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 ITECH1100 – Understanding the Digital Revolution

                                                                                          Assessment Task – Video and Disruption Report

                                                                                               Disrupting board games with GPT-3

Part 1: Research

To create any kind of text, the neural network machine learning model GPT-3, short for Generative Pre-trained Transformer, was trained using data from the internet (FloridiandChiriatti, 2020). OpenAI developed a method that takes a tiny bit of input text and outputs massive amounts of relevant and complex machine-generated content (Ciolino, Kalin andNoever, 2020). The deep learning neural network used by GPT-3 is a model that makes use of more than 175 billion machine learning parameters. Just to give you an idea of size, the Turing NLG model developed by Microsoft was the biggest trained language model prior to GPT-3, and it contained 10 billion parameters. As of the beginning of the year 2023, GPT-3 is the most extensive artificial neural network ever created. As a consequence, GPT-3 outperforms all competing models in terms of its ability to generate natural-sounding text.

The development of a chess engine with the help of GPT-3 is intended to serve as a test to determine whether or not a single AI model is capable of doing many tasks simultaneously, such as playing chess and creating tales. The currently available AI solutions are considered Narrow AI, which means they are only capable of performing a single function. AI models such as GPT-3 have the possibility of achieving Artificial General Intelligence (AGI), which refers to AI that is capable of completing a variety of activities in the same manner as humans (Gaikwad, Rambhiaand Pawar, 2022).

Part 2: Brainstorming disruptions

[a] It may not seem like there is any connection between a language model and the strategy game chess, but there is. However, if we recast chess as a challenge in text production, we can mislead it into playing. A letter and a number designate each chessboard square. The horizontal squares are labelled from a to h, and the vertical ones from 1 to 8. Thus, the coordinate pair, such as a1 for the bottom left square, may be used to uniquely identify each square.

[b] The notation system used to record chess games is often referred to as PGN (Portable Game Notation). By providing the PGN of the current game and asking the computer to predict the next word, we may transform the act of playing chess into a text generation job using these notations (move) (Mills, 2020). It is likely that some chess games were included in GPT-3’s massive input data. This will provide us a chance to see GPT-3’s ability to zero in on its targets and successfully manipulate the game. To do this, one may be using the OpenAI APIs, which will create a new era of board games.

[c] It is feasible to improve one’s ability to play chess at the GPT-3 level by:

  1. The use of high-quality chess games as a training dataset for fine-tuning the model.
  2. Modifying the format of the content so that it is simpler to understand.

For instance, adding a tip to the description that specifically lists all of the currently held seats on the board. Because the AI would no longer need to “guess” the present condition, I think this would result in a significant reduction of unlawful movements.

[d] One other modification that might be made to utilising GPT-3 in board games is to list all of the potential captures on the board. It will assist the AI in avoiding making any mistakes. It’s possible that using such strategies counts as cheating. However, the vast majority of AI chess engines use this method.

Part 3: Disruption

When it comes to evaluating the functionality of a product or piece of software, gamification is often regarded as one of the most effective ways. Games such as chess, poker, and go are usually taken into consideration for this kind of “testing.” Early access to GPT-3 was granted exclusively to those who had been invited to do so; as a result, several developers and engineers who were granted this privilege produced a great number of intriguing demonstrations (Mills, 2020). Given that GPT-3 is a language model, the process of teaching it to play Chess was significantly different from that of prior AI-based Chess-playing systems. The game of chess was recast in GPT-3 as a challenge involving text production.

Part 4: Ethics

The fact that GPT-3 was trained using data that had not been filtered in any way is the root cause of biases, which are strictly a technological flaw. One risk associated with this AI that is not related to its underlying technology is the potential for humans to abuse it in unethical ways. Ethical problems can be caused either by the technology in and of itself or by the way that humans with bad intentions use the tool. Because the model is able to successfully put together coherent information that are impossible to distinguish from human writing, it is possible, for instance, board games player can apply unethical means to win the games. This is because the model is able to successfully put together coherent paragraphs. When it comes to ethics, unethical usage of AI may have catastrophic effects. Because it is such a powerful tool, engineers have a responsibility to make every effort to prevent it from falling into the wrong hands. Spreading false information with the GPT-3 is a grave breach of honesty and integrity, which is why engineers have this obligation.

Additionally, OpenAI just just announced the introduction of ChatGPT, a model that is related to InstructGPT and is designed to provide verbal interaction with the user while they are playing board games. A user is able to inquire and follow-up questions instead of simply acting on a prompt when using ChatGPT, which was also developed using reinforcement learning with the goal of trying to align output with user intent. This reinforced artificial intelligence model, similar to InstructGPT in this regard, is able to acknowledge its errors, question incorrect premises, and decline inappropriate requests. As a result of the way in which their output system is designed, ChatGPT and InstructGPT are obviously more ethical engineering solutions.

Part 5: Visualisation

Source: Board Games – worldwide, 2023

Apps for playing board games are aesthetically reminiscent to classic tabletop games, albeit they often include some digital twists. The app of the well-known board game Monopoly has animated 3D visuals and a multi-player mode.’s mobile app pairs users against opponents of a similar skill level in online play and provides a plethora of challenging chess challenges (Board Games – worldwide, 2023).

  • By 2026, the market is predicted to be worth US$4.67bn, with a compound yearly growth rate (CAGR) of 8.81% from 2022-2026.
  • There will be $1,788.00M in IAP sales from the Board Games market by 2022.
  • Board Games paid app revenue is expected to hit $50.47m in 2022.
  • For the Board Games industry specifically, forecasts place ad sales at $1,288,000,000 by 2022.
  • There will likely be 4,847.1 million downloads in the Board Games category by the end of the forecast period (2022).
  • Revenue per download is forecasted to average $0.65 USD as of right now.
  • The United States generates the greatest income worldwide ($1,685.00m in 2022) according to a global comparison.

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