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!!!

Keep Exploring!!!

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.

Keep Exploring!!!

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!!!