AI in Movie Making - Pros vs Cons

• Easy Experimentation
• AI = Best Craftsmen
• AI cross-pollinates things that exist
• Lack of Consistency vs Quality at this point
• Background, Color changes, and designs can happen with AI

Keep Exploring!!!

Good vs Great Solutions

 Solutions can be built with different levels of accuracy/scalability based on Talent, Time, and Money

• Product built with 20$ Upwork contractor
• Product built with 400$ up-work expert
• Product built with 40K FTE
• Product built with 100K FTE Experienced Research / Dev

How do you differentiate each one based on architecture, performance, and benchmark against the best? 

Steve Jobs: The difference between good people and great people in software is 50-to-1

“I’ve always considered part of my job was to keep the quality level of people in the organizations I work with very high. I mean that’s what I consider one of the few things I can contribute… pic.twitter.com/NBlk3UoWSC

— Startup Archive (@StartupArchive_) November 17, 2024

Keep Learning!!!

Reasoning vs Next word Prediction vs LLM

 

Keep Exploring!!!

Proplens - GenAI Powered Real Estate Solution :)

 

We are in news !!!  Link

Happy Responsible, Accurate, Consistency and Low Latency Adoption :)

Knowledge vs Perspective vs Learning

• Focus on understanding over copy-pasting
• Code can be replicated, but understanding cannot
• Embrace experimentation and learning from failures
• Build deep knowledge of system behavior:
• Identify potential failure points
• Understand edge cases
• Document limitations
• Quality is everyone's responsibility

Keep Experimenting!!!

Vision Use case

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.

Keep Exploring!!!

Prediction - 🔍 Anticipating AI's Big Shift in 2025: OpenAI’s Focus on Domain-Centric Solutions

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."

Keep Exploring!!!

#AI #OpenAI #DomainSpecificAI #Innovation #MachineLearning #FinTech #Healthcare #FutureOfAI

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

Keep Exploring!!! 

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

Keep Thinking!! 

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

 

Keep Exploring!!!

🤖 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

Keep Exploring!!!

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

Keep Exploring!!!

The Human Animal is just a Machine - Warren McCulloch

 

Keep Exploring!!

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.

Keep Going!!!

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

Keep Exploring!!!

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.

Keep Exploring!!!

🚀 Celebrating a Year of GenAI Use Case Success in Retail! 🎉

It's been over a year since my Retail adoption #GenAI use case went live for a leading U.S. specialty retailer on August 17, 2023. 

The results? Double-digit improvement in user page conversions! 📈

🔍 Key Insights:

Innovation Over Integration: Initially, I didn't showcase #GenAI use cases to the #CTO. Instead, I presented a #ReimaginedWorkflow for:

• Customers
• Support analysts
• Procurement teams
• Digital Asset Management

Rethinking AI/ML Implementation: It's not about wrapping AI around existing processes. True impact comes from:

• New engagement models
• Innovative interactions
• Blending creative solutions

Success Factors in Production:

• Data Quality 🏆
• Innovative use of GenAI
• Tailored solutions (not patchwork fixes)
• Rigorous testing in production environments

Beyond Demos: Real adoption comes from solving genuine user needs, not just showcasing capabilities.

• 💡 Lesson Learned: To make #AI truly impactful, we must explore new ways of engagement, foster creativity, and innovate by combining diverse ideas.
• 👥 Collaboration Opportunity: Are you working on similar AI strategies or use cases? Let's connect and collaborate! Share your experiences in the comments.

#AIStrategy #RetailInnovation #DataDrivenDecisions #DigitalTransformation #AIAdoption #TechLeadership #InnovationInRetail #AISuccessStory

Who else is seeing success with GenAI in their industry? Let's discuss! 👇

🚨 4 Overlooked Pillars of AI Ethics: Profits vs. Principles 🚨

In the rush for AI dominance, we're neglecting crucial ethical foundations. Here are 4 pillars often sacrificed at the altar of profit:

These policies will shape the future of AI. We can't wait - the time to act is NOW.

🧠 Data Sovereignty Rights

• Overlooked because: User control cuts into valuable data assets
• Reality check: Our digital lives aren't corporate property

🏥💰 Regulatory Oversight in Critical Sectors

• Overlooked because: Slows time-to-market in lucrative industries
• Reality check: Unchecked AI in healthcare/finance risks lives & livelihoods

📚 Transparent AI Lineage

• Overlooked because: Transparency can expose flaws & biases
• Reality check: Black-box AI erodes trust & accountability

🎨 Recognition of Human Creativity

• Overlooked because: Crediting sources complicates IP claims
• Reality check: AI thrives on human ingenuity – let's honor that

These aren't just ethical niceties – they're essential for sustainable, trustworthy AI. Short-term profits shouldn't overshadow long-term societal impact.

We need AI that serves humanity, not just shareholders. It's time to realign our priorities. Which pillar do you think is most crucial? Why is it being overlooked?

#AIEthics #ResponsibleAI #TechForGood #AIAccountability #DigitalRights

Tag a tech leader who needs this wake-up call 👇

Keep Advocating ResponsibleAI!!!

TechForGood vs TechForProfits !!!

Understanding Index RAG: Data Storage vs. Retrieval

In the realm of information retrieval and artificial intelligence, Index RAG (Retrieval-Augmented Generation) has emerged as a powerful technique. To fully grasp its potential and limitations, it's crucial to understand the distinction between data storage and retrieval, particularly in the context of indexing strategies. This post will explore two different indexing approaches and their implications for handling queries, especially multipart questions.

The Indexes

Index 1: Broad and Diverse

Composition: 20 pages from history + 20 pages from geography + 20 pages from maths

Strengths:

• Versatility: Covers multiple subjects, enabling efficient responses to multipart questions
• Diversity: Offers a well-rounded breadth of content across different fields

Index 2: Deep and Focused

Composition: 200 pages focused solely on history

Strengths:

• In-Depth Knowledge: Provides comprehensive depth on history, ideal for complex historical inquiries
• Rich Content: More pages dedicated to one subject increases potential for detailed responses

Trade-offs

Breadth vs. Depth

• Index 1: Offers breadth across subjects but may lack depth for in-depth analysis
• Index 2: Delivers depth in history but falls short on breadth for interdisciplinary queries

Complexity of Queries

• Index 1: Can handle complex, multipart questions effectively due to subject variety
• Index 2: May struggle with multipart questions spanning multiple disciplines

Information Quality

• Index 1: Information may be less densely packed with specialized detail
• Index 2: Provides rich historical data but lacks subject diversity

Challenges with Multipart Questions

Consider a multipart question involving history and mathematics:

Using Index 1:

• Pros: Can provide relevant information across both subjects
• Cons: Detail may not be as profound, potentially leading to surface-level insights

Using Index 2:

• Pros: Historical aspect might be well-covered
• Cons: Absence of mathematical content results in an incomplete answer

Implications for RAG Systems

Query Processing:

• RAG systems using Index 1 may need sophisticated algorithms to balance information from different domains
• Systems using Index 2 might require additional steps to supplement missing interdisciplinary information

Content Generation:

• Index 1 allows for more flexible content generation across topics
• Index 2 enables deep, nuanced responses within its specialized domain

System Architecture:

• Index 1 might benefit from a modular architecture that can efficiently combine information from different subjects
• Index 2 could leverage specialized language models fine-tuned for historical content

Conclusion

The choice between a broad, versatile index (Index 1) and a deep, focused index (Index 2) significantly impacts the retrieval effectiveness of an information system. Understanding these dynamics is crucial for users and developers alike to create effective RAG systems.

When designing or using RAG systems, consider:

• The nature of expected queries (single-domain vs. interdisciplinary)
• The required depth of information
• The system's ability to synthesize information from multiple sources

By carefully weighing these factors, one can optimize the balance between data storage and retrieval capabilities in Index RAG systems, ultimately enhancing the quality and relevance of generated responses.

GenAI Two Use Cases - Two Lessons

Creative and Learning Use Case

Instead of playing sports or watching cartoons, kids nowadays are coding. 👀 pic.twitter.com/POX50bt4MH

— Vivek Naskar (@vivek_naskar) September 8, 2024

Wrong Guardrails Applied, Content for opinions

What other options

• Provide Factual data
• Do not provide recommendations for entities
• Reason for bias
• Do not rely on Guardrails

Keep Going!!!

Multimodal data platform

ETL and data pipelines are redefined in #GenAI Applications. Your #ETL now will support 

• #images, #docs, #numbers, #pdfs. Extracting and storing insights/vectors / structured databases 
• Everything together creates the new #GenAI #Multimodal data platform. 
• #Multimodal #insights from all forms of data
• #Proplens is working to align this perspective for our customers for richer insights and perspectives. #productlessons #genai #data

Keep Loading and Learning!!!

Navigating the Tradeoff Between Income and Responsibilities in Freelance AI Consulting and Corporate Jobs

• My friend - Hey Siva, you seem busy. 
• Myself - Yes, I have some classes and consulting. 
• My friend - So, you're earning more money? 
• Myself - Not necessarily. Some leads work out, some don't. Sometimes, even after presenting the architecture, there is no convergence. 
• My friend - That's common. 
• Myself - The same uncertainties exist in a corporate job, where ideas may not align. However, you get to work closely with founders. 
• My friend - That's true. 
• Myself - Even if your idea fails in consulting, you have a direct connection to lead, discover, and strategize. 
• Myself - In Freelance AI consulting, the outcome is clear - it either works or it fails, and I deal with it directly. No regrets :)

Freedom entails risks, but it's worth it. You pave your own path.

Sure, feel free to ping me if you are interested in harnessing the power of AI.

Keep Exploring!!!

Intelligence with GenAI

When learners can see 'Intelligence with GenAI'. It is very heartening to see solutions built during the session :)

Some feedback after the 12-hour GenAI session:

• We have a lot of data; we can use LLM to add intelligence. A lot of IoT sensor data is present, but only fixed reports are available.
• I have only used #ChatGPT; now I see a ton of tools to explore.
• Infra intelligence - LLM is used to understand the complete ecosystem of servers, leverage logs + intelligence to find server owners, and alert on reallocation / upgrades.
• Automate Data Analyst work to analyze keywords, user query patterns. This saves 65% of my efforts.

Keep Exploring!!!

More Smarter Agents + LLMs = Superior Intelligence

 

Keep Exploring!!!

Latency is a never ending learning

• Latency with cache
• Latency with semantic cache
• Latency with Indexes
• Latency with Graph Queries
• Latency with Optimal Value for Top K

Benchmark against domain dataset

Good Data = Good Strategy = Quality Experiments

Happy Low Latency!!!

How to avoid this scenario - Gartner Predicts 30% of Generative AI Projects Will Be Abandoned After Proof of Concept By End of 2025

What is required to Turn Data Into AI Products ? - My perspectives

• Experimentation on Different levels of data / Summary / Key-Values / Multimodal
• Data curation / Setting up Relationships
• Adding domain knowledge

The main reasons cited are:

• Poor data quality
• Inadequate risk controls
• Escalating costs
• Unclear business value

So, the lesson is, my perspectives are:

• The model is not lift-and-shift — customize it for your needs. A demo that works may not be the solution you need.
• Build your data and benchmark with your data. Do not rely on benchmarks that do not reflect your data.
• Have an LLM cybersecurity, data governance, and guardrails in place. Do not trust the LLM until your first 100 users are happy with it.
• Escalating costs—first, get the accuracy right, then reduce costs. You cannot achieve everything at once.
• Unclear business value — Do not force-fit LLM use case to get a promotion. Only opt for it if it genuinely adds value.
• If someone promises a working solution in 1 month remember it can be selling a prototype, not a production-grade solution

Keep Exploring!!!

Working as AI Advisor

• Close proximity to decision-makers
• Tech will be commodity but Domain + Tech is true knowledge
• Loyalty works in long term, talent gets visibility in short term
• Take time and provide accuracte inputs, Optimism + Clear perspectives matter
• Building relationships, Experimenting on meaningful projects, Novelty to build solutions / integrate past experience + latest tech to match quality matters
• Balancing business vs tech roadmap, Being able to spot features that differentiate the rest
• In the end everything may fail but there is a satisfaction in the Journey

Keep Exploring!!!

Painful Moments - Potential People vs. Wrong Mindset

While evaluating answers: Some candidates document well, attempt, and submit answers but miss the basics. This reflects both intent and missed guidance in learning. High potential is evident, but basics are either overlooked or dot-connecting skills are lacking.

While teaching: Some PhD/lateral folks tend to generalize everything or focus on proving theories break. Learning is not about proving your knowledge but about gaining a balanced perspective. One class is not sufficient to judge anything. Observing these types of learners makes me feel sad as they are so short-sighted.

Education is not mindset; experience does not mean competency!!!

Keep Exploring!!!

Memorization vs Generalization

Memorization vs Generalization

When you develop #GenAI apps, After a certain stage, When things work fine, The immediate next question is

• Model is memorizing or Learning patterns
• Test with variations / Analyze on patterns of responses

I don't want my life to be memorization - Company1 - Company2 ..., Exploring out of comfort zones provides diverse perspectives.

Earlier I had time to regret, Now I don't have time to think about anything. A long day of managing and solving different problems and different lenses of execution. Sometimes some experiences don't fill your pocket but fill your soul. In the end, I want to smile at death, I have tried all my wishlists.

Keep Exploring!!!

GenAI - LLM - Startup Learning War

 

Latency Experiments - Link

Keep Exploring!!!

The Uniqueness of Customer Data and the Importance of Automated Cleanup

In the digital age, managing customer data can be a daunting task. Here are some points to consider:

• Each customer's data will be unique.
• Each customer's data will encounter parsing issues.
• Each customer's data will disrupt pipelines.

No data is pristine; changes should not be spontaneous. When dealing with data, especially on a large scale, it's imperative to have a robust process in place. Having a process to automate cleanup is essential to scaling your solution.

Keep exploring!

KG vs RAG

• First, we solved with Prompt
• Next, we solved with RAG
• Next, we did Summary, RAG
• Next, we moved to Txt2SQL

There is some leftover space for Graph

Build a product and use tech according to needs, No Forefit in the equation :)

Keep Exploring!!!

Learning is Non-Linear

Some days are Sigmoid. Some days are Relu. Novelty is not inventing new stuff but stitching the right techniques in the right proportion.

A few more I relate to my work style :)

• Persist until you figure things out, digging deeper and deeper into unfamiliar systems until it finally clicks, and you understand how they work.
• Have a broad understanding and creative use of Algos, SQL, LLMs, etc. Teach until you get first principles right
• Have the intellectual curiosity to learn about the many new areas you’ll be taken into. Failures today are arsenal for tomorrow.
• Have a willingness to lean into uncertainty. It's okay if a bad day happens.
• Magnitude improvements and novel capabilities. Keep collecting perspectives.
• You will face unique engineering and AI challenges that will make a meaningful impact. To be authentic, you have to have a lot of scars.
• A focus on outcomes, not time-tracking. Yeah, sometimes easy looks complex, complex looks easy.
• The database is always important. Are not afraid of legacy justice systems and would rather make them work than give up.

Keep Going!!!

Txt2SQL

Txt2SQL is easier in straightforward examples, Real database has a ton of complications

Example-

Columns can be generic, Attribute1, Attribute2, We may use Attribute1 for key, Attribute2 for Value. A ton of learning working on it, still trying to get a hold :)

• Hints for clause level
• Hints for join 
• Verbose hints
• Examples in database specific formats
• Categorize on type of errors - Syntax, Missing columns, Missing right joins

Keep Exploring!!!

LLM perspectives

• LLM - Fast, Simple, and Dumb Sometimes
• Prompts - Looks simple but when structured relevant to context can give magical results
• With extended context length, embedding it looks more magical with the abstraction of representations of useful knowledge

LLM generation kids / learning using LLM products will have a different perspective of thinking / before and after ChatGPT :)

Experimentation, Always something to find a solution :)

Some questions and answers take days or weeks, and sometimes the approach moves from LLM to NLP, It's a blend of techniques to make things work.

• How do we optimize RAG with internal documents, Original vs Summary vs Intents, What works best? 
• How do we merge external data? How can we keep versions and relevance?
• More than LLM work, The heavy lifting is for Data preprocessing/cleaning / Embedding on summary 
• When to use LLM vs Multimodals?
• What is the benchmark for our domain and how much do we meet it consistently?
• The transition for LLM, LLM+KG, Creating the data mapping..

A lot of challenges but one at a time, Balancing Consistency, Accuracy, and Latency. If you want to solve real problems you can connect/explore potential learning experimentation opportunities / dedicate some learning hours. Please drop a note to career@proplens.ai

#learnings #NLP #Datascience #RAG #LLMs #perspectives #Datascience 

Keep Exploring!!!

RAG and Prompts - Learning Evolves

In one particular use case, it's a constant process of experimentation and iterations.

• Step #1 - Let me try with prompts - It works but not consistently
• Step #2 - Let me try with a vision prompt - It takes time. 
• Step #3 - Let me merge everything in the database and check. 
• Step #4 - Routing takes 2 seconds, querying takes 2 seconds, and prompting takes 2 seconds. 
• Step #5 - Let's keep accuracy and latency separate, divide everything into separate tracks, and sort out the basics.

Some successes, some lessons, and some learning.

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Success and Failures in Pushing Ideas in Startups vs Matured Companies

Over the past 4 months, I've been working with really small teams, and the difference in communication dynamics compared to larger teams has been striking.

In my previous roles in product and consulting, I achieved success but with a considerable amount of effort spent convincing and negotiating with numerous people. Here are some of the specific challenges faced when pushing ideas in larger, more mature companies compared to nimble startups:

Increased Lines of Communication: With more team members in mature companies, ensuring everyone is on the same page becomes significantly harder. There's a higher risk of changes in approach, iterations, feedback, and information being lost or mistranslated as it travels through various levels. In contrast, startups often have flatter structures, making communication more direct and less prone to distortion.

Slower Decision-Making Processes: Larger teams often have more layers of approval, which can slow down decision-making. Every stakeholder has their own priorities and concerns, adding to the complexity. Startups, with their smaller teams, can often make decisions more quickly, which allows for faster iterations and innovation.

Greater Need for Consensus: In smaller teams typical of startups, reaching a consensus or getting buy-in for new ideas is often easier. Larger teams in mature companies require more effort to align everyone's visions and goals. This can lead to lengthy discussions and compromises, which may dilute the original idea.

More Stakeholders to Convince: Larger teams come with more stakeholders, each with their own perspectives and interests. This multiplicity can make it challenging to get everyone on board with a new idea. Startups, on the other hand, usually have fewer stakeholders, and the founders or key decision-makers are more accessible, simplifying the process of getting buy-in.

However, the journey you take, whether in a startup or a mature company, will reward you for the risks and decisions you choose to travel with. Each environment has its own set of challenges and rewards, but understanding these dynamics can help in navigating them more effectively.

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Data Privacy vs Cheap value vs Compromises

How your data is bought at Low costs and leveraged to build ML Models !!!

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LLMs - Production vs Prototype

 #LLM is #Cool! #PromptEngineering is great. Demonstrating a prototype is beneficial, but for a production application, preparing data and consistently getting Top K results are essential. The ability to handle diverse queries, manage large datasets vs. remembering minimal key data are significant trade-offs and design choices that come with domain knowledge. Building a working solution is easy, but achieving predictable, low-latency, and consistent behavior requires constant iteration and evolution. #Perspectives and #AppliedLearning are key. #AI #MachineLearning #DataScience #Prototypes #ProductionReady #DataPrep #Latency #Consistency #Iteration #

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Why big companies failed to build LLMs

 

How Alexa dropped the ball on being the top conversational system on the planet
—
A few weeks ago OpenAI released GPT-4o ushering in a new standard for multimodal, conversational experiences with sophisticated reasoning capabilities.

Several days later, my good friends at PolyAI…

— Mihail Eric (@mihail_eric) June 11, 2024

• Data was poorly annotated. Documentation was either nonexistent or stale.
• Experiments had to be run in resource-limited compute environments. 
• That meant for months our internal annotation team had been mislabeling thousands of data points every single day
• antagonistic mid-managers that had little interest in collaborating for the greater good
• Duplicated efforts due to no shared common ground 

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LLM Perspectives - Familiarity vs Randomness

 

There's a big difference between solving a problem from first principles vs applying a solution template you previously memorized. It's like the difference between a senior software engineer and a script kiddie that can't code.

A script kiddie that has a gigantic bank of scripts… https://t.co/l2W8FUnwj8

— François Chollet (@fchollet) June 10, 2024

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Next Real - Startup - Healthcare + AI

 

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The Power of Titles: A Strategic Path to Influence and Impact

 My take from video - Link

Phase I: The Importance of Titles

• Influence with Titles: Titles matter because they shape perceptions and open doors.
• Decision-Making Power: Titles matter because they provide authority and credibility.
• Risk-Taking: Titles matter because they create platforms for bold actions.

Phase II: Embrace Risk on the Road to Titles

• Strategic Risk taking: Leverage every opportunity to step into roles that accelerate your journey to influential titles.
• Proactive Role Selection: Choose roles that enhance visibility and pave the way to coveted titles.

Phase III: Deliver Impact, Titles Will Follow

• Impact-First Approach: Focus on making meaningful contributions; impactful deliverables naturally lead to recognition and prestigious titles.
• Purpose-Driven Path: Prioritize routes that enhance your impact; the right titles will follow in recognition of your value.

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Transfer Learning Notes

Teach the process/approach for students to get better clarity :). My answers for below question 

 When does Transfer Learning work?

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Humanoids

#humanoids = #llm powered decisions + #robotics + custom models trained for specific tasks, Build custom models to suit specific needs https://t.co/sxcaqCk46U

— Siva (@sivaram2k10) May 12, 2024

 

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Embracing Lifelong Learning: A Call to Rethink Skills, Failure, and Progress

In an era where innovation propels us forward, the history of automobiles and airplanes serves as a powerful reminder of human ingenuity and resilience. These inventions, perfected over decades, symbolize our capacity to adapt and trust in both progress and the individuals behind it. Just as we have placed our trust in these revolutionary technologies, it is imperative that we apply the same open-mindedness and commitment to the world of personal and professional development—especially in emerging fields like AI.

Learning as a Natural Process

Learning, particularly in artificial intelligence, should not be treated as an imposition but rather as a natural extension of our innate curiosity and growth. Constraining this process to fixed timelines or comparisons only hampers the genuine learning experience. Instead, the focus should be on addressing challenges that resonate personally, which ensures a more authentic and impactful engagement with the subject matter.

The Power of Continuous Improvement

One of the most empowering aspects of learning is that it does not require formal recognition or titles. True learning comes from a steadfast commitment to experiment, fail, collaborate, and relearn. By prioritizing personal skills and interests, each individual paves their own unique path towards their goals. This journey enriches not only the learner but also those around them, transforming interactions and leading to mutual progress.

Failure is Okay, Experience and Journey matters

Moreover, undergoing failures—be they in products or personal lessons—are crucial in shaping a more empathetic and knowledgeable individual. These experiences teach resilience and provide unique insights that can be shared with others. In doing so, a learner evolves from a mere participant in their field to a mentor and leader who uplifts and educates, rather than exploiting the potential of others.

Keep Learning

Embrace the process of learning as you would trust the safety of a car or a plane. Dive deep into your passions, allow yourself the freedom to fail, and rise with a richer understanding and capability to contribute positively.

Remember: the journey of learning should be liberating, not limiting. Let's commit to making learning a part of our lifestyle—to continuously develop skills that drive us towards our ultimate destinations.

Keep learning, growing, and influencing. The road ahead is as exciting as you choose to make it.!!!

Run prompts locally - lmstudio

• Download https://lmstudio.ai/
• Download mistral model
• Run Prompts Locally

My quick ref tutorial I looked up - Link

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Leadership in Action

Beautiful Lines from the post

• Strategy changes rarely come from a fancy offsite. They come from late-night emails or hallway conversations 
• He never throws anyone under the bus except to take some blame for himself

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Professional Growth Must Come From Personal Growth

Brilliant talk, Loved it. Do not play politics, build on your values.

Thanks for highlighting my quote, Brilliant talk.

Professional Growth Must Come from Personal Growth" was a brilliant talk. It focused on understanding career growth versus values versus visibility. Often, it's a mix of factors that contribute to growth: contribution, visibility, and access to high-value work. Everything involves teamwork. Growth stems from self-centered visibility versus growing as a team; both are traits of a leader, whether selfish or selfless.

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Developing Your Skills in Data Science and AI

You don't need a title to learn/work in data science
You don't need to start with what everyone is recommending.
Focus on a few areas of AI, not the complete landscape.
Seek out mentors to guide you.
Learn by collaborating, solving problems, and asking for help in AI-related issues.
Do not believe that the growth path is the same for everyone; avoid a copycat approach.
Sometimes, you need to work without titles to really excel in your areas of interest.
Experiment / Look for Real-time problem-solving, and proposing solutions
Be resilient setbacks are okay, Pause and find alternate solutions
Consistently seek understanding and clarity. 
Roll up your sleeves, not all the time, but only for meaningful AI work. Remember, you are not a machine. Strive at a consistent pace.
Learn to differentiate Pro AI vs AI Hype and develop intuition to spot it

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Job vs Solopreneur vs Ideas vs Intuition vs Learning

In the corporate world, navigating the hierarchy of influence often poses a challenge, particularly when it comes to balancing the time required to sell personal ideas against completing assigned tasks. Jobs typically compensate us for specific deliverables, not for the incubation or pitching of our innovative ideas.

The process of advancing personal ideas depends heavily on the organizational culture. When there is genuine acceptance and support for innovation, pushing personal concepts becomes not only feasible but also encouraged. However, more often than not, we encounter environments where ideas are welcomed in discussions, but follow-up and implementation fade away, lost to immediate business priorities.

In such scenarios, one may feel compelled to take on a solopreneurial approach. This involves stepping away from the structured organizational roles and exploring the uncertain waters of individual innovation. By adopting the role of a solopreneur, we give ourselves the opportunity to fully develop and test our ideas without the restrictions and influences of corporate agendas.

Living through this uncertainty requires resilience. It isn't merely about having a groundbreaking idea; it's also about having the tenacity to bring that idea to MVP amid fluctuating circumstances. As solopreneurs, we maneuver through diverse challenges, test our limits, and learn invaluable lessons about both our crafts and ourselves.

The Journey is the Experience and the wealth is the Learning 

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Supply Chain Expertise and GenAI Insights

I have worked for Microsoft Xbox supply chain and UPS. Now If I have to add a GenAI lens to my past. Here are my perspectives.

At Microsoft's Xbox division, I managed both forward and reverse supply chain logistics, covering repairs, refurbishment, and warranty services. By integrating GenAI, we can significantly enhance product defect detection, implement targeted product improvements, and streamline supplier communications, elevating overall efficiency and product quality.

At UPS, I collaborated with the team to develop and pitch AI/ML strategies for third-party logistics, focusing on operational efficiencies, visibility, planning, forecasting, and optimization. With the infusion of GenAI, the potential applications could extend to sophisticated chatbots and digital assistants, further refining company policies, fostering broader AI adoption, and enhancing supplier communications.

I am committed to pushing the boundaries of supply chain management with innovative GenAI-driven solutions and look forward to collaborating with startups/product companies in this space. If you are an SMB, have historical data, looking to onboard in AI, Let's connect sivaram[at]phygitalytics.com. 

Keep Learning!!!

    

Insights, Innovations, and Lessons: Exploring Computer Vision and Generative AI Hybrid solutions

Hoping to share more insights on below Vision + GenAI Journey

1. Captivating Success: Harnessing Vision and Generative AI to Mitigate Food Waste

Unveiling a remarkable deployment where vision technologies, coupled with Generative AI, are being leveraged in a significant initiative to reduce food waste. A partnership between a renowned condiment brand and a leading technology provider exemplifies the powerful application of AI in environmental sustainability.

2. Dual-edged Experiences: Enhancing Product Details and Vision Technology Setbacks

This segment will delve into the mixed outcomes from integrating Generative AI in enriching product detail pages, and the limitations encountered with computer vision technologies. We’ll share an analysis of the decision-making processes in either developing custom vision models alongside Generative AI or opting for off-the-shelf solutions, outlining the key challenges and learnings from both paths.

3. Learning from Setbacks: Challenges in Vision for Skin Care Innovations

Not all ventures yield success, and in the explorative landscape of AI, the application of vision technology for skin care solutions has faced its own set of challenges. This case will reveal the hurdles faced during implementation and the pivotal lessons learned, emphasizing the importance of iterative testing and adaptive strategies in technology application.

Summary:

In wrapping up, the session will highlight the critical takeaways from the successes and setbacks observed in integrating computer vision and Generative AI across different industries. Attendees will gain a nuanced understanding of the practical applications, scalability issues, and strategic decisions crucial for leveraging these cutting-edge technologies effectively.

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Career Perspectives

Few key things that apply to me :)

• Build key relationships by networking and offering something valuable - ideas/playbook / POC.
• Fifth, select challenging projects that promise significant early wins, even if they require extra effort initially.
• Finally, at the end of the month, create a reflective document detailing your observations to better understand and improve your approach.

Ref - CAREER ADVICE: First 30 days as an exec.

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Windows Ad - 1988

 

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Evaluating Common Sense AI Frameworks vs. Business-Driven Realities

The following compares two contrasting approaches: a common sense AI framework that focuses on ethical ideals, and the business-focused reality that often prioritizes profitability and market demands:

People-Centric vs Business-Centric Approach

• Common Sense AI: Prioritizes individuals' needs and well-being.
• Business Reality: Focuses on enhancing business profitability and operations.

Encouraging Learning vs Driving Engagement

• Common Sense AI: Advocates for learning and the personal development of users.
• Business Reality: Strives to increase user engagement which often translates to increased revenue.

Facilitating Connections vs Forming Opinionated Groups

• Common Sense AI: Aims to help people create meaningful connections without bias.
• Business Reality: May encourage the formation of groups based on strong opinions, which can boost platform activity.

Trustworthiness vs Promotion of Varied Information

• Common Sense AI: Committed to being trustworthy in the information it provides or promotes.
• Business Reality: May distribute all types of information, irrespective of accuracy, to cater to diverse user demands.

Privacy Defense vs Utilizing Data

• Common Sense AI: Upholds the privacy of users as a fundamental principle.
• Business Reality: Sometimes utilizes user data without explicit consent to maximize business opportunities.

Safety for Minors vs Conditional Apologies

• Common Sense AI: Ensures the safety of children and teens as a priority.
• Business Reality: Focuses on rectifying issues only when necessary to maintain public image while keeping business priorities intact.

Transparency and Accountability vs Limited Oversight

• Common Sense AI: Maintains high levels of transparency and holds itself accountable to stakeholders.
• Business Reality: Oftentimes finds methods to collaborate or operate without stringent checks, prioritizing flexibility and operational efficiency.

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AI in Beauty / Skin Care

Congratz Khusbhu, Mastering Vision + Beauty domain takes well calculated approach. This implementation is great example.

Selecting the right solution approach is the key

• Decision of Build vs Buy Model 
• Market testing
• Model Evaluation
• Data Compliance

Build is an expensive route in this case as it needs Deep Expertise in Vision plus Data Collection to culture.

Build vs Buy Solutions

• haut 
• Perfect Corp 
• Skintelligent  
• ModiFace 

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AI Solution Strategy Perspectives

Over the last few days, there has been intense discussion around product architecture, design, and adoption. Here are some key points:

• Balancing the adoption of Large Language Models (LLMs) with considerations of cost, consistency, and latency.
• The architecture should be flexible enough to allow plug-and-play integration of different models.
• Every solution must have some differentiation, value add, or a "secret sauce".
• AI tends to perform best when combined with human input (AI + Human in the loop).
• From time to time, use a "convince with code" approach to demonstrate solutions.

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OpenAI - Prompt King - Prompt Usage Patterns

The interesting thing about OpenAI is the prompt history. The information below is a gold mine:

• Commonly used prompts and their responses.
• Ranking responses based on user feedback.
• Distribution of prompts across different domains. (Health, History, News, Tech)
• Caching of prompts and answers for quicker access.
• Low latency approach to handling cache versus read operations.
• Asynchronous processes involved in domain detection, intent detection, and retrieval.
• Various combinations of indexes are used to optimize searches using golden data, cached data, summary data, and raw data.

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Good Paper Read - THE LANDSCAPE OF EMERGING AI AGENT ARCHITECTURES FOR REASONING, PLANNING, AND TOOL CALLING: A SURVEY

THE LANDSCAPE OF EMERGING AI AGENT ARCHITECTURES FOR REASONING, PLANNING, AND TOOL CALLING: A SURVEY

Key Summary notes

• AI agent architectures are either comprised of a single agent or multiple agents working together to solve a problem.
• Agent Persona. An agent persona describes the role or personality that the agent should take on, including any other instructions specific to that agent
• ReAct. In the ReAct (Reason + Act) method, an agent first writes a thought about the given task. It then performs an action based on that thought, and the output is observed
• Reflexion. Reflexion is a single-agent pattern that uses self-reflection through linguistic feedback

Dify is an open-source LLM app development platform

Beyond LLMs: Agents, Emergent Abilities

1. Agent is able to split / create smaller subtasks

2. Persona can be set for the agent, Few shot examples supplied to get the context

3. Multiple agents created for different purposes

4. Custom Agents / Agent to Agent communication etc..

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Career Blues and perspectives

My selected list from below bookmarked articles

• Try to become a new person every 3-6 years.
• Try to solve big problems as fast as possible. 
• Adding a little bit of extra productivity to every day is great advice. The challenge can be finding the time, which means you need to subtract time from some other activities.
• I write only when inspiration strikes. Fortunately it strikes every morning at nine o'clock sharp — W. Somerset Maugham
• Focus more on my own future rather than on what others think.
• Set clear short-term and long-term career goals.
• Enjoying programming and having the time outside of work. When it's a passion it all becomes a lot easier.
• Effort and consistency trumps all
• I personally believe anybody can find success if they just focus on the journey and the joy of coding, make sure they show up and participate, be present, stay curious and playful, and don't expect any rewards for their efforts. The real reward is having fun and feeling fulfilled while you're creating something.

Bookmarks

• Measuring personal growth
• The unreasonable effectiveness of just showing up everyday
• Outstanding Programmers
• My 25-Year Engineering Career Retrospective

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Different stages of ML / DL Learning

I want to learn ML -> Take a course 
I know the basics from the course -> Try the code examples 
I tried but I don't know what's next -> Find a use case 
I found a use case -> Collect data 
I collected the data -> Model the ML problem 
I built an ML model -> Create an API to consume it 
I built an API -> Dockerize it 
Is the API scalable? -> Check options such as serverless functions, Endpoint providers like Anyscale / SageMaker Endpoints, GCP, Azure Inferencing 
I deployed the model -> Version your models using MLFlow 
When to update -> Audit / Track data 
What tools to learn -> Align with what your organization uses and cloud vendors

Learn to walk before you try to fly. Everything is incremental learning. Keep going!!!

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Good vs Bad Leaders

From my 2 decade observations,

• Title <> Experience 
• Title <> Knowledge 
• Good failures can be Good knowledge experiences but people without Titles

I have worked with all ages 20's, and 30's. My peers.

• With my juniors, I give them context, references, and How I would do in their situations.
• With my seniors, I get only what to be done, I don't mind, work comes to me what they can't do
• With my peers, When asked for input, I share the same
• You meet people with Ego without knowledge. Not knowing and pretending is more dangerous.

Be an Empathetic Leader and Manager. Be accountable for your work

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Good Read - Is mid-career unemployability the big issue no one's talking about?

Good Read from thread - Is mid-career unemployability the big issue no one's talking about?

Some key points from the thread

• Hit refresh button and run your own startup in any sector build new skills explore new opps
• Help others as much as possible.
• I agree with you and I believe most experienced professionals are actually wasting their expertise by working as an employee and paying lakhs in income taxes
• I think everyone who is good at what they do and are over 40 should learn to become a consultant.
• There is a lot of value in being able to bring people together, solving problems holistically and from first principles
• Building your career progression to have higher impact, bigger outcomes, critical decision making and mentoring the next tier is the way to go. 
• The skillsets you have acquired through your career is the USP that you have
• Growth in terms of skills, decision making, managing complexity or mentoring people
• Putting a self-paced learning system in place, ranging from making time, choosing learning goals, and modes that work for you
• Embracing lifelong learning opportunities, staying updated with industry trends, and acquiring new skills can help seasoned professionals remain relevant and valuable in a competitive landscape. 
• Most importantly, one must maintain a strong professional network and stay visible in their industry to stay top of mind among one's peers and discover new opportunities.

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Data Science & Data

Every project is a learning experience. Data science is based on "Data". Working with no data, less data, or encrypted domain knowledge with minimal data has been challenge over the past 4 years. Yet, even when data is plentiful, there remains a balancing act between leveraging it effectively and mitigating trust issues, as collaboration can sometimes be overshadowed by the scramble for credit. Everyone wants to work on a model, not on data, the old google paper still comes into their eyes :). The current trend is to train large language models (LLMs) on uniform datasets, yet this approach glosses over an important truth: no dataset can capture the full spectrum of reality. Issues such as digital poverty, underrepresentation, and inherent biases are embedded within the data we collect. Without addressing these challenges, solutions can be superficial and short-lived. Moving fast with a lot of guardrails is essentially a band-aid, not a solution. Take a step back and balance data vs model. Build something that lasts forever not for paychecks!!!

Keep Thinking!!!

Why we don't see good AI / ML work

Why we don't see good AI / ML work. Good AI / ML work depends

• Choosing a use case with a strategic and AI-focused approach. Selecting the use case that has a balance of vision/strategy / applying AI lens
• Ensuring access to adequate data for training and deployment
• Garnering robust business support
• Acquiring or developing tools to deliver meaningful AI/ML contributions

Struggling with your AI strategy? Let's connect and navigate it together.

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Anyscale Endpoints discussion

Step #1 - Anyscale signup

Step #2 - Notebook for Deploying Diffusion models

Step #3 - Deploying Service Command

Step #4 - Service Deployment

Code Example - 

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