Category Archives: IVC

AI Splainer for regular people or clarity

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I’m going to give you my perspective of understanding how AI works

I would like to offer my understanding of how AI works with my experience in BI and a lot of researchi.

First, in my opinion this AI consist of these important areas of the logical real world areas

Statistics

Algorithms

Patterns

Data.

Predictions

Semantics

First, this is meant to be at the normal person level. Of course they’re my opinions. I will go into technical detail later.

It is important to understand the difference between our intelligence and artificial intelligence, mainly artificial intelligence consist of memorization, patterns in huge amounts of data and calculations. The result, it is important to remember that you as a human have a ability to override your thoughts or predictions, it’s good sometimes that.also, as a human, sometimes you have that intuition? Many times this would be in conflict of what artificial intelligence would tell you. i’m not making a judgment I’m just observing

Artificial intelligence, I will point out is no better than human intelligence . In fact, AI is modeled on human intellgence

AI is faster, it can consume, vastly more data quickly, and you can correlate, that does not make it better. It’s just different. It is useful and faster

I would like to help compare and contrast for many of you, what we have done over the years and where we are now I believe there’s to many synonyms saying the same information and rather then a simple comparison and relationship, as well as many mathematical concepts, and even that many of these have existed for a while and that are being called by different names. It’s not new or different. Mathematics is still the center and it’s always been there., this is to clear things up, you may know more about how this things work then you realize

Oneiof the basic parallels or similarities is in BI, we focused on Files, tables, dimensions and of course fields pand metric and KPI’s. Surprisingly, it is very similar in artificial intelligence, and the focus on on algorithms,, and features which many derived from fields and dimensions which many are also derived from data fields, statistics or other things again, I’d like to point out that there is a correlation and a relationship of featured

It is important to realize previously because of technology constraints. , we would focus on these correlations and relationships to be hard wired any coded in physical files in artificial intelligence. Currently, we can perform this kind of relationship discovery, and correlations and memory encoding differently, but it’s still the same conceptionally., obviously, this can be done on a greater scale and quicker

This is just one of many correlations I will be pointing out. Thank you.

AI LLM New Project Roles including Prompt Engineering and Role AI Scientist and integrated data and information

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I’d like to offer some advice on transitioning skills and knowledge skills and knowledge who worked hard to retainto include some of the new AI and NLM developments it’s actually less impactful, and better than you may think

Well, I dressed up in a little bit for now let’s talk about prompt engineers You most likely have currenb SME’s or expert on your current data and requirements

First, do you notice I used the term AI scientist Instead of data scientist , A data scientist Currently is actually a AI model scientist and they will help you apply. We are concerned here with how a lot of folks have opinions heuristic no not necessarily fact-based already we are going to suggest some techniques, and provide some mentoring to explore this important factor in AI is proper training we specialize in providing techniques and mentoring in separate information which is Formulated and an opinion and facts or Data, which, cannot change

There is a series of steps involved in preparing for the use of Data inAI and Chat in the form of LLM models. This is not much different and you may have most of the information already gathered in order to properly design the requirements for your model we would collect the phone.it is important to realize the steps are critical, for you have confidence in your models putput which will be your result of integrating, your Word documents, your Presentations,, spreadsheets, and, of course your actual data.

We wiKeaton, Billy, and modeling of information words versus modeling and requirements for data preparation . There is a difference that is extremely important and in line with what you’ve been doing.I know that Data preparation is not glamorous, but in my 20+ years you’ll get nowhere without proper data preparation you can’t AI it you can’t impute it you need to discuss requirements with people and write them down and then execute it The AI will make the legwork, faster. but in the end you’ll have to review it otherwiseit otherwise you may end up needlessly retracing your steps based on Improper preparation I know that Data preparation is not glamorous, but in my 20+ years you’ll get nowhere without proper data preparation you can AI it you can’t imputed you need to discuss requirements with people and write them down and then execute a I will do it. Faster time is the legwork, but in the end you’ll have to review with Stuck you may end up needlessly retracing your steps based on.I know that Data preparation is not glamorous, but in my 20+ years you’ll get nowhere without proper data preparation you can AI it you can’t imputed you need to discuss requirements with people and write them down and then execute a I will do it. Faster time is the legwork, but in the end you’ll have to review with Stuck you may end up needlessly retracing your steps based on. improper preparation. This can be at Floydd by phone, the proper steps.

1. Word document

2. Presentations

3. Spreadsheets

4.Data reports

5. Data quality report for AI preparation

6.Internet

7.Other Sources (Network,Internet or Local)

We have suggested tools/techniques/open source.and suggestions for each of these. Don’t let that bother you, however, is important with today’s capabilities in AI integrate your words your thoughts, your abstraction, and your actual data together in order for you. They’re trustworthy results from your AI.

We will be providing a separate post on each of these and then finally how they come together our point is that the what you’ve been doing to understand and form requires for tradition BI can be reutilized and extend it for AI

ChatGPT answer this?

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This White line is the time that we recorded history ,the Blue area is Pre History. So comparatively, it’s like explaining your whole life by the last 30 seconds and of course convincing everyone that you had it all figured out .

One last note by the way, we’ve only been eating the processed food we’re eating now for the last five seconds 

Critical thinking, in data methods and modern data analytics

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Finally, in current IT and AI ,I think we are missing the forest for the trees. Actually we have been throughout the history, we have been decoding, deciphering, and learning about Logical thought, thinking we several examples, like hieroglyphics, and Socrates, obviously two ddvergent examples, one of deciphering.one, of critical thinking and I think we are missing the forest for the trees. Deep platform language that this process is a plate is in large degree Arella logical process and what was learned 2000 years of history is much much more important and relevant and relevant We seem to focus intently upon big data data analytics, etc. we seem to forget that a relationships of transactions to our derived information and thought processes for our perception which is our reality and often that is misguided. We get caught up in pursuing the truth however, we forget that the truth, for one person may not be the truth for another person hence perception. It is not a question of whether it’s right or wrong. It’s a question of what is affecting your reality, do you really care whether it’s right or wrong or you care you can change and outcome. More to come….

Information Value Chain Story

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I’d would like to discuss the information, value chain and how we interpret information. However since your here and, we have time , we don’t make decisions based on data or transactions we make decisions based on information, which is in essence thru visualization and trends,, which is the result of calculations, summarizations visualization of a tremendous volume of data.

It is an is important distinguish between data and information. our perception of reality and the information we make decisions on is based on, whether or not it is accurate. Information, quality or scoring often determine how far we would go. We don’t look at the detail, we don’t want to or can’t comprehend or understand, the detail of the information we want to understand trends that will lead us to decisions to change the trends. In the next article we will examine a client I had, and which I took this journey with much detail however, the focuses on high-level impactful decision

The Takeaway is that you need to understand causality not correlation you need to understand why things happen in essence. I in brutal honesty, which is that you understand what things you can change vs thing you can’t change how you gonna affect your future reaching your goal. We will be exploring this in detail step-by-step.