Welcome to Kaytek's Easy AI (Artificial Intelligence) Landing Page
Artificial Intelligence is nothing but Augmented Intelligence
Our Experiences on AI / ML (Machine Learning) / DL (Deep Learning) (cont)
New ! On 12th December 2020, Kaytek Director Mahesh Khatri conducted an online training session on Oil Geopolitics & the basics of Artificial Intelligence (AI) with different applications of Deep Neural Networks (DNN's) & AI's usage in the Oil & Gas industry vertical. (4th January 2021).
Microsoft Udacity Scholarship
Mr Mahesh Khatri was one of the 300 global participants awarded a Udacity Microsoft Machine Learning Scholarship for Microsoft Azure 2020. From an initial pool of over 36,500 applicants, Udacity chose 10,000 learners who worked for 3 months and from which the top 300 were chosen to be a part of a 4 month Udacity Nanodegree program - Machine Learning Engineer with Microsoft Azure. AI is poised to change the world and it is never too late for anyone to learn - 4th November 2020.
A decade back, Mr Mahesh Khatri received Microsoft's Top Software Architecture Awards at their consecutive Tech-Ed conferences in India (2009 & 2010). These prizes gave an opportunity to learn crucial pieces of the then newly launched Microsoft Cloud - Azure & Sharepoint.
Since then, the rise of Cloud Computing has been perhaps overshadowed by the rise of AI. The huge spur of AI technical research is pushing humanity to re-learn the basics. For the same, we are happy to announce that Microsoft & Udacity gave another opportunity to Mr Mahesh Khatri to dig into the AI & Cloud trenches, by selecting him for the Machine Learning Foundation Course Scholarship program for Microsoft Azure - 14th July 2020.
Entity Embeddings has moved to a new section - 16th January 2020.
MIT Sloan Management Review Twitter Chat Interactions
Xmas Seasons greetings. As 2020 beckons, a good time to look at Winning AI strategies for your organization. Tweet thoughts as part of a MIT Sloan Management Review Twitter chat. The following topics are covered : Data Cleaning for AI, Where to start, A Big Bang Project versus many small ones, AI Hype, Competitive Landscape, Business Context Assessment, Organizational Alignment, Data sharing & collaboration challenges, Impact on workers, Types of Organizational leaders required, etc. A 5 minute read - 26th December 2019.
Implementing Artificial Intelligence - 3rd September 2019.
India is at # 2 position on a Global Artificial Intelligence Vibrancy Index as per the lastest 2019 AI Index study by Stanford Institute for Human Centred Artificial Intelligence. It is a very exhaustive study across the multiple metrics data which has been sourced independently organized under the 3 categories of Research & Development, Economy & Inclusion. As expected, USA leads the ranking. The surprising ranking is of China at # 3, right after India. However, India does have a long way to go on the Research & Development group of metrics. A very insightful & useful tool. More at the Stanford AI Vibrancy Index page - 18th December 2019.
Baby steps on an AI journey - 31st October 2019.
One of the industries where AI is poised to have a huge impact in the future is the retail sector. Perhaps not only a negative impact due to perceived job losses caused by AI based automation, but also a positive impact caused by higher productivity. Can Retailers learn fast ? - The Mystery Of The Misplaced Shoe...Can AI Help ? - 31st May 2019.
Article Can I get a rAIse ? - CHAAI Se Charcha - An appraisal discussion between a fictitious human worker and an organization's CHAAI (CHief Appraisal AI Officer) - 30th January 2019.
Understanding the Resnet (Residual Network) Block in Convolutional Neural Networks (CNN) for AI - Computer Vision
For solving Artificial Intelligence (AI) Computer Vision problems, Convolutional Neural Networks (CNN's) have been always popularly used. Within CNN's there was a breakthrough a few years back when the Resnet (Residual Network) Block was introduced as an architectural innovation to reduce the problems of adding extra layers in the network architecture.
The original technical paper released on Resnet unfortunately did not contain an easy explanation of the terminology used in the Resnet Block diagram (Figure 2) in the same. To help ease understanding of the same, an article Resnet Block Explanation with a Terminology Deep Dive along with an accompanying presentation has been released on Medium. - 23rd October 2018.
Neural Networks are the secret sauce of Artificial Intelligence (AI) - Article (Yet Another) Neural Network Terminology Upto WX + B Stage - 27th September 2018.
Artificial Intelligence (AI) Maths captures real world knowledge - Article Entity Embeddings package real world knowledge for Artificial Intelligence (AI) algorithms - 1st August 2018.
Whatsapp Meets Google Artificial Intelligence (AI) - Article You are obsessed with Whatsapp analysing more than 700000 lines of Whatsapp chats using Google AI's Natural Language Cloud Services - 27th March 2018.
Viewpoint - Approaches to AI / ML / DL Education - Specific Technology versus Generic Learnings
One of the valid concerns expressed by experts is the choice of technologies to focus for people entering this field. In one specific podcast, the discussion was on Pytorch versus Tensorflow.
There is already an abundance of AI / ML / DL educational offerings and technologies out there. The pace of innovation will not slow down.
The learning approach should always be to extract generic learnings from specific AI / ML / DL technologies so that future learnings and re-learnings are easier and faster when the next AI / ML / DL tool or technology arrives.
It may be much much tougher, take a much longer time, but the results in terms of long term conceptual learnings will be worth the effort. DL Giants such as Geoffrey Hinton spent years toiling away before getting meaningful results. The impatience and haste shown by many beginners to the field reminds one of the kindergarten story of The 3 Little Pigs.
Viewpoint - Thoughts on FastAI Courses - The 'Top Down' Versus 'Bottoms Up' Approach
Jeremy Howard, the founder of FastAI with over 30 years of ML & coding experience is obviously well qualified. He has positioned the course as different from all the other courses in the market via a 'Top Down' approach which is code heavy and digs into the Maths whenever required on a need to basis. However,'Being different' need not mean 'Being easier to understand'.
For beginners; the 'Top Down' approach parachutes learners immediately to the peak of Mount Everest. Without having struggled to the top. From the peak, we get a great view standing on top of the mountain of FastAI and Pytorch based on his 3 decades plus experience. The results of Fast AI as demonstrated by consistent world class contemporary benchmarks are indeed remarkable. They give us an immediate starting point to use this world class library.
We are dazzled with a 'Shock & Awe' feeling. However, we have not really climbed our way to the top. We need to do a detailed deep dive into the code. Which means that a bottoms up approach will help people who are struggling currently to get their basics & foundations right. The hierarchy of code understanding on a bottoms up basis should be in this sequence : Python - Numpy - Pytorch - FastAI.
It is interesting that Jeremy Howard has planned the next version of fast.ai part 2 to be a 'Bottoms Up' approach.
Last updated on 4th January 2021.
Created on 17th October 2018.