Data & AI, distinguish them !
The first question I’d ask is, what data?
Data has meaning only in a context. The term “data” as used today can mean many things: facts, figures, statistics, evidence of something not yet proven but merely alleged to be true. So the first question would be whether we’re talking about factual data or if its interpretation is necessary.
Facts are very important !
They are the basis of truth and provide a firm foundation from which to proceed. The rule is: “when in doubt, rely on facts.” Facts alone are not enough, however. It is often necessary to make sense out of the facts or derive conclusions from them.
Facts are often used to support an allegation, which is not a fact but merely the assertion that something is true. In order to avoid confusion, it’s best to use the term ‘evidence’ instead of ‘fact.’ Evidence may be interpreted in different ways by different people. The data itself does not determine meaning; it only provides a basis for making decisions. A simple example of such an interpretation is the case in which two people, one American and one Russian, look at a glass that contains half-full water (the ‘glass’ being symbolic for a situation). The American will say it’s half full while the Russian says it’s half empty. Both are correct from their points of view.
The American is bothered by the remaining water in the glass, while the Russian is happy it’s not completely full. They have different perspectives and are both right from their point of view. The glass can also be symbolic for many issues in human society. The American and the Russian each have their own perspective regarding the issue, but both are right in their way since all perspectives are valid.
The second question i’ll ask is, what’s data?
My first thoughts were to discuss the meaning of “data” and “AI”, in order to better understand what we are talking about here. In English, the word data is a plural noun referring to pieces of information. AI is an abbreviation for artificial intelligence.
Data and AI are two separate concepts that come together in the field of data science. Data is a collection or set of information, while artificial intelligence is a type of software designed to simulate human behaviour.
When merged, the field of data science encompasses various disciplines such as statistics, machine learning, computer programming and bioinformatics. AI is a subfield of computer programming that focuses on developing machines with human-like intelligence.
What’s AI?
AI is the application of mathematical algorithms to big data in order to solve problems. Some examples include chatbots, self-driving cars, and virtual assistants such as Siri or Alexa. Most of us have come across AI in everyday life, even if we don’t know it. For example, websites like Google and Amazon use algorithms to identify patterns so that they can recommend products or services for a given search query.
Access to vast amounts of data is also key. Data science relies on the availability of large datasets, such as social network data and transaction records. You are getting an AI to generate text on different topics. This is an experiment in what one might call “prompt engineering”, which is a way to utilize GPT-3, a neural network trained and hosted by OpenAI. GPT-3 is a language model. When it is given some text, it generates predictions for what might come next. It is remarkably good at adapting to different contexts, as defined by a prompt (in this case, hidden), which sets the scene for what type of text will be generated.
Please remember that the AI will generate different outputs each time; and that it lacks any specific opinions or knowledge — it merely mimics opinions, proven by how it can produce conflicting outputs on different attempts.
