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Dynamics of building a self-organising network logic

To build a “knowledge & intelligent system”, I have come to believe that we need to build an organic-based system that develops intelligence naturally rather than using silicon-based system that needs rigid pre-coded algorithms that do not scale and have very less room to manoeuvre. There are many advantages to building it as an “organic-based” system that work with data-realised ...

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Step variant non-sequential programming models for AI systems

Based on what I have described in the previous series of blogs on AI, it can be seen that the process is an uncontrolled sequence of execution, driven solely by the state of raw data that can be sensed. This type of uncontrolled execution is nice as a research tool. Without the ability to program or control, it is of not much use to ...

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How to use the data collected as knowledge?

Like I have written before, we need to rethink the binary data representation that we have currently and evaluate the effectiveness of it for an AI application. My take is that we should not collect the raw data as a binary encoded discrete scalar value periodically, but we need to use the data directly from the source and collect knowledge ...

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The missing depth dimension in data

As I have been writing in my blogs on AI, the data representation that we have, is not suited for writing a knowledge and intelligent system. Data needs to be the primary focus and remain the primary focus to implement true AI. So, we need to find a different way of representing data by taking into consideration all the requirements ...

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Data continuity, data sampling and discernible change

As I have mentioned in many previous blogs, data continuity is one of the many features that is lost in data that is pre-collected and used for AI. But, what is data continuity? Why do I say losing continuity in data results in information loss? Is it possible to pre-collect continuous data at all? Is data continuity related in any ...

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Data-realised algorithms vs logic-based algorithms

In my previous blog I had mentioned how it is better for us to leave the data as is in the various formats it is present, as is, instead of collecting them periodically by trying to convert them into a rigid format, thus losing information present in the data. The obviously immediate question that arises is “The data is already ...

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Data representation for AI

I had written in the previous few blogs why a digital representation of data or a numerical representation of data does not fit the bill for AI. The problem with any of these is that they reduce the continuous, multi-dimensional data that naturally have relations, down to a multiple, disparate, unrelated discrete scalar values which has lost a lot of ...

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Requirements on data representation for AI

The best saying that applies to all AI and data analysis programs is “Garbage in, garbage out”! We all agree that we have terabytes and terabytes of data which is going towards petabytes of data. Yet, the amount of useful knowledge that we can derive and use from it is a pittance compared to the amount of data present. We ...

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What is logic in relation to AI?

As I had written in my previous blog, I don’t believe a true AI can be a computer program. The question that arises is why not? And if not, what can it be? As I have said, I find that computers are just that, a system that does mathematical computations and nothing more. While we claim that our computers are ...

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