlusion
- No Manual Calibration
- With a good setup, half of the complexity and noise can be eliminated.
Deep Learning - Machine Learning - Data(base), NLP, Video - SQL Learning's - Startups - (Learn - Code - Coach - Teach - Innovate) - Retail - Supply Chain
How to Implement the Use Case Correctly
Prediction:
OpenAI is set to shift towards domain-centric solutions, making 2025 a transformative year for AI. This transition is based on the data collected and learned from APIs serving different domains, focusing on context window improvements, reasoning patterns, and cross-modal integration. This will significantly enhance decision-making in critical sectors like FinTech and healthcare. By tackling technical challenges and integrating user feedback, these advancements will result in more powerful, tailored AI applications that will reshape entire industries.
Expanding Beyond Language Models
Today, OpenAI is primarily recognized as a leading provider of large language models, but its true capabilities extend much further. Its question-answering abilities, for instance, are exceptionally powerful and evolving rapidly. As clients integrate this technology into critical sectors like FinTech and healthcare, they will unlock new levels of context window improvements, and cross-modal integration and reasoning by adopting techniques like tree of thought, chain of thought, and graph-based approaches, enabling AI to think and deduce more effectively. Feedback from users will be pivotal in this journey, guiding organizations on how best to structure information flows and assess when to fine-tune models, use Retrieval-Augmented Generation (RAG), or determine the optimal use of short-term and long-term memory. This constant feedback loop will allow AI to achieve unprecedented levels of contextual understanding and adaptive reasoning, creating models that align more closely with complex real-world needs.
"OpenAI's journey is no longer just about language—it's about thought and contextual adaptation."
Building Resilient and Adaptive Systems
These advancements will likely lead to the development of more resilient and adaptable systems. Future systems will not only enhance decision-making but also push reasoning capabilities into new territories, setting the stage for increasingly sophisticated agents and refined RAG architectures. These improved architectures are expected to reduce hallucinations, boost accuracy, and lead to products that are more responsive to real-world challenges. Overcoming issues like catastrophic forgetting, hallucinations, and knowledge manipulation will be critical, positioning these systems as robust, reliable solutions across industries.
"Resilient, adaptive AI systems will transform decision-making and redefine industry standards."
Addressing Technical Challenges
Currently, accuracy challenges remain in areas such as domain-relevant embedding, balancing retrieval techniques against accuracy and latency, chunking methods based on usage or query types, contextualization, and routing or re-ranking processes. Yet, these elements are essential for advancing the capabilities of AI models. Despite these ambiguities, ongoing data processing and analysis are paving the way for more focused, domain-specific AI products. Within the next six to eight months, we’re likely to see a new wave of AI-driven applications, from highly specialized agents to RAG applications and APIs crafted for specific industries.
"Technical hurdles are simply steps toward the next wave of AI-driven, domain-specific innovation."
The Transformative Potential of 2025
The year 2025 is set to be a pivotal moment in AI, marking the dawn of domain-centric solutions that will reshape how AI interacts with our world. As more industry-specific applications emerge, OpenAI’s technologies will bring powerful, tailored solutions closer to reality.
"2025: The year AI becomes truly domain-centric, reshaping industries with precision, customized models, and highly accurate agents and RAG systems."
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#AI #OpenAI #DomainSpecificAI #Innovation #MachineLearning #FinTech #Healthcare #FutureOfAI
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:
— Haider. (@slow_developer) October 19, 2024
• AGI will have the ability to improve itself
• AGI will work on the next generation of AGI pic.twitter.com/CMvmgTc0oz
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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
AI Companion Market = Unethical misuse
The Dangerous Reality of AI Companionship Apps: Hidden Threats 🚨
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
Keep Thinking!!
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
🎯 Problem-First Approach
⚙️ Building for Scale
🚀 Product Development Reality
🔄 Blended Role Benefits
Workshop facilitation
#GenAI #ProductDevelopment #AI #Innovation #StartupLife #TechTransition #Leadership
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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
The Kitchen Sink JD
The Interview Gauntlet
The Missing Pieces
A Call for Pragmatism
Instead, focus on core competencies that drive real value:
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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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.
Keep Going!!!
Prompt Caching Analysis
Caching is enabled automatically for prompts that are 1024 tokens or longer.
Prompt Caching is enabled for the following models:
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.
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For questions/feedback/career opportunities/training / consulting assignments/mentoring - please drop a note to sivaram2k10(at)gmail(dot)com
Coach / Code / Innovate