ai in games research paper

• DOI: 10.1109/IWCMC48107.2020.9148327
• Corpus ID: 220890418

Recent Research on AI in Games

• Boming Xia , Xiaozhen Ye , Adnan Omer Abuassba
• Published in International Conference on… 1 June 2020
• Computer Science

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Designing a popular game framework using neat a genetic algorithms, a short analysis of the application(s) of intelligent agents in computer games, evolutionary algorithm in game – a systematic review, ai-based flappy bird game with dynamic level generation, lucy-skg: learning to play rocket league efficiently using deep reinforcement learning, master genie -an ai conversational chat bot game based learning, ai development in video games, materializing the abstract: understanding ai by game jamming, optimizing stage construction and level balancing of match-3 puzzle game with ppo algorithm machine learning, modeling, replicating, and predicting human behavior: a survey, 56 references, a panorama of artificial and computational intelligence in games.

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Winning Is Not Everything: Enhancing Game Development With Intelligent Agents

Deep reinforcement learning for general video game ai, towards interactive training of non-player characters in video games, intentional computational level design, on multi-agent learning in team sports games, using a surrogate model of gameplay for automated level design, generating levels that teach mechanics, hrlb⌃2: a reinforcement learning based framework for believable bots, artificial intelligence and games, related papers.

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Deep learning applications in games: a survey from a data perspective

• Published: 04 December 2023
• Volume 53 , pages 31129–31164, ( 2023 )

Cite this article

• Zhipeng Hu 1 ,
• Yu Ding   ORCID: orcid.org/0000-0003-1834-4429 2 ,
• Runze Wu 2 ,
• Lincheng Li 2 ,
• Rongsheng Zhang 2 ,
• Yujing Hu 2 ,
• Feng Qiu 2 ,
• Zhimeng Zhang 2 ,
• Kai Wang 2 ,
• Shiwei Zhao 2 ,
• Yongqiang Zhang 2 ,
• Ji Jiang 2 ,
• Yadong Xi 2 ,
• Jiashu Pu 2 ,
• Wei Zhang 2 ,
• Suzhen Wang 2 ,
• Ke Chen 2 ,
• Tianze Zhou 2 ,
• Jiarui Chen 2 ,
• Yan Song 2 ,
• Tangjie Lv 2 &
• Changjie Fan 2  

This paper presents a comprehensive review of deep learning applications in the video game industry, focusing on how these techniques can be utilized in game development, experience, and operation. As relying on computation techniques, the game world can be viewed as an integration of various complex data. This examines the use of deep learning in processing various types of game data. The paper classifies the game data into asset data, interaction data, and player data, according to their utilization in game development, experience, and operation, respectively. Specifically, this paper discusses deep learning applications in generating asset data such as object images, 3D scenes, avatar models, and facial animations; enhancing interaction data through improved text-based conversations and decision-making behaviors; and analyzing player data for cheat detection and match-making purposes. Although this review may not cover all existing applications of deep learning, it aims to provide a thorough presentation of the current state of deep learning in the gaming industry and its potential to revolutionize game production by reducing costs and improving the overall player experience.

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Created by Jason M. Allen using the generative Artificial Intelligence platform Midjourney . The painting became a news story when it won the 2022 Colorado State Fair’s annual fine art competition on 5 September, becoming one of the first AI generated images to win such a prize.

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Acknowledgements

We would like to thank Jiajun Bu and Weixun Wang for their generous and helpful discussions, as well as their constructive suggestions.

This work is supported by the Key Research and Development Program of Zhejiang Province (No. 2022C01011).

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Hu, Z., Ding, Y., Wu, R. et al. Deep learning applications in games: a survey from a data perspective. Appl Intell 53 , 31129–31164 (2023). https://doi.org/10.1007/s10489-023-05094-2

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Current approaches to simulating interactive environments with neural models include methods like Reinforcement Learning (RL) and diffusion models. Techniques such as World Models by Ha and Schmidhuber (2018) and GameGAN by Kim et al. (2020) have been developed to simulate game environments using neural networks. However, these methods face significant limitations, including high computational costs, instability over long trajectories, and poor visual quality. For instance, GameGAN, while effective for simpler games, struggles with complex environments like DOOM, often producing blurry and low-quality images. These limitations make these methods less suitable for real-time applications and restrict their utility in more demanding game simulations.

The researchers from Google and Tel Aviv University introduce GameNGen , a novel approach that utilizes an augmented version of the Stable Diffusion v1.4 model to simulate complex interactive environments, such as the game DOOM, in real-time. GameNGen overcomes the limitations of existing methods by employing a two-phase training process: first, an RL agent is trained to play the game, generating a dataset of gameplay trajectories; second, a generative diffusion model is trained on these trajectories to predict the next game frame based on past actions and observations. This approach leverages diffusion models for game simulation, enabling high-quality, stable, and real-time interactive experiences. GameNGen represents a significant advancement in AI-driven game engines, demonstrating that a neural model can match the visual quality of the original game while running interactively.

GameNGen’s development involves a two-stage training process. Initially, an RL agent is trained to play DOOM, creating a diverse set of gameplay trajectories. These trajectories are then used to train a generative diffusion model, a modified version of Stable Diffusion v1.4, to predict subsequent game frames based on sequences of past actions and observations. The model’s training includes velocity parameterization to minimize diffusion loss and optimize frame sequence predictions. To address autoregressive drift, which degrades frame quality over time, noise augmentation is introduced during training. Additionally, the researchers fine-tuned a latent decoder to improve image quality, particularly for the in-game HUD (heads-up display). The model was tested in a VizDoom environment with a dataset of 900 million frames, using a batch size of 128 and a learning rate of 2e-5.

GameNGen demonstrates impressive simulation quality, producing visuals nearly indistinguishable from the original DOOM game, even over extended sequences. The model achieves a Peak Signal-to-Noise Ratio (PSNR) of 29.43, on par with lossy JPEG compression, and a low Learned Perceptual Image Patch Similarity (LPIPS) score of 0.249, indicating strong visual fidelity. The model maintains high-quality output across multiple frames, even when simulating long trajectories, with only minimal degradation over time. Moreover, the approach shows robustness in maintaining game logic and visual consistency, effectively simulating complex game scenarios in real-time at 20 frames per second. These results underline the model’s ability to deliver high-quality, stable performance in real-time game simulations, offering a significant step forward in the use of AI for interactive environments.

GameNGen presents a breakthrough in AI-driven game simulation by demonstrating that complex interactive environments like DOOM can be effectively simulated using a neural model in real-time while maintaining high visual quality. This proposed method addresses critical challenges in the field by combining RL and diffusion models to overcome the limitations of previous approaches. With its ability to run at 20 frames per second on a single TPU while delivering visuals on par with the original game, GameNGen signifies a potential shift towards a new era in game development, where games are created and driven by neural models rather than traditional code-based engines. This innovation could revolutionize game development, making it more accessible and cost-effective.

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Google used AI to recreate Doom — and people can't tell it from the real thing

The team says they answer an important question related to the future of AI gaming

Google researchers used AI to recreate the classic first-person shooter Doom. Videos were made of the gameplay and humans were only slightly better than random chance at recognizing which were real clips and which were generated by AI.

In a recently submitted pre-print paper, the researchers presented GameNGen which they say is the first game engine powered entirely by AI that allows players to interact with the game over long stretches of time at high quality.

Using their AI game engine , the scientists recreated the game Doom at 20 frames per second. The shooter was pretty playable as the AI allowed players to attack enemies, open doors, and it also kept track of their ammo and health levels.

The researchers explained that while it may seem tempting to assume that AI game generation is just very fast AI video generation, there’s more to it than meets the eye. To pull this off they needed special type of models that wouldn’t collapse to meet the demands of generating an interactive space.

This answers an important question about AI games

The team said their work answers an important question about the future of games created by AI: Is it even possible? “In this work we demonstrate that the answer is yes,” they wrote.

We’ve already seen a demo by Nvidia of a game environment where players could interact with AI-powered NPCs but human designers created the small world it occupied. 

The Google Research team worked with Doom playing on a 320 x 240 resolution — roughly what you see when a YouTube video plays at 240p. The AI game engine was trained on 900 million frames, a task which would be made significantly more taxing if it were to be trained on one of today’s best-looking games like Horizon Forbidden West .

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What this means

But, as the researchers said, their work proves that in principle, creating an on-demand RPG is possible. As the researchers themselves pointed out, their work leads to many interesting questions. A crucial one is how AI games would be created in the first place. 

To generate enough data for them to replicate Doom, they had to create AI bots that would record their own gameplay. Using an AI game generator like GameNGen means the models need to be trained on something.

In other words, if you’re already thinking of setting up your own AI-game studio, know that to generate an AI game you need gameplay data. However, generating this data requires an existing game. If you do manage to crack this chicken-and-egg situation, then you’ve stumbled on a game-changing (and game-creating) plan.

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• 30 August 2024

Researchers built an ‘AI Scientist’ — what can it do?

• Davide Castelvecchi

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Could science be fully automated? A team of machine-learning researchers has now tried.

‘AI Scientist’, created by a team at Tokyo company Sakana AI and at academic labs in Canada and the United Kingdom, performs the full cycle of research from reading the existing literature on a problem and formulating hypothesis for new developments to trying out solutions and writing a paper. AI Scientist even does some of the job of peer reviewers and evaluates its own results.

AI Scientist joins a slew of efforts to create AI agents that have automated at least parts of the scientific process. “To my knowledge, no one has yet done the total scientific community, all in one system,” says AI Scientist co-creator Cong Lu, a machine-learning researcher at the University of British Columbia in Vancouver, Canada. The results 1 were posted on the arXiv preprint server this month.

“It’s impressive that they’ve done this end-to-end,” says Jevin West, a computational social scientist at the University of Washington in Seattle. “And I think we should be playing around with these ideas, because there could be potential for helping science.”

The output is not earth-shattering so far, and the system can only do research in the field of machine learning itself. In particular, AI Scientist is lacking what most scientists would consider the crucial part of doing science — the ability to do laboratory work . “There’s still a lot of work to go from AI that makes a hypothesis to implementing that in a robot scientist,” says Gerbrand Ceder, a materials scientist at Lawrence Berkeley National Laboratory and the University of California, Berkeley. Still, Ceder adds, “If you look into the future, I have zero doubt in mind that this is where much of science will go.”

Automated experiments

AI Scientist is based on a large language model (LLM). Using a paper that describes a machine learning algorithm as template, it starts from searching the literature for similar work. The team then employed the technique called evolutionary computation, which is inspired by the mutations and natural selection of Darwinian evolution. It proceeds in steps, applying small, random changes to an algorithm and selecting the ones that provide an improvement in efficiency.

To do so, AI Scientist conducts its own ‘experiments’ by running the algorithms and measuring how they perform. At the end, it produces a paper, and evaluates it in a sort of automated peer review. After ‘augmenting the literature’ this way, the algorithm can then start the cycle again, building on its own results.

The authors admit that the papers AI Scientists produced contained only incremental developments. Some other researchers were scathing in their comments on social media. “As an editor of a journal, I would likely desk-reject them. As a reviewer, I would reject them,” said one commenter on the website Hacker News.

West also says that the authors took a reductive view of how researchers learn about the current state of their field. A lot of what they know comes from other forms of communication, such as going to conferences or chatting to colleagues at the water cooler. “Science is more than a pile of papers,” says West. “You can have a 5-minute conversation that will be better than a 5-hour study of the literature.”

West’s colleague Shahan Memon agrees — but both West and Memon praise the authors for having made their code and results fully open. This has enabled them to analyze the AI Scientist’s results. They’ve found, for example, that it has a “popularity bias” in the choice of earlier papers it lists as references, skirting towards those with high citation counts. Memon and West say they are also looking into measuring whether AI Scientist’s choices were the most relevant ones.

Repetitive tasks

AI Scientist is, of course, not the first attempt at automating at least various parts of the job of a researcher: the dream of automating scientific discovery is as old as artificial intelligence itself — dating back to the 1950s, says Tom Hope, a computer scientist at the Allen Institute for AI based in Jerusalem. Already a decade ago, for example, the Automatic Statistician 2 was able to analyse sets of data and write up its own papers. And Ceder and his colleagues have even automated some bench work: the ‘ robot chemist ’ they unveiled last year can synthesize new materials and experiment with them 3 .

Hope says that current LLMs “are not able to formulate novel and useful scientific directions beyond basic superficial combinations of buzzwords”. Still, Ceder says that even if AI won’t able to do the more creative part of the work any time soon, it could still automate a lot of the more repetitive aspects of research. “At the low level, you’re trying to analyse what something is, how something responds. That’s not the creative part of science, but it’s 90% of what we do.” Lu says he got a similar feedback from a lot of other researchers, too. “People will say, I have 100 ideas that I don’t have time for. Get the AI Scientist to do those.”

Lu says that to broaden AI Scientist’s capabilities — even to abstract fields beyond machine learning, such as pure mathematics — it might need to include other techniques beyond language models. Recent results on solving maths problems by Google Deep Mind, for example, have shown the power of combining LLMs with techniques of ‘symbolic’ AI, which build logical rules into a system rather than merely relying on it learning from statistical patterns in data. But the current iteration is but a start, he says. “We really believe this is the GPT-1 of AI science,” he says, referring to an early large language model by OpenAI in San Francisco, California.

The results feed into a debate that is at the top of many researchers’ concerns these days, says West. “All my colleagues in different sciences are trying to figure out, where does AI fit in in what we do? It does force us to think what is science in the twenty-first century — what it could be, what it is, what it is not,” he says.

doi: https://doi.org/10.1038/d41586-024-02842-3

Lu, C., Lu, C., Lange, R. T., Foerster, J., Clune, J. & Ha, D. Preprint at arXiv https://arxiv.org/abs/2408.06292 (2024).

Ghahramani, Z. Nature 521 , 452–459 (2015).

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Szymanski, N. J. et al. Nature 624 , 86–91 (2023).

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Familiarize yourself with UP's policies on using AI . If you use a generative AI tool for writing, be transparent with your professors. Acknowledge your uses of the tool (such as editing your writing or translating words) within your paper, in a note, or in another suitable location like an appendix. 

Ethical Question:

• Should generative AIs be credited the same way as human authors? Citations are used to credit the work of other people and give readers a path to the sources used. A reader can then find and look at those sources to make their judgment on things like authority and accuracy. AI tools generate text in human language but may not accurately identify specific sources used. However, they can fabricate convincing citations to sources that don't exist. 

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Guidance for citing ChatGPT and similar AI tools is emerging while continuing to be debated ( more from APA ).

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Reference List Example:

OpenAI. (2023). ChatGPT (Mar 14 version) [Large language model]. https://chat.openai.com/chat

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Example: (OpenAI, 2023)

In-Text Quote:

Provide the prompt you used and any portion of the relevant text that was generated in the text of your paper: 

When prompted with “Is the left brain right brain divide real or a metaphor?” the ChatGPT-generated text indicated that although the two brain hemispheres are somewhat specialized, “the notation that people can be characterized as ‘left-brained’ or ‘right-brained’ is considered to be an oversimplification and a popular myth” (OpenAI, 2023).

Guidance for citing ChatGPT and similar AI tools is emerging while continuing to be debated ( more from MLA ).

"Prompt." Tool Name, Version, Producer, Date,   URL.

Works Cited List Example:

“Describe the symbolism of the green light in the book The Great Gatsby by F. Scott Fitzgerald” prompt. ChatGPT , 13 Feb. version, OpenAI, 8 Mar. 2023, chat.openai.com/chat.

"What is Carrie Mae Weems' most influential work and what are its themes?" prompt. Gemini . 8 Feb. 2024 version, Google, 16 Feb. 2024, https://gemini.google.com/app.

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Examples: ("Describe the symbolism") 

("What is Carrie")

Guidance for citing ChatGPT and similar AI tools is emerging while continuing to be debated   ( more from Chicago ).

For student papers or research articles, cite the AI language tool as a footnote. Don't cite AI tools in a bibliography or reference list unless you can provide a public link to the conversation. 

Footnote example (if information about the prompt has been included within the text of your paper):

1. Text generated by ChatGPT, March 7, 2023, OpenAI, https://chat.openai.com/chat. 

Footnote example (including information about the prompt):

1. ChatGPT, response to "Explain how to make pizza dough from common household ingredients," March 7, 2023, OpenAI, https://chat.openai.com/chat. 

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Department of Education

New research highlights opportunities and challenges of ai chatbots in higher education.

The new study is a crucial step in consolidating knowledge about AI in education and in particular AI chatbots, urging caution against overly optimistic or pessimistic views, and calling for more grounded research approaches to better understand the true impact of AI chatbots in the classroom and on teaching and learning practices. 

The study examines the emerging research area of Artifical Intelligence (AI) chatbots in Higher Education, focusing specifically on empirical studies conducted since the release of Chat GPT. The review includes 23 research articles published between December 2022 and December 2023 exploring the use of AI chatbots in Higher Education settings.

The study takes a three-pronged approach to the empirical data; examining the state of the emerging field of AI chatbots in Higher Education, the theories of learning used in the empirical studies and it scrutinizes the discourses of AI in Higher Education framing the latest empirical work on AI chatbots.

Study reveals gaps and overhyped expectations

Many studies reviewed did not use established learning theories to analyze AI chatbots, indicating a gap in how these tools are being understood from a learning perspective. This suggests that the empirical work does not yet offer insights into the mechanisms of learning that chatbots may facilitate.

The study highlights a tendency in the literature to use exaggerated language in both dystopian and utopian manner, with some claims about AI chatbots' potential being unsupported by the empirical evidence.

Lack of consistent framework

The research also shows that while AI chatbots are being explored across various disciplines, there is no consistent framework for understanding their effects on education. Replication studies are needed to determine how students engage with chatbots and how such interaction may affect their learning. Teachers are skeptical to the value AI chatbots bring to teaching and learning practices.

Link to the study 

Generative AI chatbots in higher education: a review of an emerging research area Springer (2024), Open Access

Cormac McGrath , Associate Professor, Department of Education, Stockholm University.

Alexandra Farazouli , PhD student, Department of Education, Stockholm University.

Teresa Cerratto-Pargman , professor of Human-machine interaction, Department of Computer and Systems Sciences, Stockholm University.

The Research Group on Higher Education Learning Practices at Stockholm University engages in theoretical and empirical research on different aspects of higher education.

Last updated: September 3, 2024

Source: IPD

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The convergence of AI and immersive environments

The convergence of AI and immersive environments, including the metaverse, is not just a technological trend but a paradigm shift in the digital landscape, in how we interact with digital environments and each other.

This technological synergy promises to revolutionize industries, transform social interactions, and create new economic opportunities. From AI-generated content that populates vast virtual worlds to intelligent avatars that enhance our digital presence, the possibilities are both exciting and profound.

This combination is blurring the lines between the physical and digital worlds, creating immersive experiences that were once thought impossible. For businesses, this convergence presents both opportunities and challenges. It offers new channels for customer engagement, novel business models, and unprecedented ways to collaborate and create value. But challenges need to be addressed: technical hurdles around scalability, interoperability, security, and energy efficiency need to be overcome. Equally important are the ethical and social considerations, including privacy concerns and issues of digital inequality.

This Point of View looks at how AI is helping to democratize content creation in the metaverse, discusses how AI agents improve user experiences and interactions in virtual spaces and explore how AI-augmented spatial computing change the way we see and use digital information in our physical environment.

About Capgemini Immersive Lab

We are poised at the start of a new digital experience – the metaverse.

Accelerating the convergence of the physical and digital worlds, the metaverse offers a multitude of new ways for us all to experience things differently – whether at work, home, or play. The metaverse represents the next stage of the Internet, enabling billions of people to connect in virtual worlds, experiencing real-life feelings. 

These public and private decentralized 3D virtual spaces deliver online digital CX (customer experience) and EX (employee experience), generated by a sense of presence and space, speech, gesture, touch and even smell. Across sectors, organizations are eagerly talking about what the metaverse could mean for their marketing, their operations, and their employees.

But the metaverse is not just spaces to socialize, play games, and shop. Perhaps the platform’s greatest longer-term value is that it gives businesses the potential to build personalized and sustained relationships with customers, and develop immersive experiences for employees, helping them to work more efficiently, collaborate more effectively with colleagues, and attract and retain scarce talent.

Now is the time for businesses to understand the practical application of the metaverse – and how to explore its future potential. Experience things differently.

This week: the arXiv Accessibility Forum

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Computer Science > Artificial Intelligence

Title: ai in human-computer gaming: techniques, challenges and opportunities.

Abstract: With breakthrough of the AlphaGo, human-computer gaming AI has ushered in a big explosion, attracting more and more researchers all around the world. As a recognized standard for testing artificial intelligence, various human-computer gaming AI systems (AIs) have been developed such as the Libratus, OpenAI Five and AlphaStar, beating professional human players. The rapid development of human-computer gaming AIs indicate a big step of decision making intelligence, and it seems that current techniques can handle very complex human-computer games. So, one natural question raises: what are the possible challenges of current techniques in human-computer gaming, and what are the future trends? To answer the above question, in this paper, we survey recent successful game AIs, covering board game AIs, card game AIs, first-person shooting game AIs and real time strategy game AIs. Through this survey, we 1) compare the main difficulties among different kinds of games and the corresponding techniques utilized for achieving professional human level AIs; 2) summarize the mainstream frameworks and techniques that can be properly relied on for developing AIs for complex human-computer gaming; 3) raise the challenges or drawbacks of current techniques in the successful AIs; and 4) try to point out future trends in human-computer gaming AIs. Finally, we hope this brief review can provide an introduction for beginners, and inspire insights for researchers in the field of AI in human-computer gaming.

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Speaker 1: There are so many AI writing apps, but only a handful that are specifically designed for researchers and academics. And here are my favorites. The first one you need to know about is PaperPal. Now with PaperPal, when you sort of like sign up to their subscription, you also get all of these other things, but it's important that they do have kind of a semi-writing service, okay? This means that they can provide structures, they can provide sort of hints, but they don't explicitly write stuff for you. But I'm gonna show you why I think this is an AI writer, because when you click here onto new web document, it'll take you to this normal like word processing layout. And here it says start typing or pasting text. Now PaperPal really is about editing, but I'll show you across here where we've got all these other options. All these other options mean that we have the ability to create templates. And when we have templates, we've got outlines. Outlines for a research article, case report, essay, statement of purpose. And this is where the AI writer component really, really helps academics. Look at all of the different things you can do here. So let's say I wanna start a research article. I click up here, I say I want the introduction, and I'm in these areas. So I'm in physical sciences. And I wanna say, okay, I wanna start a research article about transparent electrodes. Okay, so two words. Let's see what it does with two words. Ensure that your text contains a minimum of 10 words. Okay, so that's what I really want. Eight, ooh, novel materials. There we are, gotcha. So generate there. And so one thing I like about this is that it doesn't do all of the stuff for you. It's an important part of the writing process, which is the first step of creating a structure from which you can sort of like build upon. So here we are. This is all of the stuff that it sort of like said I should include in the introduction. So let's just write introduction here as a little placeholder, donk. And then underneath, I'm gonna say insert. So here we are. This is the introduction. So it's saying I need something like talking about how transparent electrodes have gained significant attention in recent years. Then the development of transparent electrodes is crucial for the advancement of OPDV devices, flexible displays, blah, blah, blah, blah, blah. All of that stuff is really important. This is the structure that you should follow when writing an academic introduction. And you can see it's just giving me little options to build upon. And so here it says, what are the properties and performance of transparent electrode materials used in OPD devices and flexible displays? So here it's asking me what is my actual research question, what my aim and objective is, and my hypothesis is at the end of that. So this is how I would use this as an AI writer. You can see you've got a load of other options as well. We've got case reports, essays, statement of purpose. We can brainstorm. So here it says I need something. I don't know quite what I need. There's nothing more intimidating than a blank page. So here we've got all of the brainstorming option. How can paperpower help? Okay, or devices, there we go. Okay, so brainstorm. Now it's going to go away and it's going to brainstorm ideas for me. And here we are. This is where it's a great writing tool because it doesn't give you like the text because let's face it, we can all be a little bit lazy and if it just spills out text, it's so easy just to copy and paste it across and be like, ooh, I'm done. You can even convince yourself that you're done, even though you know you should go through it, read it, put in citations, references, paraphrase it for your own work, all of that sort of stuff, but we don't because we're lazy. So here, this is sort of like not allowing you to cheat. So here we are. That's how we can use PaperPal as an AI writer for academia. We can also see we've got title, abstract, keywords, summary, study highlights, email, and also if you're emailing a particular journal, you've got all of the options down here. Absolutely love it. I would use this as a first port of call for creating the template that I would use to write any sort of like academic document and even emails to a journal. So that's PaperPal. What's next? You'll like it. The next tool is Jenny.ai. Now, Jenny.ai has gone through a load of iterations over the last year. They've really cleaned up the interface and I love what they're doing at the moment. Here is the interface. When you log in, you click create a new document. This is what we've got. It says, what are you writing today? And it asks you to give you a prompt and it will tell you here whether or not it's a weak prompt or a good prompt. So let's start writing and see if we can get it to sort of like kick out something that's incredibly useful for us to build upon. Five hours later. People. Okay, great prompt. Thanks, Jenny. Here we are. Start writing. Let's review. The attractiveness of beards on YouTube. And here we are. This is where now Jenny sort of like starts to come into its own because it starts spilling out stuff and saying, do you want this? Do you want this? Do you want this? It can be a little bit overwhelming in my experience but here we are. We can just accept that and it's already given us a reference down here and it is a really great way. There we are. Now it's given me more and I can accept that. It's just a way of like putting you in the driver's seat by giving you options about what to include and what not to include. Once we sort of like have a look at this, there are loads of AI commands down here. We've got continue writing, write introduction, write conclusion, write opposing arguments, write more with more depth and we've got AI chat. Now one thing I would do over here first of all before I do anything is ask AI chat that asked Jenny to generate an outline for me. Okay, here is the output. Here's everything I would want to put into this particular document. So I'm going to copy and paste it here and then you can see I've then now got a structure. Introduction A and then I've got the role of YouTube in shaping beauty and grooming standards and then I've got the sort of different sort of ways that I could structure it. So that A was meant to be there. Overview of the importance and then saying B, the concept. So I like to sort of like just put this into bullet points that I can then build out on which is just such a great way to start yourself thinking not only about the structure but also how the story of a particular bit of academic writing will flow. You start broad and you get narrower and narrower. So the fact that this spills out information is really important but getting, you know, asked Jenny to create a structure is just as important in those early stages and you can see I've already now got a structure that I can build upon. Continue writing. Jenny is writing. There we are, here we go. So it's going, it's going. So with the settings you do have the ability to turn it into a tool so that you are working at your best. I would probably initially to turn off autocomplete and just work on that broader structure and then putting in my own words and then using AI commands to get all of the different sort of like enrichments to my text and then also using the cite option. This cite option is really great because it means that you don't have to go to something like size space or anything like that to, oh, no, that's not what I want. Perception of beers in different culture. Now it's saying State University of New York, learning how to apply lipstick and set their hair in curlers from their mothers. What most boys grew up learning. Okay, well that's kind of on track but not quite so. You can add citations, you can do stuff that just makes writing so much easier and dare I say it, almost fun. Another tool you should know about is Yomoo. Yomoo AI is very much like Jenny AI. Here we can create a new document and here's it's slightly different and it does ask you to create a structure as you are sort of like setting up your document which I really like. Oh, why can't I spell today? Okay, there we are. And then I've got outline and ideas. Yes, that's exactly what I want. Oh, I can only click one. What do I want, what do I want? Oh, an outline. So now it's going away and it's creating that first draft structure for me and that was very, very quick. You can see that it's quite a significant amount of text and structure. The science behind beard smell and its impact on attractiveness. Culture and historical perspective on beards and their smells. It stayed very, very much on brief and it's given me a chance to kind of get that structure in place early on. And so now when I start writing, I can see document AI, write an introduction. So Yomoo is writing. Now it's writing an introduction where it says this. You can see it's quite a big amount of text really because Jenny was sort of like sentence by sentence by sentence and it can be hard to kind of like keep track of the overall theme of what you're writing when it's just spilling stuff out. But here it's got much bigger sort of like text blocks that it's generating. And overall I think it's done a really fantastic job at sort of like that first draft. So here I like it. I haven't seen anywhere where I can put in references like in Jenny, but I can export into Latex, Microsoft Word where I can add my own references later using Zotero or Mendeley or something like that. And you have got all of the different kind of options for sharing, saving, and creating a custom style. So overall, this is a great tool if you just want to sort of like get that first draft out of the way and then you can work on refining it with other tools a little bit after. If you like this video, go check out this one where I talk about unleashing the power of PaperPal AI for academic writing and increasing the success rates of your papers when you submit them to journals. Go check it out now. Thank you.