Artificial Intelligence terms that professionals should know

Artificial Intelligence has been a hot topic in the media over the past year. There are many examples of AI-generated text, artwork, and even video. AI is again at the forefront of public conversation, with many citing lawyers and accountants as vulnerable.

Artificial intelligence will not replace professionals with experience immediately, but those who can use AI effectively can quickly surpass those who cannot. Artificial intelligence is changing how many people work and will continue to do so. Your clients will need your AI insight, and you may also need to share your AI knowledge with your organization.

How can you ensure that you stay caught up?

You may know more than you thought about AI – you may have been using AI every day for years. This article will provide you with the essential AI terms you should know to participate in AI conversations at your workplace.

What is Artificial Intelligence

Artificial intelligence (AI) is a broad definition of a branch in computer science whose goal is to create intelligent machines that can perform tasks that require intelligence. It includes problem-solving and decision-making. AI is used daily, from text editors to autocorrect and chatbots to digital assistants like Siri.

What does an algorithm mean

A set of rules that computers or calculators follow to solve problems. We use an algorithm to solve problems involving long division in math equations. Google and other search engines use algorithms to provide the most relevant results for users.

What does machine learning mean

Machine learning is a subset of AI that learns without explicit instructions. It does this by inferring patterns from data using statistical algorithms and models. Social media feeds and product recommendations are examples of ML.

What is Natural Language Processing

On the other hand, natural language processing (NLP) focuses more on creating human language. This includes both spoken and written language. It does not focus on robotic speech or restricting text. Natural language processing uses algorithms to extract and analyze language data in a manner that computers can understand. Machines must be able to process vast amounts of data, organize them and translate and produce content that looks human.

What is natural language searching?

Natural language search is a search method that allows users to interact directly with computers or search engines using their everyday language rather than formalized queries or commands. Natural language search is a way to find a gym that focuses on fitness. It doesn’t matter if the name of the gym includes the word gym or not.

You don’t need to include everything in your search query (gym, studio, fitness and yoga, CrossFit, health club, and more) to receive all-inclusive returns. Typing like a natural person is more fun than typing like a robot. With a bit of machine learning help, your results will stay local.

As an expert, you can search through documents, such as research briefs and reports, without using exact match language.

What is data mining

Data mining is a process that involves looking for patterns, relationships, and correlations within large datasets. Technology systems can analyze data in a way that humans cannot do. They can identify anomalies at a level of scale that is unimaginable. This analysis can help predict outcomes, find potential mistakes, and spot questionable trends. The information gained from this analysis is helpful in many ways.

Data mining: What you should know

Your recommendations will (hopefully!) keep improving. A store can provide relevant suggestions by analyzing the buying patterns of other people who are also interested in or buy the same products that you do. The same principle is used in Netflix recommendations and targeted online advertisements.

What is the difference between unstructured and structured data

Structured data can be defined as organized data in a specific structure. Quantitative data is another name for structured data. It’s objective and easy to store and export into Microsoft Excel or other databases. Data mining is easier because the data is organized consistently and recognizable. It is easier to distill and analyze structured data.

Unstructured data, on the other hand, needs to be organized. Unstructured data is not scheduled and has no externally-defined structure. It cannot be exported, stored, or collected. It’s what most companies deal with every day. This includes most text-heavy information, including reports, Microsoft Word files, emails, webpages, etc.

Structured and unstructured data

For decades, structured data has enabled you to quickly complete your searches and inquiries. Because the data is structured and objective, the results that you receive are accurate.

Structured data is easily analyzed, such as the transactional data in a sales report – e.g., Rep X has sold Y units this year, resulting in a revenue of $$. The detailed feedback that the same rep received from users during the implementation of the product is unstructured and difficult to analyze and quantify.

Recent AI advances, like Large Language Models (LLM) and Foundation Models (FMF), have primarily used vast unstructured data repositories to create new insights. For more information, see the article on machine learning.

What are big data

“big data” refers to data sets that are too complex or large to be processed by conventional data processing software. Big data is the combination of structured data, semi-structured data, and unstructured information. Big data can be anything from customer databases to all the news on social media sites or even trade data at the New York Stock Exchange.

What does AI mean to your future

The above information is well understood and has been used in professional products for many years, even decades. Remember when you first typed in plain language to search for what you wanted? This was an example of AI. Since you have been using these AI concepts in your everyday life for many years, you are likely familiar with them.

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