Before we can use something meaningfully, we must know what it is and why it is essential. Such is the case for an artificial intelligence software definition, as we must first understand its purpose.
It could be argued that until a majority agrees on meaning, then the entity of question has no meaning even in the general sense. Otherwise, it becomes merely a collection of characters.
Learning the purpose of artificial intelligence can be challenging because it’s used in so many ways. This is why we see so many deep discussions, debates, and agreements about AI and its fuzzy connotations. And it doesn’t help that so many previous generations in the fields of science and technology have attempted to use the term. People during the early time would often ask, “Does artificial intelligence exist?”
The good news is that even if we disagree on a solid definition of AI, the reason is that we most likely are seeking to use it in different ways. And that’s good because it implies the power and the many features of artificial intelligence. One of those features leads us to deeper neural networks and other high functions.
Discerning intelligence
Intelligence can be considered in many several ways. We could say that intelligence pertains to specific mental functions that consisted of these activities:
Learning: Being able to collect, process, and retain new information.
Reasoning: Being able to evaluate and access information in many ways.
Understanding: Understanding the results of evaluating information.
Grasping truths: Assessing the credibility and validity of the information.
Seeing relationships: Divining how validated data interacts with other data.
Considering meanings: Applying current beliefs and truths to situations that are consistent with their meaning.
Distinguishing facts from belief: Understanding if reliable sources properly support data can be proven to be valid.
As you can see, this list could get very long in a hurry, but we can’t forget that the list itself is subjective – and vulnerable to interpretation – simply because everyone could see it differently.
A systemic way of using artificial intelligence
Putting AI to work requires a systemic approach that using sound logic. It helps to lay out a flow chart that indicates the required AI steps.
Here are some basic steps for implementing and learning to use artificial intelligence:
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- Establish goals based on current needs.
- Evaluate the value of any existing data and information that supports the goal.
- Collect additional data that could further support the goal.
- Use a format for the new data so that it is consistent with existing data.
- Determine the relationships between new and existing data.
- Determine if goals have been achieved.
- Adjust goals when warranted by the new data, and evaluate its effect on future probability of success.
- Repeat Steps 2 – 7 as necessary until the goal has been achieved, or at least until all possibilities for achieving the goal have been exhausted.
Even when we manage to develop algorithms and grant access to information that support the entire process within a powerful computer, the machine is often limited by its own capability in its quest to collect intelligence.
In the end, computers cannot understand something because it depends on parameters and machine processes to evaluate the data. It can only think logically and in a mechanical manner. Artificial intelligence software and computers are not able to separate truth from a mistruth through this method.
While there are many recent cases where algorithms are learning more and more about thinking for themselves, we are not there quite yet. But it’s only a matter of time before that changes. – I think everyone would agree with that.
