The curse of Social Media and State of Education - Dark Future

With the growing amount of selfies, pics data shared across quora, facebook, Instagram, tiktok. We need to think of

• Impact of the content on the psychology of kids
• Emotional Maturity / Bias / Impact on Kids / Children
• Loss of productivity / Distraction
• There is considerably more bad content than the good content
• How does these content fuel for better thinking / better focus
• Who is responsible for moderating all the online content? How do I know as a parent the influence on my Kids
• The state of education/mindset hasn't changed with this cheap data plans. Instead, we started losing our own individual thinking
• This is a follow the crowd pattern approach. Developed countries do not have so much of exams but produce better research and innovation. With the state of education, we produce more failures and ship abroad few successful students
• Changing students to merely solving problems within time constraint does not develop creativity or curiosity
• If 90 kids fail in this education system what do they end up? Delivery boys?
• Population can't be controlled by force. It needs education, awareness, and responsibility

Better metrics for a better future generation and better thinking towards

• How do we make people self-sufficient
• How has education improved scientific thinking
• How has education improved morality
• How has education promoted social responsibility
• Are things affordable for the poor and needy
• What have we done to create ideas at grassroots

Creating a better intelligent generation needs more thoughts than just about GDP

Keep Thinking!!!

The Curse of Cheap Data Plans

Many time I wonder cheap data plans are a curse, not a boom. I see more often these days

• More time I personally spend on Youtube
• Forwards of TikTok/ Halo Status videoes
• Rechecking same repetitive news everywhere

I have lost a lot of sleeping hours. Google Youtube recommendation is the most unfair recommendation. Providing extremely similar recommendations. There is no mix of different sources. Sometimes tailored information is not what we need, we need the raw data.

Too much of personalization is a curse. You will lose yourself biased on your perspectives. Sometimes raw information makes more sense than tailored information.

Escape the Web!!!

Day #277 - Tracking Objects - Deep SORT

What is Deep Sort ?
Simple Online and Realtime Tracking with a Deep Association Metric

How it works ?
It performs Kalman filtering in image space and frame-by-frame data association using the Hungarian method with an association metric that measures bounding box overlap

Paper - Link


Happy Learning!!!

Day # 276 - Segmentation, Age-Gender Estimations

This post is based on current learning's exploring segmentation and age/gender detection.

Experiment #1 - Used the existing model and took faces from edouardjanssens.com for both men / women from 1-100 in increments of 5. The actual result and predicted chart summary

Demo Code

Experiment #2 - Getting Started with Segmentation, Experimented with Repo - https://github.com/divamgupta/image-segmentation-keras

Few tweaks in code

Demo Code
Happy Learning!!!

AI bubble burst

Good one, Mar 2019 article but still relevant - AI Read
Key Points

• “In 40 percent of cases, we could find no mention of evidence of AI"
• “Companies that people assume and think are #AI companies are probably not.”
• "AI catnip to investors"

Everything starts with Data, Domain, and Value-driven use cases. AI comes after good Domain and Data Knowledge. AI is a value addition on top of the existing insights. 

• Today - How is my business doing (Transactional Reports)
• How was my product sale X for last 3 months (BI Reports)
• What sells together with X (Apriori /Recommendation)
• How much I can forecast to purchase those items together based on previous historical data (Forecasting)

Everything needs to sum up to align with Data Story, Value addition to business. Without right use cases/ quality data, whatever we demo with sample data /deep networks, we will never succeed in the real world #intelligence #artificialintelligence #perspectives. Cool AI demos != Cool Real-world Implementation.

Happy Thinking!!!

Day #275 - When will #deeplearning finally die out?

Interesting answer in quora for question - "When will #deeplearning finally die out?" by Sridhar Mahadevan

Key points - Limitations/ concerns pointed out  

• Key point #1 - Minimizing error over a #training set, no matter how large, is not enough to solve the AI problem;
• Key point #2 - The true test of a scientific theory is not its accuracy at making predictions over some #fixeddataset, but the level of #insight it gives us into a problem
• Key point #3 - Human vision is far more complex than Imagenet benchmarking #datasets set of images.

#DeepLearning has a lot of proven success in Vision, Text, Numbers game. This article questions some of the #assumptions.

#AI evolving. This article gives perspective towards reality vs hype,  understand the #possibilities / #limitations / #challenges of #DeepLearning.

Read Sridhar Mahadevan's answer to When will deep learning finally die out? on Quora

Happy Learning!!!

Dlib on Windows 64 bit Platform

1. Download and Install cmake from https://cmake.org/download/ (Windows 64 bit download)
2. From anancoda prompt - pip install cmake
3. Install Dlib - pip install dlib

Happy Learning!!!

The Business of Facebook

When we think everything in terms of Data. We would need to look at Data Goals, Business Goals, the story behind data.

High-Level Aggregated Metrics - What would be the data metrics in Facebook

• Number of new users added
• Number of new places added
• Number of pics uploaded, Growth of data video, pics
• Number of conversations for new users
• Average time spent by users across ages, professions, country, ethnicity
• Most viewed content across age groups
• Most viewed contents across domains / Professions
• Number of new advertisers added
• Sale / Conversion / Influencers 

Individual User Specific Information

• Your likes/dislikes
• Your background, education, ethnicity
• Behavioral traits
• Your app usage 

Unconsciously we do reflect a lot of our interests in social media. This makes us more aligned/biased if we are suggested based on our historical data. Always keep changing your patterns of thinking, learning, experimenting. Instead of Facebook, I prefer to chose to share things in my own private blog. I don't believe pics reflect the emotions of a person. I remember the lines in Macbeth "Fair is Foul, Foul is Fair"

Your bits and bytes are tracked and recommendations are given. Always timeslice your tasks. We are here only for a limited time. Sometimes we need to create those moments by experimenting, taking our learning to the next level. This Life is precious to get carried away with selfies and pics. Look beyond and find what we can do, learn to make the world a better place.

More Reads - How Facebook got addicted to spreading misinformation

Happy Learning!!!

New Age BI = Kimball + Big Data for unstructured data + AI capabilities

Kimball and Inmon are driven off of structuring data

Inmon

• Bill Inmon is centralized DW proponent
• Inmon defines data warehouse as a centralized repository for the entire enterprise
• Data warehouse is at the center of the Corporate Information Factory (CIF)

Kimball

• Kimball defines data warehouse as “A copy of transaction data specifically structured for query and analysis”
• Kimball kept getting more correct due to global corporations and their need for distributed DW
• Kimball is the distributed Data Marts proponent.
• Kimball defines business processes quite broadly.

Current Status

• Both those concepts are currently changing based with BigData, Inmem Processing, Columnar
• databases and machine intelligence
• We need both tactical plus bigdata plus analytics

Today BI = DW + AI capabilities + Big Data

• BI & Analytics is spread across multiple components; we cannot invest in centralized DW as systems need to be agile enough to accommodate changing data needs
• Knowledge discovering is an ongoing process
• Data is structured, unstructured. Strategically data is changing.
• Making it smaller datamarts is more manageable

Kimball + Big Data for unstructured data + AI capabilities = New Age BI

The goal of analytics is to move away from historical to real-time recommendations

New Age #BI = #Kimball + #BigData for unstructured data + #AI capabilities

• #Kimbal - Build Datamarts to slide and dice through data
• #BigData - Find your digital footprint, Look at realtime trends/sentiments
• #AI - Use AI to find insights from your customer, users, demands, patterns

Happy Learning!!!

Day #274 - Re_id Notes from papers / Analysis - Reidentification of person from historical data

Approach

• Extract Features
• Cluster to find similar faces
• Approximate k-NN search

Survey on Deep Learning Techniques for Person Re-Identification
Classification Model

• Using SIFT, Color Histograms
• Determining the individual identity (aka class)
• Image Categorization by Age / Gender and Search

Siamese Network 

• Learning a similarity function, which takes two images as input and expresses how similar they are.
• Triplet Siamese model, Pairwise Model
• Triplet models - The triplet loss function takes face encoding of three images anchor, positive and negative.  Here anchor and positive are the images of same person whereas negative is the image of a different person

Face Search at Scale: 80 Million Gallery
Key Points

• Represent objects with feature vectors 
• Employ an indexing or approximate search scheme in the feature space

Performance-oriented Design

• Fast filtering step (Approximate k-NN search)
• Re-ranking step (K Candidates Deep Feature Similarity)

Using Siamese Networks - Retail Use Cases

• Scenario #1 – Find a person in Camera1 and Find him across all other cameras
• Scenario #2 – Find a person at Entrance and Track him across in-store video
• Scenario #3 – Retrain this for every +/- 10 minutes, Dynamically Track for every single customer, Retrain as Class – Query Image Scenario

Happy Learning!!!