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Emerging Technologies in CS

Armaan Kokan

December 18th, 2022

Computer Science is an enormous umbrella with a lot of subtopics. Each technology has its functions and benefits. Today we will look at some of the emerging technologies and what they are.

Artificial Intelligence (AI)- This is something you have probably already heard of. AI in simple terms is a machine taught to do tasks that normally require human intelligence. Below are some examples of famous AIs.

  1. Deep Blue- This was a supercomputer developed by IBM to play chess. In 1997, Deep blue beat Grandmaster Garry Kasparov in a game of chess under normal time controls. In only 19 moves, the grandmaster resigned from playing the game. The AI used custom VLSI chips to carry out the alpha-beta search algorithm to make an effective move each time. VLSI (Very large scale integration) created an integrated circuit by incorporating tens of thousands of transistors into one chip.

  2. CICERO AI- Brand new AI that Meta (previously known as Facebook) released recently. This AI was made to beat people in a game called Diplomacy. This game is a strategy game where players (2-7 players) must discuss with each other before making moves (the average time for one move is 15 minutes). The players have to be able to negotiate, lead, and listen to multiple different viewpoints at the same time. This AI has successfully beaten many people in the game. The goal of Meta though is to not create an AI that can win this game but to use this technology to help create more effective chatbots that can hold long conversations with humans.

Machine Learning- The development of a system that can adapt and learn without any human intervention. Often needing a lot of samples, it will recognize patterns and draw inferences from them. A very common example used to teach in schools is the beagle vs bagel machine learning algorithm. Here the algorithm will be able to tell if something is a beagle or a bagel depending on the picture fed to the system. Below are some examples of famous ML algorithms:

  1. Linear Regression- This ML takes the independent variable (x) and the dependent variable (y) by fitting them into a regression model. This is often used for statistical purposes where certain relationships can be represented as a straight line (y= mx + b) where m is the slope and b is the y-intercept.

  2. Artificial Neural Networks (ANN)- This is an algorithm that mimics the human brain to solve complex tasks. It has three interconnected layers that process the input data. These are used in home automation devices such as door locks, thermostats, smart speakers, lights, and appliances. The first layer sends the input to deeper layers. The second layer (also called the neural layer) transforms the data into understandable pieces. The third layer sends the final output for the question.

Data Analytics- Process of examining data sets to find trends and draw conclusions from the data. This subtopic is also part of data science. There can be many examples of data analytics where companies can serve their customers with useful products by looking at what they want, and the current products they are using. This field can also be used to improve the insurance industry. You can better predict accidents using data from the past and the present. This predictability can provide better prices to customers, thus helping the company invite new customers. They can also add further types of discounts which previously were not a variable to account for.

Other fields of computer science include AR/VR, UI/UX design, and quantum computing. Some other topics include cybersecurity, natural language processing, computer vision, and telepresence. We will focus on some of these other topics in the upcoming weeks.


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