Information and knowledge explosion

Has the meaning of knowledge and access to knowledge changed over the past fifty years?

Information is a fact and when it is used to practical use it becomes knowledge and skill. 

The nature of "knowledge" has arguably changed over the past fifty years. Fifty years ago, knowledge was often seen as something static and authoritative, something to be aspired for. Now, it's increasingly recognized as dynamic, fluid, and constantly evolving through critical thinking, questioning, and adaptation.

Thanks to Claude Shannon and the digital revolution he started, it has fundamentally reshaped our relationship with information and knowledge, creating both exciting opportunities and new challenges to navigate. The democratisation of knowledge and rapidly evolving fields of knowledge makes continuous learning and updating of knowledge important to stay relevant. 

Advanced data analysis tools, its insights and powerful search engines have put knowledge generation fast and within reach of any citizen of the world.

Rise of AI tools has made research, translation, fact checking and personalisation of information (how it affects and benefits the user) within everyone's reach.

We also find that

• Availability and use of digital storage server tech
• open source public knowledge sources like wikipedia and youtube 
• user generated content from blogs, vlogs and 
• interactive content from quora etc and user product reviews on ecommerce sites               has resulted in a splurge of different types of info in public domain for consumption. 

The past 50 years has seen more growth and expansion of knowledge, in my opinion, than what was there since dawn of the universe. Our little brain is getting crammed with more and more knowledge and information 

Where is information and knowledge explosion leading us to ?

George 

Advantages of using Open source Large language models..

Top 3 Advantages of Open Source LLMs:

1. Transparency and Trust: You can scrutinize the code, training data, and workings of the model, allowing for greater trust and confidence in its outputs. This also facilitates independent audits and ethical considerations.
2. Customization and Flexibility: You are not locked into a proprietary system. You can tailor the LLM to your specific needs, modify its algorithms, and even host it on your own infrastructure.
3. Cost-Effectiveness: Open source LLMs usually come without licensing fees, reducing initial costs and offering predictable long-term expenses. You can potentially avoid vendor lock-in and adapt the model for cost-efficient deployment.

7 More Benefits of Open Source LLMs:

4. Rapid innovation and improvement: The open community can contribute to bug fixes, optimizations, and feature enhancements, accelerating the model's progress and leading to faster advancements.
5. Democratization of AI: Open source LLMs make this powerful technology accessible to a wider range of individuals and organizations, fostering inclusion and innovation beyond large corporations.
6. Reduced bias and increased fairness: Transparency and community scrutiny allow for identification and mitigation of potential biases in the model's training data and algorithms.
7. Enhanced security and control: You have greater control over data privacy and security when hosting the LLM on your own infrastructure and having access to its internal workings.
8. Support and collaboration: A vibrant community offers technical support, knowledge sharing, and collaboration opportunities, aiding in troubleshooting, adaptation, and deployment.
9. Experimental playground: Open source LLMs provide a platform for researchers and developers to experiment with new algorithms, applications, and use cases, driving further advancement in the field.

These are just some of the many benefits of using open source LLMs. They offer a powerful and flexible way to leverage the potential of AI, fostering transparency, collaboration, and innovation for individuals and organizations alike.

Economic potential of the new tech platforms

https://youtu.be/rQEh7d-qa38?si=ipmYIxAuwGx7lrSv

In her TED Talk titled "Why AI Will Spark Exponential Economic Growth," Cathie Wood who speaks so well with clarity discusses five innovation platforms that are evolving at the same time: *artificial intelligence, robotics, energy storage, blockchain technology, and multi-omic sequencing*. 

(the last one relates to gene editing Crispr tech etc...)

She believes that these platforms have the potential to dramatically accelerate from the present *$10 trillion market capitalization of these new tech platform companies to $200 trillion (2x global GDP now) by 2030* - just these 5 innovation tech platforms.. 👍

Just the autonomous taxi platform which combines three of these five technologies of AI, Robotics and energy storage will boom to $10 trillion in the next 5-10 years and the market cap of the companies by $50 trillion.

Early 1900s when general purpose technologies like the telephone, electricity, and automobile were invented, these technologies transformed the world and led to periods of rapid economic growth.

She also discussed how artificial intelligence could be used to transform healthcare and the financial services sector.

Wood's talk is an optimistic view of the future of the convergence of these technologies in different ways. She believes that artificial intelligence and other emerging technologies have the potential to create a *period of unprecedented economic growth*. However, she also acknowledges that there will be challenges along the way, such as the need to *shift focus from managing disruptive innovation to managing creative destruction* that often accompanies technological innovation. 

Exponential growth of productivity leads to more comfort, ease and output at lesser costs.

My comments - What is the end result - innovation in all spheres is leading to more human comfort, more free time, better physical health and more wealth, leading to more mental diseases, psychological issues etc.

The speaker misses out on the most important tech platform on which all the existing 4 of 5 technologies run, except energy storage, ie. the Internet. 🤔🤔

Harnessing abundant clean energy for 10 billion people ..

TED talk by Julio Friedman (Chief Scientist, Carbon Direct, NY based Carbon management firm) on energy.

 https://youtu.be/bwEIqjU2qgk?si=AzctDs2Fup0KvXMA

Julio Friedman's TED Talk starts by highlighting the ten major challenges of the world starting with energy, water, food, environment,  poverty,  terrorism and war, disease, education,  democracy and population.  The first three of water, food and environment again needs energy inputs for desalination, producing fertilizers, vertical farming, carbon capture etc.

Friedman focuses on how to harness energy for 10 billion people of the world by 2050 AD. 

He estimates that the world will need 60 terawatts of energy to meet the needs of 10 billion people, while the current usage is only 26 terawatts (only 8 TW is electricity). This energy needs to be abundant, sustainable, and cheap.  

The Earth receives 163,000 terawatts of energy from the sun every day, 80,000 TW bounces back while the other 83,000 TW is absorbed the earth. He says sun's energy falling on land and water generates, for example, 860 TW of wind energy across the world.

Friedman additionally showcases examples of countries from Southern hemisphere (13% world population) like Chile and Namibia that are making strides towards harnessing abundant clean energy. Chile is using its abundant solar and wind resources (with help from Japan) to produce green hydrogen and ammonia, while Namibia with Japanese help, is developing a massive solar and wind project with the potential to export clean energy to Europe and the rest of the world.

He acknowledges the challenges of infrastructure development, innovation, and investment needed to transition to a clean energy future. 

He proposes solutions like systemic decades long investment mechanisms generating employment and prosperity, long-term off-take agreements, and collaboration between governments and private investors to help provide all the energy needs for the world by 2050 AD.

Markov Models

Markov Models: Predicting the Future based on the Present

Markov models are stochastic models used to predict the future behavior of systems by looking at their current state. They rely on the Markov property, which states that the probability of the next state depends only on the current state, not on the system's previous history.

Think of it like walking a dog on a leash. The direction you take next (turn left, go straight, etc.) only depends on where you and the dog are right now, not where you've been before.

Here are some key features of Markov models:

• States and Transitions: The system is represented by a set of possible states (sunny/rainy, healthy/sick, product A/product B) and transitions between those states (sunny transitions to rainy with a certain probability).
• Probability Matrix: The probabilities of transitions between states are captured in a matrix, called the transition matrix. This matrix allows you to calculate the likelihood of reaching any future state from the current one.

Types of Markov Models: There are different types of Markov models, depending on the complexity of the system being modeled.

• Discrete-time Markov models: Transitions happen at defined intervals (days, hours, etc.).
• Continuous-time Markov models: Transitions can happen at any time.
• Hidden Markov models: The actual states are hidden, and you only observe their outcomes (coughing, buying product A).

Applications of Markov Models:
Markov models are used in various fields for prediction and analysis:

• Finance: Forecasting stock prices, predicting customer churn in banks.
• Weather prediction: Modeling weather patterns, predicting rain/snow.
• Bioinformatics: Analyzing gene sequences, identifying protein structure.
• Natural language processing: Predicting the next word in a sentence, machine translation.
• Robot navigation: Planning robot movement paths, avoiding obstacles.

Learning more about Markov models:

If you'd like to explore further, here are some resources:Wikipedia article on Markov models: https://en.wikipedia.org/wiki/Markov_model
MIT OpenCourseware: Introduction to Probability and Statistics: https://ocw.mit.edu/courses/18-05-introduction-to-probability-and-statistics-spring-2014/
Interactive Markov model simulator: https://www.markovlab.ai/

Root Cause Analysis

Root cause analysis (RCA) is a structured approach to uncovering the underlying, fundamental reason for a problem or incident. It goes beyond simply treating the symptoms and delves into the "why" behind the "what".

Think of it as peeling an onion - you keep removing layers until you reach the core, the true reason for the issue. By understanding the root cause, you can implement effective solutions that prevent the problem from recurring.

Here are some key points about RCA:

• Focuses on prevention: Unlike traditional problem-solving that focuses on immediate fixes, RCA aims to prevent future occurrences by addressing the root cause.
• Systematic approach: RCA involves using various tools and techniques like the Five Whys, Fishbone Diagram, and Pareto Principle to systematically analyze the problem, identify contributing factors, and trace them back to the root cause.
• Applicable to various fields: RCA is used in diverse fields like business, healthcare, engineering, IT, and manufacturing.

Benefits of using RCA:

• Reduces costs: By preventing future problems, RCA can lead to significant cost savings through avoided downtime, repairs, and rework.
• Improves quality: Addressing the root cause of issues leads to better quality products, services, and processes.
• Enhances safety: Proactive identification and mitigation of root causes can improve safety and prevent accidents.
• Promotes continuous improvement: RCA fosters a culture of learning and continuous improvement by encouraging deeper analysis and problem-solving.

Visit to Bangalore Tech Summit 2023.

 Tech Summit Day 2 visit by George Easaw, Alliance University

The first session I attended was in the JC Bose Hall on the topic - Tech interventions for a sustainable future, which was moderated by Guruprakash Sastry, Head of Climate Action at Infosys. Santosh Subramaniam, CIO from Veolia and Prof Claudio Tusson gave interesting talks on the impact of technology on Climate change.. The session was  interesting.

The second session that I attended was in the CV Raman Hall on the Future of Quantum Computing moderated by Doraiswamy from Veolia. , the panelists were Krishna Palem from Accelequant, Dinakaran from IBM Research and Prof. Anil Prabhakar from IIT Madras. The session was quite informative and gave an idea of the future of quantum computing which would not be possible in the near future and would take at least 30 - 50 years.

The third session was on the rapid evolution of Generative AI. The moderator was Ranjan Mani from Atlassian, the panelists were Ulhas Nambiar from Accenture, Amogha from Mudskipper and Ganesh from Gnani. The deliberations were very informative and involved a lot of discussions.

The next two sessions we were at the Ramachandran hall where the talks were based in biotech and keeping epidemics at bay. And on riding the agritech wave. The moderator was Patil, CEO of Krishikalpa and the panelsist were Sunil Jain from Agrostar, Basavaraj from Criyagen and Ravi Sajjan from Hunnugdna.. The deliberations were on the impact of technology in the green field environment and the deliberations were quite helpful and informative.

The talks were of very good quality. The speakers were eminent personalities from different fields of science, technology and industry.  As  all the sessions were parallelly running in 4 to 5 different halls, it was difficult to attend all the sessions, however we selected the interesting ones and attended them.

George

Hacking natural photosynthesis ..

An interesting TED talk on how *digital twin mediated gene therapy* can help hack the only 20% efficient natural plant photosynthesis process to produce more carbos and proteins, to cost effectively satiate the increasing energy and protein needs of our growing global population in the coming decades using the example of white cow pea in central Africa .. 👍👍🙏(red cow pea is വൻപയർ)

Thanks to the Jewish  scientist Paul Berg who in 1962 opened up the field of *Genetic Engineering* to the whole world for the first time .. 🙏🙏

https://youtu.be/s_gjrvhPKt0?si=KDBj_bOQFvPkfAa1

Why Jews are unbeatable ?

Top 10 ways how Jews impacted the world (and why the world recognises them for their brilliance and contribution to the growth of the human race .. )

1. Google- Sergei Brin, Larry Page (1998) - both Jews

2. Capitalism- David Ricardo (with Adam Smith) (1815)

3. Atomic bomb - Robert Oppenheimer (1945)

4. Polio vaccine - Karl Landsteiner (1908), Jonas Salk (1955) (both Jews)

5. Cholera and Bubonic vaccination - Waldermar Haffkine (1893)

6. Mass-energy equivalence E=mc2 - Albert Einstein (1905)

7. Carbon-free Chromium/Stainless steel - Hans Goldsshmidt (1893)

8. Genetic Engg - Paul Berg (1962)

9. Pacemaker and defibrillator - Paul Zoll (1958)

10. Light Amplification by Stimulated Emission of Radiation (Laser) -  Theodore Maiman (1960) / Albert Einstein (1917) (both Jews)

The world continues to benefit from them and cannot write them off .. In addition Jews have got one third of all Nobel Prizes conferred so far .. 🙏🙏🙏🙏