Storytelling Tips

Tips

• SOS - Sense, Observe, Store
• RSI - Real, Specific, Incidents
• Stories of Self
• Stories of Outsiders

Perspectives

• Connect with person
• Then build your story
• Build your brand / Narration
• The hero of the story is the audience
• The story connects more with data

Personality

• Self-awareness / Brand Building
• Self-awareness
• Key takeaway - Two styles 

Aristotle Principles

• Ethos - Credibility
• Pathos - Relate
• Logos - Data

Keep Learning!!!

Cracking the code of 10 minutes delivery, Decoding - Data, Intelligence, Science behind it and Impact

If I get my favorite dish in 10 mins, It would be an awesome moment, the gap between wish vs reality is getting too narrow. Let's analyze closely the data, pattern, and science that bring reality to the promise of 10 minutes delivery.

This timed delivery has made a dent in history too. Domino's had great growth with 30 mins pizza delivery promise when it was competing against pizza hut in the late 70s (1979). This idea of the timed, same day, 30 mins had evolved across Amazon Prime, same-day delivery, swiggy instamart.

This worked best / works for groceries / retailing. 

How does this work? we will look at the pieces of the puzzle that eventually connects the solution.

As a retailer, for example, Amazon, I would know

• Top selling items in each category
• Forecast for those items by region / city / etc
• With forecast/demand aspects I would keep a reasonable amount of stock preordered with essentials / all-time items / seasonal items
• Most of the warehouses would be available near metros / little away from metros
• In order to fulfill same-day delivery, you will have to keep some stocks based on future estimates
• This same-day delivery may be available more for products of certain price range / Higher probability of sale / Vendor warehouses are available within the vicinity
• Assume the stock is available, the next big thing is delivery

Routing

• Logistics, Delivery, Routing/packing/delivery is the key
• With Options to plan based on traffic patterns / leveraging on-demand vendors/availability of labor this delivery is also feasible
• There could be multiple delivery schedules in certain routes to accommodate this dynamic same day delivery
• The delivery I have observed is done with two-wheelers, 4 wheelers to cover both times, handle traffic, complete more deliveries

Instamart

• Many times when we walk near HSR max, you will spot a three-floor building without any details and a lot of swiggy delivery boys waiting and collecting items
• They are spaces that act as fulfillment centers within the city to deliver items within 30 minutes

How 10 minutes delivery is possible in Food?

• Compared to other options Food has other factors #freshness #quality
• Based on previous orders/customer patterns, Apps providers would know peak demand area / repeated buying customers, frequently ordered items
• A few places where I have seen freshness handled is STAR Biryani. They prepare Biryani at 10AM, 1PM, 5PM, 7PM, They prepare food in multiple time slots based on demand as well as change based on observed behavior
• This does results in repetitive work, labor, working round the clock to serve #fresh, and meeting #quality
• Today we spot a lot of cloud kitchens, This 10 minutes delivery can very well be handled within 2 to 3 km of the vicinity
• Forecasting, Allocating delivery specifically for these orders, Shortest routing approach based on current traffic, Most of this is already handled in their current App for the usual 30 mins normal order delivery
• Somewhere during the presentation, I captured this screenshot, Zomato / Swiggy know both consumers, restaurants very well.

Caution - Food Quality

• My personal observation, Out of 10 places only 1 or 2 places I have been a repeat customer, What we see in Pic vs What gets delivered vs Health issues is always a warning
• During my childhood monthly once/twice was our usual restaurant dining routine, it was expensive and not affordable
• The 20s/30s We would be able to explore/experiment with food but the preservatives, artificial color, a sedentary lifestyle will reflect the consequences in the 40s
• This ready-made food consumption in the long term when it exceeds the limits will be a health hazard, Garbage in Damage Done :)
• The target segment would be primarily working segment / above middle class, Easily today for 2-3ppl you would spend 1000+

Everything, when it exceeds the permissible limit, will become an addiction in long term. 

It was a little long story, let's get back. 

I was thinking of another business idea. If I could prepare an API-based approach where we could directly provide the recommendations/promotions to vendors we can make it more R2C, Restaurant to consumers directly than R2A2C Restaurants to App Providers to Consumers.

I hope some perspectives were interesting :)

Keep Exploring :)

Metaverse and Computer vision

I was listening to Lex Fridman and Mark Zuckerberg's podcast on metaverse. Some insights/ideas I got out of the podcast. In a metaverse environment, the goal is to make it closely mimic your physical presence by paying close attention to representing/capturing your real self. The key vision use cases that would play a key role in a real-time environment/experience for users.

• Emotions tracking
• Facial Expressions
• Tiny gestures/remarks unique to the personality
• Face tracking
• More realistic presence for touch/feel senses
• Your AR / VR device is going to be enhanced to track these details

From a computer vision point of view

• Creating a deep fake representation/photo realistic / cartoon representation of yourself
• Representing your emotions, lip movements, expressions
• Adding your voice modulations in discussions
• Altering your face to sync with real-time expressions and movements
• Background changes, clothing, and presentations
• Creating your avatar for business, entertainment, family different groups

Revenue approach?

You might end up using it for different use cases like

• Gaming / Entertainment / Business / Collaboration Multiple avenues of opportunities
• Buying / selling / clothing / styles / accessories in Retail / Fashion domain
• Influencer-based sales/advertisements.

This could potentially lead to a long-term subscription-based model.

Today with affordable smartphones, high-speed connectivity Spotify, youtube we spend lots more time than 10 years back. In another 5 years, These could be replaced with affordable 50$ AR / VR where we might split our time spending across social media/youtube/metaverse, etc..

Keep Exploring!!!

Gartner Hype Cycle Observations..

 

• Plateau of Productivity - Predictivity Analytics
• Enlightenment Stage nearing Productivity - Text Analytics, DBMS Analytics, RDBMS with Analytics APIs
• Currently Setting / Approaching Standardization - Video Analytics, MLops
• Innovation Trigger - Feature Stores / Synthetic Data / Reinforcement Learning

Invest in Talent, Learning, projects based on business need, impact, maturity of the architecture

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AI Noise Cancellation

I hadn't tried it successfully, This seems to be a far better solution in meetings. Finally caught up with it. I had an Nvidia GPU Machine.

Installed Nvidia RTX Voice. The system device input remains the same

Added Nvidia RTX Voice to use the default input and cancel the incoming noises

Since RTX voice will get a stream of input and cleared voices, the same can be configured for other apps like Teams, Chrome etc..This is teams settings

This is chrome setting

Time for noise free discussions :) 

Keep Exploring!!!!

Experience vs Algorithms vs Design vs Polyglot expertise

1. If I solve all algos and core data structures - Does it make it a good programmer - Yea Possibly he can solve build solutions
2. What do I do in my work ? - Understanding data, domain, customer problems, applying the lens of data + ML + BI finding potential solutions 
3. Where does this experience come from? - Similar domains, problems, building products 
4. What does experience mean ? - Collection of different roles / functions / projects / products 
5. Did I do only development or support or testing or performance? - When you build solutions you have to wear multiple hats to build them. There is no hard boundary for each role. To think from a customer perspective and building solutions is different from building solutions and how customers will use it
6. Do I remember all algos, code now ? - Now, Some I remember, Some I learn as I apply
7. Do I need to learn to practice every day? - There is no boolean way of answering for knowledge to say you know or don't know. As long as you can build solutions and code up you are good enough to solve customer solutions.

There is no one definition of skills. Do not go by what is being dictated. Building solutions takes as much time as you learn core cs basics. Expertise comes with time and experiments, not just coding standard problems.

Keep Thinking!!!

Boolean System of Education / Learning

Always we get evaluated for success/failure. There is no place for passion/experimentation/work on something in long term. 

• Pass / Fail
• Engineer / Doctor
• Data Scientist / DB Developer

The flaw that you get graded and evaluated fit or fail is itself wrong. How many times have we passed an exam but had to relook to learn a concept? From applying/solving Data Science use cases vs when I had to revisit basics. I had to relearn everything because In real-world scenario data/use case / ML Algo / Accuracy / Deployment those take-up time and focus, not first principles. Balancing both building blocks vs implementation needs time. 20 years gave me a perspective of how it evolves. 2 years of master's helped me unlearn/relearn. Still, I try to bridge the gaps. Sometimes the questions help me to find another convincing answer not a namesake answer. Multiple choice questions can deem you as failed but your perspective may be beyond those MCQs, When you learn a skill some things could be intuitive not remembered from an exam point of view. When you do several hands-on experiments, you can relate/connect better? What you read/see/hear may not connect well.

• Learning is endless and it cannot be evaluated with point-in-time marks/evaluation.
• Good communication skills do not mean good technical skills
• Certifications do not mean you have the required skills
• Titles do not need to reflect competency 

Everything is a combination of skill/opportunities/timing/exposure. Everyone has their own time to learn/grow. You just need to be sure Am I better than my previous version. 

Life seems too short as I keep stepping into the 20+ exp zone. Follow your path, retain your uniqueness.

Experience = Collecting different experiences/roles/projects, Not working on the same things for X number of years. Collect more memories to connect/relate and build the big picture. 

Keep Thinking!!!

Focus vs Non-Critical Interruptions vs Meetings

 

Keep Exploring!!!

Managing a Project vs Managing a Team vs Managing Self

Managing Project

My working style is

• Prototype as much as possible to plan for tasks
• Guided knowledge with some prep work
• Formulate experiments / options / blockers / alternatives
• I prefer to know the big picture, clarity with some early prep and continuous learning to guide/unblock as needed

Other ways of Managing project

• Task-based / Time based
• I prefer to go with Know-how and know things from an implementation perspective
• Everything is connected knowledge, You cannot be hands-on in every area but knowing / measuring / probing to understand from the end solution point of view matters

Managing Team

• Where they are currently vs What is their goal
• Look for a long term perspective, What aligns to their growth path
• More the team less the time you can dedicate for every one

Managing Self

• Am I learning things I like to do 
• Does this align with my long term perspectives
• What I do to refresh my first principles
• Do I keep learning / connecting the dots with the big picture?

Career/title/skill everything is a point in time snapshot. Even the strongest will become weaker at some point in time. Keep ego/arrogance aside, life is too short to feel I Am the smartest, sometimes it is more about how you genuinely support/understand and get things going. A big no for services or pure people management roles. Pick and choose what best works for you.

Keep Going!!!

Research Paper Reads - Fruit Freshness

Paper #1 - Deep Learning Based Classification System For Recognizing Local Spinach

• Five types of spinach. (a)Jute Spinach, (b)Malabar Spinach, (c)Red Spinach, (d)Taro Spinach, (e)Water Spinach.
• Because our dataset contains different sizes of the images. 224x224x3 is the input shape of our model

Paper #2 - Automatic Plant Cover Estimation with Convolutional Neural Networks

• This dataset contains 7,200 images with 750 training and 150 validation images per class and is therefore also balanced
• Our network consists of two main components: a feature extractor backbone and a network head
•  The backbone consists of one of the abovementioned standard classification networks potentially in conjunction with a Feature Pyramid Network to increase the output resolution

Paper #3 - Fruit Freshness Grading Using Deep Learning

• Fruit colour is derived from natural pigments when ripening, enzymatic and non-enzymatic browning reactions lead to the formation of water-soluble dark colours
• Visual characteristics, e.g., shape, wholeness, spots, bruises, and blemishes, can reflect the speed of fruit deterioration
• The consistency of physical shape may indicate the thickness of fruits that may have implications of its capability to defend against diseases
• Geometric changes are a frequently observed result of fruit degradation
• Texture is another important measurement of the level how a fruit has decayed
• Fruit texture, colour and shape are three important visual features for fruit quality grading

• Added random noises follow the sequential order: Random brightness adjustment, random contrast, and random erasion

• In total, there are (approximate) 4,000 images collected with each type of fruit about 700
• The freshness grading is scaled from 0.0 to 10.0 with 0.0 indicating total corruption and 10.0 for total freshness 
• We define the fruits being harvested as absolute freshness with a numerical level description of 10.0

Paper #4 - Machine Vision based Fruit Classification and Grading - A Review

• Some extraction methods like Speeded Up Robust Features (SURF), Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) 
• Features of fruits like color, size, shape and texture
• Automatic sorting system that can perform fast, save time and reduce manual labor
• The basic steps of the automatic image-based fruit grading are: fruit image recognition, fruit object recognition

Color features extraction methods broadly fall in two categories:

1. Global methods (global color histogram, histogram intersection, image bitmap)

2. Local methods (local color histogram, color correlogram, color difference histogram)

# Fitting K-NN to the Training set

from sklearn.neighbors import KNeighborsClassifier

classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)

classifier.fit(X_train, y_train)

# Predicting the Test set results

y_pred = classifier.predict(X_test)

Paper #5 - Deep Learning for Automatic Quality Grading of Mangoes: Methods and Insights

• Augment the CNN classifier with a convolutional autoencoder
• The rationale of considering such networks is as follows: 1) the presence of autoencoder
• forces the network to remember essential information for reconstruction when extracting features for classification, thereby having a regularizing effect; and, 2) the latent features learned by the network could benefit other downstream tasks as they contain the compressed information for reconstruction;

Data Augmentation

• Horizontal or vertical flip, each with 50% probability;
• Brightness, –20 to +20%;
• Contrast, –10 to +10%;
• Rotation, –20 to 20 degrees;
• Zoom in/out, 0.8x to 1.25x

The ConvAE-Clfs consist of 3 components:

• A convolution-based encoder that compresses an image into a latent vector;
• A convolution-based decoder that reconstructs the image from the latent vector and some intermediate features;
• A fully-connected classifier that takes the latent vector as input and gives the class prediction.

The proposed convolutional autoencoder-classifiers were shown to have no clear advantage over the single-task CNNs, but the result should be verified with larger datasets and more related tasks

Paper #6 - Deep Learning and Machine Vision for Food Processing: A Survey

• An MVS includes two main parts to enable objective and non-destructive food evaluation: 1) acquiring and 2) processing

• Fruit and Vegetable Identification Using Machine Learning for Retail Applications
• GuavaNet: A deep neuralNet: A deep neural network ar al network architecture for automatic sensory evaluation  to predict the degree of acceptability for Guava by a consumer 

Keep Exploring!!!

Story telling with data - Early Thoughts

I accidentally had to skim through the book storytelling with data. The context of data presentation and some examples sharing it here.

Example #1

Chart #1

Updated Chart #1

My Observation - Subjective outcomes so bar chart would convey highs/lows effectively

Example #2

Chart #1

Updated Chart #1

My Observation - One-liner info added. Plus volumetric growth is projected.

Example #3

Chart #1

Updated Chart #1

My Observation, Line chart is less clutter in terms of low / high patterns

Summary

• Understand the context
• Choose an appropriate visual display
• Eliminate clutter
• Focus attention where you want it
• Think like a designer
• Tell a story

Disclaimer - Subjective topic, lies on individual choices and preferences though :) What you can interpret.

Keep Exploring!!