3 Minute AI School Bytes Episode 3 – Features & Fields
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3 Minute AI School Bytes Episode 1 – AI Explained
Information
AI Splainer for regular people or clarity
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 Beneath the covers!
Artificial intelligence as has four basic subject areas. They are as follows:
Statistics or, features
Linear algebra
Calculus
Probability
I would like to focus on statistics or AI eatures.
Information including statistics are derived from data ,data is real potential. It’s not calculated or surprised that will come later. And it most probably reflects some type of transaction
Information which is derived from data, can be standardized by reference data, or calculated of cours,e
Features can also be transformed, standardized and, consist of calculated information and be in many cases, change from its raw form
Then, of course we have to consider the algorithm you may choose to predict or especially recently todays generative content
It is also worth considering that in the early AI days much data concerns came from structured , sources, tables, or dimensions and columns, or as a attributes this data, what is the model and kept in and RDBMS recently we are able to process mature what we would call a structured data. This presents both great opportunity, and a greater chance for not understanding the context of the data.
My main conclusion is that you have to be careful that you consume data or feed it to your AI with a forensically traceable and verifiable source. In other words, he a chain of custody as you would in the real world. You cannot just take a prediction and not understand how it was. Arrived at. Like in the real world, there’s a difference between a guess and a well thought out prediction
Something you may want to know before you go that way , We have yet learned how our judgment works. Apparently, it’s a mixture of learned knowledge, experiences in human intelligence
Data Mind Set interview Information Value Chain
Pending Data Apocalypse
Artificial Intelligence Codex
This is a brief blog to help understand the synergies and similarities between AI & BI We will start our at what drives the process, which is the business goals I will be publishing sevral guides and templates\ to help you in this but is important this is written for your team and others to evaluat
Businemss Intelligence
This path will involve recording and providing the deliverables so let your development team translate your requirements into business and technical deliverables
Equally we will use these deliverables as a springboard in defining, creating and documenting interesting business and technical deliverables into functional deliverables from the business that the AI development team can use
BI Dimensionns
BI dimensions is it area we will cover quite a bit. It is similar here distinctly different from AI use of the term in PI. A dimension is a high-level category or a category of glossary terms or attitude from a file or table, bottom line in BI. It’s a grouping of separate fields or an AI each field is called equally a feature, or a dimension, or a variable. In AI it is around the algorithm selected. We will cover this in detail.
BI Metrics
AI measure a KPI or metric that is piece part of many data elements and several parts of dimensions which is the name of the dimension or category in business terms
AI Features
In AI features is interesting, and a bit different than the grooving of separate fields or data elements or attributes, which is called business dimensions. A feature has several definitions. The primary definition is a field that that in part of an algorithm chosen and impacts the algorithm based on the input in AI. There are many techniques and tools to.A high features is interesting, and a bit different than the in AI there are many tools and techniques for feature selection. They are very similar and based on the tools and techniques used in BIA to define attributes who will discuss in detail later.grooving of dittos, which is called business dimensions. A feature has several definitions. The primary definition is a field that can choose or impacts the model based on the input in AI. There are many techniques and tools to.in AI there are many tools and techniques for feature selection. They are very similar and based on the tools and techniques used in BI to define attributes who will discuss in detail later.
AI predicted Content
Here too, we have not standardized on description. The predicted content is commonly known as the generated content. this is important in both models types, both expert AI, and knowing that there are features, however, LLM specifically creates generated content, or its prediction of your contest based on your questions or prompts
AI & BI Synthesis
I created a an approach and a short video, sharing the best of both worlds and suggesting the best approach to get the best of both worlds
Here is the video
