Toolboxes

 

Hello folks, creatives and technology-oriented scientists! One of the biggest problems in science people face in terms of problem solving is not the machinery, not their resume choices, not their characters and not their ideas, but the global reality of how to work with data. So, let’s get to the meat of our today’s story.

A. The Burning Problem

Mere possession of the equipment doesn’t make you a scientist… If someone merely buys the SPSS packs of IBM, did he become an analyst? For the purposes of the “Luminous Quanta” project, today we will unravel the reality of world-class science, works of other people as a matter of fact as well as founding our own in the future.

Assume we’re in the middle of a high-profile organization, multinational or not it doesn’t matter but rather into the spirit of robust international science and dealing with business development and global information. There is a team, there are products, there are departments, there is strategy. Observing the technological aspects of their endeavors, a team of engineers as well as marketers is actually responsible for running 10 different pieces of computational codes, running in 7 different software programs mathematically oriented. The question couldn’t be simpler: How can a scientist, the single wonder, actually rely on equivalent access with companies such as Google, Apple or IBM?

The case with AI is actually similar. AI applications and mathematical algorithms dealing with novel innovations of AI actually need training data, public, private, constructed, whatever… For example, when I have been studying in the Google Analytics Academy we were given access to the Google Official Merch Shop, an account in Google Analytics – now is known as GA4 – in which there was unparalleled evolution and development in terms of the metrics, dimensions and conversions displayed in the Google Analytics equipment. Google Analytics gives you the option to extract data from it in PDF, computational sheets (Excel for example), as well as CSV files. Now we have a primary sense of how to use statistical data in AI. But as we can immediately imagine, the more developed and skyrocketed the shop the more reliable the AI models generated.

From the very moment we start dealing with the statistical errors in science we realize that a simple individual who is not Google or who simply has a blog that makes articles, possesses rather improbable chances of ever dreaming he will reach the level of Google Merchandising projects. His measurement equipment, that is the same Google Analytics technology used by everyone else, simply portrays 5-6 figures and nothing more. Furthermore, there are dozens of products, platforms, processed data and technologies that actually promise they possess the tools and offer the toolboxes to make positive change, let’s name a few – my own interests as well -: Anaconda, Jupyter Notebooks, H2O.ai, BlueQubit Quantum Computing Software, Public Data from Kaggle and Google, Excel, IBM, Google Analytics for Digital Marketing, MATLAB and Octave and many other data studios…

So far, bearing in mind my own concerns, I named above 7 personal choices of data studios and the contemporary pace of life as well as multi-faceted creativity of different labels than technology, you may be a writer, a marketer or an artist, simply make us ask: What will you catch up first and to what extent?

B. The Solution: Cognitive Toolboxes

As I said in the beginning, mere possession of the equipment doesn’t turn the individual into a scientist. It takes years of knowledge, practice and the fact that repetition is the mother of knowledge. The need is the mother of invention as well.

For the purposes of the “Luminous Quanta” project I will briefly summarize the nature of the multi-disciplinary expertise required and projected in what I like to call cognitive toolboxes.

1.) Mathematics
A fresh reminder to repeat the elementary experiences in Calculus, Linear Algebra, Statistics, Mathematic Logic, Probabilities, Elementary Differential Equations, Limits and Differentials, Trigonometry, Mathematical Physics and whatever else studied, shall be a continuous process actually vital later on.

2.) AI Principles
AI is the new electricity! It touches aspects of neuroscience and neural networks, supervised learning, unsupervised learning, genetic algorithms, Kalman filters – they were used to send Apollo to the moon by simulating the laws of the motion of planets of Kepler, gravity and kinematics -, applications in programming, large language models and computational linguistics and there are numerous concerns between General AI and Narrow AI and questions of whether this digital technology will ever reach the level of human intelligence. Getting an AI mindset is essential.

3.) Python Programming
Math and Science can reach unparalleled levels of global impact. But without programming our hands are tied up. Python, SQL and R are the languages used in data science as well as high profile AI applications. Studying a single language first for quite sometime can free wild ambitions…

4.) Quantum Physics & Quantum Computing Principles
Nature is not classical but quantum mechanical. The human mind tries to model nature and the environment around it to understand them better. Quantum Physics and Quantum Computing hold the key and the building blocks of some of the most fascinating aspects of scientific creativity: the microworld, the revolution of quantum computers, quantum AI and related technologies. Quantum Computers as a matter of fact operate on 4 different levels: the coding level, the virtual circuit level, the mathematical level and the engineering level. Can be accessed by classes, books, as well as revising math. Whoever wishes to pioneer in STEAM initiatives – Science, Technology, Engineering, Arts, Mathematics – especially in the area of natural sciences, might find himself wondering or claiming resonance around these fields…!

5.) Data Acquisition – Data Strategy
10 years ago, the collection of data was being considered Herculian feat. Today we live in the age of AI, big data and disruptive change modelling. Being proactive is better than being a follower. Being data driven is my competitive advantage.

6.) Subject Matter Research
Google is one of the best exemplifications of AI applications. But it can be fascinating to attend an event where Google pioneers with AI diagnoses in health, only to realize the lengths and the depths they are reaching in terms of founding the physical problem at hand… AI scientists should work in parallel and hand in hand with the scientists of the physical problem, not to say they need experience themselves as well, partly though… For the “Luminous Quanta” project where one scientist, myself, embarks on a journey to measure the world, all the previous function at the level of individuality all along. All by myself…

7.) Scientific Vision
Science is much more than problem-solving and dealing with phenomena, it is a journey of self-awareness as well as a vision through which we see, interpret and analyze the world. Today, AI as well can interpret the world. It is true that when dealing with AI we should bypass the grand questions of science and deal with the single task at hand, that is narrow AI so far, but seeing the big picture is equally important. What are the disciplines and the fields that possess global interest and global impact and can fire childlike passions and stimuli? Theoretical Physics, Math as The Beginning of Everything, Quanta, Technology, The Universe, Computers, Mathematical Logic and Philosophy of Sciences, The Higgs boson, Biology, Life Sciences, Cosmology, Space, Energy, etc. We should all find our inclinations concerning the previous.

8.) Reverse Engineering
Reverse Engineering is the art and science where big systems are being deconstructed to extract design information from them. In complex phenomena as well as mammoth problem-solving this is essential.

As an epilogue, setting the case and the burning desire as well as coming up with the solutions that will put our project in context, is essential to understand the rationale and the mindset behind the inventive spirit that is essential so to deal with programming applications, the general picture in other words that without it we can’t go anywhere… Have a great time folks!