(Ten minute read)

We all know that massive changes need to be made to the way we all live on the planet, due to climate change.

However most of us are not aware of the effects that artificial intelligence in having on our lives.

This post looks at our changing understanding of ourselves, due digitalized reasoning, which is turning us into digitalized

citizens, relying more on and more on digitalized reasoning for all aspects of living.

Does it help us understand what is going on? Or to work out what we can do about it?

It could be said that the climate is beyond our control,  but AI remains within the realms of control.

Is this true?

It is true that the human race is in grave danger of stupidity re climate change which if not addressed globally could cause our extinction.

We know that using technology alone will not solve climate change, but it is necessary to gather information about what is happing to the planet, while our lives are monitored in minute detail by algorithms for profit.

There are many reasons why this is happing and the consequences of it will be far reaching and perhaps as dangerous if not more than what the climate is and will be bringing.

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While biology reasoning usually starts with an observation leading to a logical problem-solving with deductive conclusions

usually reliable, provided the premises are true.

Digital AI reasoning on the other hand is a cycle rather than any logically straight line.

It is the result of one go-round becomes feedback that improves the next round of question asking to ask machine

learning, with all programs and algorithms learning the result instantly.

Example  One Drone to the next. One high-frequency trade to the next. One bank loan to the next. One human to the next.

Another words.

Digital Reasoning, is combining artificial intelligence and machine learning with all the biases program’s in the code in the first place without any supervision oversight, or global regulation

It combined volumes of data in real-time to remove the propose a hypothesis, to make a new hypothesis without conclusively prove that it’s correct.  An iterative process of inductive reasoning extracts a likely (but not certain) premise from specific and limited observations. There is data, and then conclusions are drawn from the data; this is called inductive logic/ reasoning. 

Inductive reasoning does not guarantee that the conclusion will be true.

In inductive inference, we go from the specific to the general. We make many observations, discern a pattern, make a generalization, and infer an explanation or a theory.

In other words, there is nothing that makes a guess ‘educated’ other than the learning program.

The differences between deductive reasoning and inductive reasoning.

Deductive reasoning is a top-down approach, while inductive reasoning is a bottom-up approach.

Inductive reasoning is used in a number of different ways, each serving a different purpose:

We use inductive reasoning in everyday life to build our understanding of the world.

Inductive reasoning, or inductive logic, is a type of reasoning that involves drawing a general conclusion from a set of specific observations. Some people think of inductive reasoning as “bottom-up” logic the  one logic exercise we do nearly every day, though we’re scarcely aware of it. We take tiny things we’ve seen or read and draw general principles from them—an act known as inductive reasoning.

Inductive reasoning also underpins the scientific method: scientists gather data through observation and experiment, make hypotheses based on that data, and then test those theories further. That middle step—making hypotheses—is an inductive inference, and they wouldn’t get very far without it.

Inductive reasoning is also called a hypothesis-generating approach, because you start with specific observations and build toward a theory. It’s an exploratory method that’s often applied before deductive research.

Finally, despite the potential for weak conclusions, an inductive argument is also the main type of reasoning in academic life.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning, where you start with specific observations and form general conclusions.

Deductive reasoning is used to reach a logical and true conclusion. In deductive reasoning, you’ll often make an argument for a certain idea. You make an inference, or come to a conclusion, by applying different premises. Due to its reliance on inference, deductive reasoning is at high risk for research biases, particularly confirmation bias and other types of cognitive bias like belief bias.

In deductive reasoning, you start with general ideas and work toward specific conclusions through inferences. Based on theories, you form a hypothesis. Using empirical observations, you test that hypothesis using inferential statistics and form a conclusion.

In practice, most research projects involve both inductive and deductive methods.

However it can be tempting to seek out or prefer information that supports your inferences or ideas, with inbuilt bias creeping into  research. Patients have a better chance of surviving, banks can ensure their employees are meeting the highest standards of conduct, and law enforcement can protect the most vulnerable citizens in our society.

However, there are important distinctions that separate these two pathways to a logical conclusion of what Digitized reasoning is going to do or replace human reasoning.

First there is no debate that Computers have done amazing calculations for us, but they have never solved a hard problem on their own.

The problem is the communication barrier between the language of humans and the language of computers.

A programmer can code in all the rules, or axioms, and then ask if a particular conjecture follows those rules. The computer then does all the work. Does it  explain its work.  No. 

All that calculating happens within the machine, and to human eyes it would look like a long string of 0s and 1s. It’s impossible to scan the proof and follow the reasoning, because it looks like a pile of random data. “No human will ever look at that proof and be able to say, ‘I get it.’ They operate in a kind of black box and just spit out an answer.

 Machine proofs may not be as mysterious as they appear.  Maybe they should be made to explain. 

I can see it becoming standard practice that if you want your paper/ codes/ algorithm to be accepted, you have to get it past an automatic checker – re transparency because efforts at the forefront of the field today aim to blend learning with reasoning.

After all, if the machines continue to improve, and they have access to vast amounts of data, they should become very good at doing the fun parts, too. “They will learn how to do their own prompts.”

company will enable customers to spot risks before they happen, maximize the scalability of supervision teams, and uncover strategic insights from large

The Limits of Reason.

Neural networks are able to develop an artificial style of intuition, leverage communications data to spot risks before they happen, and identify new insights to drive fresh growth initiatives, creating a large divide between firms investing to harvest data-driven insights and leverage data to manage risk, and those who are falling behind.

This will bear out in earnings and share prices in the years to come.

The challenge of automating reasoning in computer proofs as a subset of a much bigger field:

Natural language processing, which involves pattern recognition in the usage of words and sentences. (Pattern recognition is also the driving idea behind computer vision, the object of Szegedy’s previous project at Google.)

Like other groups, his team wants theorem provers that can find and explain useful proofs. He envisions a future in which theorem provers replace human referees at major journals.

Josef Urban thinks that the marriage of deductive and inductive reasoning required for proofs can be achieved through this kind of combined approach. His group has built theorem provers guided by machine learning tools, which allow computers to learn on their own through experience. Over the last few years, they’ve explored the use of neural networks — layers of computations that help machines process information through a rough approximation of our brain’s neuronal activity. In July, his group reported on new conjectures generated by a neural network trained on theorem-proving data.

Harris disagrees. He doesn’t think computer provers are necessary, or that they will inevitably “make human mathematicians obsolete.” If computer scientists are ever able to program a kind of synthetic intuition, he says, it still won’t rival that of humans.

“Even if computers understand, they don’t understand in a human way.”

I say the current Ukraine Russian war is the labourite of AI reasoning this war with all its consequence is telling us that AI should never be allowed near nuclear weapons or….dangerous pathogens.

An inductive argument is one that reasons in the opposite direction from deduction.

Given some specific cases, what can be inferred about the underlying general rule?

The reasoning process follows the same steps as in deduction.

The difference is the conclusions: an inductive argument is not a proof, but rather a probalistic inference.

When scholars use statistical evidence to test a hypothesis, they are using inductive logic.

The main objective of statistics is to test a hypothesis. A hypothesis is a falsifiable claim that requires verification.

• Most progress in science, engineering, medicine, and technology is the result of hypothesis testing.

When a computer uses statistical evidence to test a hypothesis it’s assumption may or may not be true. To prove something is correct, we first need to take reciprocal of it and then try to prove that reciprocal is wrong which ultimately proves something is correct.

Finally this post has been written or generated by a human reasoning, that see the dangers of losing that reasoning to Digital reasoning of Enterprise Spock.

All human comments appreciated. All like clicks and abuse chucked in the bin.

Contact: bobdillon33@gmail.com