What was the first expert system developed to determine the chemical structure of molecules quizlet?

Your genes play an important role in your health, but so do your behaviors and environment, such as what you eat and how physically active you are. Epigenetics is the study of how your behaviors and environment can cause changes that affect the way your genes work. Unlike genetic changes, epigenetic changes are reversible and do not change your DNA sequence, but they can change how your body reads a DNA sequence.

Gene expression refers to how often or when proteins are created from the instructions within your genes. While genetic changes can alter which protein is made, epigenetic changes affect gene expression to turn genes “on” and “off.” Since your environment and behaviors, such as diet and exercise, can result in epigenetic changes, it is easy to see the connection between your genes and your behaviors and environment.

How Does Epigenetics Work?

Epigenetic changes affect gene expression in different ways. Types of epigenetic changes include:

DNA Methylation

DNA methylation works by adding a chemical group to DNA. Typically, this group is added to specific places on the DNA, where it blocks the proteins that attach to DNA to “read” the gene. This chemical group can be removed through a process called demethylation. Typically, methylation turns genes “off” and demethylation turns genes “on.”

Histone modification

DNA wraps around proteins called histones. When histones are tightly packed together, proteins that ‘read’ the gene cannot access the DNA as easily, so the gene is turned “off.” When histones are loosely packed, more DNA is exposed or not wrapped around a histone and can be accessed by proteins that ‘read’ the gene, so the gene is turned “on.” Chemical groups can be added or removed from histones to make the histones more tightly or loosely packed, turning genes “off” or “on.”

Non-coding RNA

Your DNA is used as instructions for making coding and non-coding RNA. Coding RNA is used to make proteins. Non-coding RNA helps control gene expression by attaching to coding RNA, along with certain proteins, to break down the coding RNA so that it cannot be used to make proteins. Non-coding RNA may also recruit proteins to modify histones to turn genes “on” or “off.”

Example: Study of newborn vs. 26-year-old vs. 103-year-old

DNA methylation at millions of sites were measured in a newborn, 26-year-old, and 103-year-old. The level of DNA methylation decreases with age. A newborn had the highest DNA methylation, the 103-year-old had the lowest DNA methylation, and the 26-year-old had a DNA methylation level between the newborn and 103-year-old (1).

Example: Smokers vs. non-smokers vs. former smokers

Smoking can result in epigenetic changes. For example, at certain parts of the AHRR gene, smokers tend to have less DNA methylation than non-smokers. The difference is greater for heavy smokers and long-term smokers. After quitting smoking, former smokers can begin to have increased DNA methylation at this gene. Eventually, they can reach levels similar to those of non-smokers. In some cases, this can happen in under a year, but the length of time depends on how long and how much someone smoked before quitting (2).

Artificial intelligence (AI) hasn’t always been a lucrative field. But the technology that first made AI commercially successful – the ‘expert system’ – is commonly forgotten.

Before our modern AI-powered tools, expert systems were a notable component of computerised automation. They were the innovative trend; the ‘big thing’ in the tech of their time.

Today, though, the history of expert systems has taken a step away from the limelight. But that doesn’t mean the technology wasn’t a key milestone in the development of AI technology.

A knowledge of expert systems history, then, is crucial to truly understand the history of artificial intelligence (AI). So, let’s dive in.

What are expert systems?

Before exploring the expert systems history, it’s helpful to know what, exactly, expert systems are.

Expert systems are the first example of ‘knowledge-based systems’. They work using rules and rely on two components: a knowledge base and an inference engine.

Let’s break those terms down:

• Knowledge base: An organised collection of facts about the world and/or the task the system is designed to carry out.
• Inference engine: Applies logical rules to the knowledge base to deduce new information.

In the simplest terms, expert systems are artificial intelligence systems that specialise in one task. In other words, they’re computerised systems that act as experts in one given field.

Before it all

The expert systems history starts almost alongside the dawn of the modern computer in the 1940s, when the first digital programmable computers began to emerge.

It wasn’t long before researchers started to think about the potential of these new machines. What if they could emulate human decision-making? What if they could “think” as humans do?

And so it was that researchers started looking into artificial intelligence — and began on the path to creating expert systems.

The first expert systems

Officially, the expert systems history starts in 1965. This is when the technology saw its formal introduction by the Stanford Heuristic Programming Project. Edward Feigenbaum – the ‘father of expert systems’ – led the inaugral project.

Edward Feigenbaum was involved with both MYCIN and Dendral — two separate early expert systems.

Dendral was an expert system that specialised in analysing and identifying chemical compounds. It’s widely considered the first expert system.

MYCIN was derived from Dendral. It was another expert system — one that focused on identifying bacteria that caused infections and recommending antibiotics.

These systems didn’t try to be general intelligence. They weren’t general problem solvers. Rather, they focused on a limited (but in-depth) foundation of knowledge. And this made them one of the first successful attempts at AI software. That is, machines that appeared to analyse and ‘think’.

Reaching the heyday

The heyday of expert systems came in the 80s. During this time, two-thirds of Fortune 500 companies used expert systems.

Interest in expert systems was international. They saw increased research funding in Europe, and the Fifth Generation Computer Systems Project in Japan, which saw researchers focus (in part) on inference technology and knowledge bases.

A Symbolics Lisp Machine: an early platform for expert systems. Source

But expert systems were not without their problems. There were difficulties managing and maintaining the knowledge base. There were difficulties writing the rules that reflected the knowledge of experts. And the hype around expert systems was spiralling faster than the technology could keep up.

To paraphrase a common idiom, hype comes before a fall. And this was true for expert systems. The AI winter was coming.

Fading to obscurity?

In the 1990s and onward, the expert systems history involves the decline in the popularity and hype of the technology. As the tech world saw an AI winter, the excitement around expert systems faded.

The apparent decline of expert systems at this time has two reasons behind it.

The first, simply, is that expert systems failed to live up to the hype. They couldn’t perform the over-egged functionality that had been promised. They didn’t expand to a more general form of AI fast enough, and so they were discarded.

The other explanation is that they were absorbed by other technology tools. As expert systems became better known, programmers and developers could use the technology behind them as part of other offerings.

In short, rule-based systems became useful for more than expert systems, and so the standalone expert system stepped out of the spotlight.  

The present and future

You probably won’t hear much mention of expert systems these days. Indeed, it would seem that there are very few in use.

However, the basic tools and premises that stem from the expert systems of the past are present in modern software.

For instance, think of the rules-based systems found in automation tools. Or, consider the understanding of the need for data and knowledge in machine learning and other modern AI-powered tools. Consider the different types of database for different types and formats of data and knowledge.

The advancements in these technologies all have roots in expert systems.

An expert systems history

Expert systems are a key player in the history of automation and AI. While they’re not in the spotlight today, there was once a time where they were the height of artificial intelligence.

Useful links

• Milestones in artificial intelligence
• A history of automation: the rise of robots and AI
• The AI winter is coming
• What is the AI effect?
• AI: the snake oil of the 21st century
• Types of AI: distinguishing between weak, strong, and super AI

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What was the first expert system developed to determine the chemical structure of molecules?

What is the first expert system?

What is the expert system?

Which statement is true of integrating an expert system into the database component of a decision support system DSS quizlet?