"No one is harder on a talented person than the person themselves" - Linda Wilkinson ; "Trust your guts and don't follow the herd" ; "Validate direction not destination" ;

April 18, 2024

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!!!


April 15, 2024

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.

Keep Exploring!!!

April 08, 2024

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 - 


Keep Exploring!!!

April 07, 2024

2024 - Year of Opportunities / Lessons / Learning's

My memorable moments/projects/achievements.  

  • Build vision capability
  • 2 Granted Patents
  • Won Vision solutions for Retail, and FMCG customers
  • Built products in a consulting role (Some failed / some worked)
  • Rewrote warranty for 220million consoles in Microsoft

Next Steps

  • Teaching + Deep Dive + Part-time is my goal for sustainable health and learning
  • Pick and select a few things and deep dive and build a point of view

Keep Exploring!!!

April 02, 2024

Data is not the new oil - Alexandr Wang

Great Talk, Lot of good insights

  • Oil is a commodity and everyone has same access to oil
  • Not all data has same level of value
  • Every type of data fintech / insurance / healthcare has different 
  • Data has multititude 
  • Multiple frameworks / thoughful strategy to stitch data
  • Building block for next move is quality code / automated code
  • Earliest is Autonomous cars
  • Raw data to labelled data is First step towards quality data
  • Data = New Oil, Scale = Refinery
  • Most capabilities are taught by large scale data
  • Data Engine = Refinery for data
  • Teach a model how to access one answer better than other with a bunch of examples

Opportunity for Enterprises

  • Total data available - 99% - Private
  • Messages / Emails will never end up on internet
  • Enterprises have a lot of unused data
  • Tune General purpose model for Enterprises
  • Customer care / Legal Apps
  • Focus on big problems that matter to your business

Questions

  • Unique Data Assets
  • Better than Anyone else 
  • Unique capabilities / Differentiators
  • Cost Reduction / Customer Care / Optimization
  • AI = Productivity enhancer
  • Meaningful chunks of work
  • Health care / Financial Services
  • Data & Compute Limiting Factors
  • AI <> Replacement for humans
  • Economically viable human systems
  • There is no future here 2 years back vs This is a threat
  • Model improvement is going to get better
  • Need broader cooperation
  • Inequality with jobs / upskilling with new jobs
  • AI misuse is punished / handled severly
  • Testing and Evaluation for Systems and use case
  • Fit for purpose vs primetime
  • FDA for drugs similar regulation options, Apps approved after scruitiny
  • Public evaluation of models
  • Testers in public / private / regulators
  • Ideas + Accountability decentralized

Ref - Alexandr Wang: 26-Year-Old Billionaire Powering the AI Industry

The Truth About Building AI Startups Today

  • GPT Wrappers
  • AI Agents 
  • High Beta Opportunities
  • Idea Maze
  • Once in a Life time opportunity
  • AGI / Multimodal / Videos
  • Workflow Automation
  • RPA - Search/ Form Filling
  • Sweet Spot - Pivot LLMs Automate Government Contract
  • Dev tool companies
  • Fine Tuning LLM Models
  • Something more than Finetuning
  • Customize to private datasets (Healthcare / FinTech)
  • Cybersecurity to Cloud -> Cybersecurity for LLM
  • LLM to data access Mapping
  • Purpose trained models / Run Locally models
  • Prototype with close source LLM Models
  • Collect data in parallel for domain context
  • Partner and build private models
  • Closed source AGI = Monopoly / Dangerous
  • AI Ethics + Regulation + Measuring it

My Take

  • Prototype with close source LLM Models
  • Collect data in parallel for domain context
  • Partner and build private models

Product #1 - Syncly

  • With AI feedback analysis, Syncly instantly categorizes feedback and reveals hidden negative signals. 
  • Centralize all your feedback and take proactive actions based on real time insights to elevate your five-star customer experience.

Product #2 - Cradle

  • Protein engineering without the guesswork

Keep Exploring!!!

Transformer Walkthrough

Session #1


Session #2

Keep Exploring!!!