Crypto exchange Binance announced the relaunch of the Binance wallet with the goal of bringing better accessibility to the Web3 world.

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Coca-Cola drew plenty of criticism for rolling out an uninspired and lazily AI-generated holiday ad. Redditors made a much better version.

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Collaboration between finance, technology, and sustainability leaders is essential to sustainable value creation. But when these leaders have different objectives and approaches, it can be tough to make the long-term match.

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When you’re just starting out with programming, choosing the right computer can make a significant difference in your learning experience. As you embark on your coding journey, one of the first…

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Both chipmakers are on track to deliver outstanding growth thanks to the robust demand for AI chips.

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DAYTON, Ohio (WDTN)– November marks National ‘Thank a Business Month’ and turns out, one of the best ways to do that is with the Better Business Bureau! Communications VP Sheri Sword joined Living Dayton on Thursday with tips for viewers at home. Reputation can mean everything, especially ...

Celebrate ‘Thank a Business Month’ with BBB | WDTN.com

Which of these AI-driven stocks is the better investment right now?

Better Artificial Intelligence Stock: Palantir vs. UiPath

Better

better

Authors:
(1) Shehan Munasinghe, Mohamed bin Zayed University of AI and Equal Contribution;
(2) Rusiru Thushara, Mohamed bin Zayed University of AI and Equal Contribution;
(3) Muhammad Maaz, Mohamed bin Zayed University of AI;
(4) Hanoona Abdul Rasheed, Mohamed bin Zayed University of AI;
(5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University;
(6) Mubarak Shah, University of Central Florida;
(7) Fahad Khan, Mohamed bin Zayed University of AI and Linköping University. Editor's Note: This is Part 6 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Table of Links Abstract and 1 Introduction
2. Related Works


PG-Video-LLaVA


3.1. Overview
3.2. Architecture


Experiments


4.1. Implementation Details
4.2. Stronger Baseline
4.3. Spatial Grounding in Videos
4.4. Zero-Shot Visual Question Answering
5. Conclusion and References Supplementary Material A. Audio Modality Integration
B. Visual Grounding: Quantitative Evaluation
C. Qualitative Results for Visual Grounding
D. Quantitative Evaluations of Video-based Conversation Performance 4.2. Stronger Baseline This section provides an overview of the quantitative evaluations conducted to determine the effects of the strengthened baseline on PG-Video-LLaVA. To evaluate the impact of the enhanced baseline on PG-Video-LLaVA, we apply the benchmarking framework from Video-ChatGPT[22]. This framework measures performance on several axes critical for video-based conversational agents, including correctness of information, detail orientation, contextual understanding, temporal understanding, and consistency. In order to facilitate a reliable and reproducible evaluation, we have updated our assessment pipeline by replacing GPT-3.5-Turbo with Vicuna-13b-v1.5. This adjustment addresses the limitations in reproducibility inherent to the closed-source nature of GPT-3.5-Turbo. Subsequently, we have re-assessed both PG-Video-LLaVA and other recent models to ensure a fair and consistent comparison. The results shown in Table 1 demonstrate that PG-Video-LLaVA outperforms the foundational Video-ChatGPT model and exhibits superior performance when compared to other recent contributions in the domain. Following the quantitative assessment, the qualitative results in Figure 3 indicate the enhanced baseline’s impact on PG-Video-LLaVA’s performance. The PG-Video-LLaVA (13B) model exhibits improved accuracy in the information presented, a deeper level of descriptive detail, and a stronger alignment with the context and temporal progression of the videos. This advancement is particularly noticeable in the precise depiction of the child’s engagement with their surroundings and the giraffe’s behaviour, indicating a refined interpretation of both the activities and their settings. These qualitative insights are consistent with the quantitative results, highlighting the augmented baseline’s role in advancing PG-Video-LLaVA’s capacity in video understanding. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Shehan Munasinghe, Mohamed bin Zayed University of AI and Equal Contribution; (2) Rusiru Thushara, Mohamed bin Zayed University of AI and Equal Contribution; (3) Muhammad Maaz, Mohamed bin Zayed University of AI; (4) Hanoona Abdul Rasheed, Mohamed bin Zayed University of AI; (5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University; (6) Mubarak Shah, University of Central Florida; (7) Fahad Khan, Mohamed bin Zayed University of AI and Linköping University. Authors: Authors: (1) Shehan Munasinghe, Mohamed bin Zayed University of AI and Equal Contribution; (2) Rusiru Thushara, Mohamed bin Zayed University of AI and Equal Contribution; (3) Muhammad Maaz, Mohamed bin Zayed University of AI; (4) Hanoona Abdul Rasheed, Mohamed bin Zayed University of AI; (5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University; (6) Mubarak Shah, University of Central Florida; (7) Fahad Khan, Mohamed bin Zayed University of AI and Linköping University. Editor's Note: This is Part 6 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Editor's Note: This is Part 6 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Editor's Note: This is Part 6 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Editor's Note: This is Part 6 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Table of Links Abstract and 1 Introduction 2. Related Works PG-Video-LLaVA 3.1. Overview 3.2. Architecture Experiments 4.1. Implementation Details 4.2. Stronger Baseline 4.3. Spatial Grounding in Videos 4.4. Zero-Shot Visual Question Answering 5. Conclusion and References Abstract and 1 Introduction Abstract and 1 Introduction 2. Related Works 2. Related Works PG-Video-LLaVA PG-Video-LLaVA PG-Video-LLaVA 3.1. Overview 3.1. Overview 3.2. Architecture 3.2. Architecture Experiments Experiments Experiments 4.1. Implementation Details 4.1. Implementation Details 4.2. Stronger Baseline 4.2. Stronger Baseline 4.3. Spatial Grounding in Videos 4.3. Spatial Grounding in Videos 4.4. Zero-Shot Visual Question Answering 4.4. Zero-Shot Visual Question Answering 5. Conclusion and References 5. Conclusion and References Supplementary Material Supplementary Material A. Audio Modality Integration B. Visual Grounding: Quantitative Evaluation C. Qualitative Results for Visual Grounding D. Quantitative Evaluations of Video-based Conversation Performance A. Audio Modality Integration A. Audio Modality Integration B. Visual Grounding: Quantitative Evaluation B. Visual Grounding: Quantitative Evaluation C. Qualitative Results for Visual Grounding C. Qualitative Results for Visual Grounding D. Quantitative Evaluations of Video-based Conversation Performance D. Quantitative Evaluations of Video-based Conversation Performance 4.2. Stronger Baseline This section provides an overview of the quantitative evaluations conducted to determine the effects of the strengthened baseline on PG-Video-LLaVA. To evaluate the impact of the enhanced baseline on PG-Video-LLaVA, we apply the benchmarking framework from Video-ChatGPT[22]. This framework measures performance on several axes critical for video-based conversational agents, including correctness of information, detail orientation, contextual understanding, temporal understanding, and consistency. In order to facilitate a reliable and reproducible evaluation, we have updated our assessment pipeline by replacing GPT-3.5-Turbo with Vicuna-13b-v1.5. This adjustment addresses the limitations in reproducibility inherent to the closed-source nature of GPT-3.5-Turbo. Subsequently, we have re-assessed both PG-Video-LLaVA and other recent models to ensure a fair and consistent comparison. The results shown in Table 1 demonstrate that PG-Video-LLaVA outperforms the foundational Video-ChatGPT model and exhibits superior performance when compared to other recent contributions in the domain. Following the quantitative assessment, the qualitative results in Figure 3 indicate the enhanced baseline’s impact on PG-Video-LLaVA’s performance. The PG-Video-LLaVA (13B) model exhibits improved accuracy in the information presented, a deeper level of descriptive detail, and a stronger alignment with the context and temporal progression of the videos. This advancement is particularly noticeable in the precise depiction of the child’s engagement with their surroundings and the giraffe’s behaviour, indicating a refined interpretation of both the activities and their settings. These qualitative insights are consistent with the quantitative results, highlighting the augmented baseline’s role in advancing PG-Video-LLaVA’s capacity in video understanding. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

AI Just Got Better at Watching Videos

Authors:
(1) Albert Gu, Machine Learning Department, Carnegie Mellon University and with equal contribution;
(2) Tri Dao, Department of Computer Science, Princeton University and with equal contribution. Table of Links Abstract and 1 Introduction 2 State Space Models 3 Selective State Space Models and 3.1 Motivation: Selection as a Means of Compression 3.2 Improving SSMs with Selection 3.3 Efficient Implementation of Selective SSMs 3.4 A Simplified SSM Architecture 3.5 Properties of Selection Mechanisms 3.6 Additional Model Details 4 Empirical Evaluation and 4.1 Synthetic Tasks 4.2 Language Modeling 4.3 DNA Modeling 4.4 Audio Modeling and Generation 4.5 Speed and Memory Benchmarks 4.6 Model Ablations 5 Discussion 6 Conclusion and References A Discussion: Selection Mechanism B Related Work C Mechanics of Selective SSMs D Hardware-aware Algorithm For Selective SSMs E Experimental Details and Additional Results 3 Selective State Space Models We motivate our selection mechanism using intuition from synthetic tasks (Section 3.1), then explain how to incorporate this mechanism into state space models (Section 3.2). The resulting time-varying SSMs cannot use convolutions, presenting a technical challenge of how to compute them efficiently. We overcome this with a hardware-aware algorithm that exploits the memory hierarchy on modern hardware (Section 3.3). We then describe a simple SSM architecture without attention or even MLP blocks (Section 3.4). Finally, we discuss some additional properties of selection mechanisms (Section 3.5). 3.1 Motivation: Selection as a Means of Compression We argue that a fundamental problem of sequence modeling is compressing context into a smaller state. In fact, we can view the tradeoffs of popular sequence models from this point of view. For example, attention is both effective and inefficient because it explicitly does not compress context at all. This can be seen from the fact that autoregressive inference requires explicitly storing the entire context (i.e. the KV cache), which directly causes the slow linear-time inference and quadratic-time training of Transformers. On the other hand, recurrent models are efficient because they have a finite state, implying constant-time inference and linear-time training. However, their effectiveness is limited by how well this state has compressed the context. To understand this principle, we focus on two running examples of synthetic tasks (Figure 2). • The Selective Copying task modifies the popular Copying task (Arjovsky, Shah, and Bengio 2016) by varying the position of the tokens to memorize. It requires content-aware reasoning to be able to memorize the relevant tokens (colored) and filter out the irrelevant ones (white). • The Induction Heads task is a well-known mechanism hypothesized to explain the majority of in-context learning abilities of LLMs (Olsson et al. 2022). It requires context-aware reasoning to know when to produce the correct output in the appropriate context (black). In summary, the efficiency vs. effectiveness tradeoff of sequence models is characterized by how well they compress their state: efficient models must have a small state, while effective models must have a state that contains all necessary information from the context. In turn, we propose that a fundamental principle for building sequence models is selectivity: or the context-aware ability to focus on or filter out inputs into a sequential state. In particular, a selection mechanism controls how information propagates or interacts along the sequence dimension (see Section 3.5 for more discussion). This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Albert Gu, Machine Learning Department, Carnegie Mellon University and with equal contribution; (2) Tri Dao, Department of Computer Science, Princeton University and with equal contribution. Authors: Authors: (1) Albert Gu, Machine Learning Department, Carnegie Mellon University and with equal contribution; (2) Tri Dao, Department of Computer Science, Princeton University and with equal contribution. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 State Space Models 2 State Space Models 3 Selective State Space Models and 3.1 Motivation: Selection as a Means of Compression 3 Selective State Space Models and 3.1 Motivation: Selection as a Means of Compression 3.2 Improving SSMs with Selection 3.2 Improving SSMs with Selection 3.3 Efficient Implementation of Selective SSMs 3.3 Efficient Implementation of Selective SSMs 3.4 A Simplified SSM Architecture 3.4 A Simplified SSM Architecture 3.5 Properties of Selection Mechanisms 3.5 Properties of Selection Mechanisms 3.6 Additional Model Details 3.6 Additional Model Details 4 Empirical Evaluation and 4.1 Synthetic Tasks 4 Empirical Evaluation and 4.1 Synthetic Tasks 4.2 Language Modeling 4.2 Language Modeling 4.3 DNA Modeling 4.3 DNA Modeling 4.4 Audio Modeling and Generation 4.4 Audio Modeling and Generation 4.5 Speed and Memory Benchmarks 4.5 Speed and Memory Benchmarks 4.6 Model Ablations 4.6 Model Ablations 5 Discussion 5 Discussion 6 Conclusion and References 6 Conclusion and References A Discussion: Selection Mechanism A Discussion: Selection Mechanism B Related Work B Related Work C Mechanics of Selective SSMs C Mechanics of Selective SSMs D Hardware-aware Algorithm For Selective SSMs D Hardware-aware Algorithm For Selective SSMs E Experimental Details and Additional Results E Experimental Details and Additional Results 3 Selective State Space Models We motivate our selection mechanism using intuition from synthetic tasks (Section 3.1), then explain how to incorporate this mechanism into state space models (Section 3.2). The resulting time-varying SSMs cannot use convolutions, presenting a technical challenge of how to compute them efficiently. We overcome this with a hardware-aware algorithm that exploits the memory hierarchy on modern hardware (Section 3.3). We then describe a simple SSM architecture without attention or even MLP blocks (Section 3.4). Finally, we discuss some additional properties of selection mechanisms (Section 3.5). 3.1 Motivation: Selection as a Means of Compression We argue that a fundamental problem of sequence modeling is compressing context into a smaller state. In fact, we can view the tradeoffs of popular sequence models from this point of view. For example, attention is both effective and inefficient because it explicitly does not compress context at all. This can be seen from the fact that autoregressive inference requires explicitly storing the entire context (i.e. the KV cache), which directly causes the slow linear-time inference and quadratic-time training of Transformers. On the other hand, recurrent models are efficient because they have a finite state, implying constant-time inference and linear-time training. However, their effectiveness is limited by how well this state has compressed the context. To understand this principle, we focus on two running examples of synthetic tasks (Figure 2). • The Selective Copying task modifies the popular Copying task (Arjovsky, Shah, and Bengio 2016) by varying the position of the tokens to memorize. It requires content-aware reasoning to be able to memorize the relevant tokens (colored) and filter out the irrelevant ones (white). • The Induction Heads task is a well-known mechanism hypothesized to explain the majority of in-context learning abilities of LLMs (Olsson et al. 2022). It requires context-aware reasoning to know when to produce the correct output in the appropriate context (black). In summary, the efficiency vs. effectiveness tradeoff of sequence models is characterized by how well they compress their state: efficient models must have a small state, while effective models must have a state that contains all necessary information from the context. In turn, we propose that a fundamental principle for building sequence models is selectivity: or the context-aware ability to focus on or filter out inputs into a sequential state. In particular, a selection mechanism controls how information propagates or interacts along the sequence dimension (see Section 3.5 for more discussion). This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Why Compressing Information Helps AI Work Better

Table of Links Abstract and 1. Introduction Background and Related Work


Threat Model


Robust Style Mimicry


Experimental Setup


Results
6.1 Main Findings: All Protections are Easily Circumvented
6.2 Analysis


Discussion and Broader Impact, Acknowledgements, and References A. Detailed Art Examples B. Robust Mimicry Generations C. Detailed Results D. Differences with Glaze Finetuning E. Findings on Glaze 2.0 F. Findings on Mist v2 G. Methods for Style Mimicry H. Existing Style Mimicry Protections I. Robust Mimicry Methods J. Experimental Setup K. User Study L. Compute Resources D Differences with Glaze Finetuning In Section 4.1 and Figure 2, we discussed the brittleness of Glaze protections against small changes in the finetuning script. We also found our finetuning setup to be better at baseline style mimicry from unprotected art (see Figure 19). Authors:
(1) Robert Honig, ETH Zurich (robert.hoenig@inf.ethz.ch);
(2) Javier Rando, ETH Zurich (javier.rando@inf.ethz.ch);
(3) Nicholas Carlini, Google DeepMind;
(4) Florian Tramer, ETH Zurich (florian.tramer@inf.ethz.ch). This paper is available on arxiv under CC BY 4.0 license. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Background and Related Work Threat Model Robust Style Mimicry Experimental Setup Results
6.1 Main Findings: All Protections are Easily Circumvented
6.2 Analysis Discussion and Broader Impact, Acknowledgements, and References Background and Related Work Background and Related Work Background and Related Work Threat Model Threat Model Threat Model Robust Style Mimicry Robust Style Mimicry Robust Style Mimicry Experimental Setup Experimental Setup Experimental Setup Results 6.1 Main Findings: All Protections are Easily Circumvented 6.2 Analysis Results Results 6.1 Main Findings: All Protections are Easily Circumvented 6.1 Main Findings: All Protections are Easily Circumvented 6.2 Analysis 6.2 Analysis Discussion and Broader Impact, Acknowledgements, and References Discussion and Broader Impact, Acknowledgements, and References Discussion and Broader Impact, Acknowledgements, and References A. Detailed Art Examples A. Detailed Art Examples B. Robust Mimicry Generations B. Robust Mimicry Generations C. Detailed Results C. Detailed Results D. Differences with Glaze Finetuning D. Differences with Glaze Finetuning E. Findings on Glaze 2.0 E. Findings on Glaze 2.0 F. Findings on Mist v2 F. Findings on Mist v2 G. Methods for Style Mimicry G. Methods for Style Mimicry H. Existing Style Mimicry Protections H. Existing Style Mimicry Protections I. Robust Mimicry Methods I. Robust Mimicry Methods J. Experimental Setup J. Experimental Setup K. User Study K. User Study L. Compute Resources L. Compute Resources D Differences with Glaze Finetuning In Section 4.1 and Figure 2, we discussed the brittleness of Glaze protections against small changes in the finetuning script. We also found our finetuning setup to be better at baseline style mimicry from unprotected art (see Figure 19). Authors: (1) Robert Honig, ETH Zurich (robert.hoenig@inf.ethz.ch); (2) Javier Rando, ETH Zurich (javier.rando@inf.ethz.ch); (3) Nicholas Carlini, Google DeepMind; (4) Florian Tramer, ETH Zurich (florian.tramer@inf.ethz.ch). Authors: Authors: (1) Robert Honig, ETH Zurich (robert.hoenig@inf.ethz.ch); (2) Javier Rando, ETH Zurich (javier.rando@inf.ethz.ch); (3) Nicholas Carlini, Google DeepMind; (4) Florian Tramer, ETH Zurich (florian.tramer@inf.ethz.ch). This paper is available on arxiv under CC BY 4.0 license. This paper is available on arxiv under CC BY 4.0 license. available on arxiv available on arxiv

The Key to Better Art Style Mimicry

Hey Hackers, Hidonix has been nominated in HackerNoon's annual Startups of The Year awards in Los Angeles, California. Please vote for us here: https://hackernoon.com/startups/north-america/north-america-los-angeles-ca-usa?stup=66f3161b6ea4677e23d79637&embedable=true Read more about us below to understand why we deserve your vote. Meet Hidonix At first glance, you might think we’re just another software company. But we’re not. We’re a deep tech company dedicated to investing in R&D across groundbreaking technologies: Extended Reality, Computer Vision, Artificial Intelligence, Spatial Intelligence, Robotics, and IoT. Our mission is to push the boundaries of these technologies and integrate them into innovative, in-house platforms that we provide to various industries, including museums, congress venues, malls, hospitals, and schools. The best way to visualize who we are is through our lab—a space where we experiment with these technologies to deliver solutions that have never been achieved before. https://youtu.be/FKIl_0ua2j0?embedable=true How Hidonix achieved product-market fit One of the most innovative aspects of Hidonix, and something both our customers and investors deeply value, is the versatility of our technologies. While they are adaptable across many verticals, they are also tailored to meet the specific needs of the industries we target. We achieve this by embedding our technologies into purpose-built products designed specifically for each market. For example, our Computer Vision algorithms enable museums to deliver detailed information about their collections through a simple scan of the artworks. Meanwhile, in the education sector, we use the same cutting-edge algorithms to perform advanced facial recognition, helping schools prevent potential threats and ensure safety. The evolution of Hidonix Hidonix began in the museum field, where we successfully introduced innovative, interactive solutions to attract and engage new visitors. At the same time, we empowered internal staff with an integrated platform offering over 60 services to streamline their daily operations. These include managing artwork loans and preservation, digitizing collections, and building a comprehensive CRM. Initially, our technologies were designed specifically for museums. However, we soon realized that the same technologies could be adapted to other industries. Our indoor navigation system, for example, proved highly versatile. This discovery led us to explore and address the unique needs of various verticals by tailoring our algorithms to fit specific business requirements. This process transformed us from a small company in Italy into a financially stable organization with a team of 30+ professionals spread across Europe and the U.S., where our headquarters are now located. Milestones and achievements We’re proud of the many projects that demonstrate our expertise and innovation. Among the highlights are our collaborations with the Venice Biennale, where we supported the Venice Pavilion during the challenging COVID-19 period. Our solutions helped welcome, educate, and entertain visitors at a time when cultural initiatives struggled to engage audiences without physical attendance. Another notable achievement is our work with one of the largest convention centers in Europe. By implementing our navigation feature, we not only enhanced the visitor experience but also created new revenue streams for exhibitors and event organizers. Of course, there’s still much more to achieve. We’re currently focused on exciting U.S. projects that address critical issues like safety and security in schools. At Hidonix, we’re committed to continuous R&D, confident in our ability to make a meaningful impact across diverse sectors. Lessons learned When working with advanced technologies, we’ve realized that the market isn’t always immediately ready to embrace them. It takes time for people to understand how to best use new solutions and become familiar with innovative concepts. Not all of us are Steve Jobs—convincing others to believe in your vision can be challenging at first. To all the innovators out there: stay consistent and don’t be discouraged by initial feedback. Keep pushing forward, and if you’re truly making a real impact, people will recognize it eventually. What Startups of The Year means to us We are incredibly proud to be nominated by the community—it has strengthened our belief in what we’re building. As a team of young and talented individuals, we’re often amazed by what we can achieve when we think outside the box, driven by the hope that our technologies will make a meaningful impact on society, not just generate profits. Winning this competition would be an incredible honor and a well-deserved recognition for every team member who has put in unparalleled effort into our mission. It would also help us amplify our message about innovative ways to use technology in fields that urgently need advancement for the greater good. Conclusion Being part of the "Startups of the Year" journey is a humbling and inspiring experience for us. It reinforces our commitment to developing innovative technologies that create real value and impact across industries. This recognition, whether we win or not, fuels our passion to keep pushing boundaries, thinking creatively, and striving for excellence. We are deeply grateful for the support of our community, our talented team, and everyone who believes in our mission. Together, we’re not just building a company—we’re shaping a future where technology truly benefits society! Vote for us today! Welcome to HackerNoon’s Startups of The Year 2024 interview series, spotlighting startups from around the world that are changing the way we live, one solution at a time. If you’ve been nominated, create a brand profile and answer these questions here.
Want to nominate a startup? Follow the steps here.Looking for inspiration? Check out how other startups responded to last year’s Startups of The Year interview questions and examples of company responses here, here, and here. Wanna use this template? Click here. Hey Hackers, Hidonix has been nominated in HackerNoon's annual Startups of The Year awards in Los Angeles, California. Please vote for us here: Please vote for us here: https://hackernoon.com/startups/north-america/north-america-los-angeles-ca-usa?stup=66f3161b6ea4677e23d79637&embedable=true https://hackernoon.com/startups/north-america/north-america-los-angeles-ca-usa?stup=66f3161b6ea4677e23d79637&embedable=true Read more about us below to understand why we deserve your vote. Meet Hidonix At first glance, you might think we’re just another software company. But we’re not. We’re a deep tech company dedicated to investing in R&D across groundbreaking technologies: Extended Reality, Computer Vision, Artificial Intelligence, Spatial Intelligence, Robotics, and IoT. Our mission is to push the boundaries of these technologies and integrate them into innovative, in-house platforms that we provide to various industries, including museums, congress venues, malls, hospitals, and schools. The best way to visualize who we are is through our lab—a space where we experiment with these technologies to deliver solutions that have never been achieved before. https://youtu.be/FKIl_0ua2j0?embedable=true https://youtu.be/FKIl_0ua2j0?embedable=true How Hidonix achieved product-market fit One of the most innovative aspects of Hidonix , and something both our customers and investors deeply value, is the versatility of our technologies. While they are adaptable across many verticals, they are also tailored to meet the specific needs of the industries we target. We achieve this by embedding our technologies into purpose-built products designed specifically for each market. Hidonix For example, our Computer Vision algorithms enable museums to deliver detailed information about their collections through a simple scan of the artworks. Meanwhile, in the education sector, we use the same cutting-edge algorithms to perform advanced facial recognition, helping schools prevent potential threats and ensure safety. The evolution of Hidonix Hidonix began in the museum field, where we successfully introduced innovative, interactive solutions to attract and engage new visitors. At the same time, we empowered internal staff with an integrated platform offering over 60 services to streamline their daily operations. These include managing artwork loans and preservation, digitizing collections, and building a comprehensive CRM. Initially, our technologies were designed specifically for museums. However, we soon realized that the same technologies could be adapted to other industries. Our indoor navigation system, for example, proved highly versatile. This discovery led us to explore and address the unique needs of various verticals by tailoring our algorithms to fit specific business requirements. This process transformed us from a small company in Italy into a financially stable organization with a team of 30+ professionals spread across Europe and the U.S., where our headquarters are now located. Milestones and achievements We’re proud of the many projects that demonstrate our expertise and innovation. Among the highlights are our collaborations with the Venice Biennale , where we supported the Venice Pavilion during the challenging COVID-19 period. Our solutions helped welcome, educate, and entertain visitors at a time when cultural initiatives struggled to engage audiences without physical attendance. Venice Biennale Another notable achievement is our work with one of the largest convention centers in Europe. By implementing our navigation feature, we not only enhanced the visitor experience but also created new revenue streams for exhibitors and event organizers. Of course, there’s still much more to achieve. We’re currently focused on exciting U.S. projects that address critical issues like safety and security in schools. At Hidonix, we’re committed to continuous R&D, confident in our ability to make a meaningful impact across diverse sectors. Lessons learned When working with advanced technologies, we’ve realized that the market isn’t always immediately ready to embrace them. It takes time for people to understand how to best use new solutions and become familiar with innovative concepts. Not all of us are Steve Jobs—convincing others to believe in your vision can be challenging at first. To all the innovators out there: stay consistent and don’t be discouraged by initial feedback. Keep pushing forward, and if you’re truly making a real impact, people will recognize it eventually. What Startups of The Year means to us What Startups of The Year means to us We are incredibly proud to be nominated by the community—it has strengthened our belief in what we’re building. As a team of young and talented individuals, we’re often amazed by what we can achieve when we think outside the box, driven by the hope that our technologies will make a meaningful impact on society, not just generate profits. Winning this competition would be an incredible honor and a well-deserved recognition for every team member who has put in unparalleled effort into our mission. It would also help us amplify our message about innovative ways to use technology in fields that urgently need advancement for the greater good. Conclusion Being part of the "Startups of the Year" journey is a humbling and inspiring experience for us. It reinforces our commitment to developing innovative technologies that create real value and impact across industries. This recognition, whether we win or not, fuels our passion to keep pushing boundaries, thinking creatively, and striving for excellence. We are deeply grateful for the support of our community, our talented team, and everyone who believes in our mission. Together, we’re not just building a company—we’re shaping a future where technology truly benefits society! Vote for us today! Vote for us today! Vote for us today! Welcome to HackerNoon’s Startups of The Year 2024 interview series, spotlighting startups from around the world that are changing the way we live, one solution at a time. If you’ve been nominated, create a brand profile and answer these questions here. Want to nominate a startup? Follow the steps here. Looking for inspiration? Check out how other startups responded to last year’s Startups of The Year interview questions and examples of company responses here, here, and here. Welcome to HackerNoon’s Startups of The Year 2024 interview series, spotlighting startups from around the world that are changing the way we live, one solution at a time. If you’ve been nominated, create a brand profile and answer these questions here . Startups of The Year 2024 create a brand profile create a brand profile create a brand profile here here Want to nominate a startup? Follow the steps here . here here Looking for inspiration? Check out how other startups responded to last year’s Startups of The Year interview questions and examples of company responses here, here, and here. Looking for inspiration? Check out how other startups responded to last year’s Startups of The Year interview questions and examples of company responses here , here , and here . last year’s Startups of The Year interview questions last year’s Startups of The Year interview questions here here here here here here Wanna use this template? Click here. Wanna use this template? Click here . here

Meet the Startup Changing Museums For the Better

ለተሻለ የጅምር ለውጥ ሙዚየሞችን ያግኙ

Upoznajte startup koji mijenja muzeje na bolje

Lernen Sie das Startup kennen, das Museen zum Besseren verändert

Conozca la startup que está cambiando los museos para mejor

博物館をより良い方向に変えるスタートアップ企業

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Marvel Rivals, released on December 6, 2024, on Xbox, PC, and PlayStation, is the latest entry in the hero shooter genre. While Overwatch has long dominated this space, it may be time for a new chaotic, team-based game to take the spotlight. East and West Synergy Developed by gaming giant NetEase Games, headquartered in Guangzhou, China, with offices in Seattle, the game utilizes iconic Marvel superheroes like Spider-Man, Wolverine, and Captain America, while also allowing players to team up with villains like Venom, Magneto, and Loki in action-packed, colorful 6v6 arenas. The game boasts excellent sound design, well-crafted maps, and simple gameplay that players familiar with the genre will quickly pick up. Being a third-person shooter, Marvel Rivals showcases each Marvel legend in their iconic glory. Each character stays true to their source material, offering expressive, engaging designs that are fun to watch and play. What stands out most to me, beyond the captivating gameplay and vibrant art style, is the potential for Western and Eastern audiences to come together through characters beloved for over 60 years. Spider-Man, the Hulk — these are universally recognized figures. Even though the characters were created in America, the Marvel Universe enjoys a massive following in China, highlighted by the Marvel Universe attraction at Shanghai Disneyland. China is the largest gaming market in the world, making the financial incentive for a game like Marvel Rivals undeniable. With dedicated servers globally, the game has strong potential to unite Eastern and Western communities while fostering competition through esports. Although we don’t yet know the full roadmap for competitive esports within the game, the Marvel trademark could prove invaluable in securing big prize pools that entice players to compete for trophies and profit. https://youtu.be/8NUtV1CkyKs?embedable=true The inclusion of Tokyo-themed stages in the game is a nice touch, drawing attention to the world’s love of Japanese culture and appealing to Marvel’s Japanese fanbase. If NetEase manages this project well, Marvel Rivals could become one of the biggest games, connecting Western and Eastern fanbases, casual players, and aspiring professionals alike. Perhaps we’ll even see Chinese players gain recognition in the West for their incredible gameplay as Rocket Raccoon or Iron Man. The game even features some Chinese writing, which has left some players confused. This inclusion of Chinese characters is likely part of a broader strategy to celebrate and bring attention to Chinese culture and artists. Privacy Concerns and Political Implications If you’ve been following the news, you might know that TikTok faces a potential ban at the beginning of 2025 due to “national security” risks. I wonder if Marvel Rivals and NetEase will face similar scrutiny regarding privacy concerns, given that the company is based in China. Such accusations could have significant implications for the future of gaming. It’s already well-known that games today collect vast amounts of data on players. Perhaps this concern is premature, but it’s worth considering how technologies influence public relations and politics. Still, I believe accusations of serious privacy violations or concerns about how a Chinese company uses player data will likely go ignored. Most players care little about the political and economic implications of games as long as their gameplay isn’t affected or the costs of in-game cosmetics and DLC don’t rise. Of course, there have been instances where politics, activism, and esports have collided. The more connected the world becomes, the more issues are spotlighted and shared. Questions for the Future How will NetEase’s origins impact its ability to secure sponsorships? How much will China’s gaming industry invest in making Marvel Rivals a viable product in the competitive gaming space? Will international teams be brought in? Could major players in esports, like Saudi Arabia, get involved? There’s potential for Marvel Rivals to feature at the World Esports Cup in 2025, but many questions remain. Nevertheless, this game could serve as a valuable tool to unite global gaming culture and add fuel to the rapidly growing esports machine. China has actively promoted the global dissemination of its games as a cultural export, with several games enjoying major success abroad. The country has established a policy framework to promote traditional Chinese culture through innovative digital mediums, including high-quality games. In 2019, Beijing unveiled gaming industry development guidelines that aim to leverage games as an international medium for conveying compelling Chinese stories.
— Liz Wang, The Role of Gaming and Esports in China’s Innovation Strategy: A Cultural Export and Source of Soft Power, August 2024 Marvel Rivals, released on December 6, 2024, on Xbox, PC, and PlayStation, is the latest entry in the hero shooter genre. While Overwatch has long dominated this space, it may be time for a new chaotic, team-based game to take the spotlight. Marvel Rivals, Overwatch East and West Synergy Developed by gaming giant NetEase Games, headquartered in Guangzhou, China, with offices in Seattle, the game utilizes iconic Marvel superheroes like Spider-Man, Wolverine, and Captain America, while also allowing players to team up with villains like Venom, Magneto, and Loki in action-packed, colorful 6v6 arenas. The game boasts excellent sound design, well-crafted maps, and simple gameplay that players familiar with the genre will quickly pick up. Being a third-person shooter, Marvel Rivals showcases each Marvel legend in their iconic glory. Each character stays true to their source material, offering expressive, engaging designs that are fun to watch and play. Marvel Rivals What stands out most to me, beyond the captivating gameplay and vibrant art style, is the potential for Western and Eastern audiences to come together through characters beloved for over 60 years. Spider-Man, the Hulk — these are universally recognized figures. Even though the characters were created in America, the Marvel Universe enjoys a massive following in China, highlighted by the Marvel Universe attraction at Shanghai Disneyland. Marvel Universe China is the largest gaming market in the world, making the financial incentive for a game like Marvel Rivals undeniable. With dedicated servers globally, the game has strong potential to unite Eastern and Western communities while fostering competition through esports. Marvel Rivals Although we don’t yet know the full roadmap for competitive esports within the game, the Marvel trademark could prove invaluable in securing big prize pools that entice players to compete for trophies and profit. https://youtu.be/8NUtV1CkyKs?embedable=true https://youtu.be/8NUtV1CkyKs?embedable=true The inclusion of Tokyo-themed stages in the game is a nice touch, drawing attention to the world’s love of Japanese culture and appealing to Marvel’s Japanese fanbase. If NetEase manages this project well, Marvel Rivals could become one of the biggest games, connecting Western and Eastern fanbases, casual players, and aspiring professionals alike. Marvel Rivals Perhaps we’ll even see Chinese players gain recognition in the West for their incredible gameplay as Rocket Raccoon or Iron Man. The game even features some Chinese writing, which has left some players confused. This inclusion of Chinese characters is likely part of a broader strategy to celebrate and bring attention to Chinese culture and artists. Privacy Concerns and Political Implications Privacy Concerns and Political Implications If you’ve been following the news, you might know that TikTok faces a potential ban at the beginning of 2025 due to “national security” risks. I wonder if Marvel Rivals and NetEase will face similar scrutiny regarding privacy concerns, given that the company is based in China. Such accusations could have significant implications for the future of gaming. Marvel Rivals It’s already well-known that games today collect vast amounts of data on players. Perhaps this concern is premature, but it’s worth considering how technologies influence public relations and politics. Still, I believe accusations of serious privacy violations or concerns about how a Chinese company uses player data will likely go ignored. Most players care little about the political and economic implications of games as long as their gameplay isn’t affected or the costs of in-game cosmetics and DLC don’t rise. Of course, there have been instances where politics, activism, and esports have collided. The more connected the world becomes, the more issues are spotlighted and shared. Questions for the Future Questions for the Future How will NetEase’s origins impact its ability to secure sponsorships? How much will China’s gaming industry invest in making Marvel Rivals a viable product in the competitive gaming space? Will international teams be brought in? Could major players in esports, like Saudi Arabia, get involved? Marvel Rivals There’s potential for Marvel Rivals to feature at the World Esports Cup in 2025, but many questions remain. Nevertheless, this game could serve as a valuable tool to unite global gaming culture and add fuel to the rapidly growing esports machine. Marvel Rivals China has actively promoted the global dissemination of its games as a cultural export, with several games enjoying major success abroad. The country has established a policy framework to promote traditional Chinese culture through innovative digital mediums, including high-quality games. In 2019, Beijing unveiled gaming industry development guidelines that aim to leverage games as an international medium for conveying compelling Chinese stories. — Liz Wang, The Role of Gaming and Esports in China’s Innovation Strategy: A Cultural Export and Source of Soft Power, August 2024 China has actively promoted the global dissemination of its games as a cultural export, with several games enjoying major success abroad. The country has established a policy framework to promote traditional Chinese culture through innovative digital mediums, including high-quality games. In 2019, Beijing unveiled gaming industry development guidelines that aim to leverage games as an international medium for conveying compelling Chinese stories. China has actively promoted the global dissemination of its games as a cultural export, with several games enjoying major success abroad. The country has established a policy framework to promote traditional Chinese culture through innovative digital mediums, including high-quality games. In 2019, Beijing unveiled gaming industry development guidelines that aim to leverage games as an international medium for conveying compelling Chinese stories. — Liz Wang, The Role of Gaming and Esports in China’s Innovation Strategy: A Cultural Export and Source of Soft Power, August 2024 — Liz Wang, The Role of Gaming and Esports in China’s Innovation Strategy: A Cultural Export and Source of Soft Power, August 2024

Could Marvel Rivals Help Facilitate Better US-China Relations?

The fever around AI has many founders asking whether they should let an LLM like ChatGPT take a crack at their pitch deck. After all, if AI can write code and create marketing copy, why not trust it with your startup's story? In Short: No LLMs are essentially sophisticated pattern matchers, but I would argue unsuitable for breakthrough ideas. These models work by predicting what words should come next based on the statistical patterns in their training data – they're essentially playing a complex game of "finish this sentence." And while they might seem magical, they're ultimately bound by the patterns they've seen before, creating legible remixes rather than truly novel thought. Most models try to fake insight with a dash of randomness in their output (what AI researchers call "temperature"), but even this randomness is still just pulling from their statistical set. Your startup’s idea is the opposite of pattern matching. You're building something new, something based on your real-world insight that will break patterns. Something that will disrupt. Think about how a founder identifies a real opportunity: they notice friction in daily life, spot inefficiencies in their industry, or experience a problem firsthand that no one else has solved. This kind of pattern breaking – seeing what's missing rather than what's already there – is precisely what LLMs can't do. They can tell you all about successful companies that exist, but they can't identify the gaps that will lead to the next breakthrough. Regardless of the effectiveness of AI, creating a pitch deck isn't just about the end product. It's a mental exercise for founders to crystallize their thinking about their market, users, and competitive landscape. Skipping this process by outsourcing it to AI is like trying to get in shape by having someone else do your pushups. The real value isn't in the deck itself – it's in the mental model you build while creating it. After working with countless founders on their decks, time and time again I hear that the shiny slides were just half the value, and an often better takeaway from fundraising is the deeper understanding of their venture and how it fits into a changing world. Even if AI gets good enough in the next few years to truly create great decks, I'd still recommend founders go manual so they can experience the deep dive. Where AI Actually Makes Sense That said, AI isn't entirely useless in the pitch process. Consider using it as an occasional tool, not a crutch. Some founders have the opposite problem of having too little to say – they have too much. When you're deep in the trenches of building, it's easy to write paragraphs where sentences or even half sentences would do. This is where AI shines: as an efficient editor. Feed it any verbose slide, and it can help distill your message to its essential elements while preserving your core insights. Founder bios are an obvious candidate for this treatment – AI can help transform a meandering career history into a sharp narrative about why they’re the right person to solve this specific problem. The same approach works for market analysis slides that read like research papers, or product descriptions that get lost in technical details. Again, the mental exercise of cutting down text yourself is probably more useful in helping you understand what really matters for your business. But using AI in this way at least avoids the issues of hallucination or lacking your novel insights. Once you feel your deck is ready for primetime, AI can be used to review and practice your pitch. Tools like Fornax.ai can analyze your deck against common pitch deck tactics, highlighting where you might be breaking conventional wisdom (in good or bad ways). Additionally, platforms like Yoodli can help you with verbal pitch practice by providing AI feedback on everything from your pace to your handling of questions. The key is remembering that AI can help you refine your pitch, not create it. Your unique insights, experience, and deep understanding of the problem you're solving – that's still on you. The fever around AI has many founders asking whether they should let an LLM like ChatGPT take a crack at their pitch deck. After all, if AI can write code and create marketing copy, why not trust it with your startup's story? In Short: No LLMs are essentially sophisticated pattern matchers, but I would argue unsuitable for breakthrough ideas. These models work by predicting what words should come next based on the statistical patterns in their training data – they're essentially playing a complex game of "finish this sentence." And while they might seem magical, they're ultimately bound by the patterns they've seen before, creating legible remixes rather than truly novel thought. Most models try to fake insight with a dash of randomness in their output (what AI researchers call "temperature"), but even this randomness is still just pulling from their statistical set. Your startup’s idea is the opposite of pattern matching. You're building something new, something based on your real-world insight that will break patterns. Something that will disrupt. Think about how a founder identifies a real opportunity: they notice friction in daily life, spot inefficiencies in their industry, or experience a problem firsthand that no one else has solved. This kind of pattern breaking – seeing what's missing rather than what's already there – is precisely what LLMs can't do. They can tell you all about successful companies that exist, but they can't identify the gaps that will lead to the next breakthrough. Regardless of the effectiveness of AI, creating a pitch deck isn't just about the end product. It's a mental exercise for founders to crystallize their thinking about their market, users, and competitive landscape. Skipping this process by outsourcing it to AI is like trying to get in shape by having someone else do your pushups. The real value isn't in the deck itself – it's in the mental model you build while creating it. After working with countless founders on their decks , time and time again I hear that the shiny slides were just half the value, and an often better takeaway from fundraising is the deeper understanding of their venture and how it fits into a changing world. Even if AI gets good enough in the next few years to truly create great decks, I'd still recommend founders go manual so they can experience the deep dive. After working with countless founders on their decks Where AI Actually Makes Sense That said, AI isn't entirely useless in the pitch process. Consider using it as an occasional tool, not a crutch. Some founders have the opposite problem of having too little to say – they have too much. When you're deep in the trenches of building, it's easy to write paragraphs where sentences or even half sentences would do. This is where AI shines: as an efficient editor. Feed it any verbose slide, and it can help distill your message to its essential elements while preserving your core insights. Founder bios are an obvious candidate for this treatment – AI can help transform a meandering career history into a sharp narrative about why they’re the right person to solve this specific problem. The same approach works for market analysis slides that read like research papers, or product descriptions that get lost in technical details. Again, the mental exercise of cutting down text yourself is probably more useful in helping you understand what really matters for your business. But using AI in this way at least avoids the issues of hallucination or lacking your novel insights. Once you feel your deck is ready for primetime, AI can be used to review and practice your pitch. Tools like Fornax.ai can analyze your deck against common pitch deck tactics, highlighting where you might be breaking conventional wisdom (in good or bad ways). Additionally, platforms like Yoodli can help you with verbal pitch practice by providing AI feedback on everything from your pace to your handling of questions. The key is remembering that AI can help you refine your pitch, not create it. Your unique insights, experience, and deep understanding of the problem you're solving – that's still on you.

Your Startup Deserves Better Than AI’s Pattern-Matching Pitches

የእርስዎ ጅምር ከ AI ጥለት-ተዛማጅ እርከኖች የተሻለ ይገባዋል

Ihr Startup verdient mehr als die Mustererkennungs-Pitches der KI

Su startup merece algo mejor que los discursos de búsqueda de patrones de la IA

A túa startup merece algo mellor que os lanzamentos de coincidencia de patróns da IA

あなたのスタートアップは、AI のパターンマッチングのピッチよりも優れたものに値する

თქვენი სტარტაპი უკეთესს იმსახურებს, ვიდრე AI-ის შაბლონების შესატყვისი მოედანი

ការចាប់ផ្តើមរបស់អ្នកសមនឹងទទួលបានប្រសើរជាង AI's Pattern-Matching Pitches

귀하의 스타트업은 AI의 패턴 매칭 피치보다 더 나은 것을 받을 자격이 있습니다.

Jūsų startuolis nusipelno geresnių nei dirbtinio intelekto modelių atitikimo pasiūlymai

Váš startup si zaslúži lepšie ako umelá inteligencia na výber vzorov

Оғози шумо сазовортар аз сатҳҳои мувофиқи намунаи AI аст

สตาร์ทอัพของคุณสมควรได้รับสิ่งที่ดีกว่าการนำเสนอการจับคู่รูปแบบของ AI

As a PhD student, you’re likely feeling a mix of excitement and overwhelm as you embark on your research journey. Reflecting on my own experience, I remember the challenges and limitations of the tools available on the market to assist with conducting your literature review. During the COVID period, tools like Research Rabbit and Zotero were a bit more rudimentary and easier to use, and they proved invaluable in my first year. However, this realisation prompted me to share insights on how to effectively utilise such tools. This short blog was inspired after watching the potential of AI tools showcased in “Write A Masterpiece Systematic Literature Review With AI [Next Level Strategies]” by Andy Stapleton, Please note that the advice and approaches discussed in this blog reflect my personal experience and are not professional academic advice. In this blog, I will introduce the Triple T Approach — Tactics, Techniques, and Tools — to streamline literature reviews. By integrating strategic planning, methodological execution, and AI tools like ChatGPT, Elicit, and Litmap, this approach ensures a comprehensive and efficient review process. Be Aware of the Texas sharpshooter! Before diving into the Triple T framework, it’s crucial to heed the advice of being wary of the Texas sharpshooter fallacy. that refers to a scenario where a person, lacking actual shooting skills, randomly fires bullets at a barn wall. Subsequently, they paint a bull’s-eye around a fortuitous cluster of bullet holes and proclaim themselves a sharpshooter. Falling into this trap can be tempting, particularly with tools that produce seemingly strong evidence, which may obscure their biases. This is why it’s essential to question everything produced by AI, by adopting a philosophical skepticism mindset. It’s crucial to question everything produced by AI to provoke awareness of any bias existing in the data set and challenge our assumptions while evaluating evidence generated by GenAI tools and exploring different perspectives and their implications on our research direction. This logical pitfall warns against being drawn to tools that appear to provide strong evidence, potentially concealing biases. Utilizing these tools effectively requires critical thinking skills; in this context, these skills act as your shooting skills for the Texas sharpshooter fallacy! This is why I always recommend reading more about this topic, as it introduces essential life skills to help not only grow your research but also grow as a researcher. This approach ensures a comprehensive and efficient review process while also keeping in mind the limitations of current AI tools and their biases. When choosing each tactic, try to link it with appropriate tools and techniques while considering how they align together and the implications of such an approach on the limitations of AI tools. I have only focused on the bias challenge, but of course, there are many limitations awaiting AI in academia. Always remember that you are leading the tools, not the other way around. What is the Triple T Approach? The Triple T framework breaks down into Tactics, Techniques, and Tools. Tactics involve strategic decisions in structuring the review, conducting thorough literature searches, and synthesizing information. Techniques encompass methods such as AI-assisted structuring and analytical reading, while Tools consist of resources like ChatGPT and Mendeley for efficient literature management. Applying the Triple T Approach to Your Literature Review Workflow: Here’s a quick walkthrough of some of the concepts and strategies applied in the approach to ensure a methodical and tool-driven process, enhancing the quality and depth of your review. 1. Tactics: • Structuring the Review: Develop a clear, organized structure for your literature review. • Comprehensive Literature Search: Systematically search for and select relevant literature. • Synthesizing Information: Integrating findings from various sources into a cohesive narrative. 2. Techniques: • AI-Assisted Structuring: Using AI to generate a preliminary structure for your review. • Targeted Literature Discovery: Employing specific queries based on your review structure to find relevant literature. • Analytical Reading and Organization: Critically reading and categorizing literature for easy reference and synthesis. 3. Tools: • ChatGPT and Perplexity: For generating the initial structure of your literature review. • PaperPal and Jenni AI: Tools to assist with proofreading your initial drafts and refining your literature structure. • Elicit or Scispace: To search for and summarize seed papers based on your structured queries. • Litmap and Connected Papers: For discovering related literature reviews. • Mendeley or Zetro: To manage and organize your references and PDFs. • Docanalyzer.AI: For analyzing documents and aligning them with your review structure. By aligning your research with this approach, you’ll streamline your workflow and lay a robust foundation for your literature review paper. Of course, this approach is flexible. For example, when you start structuring the plan for your literature review, you could use tools like Perplexity and then draft and proofread the initial paper structure using PaperPal. However, you could also gather information before you do any planning. Thus, a tactic like synthesizing information will need some techniques for targeted literature discovery through Elicit. So, please add or update any section of this approach that you feel fits your needs! As a PhD student, you’re likely feeling a mix of excitement and overwhelm as you embark on your research journey. Reflecting on my own experience, I remember the challenges and limitations of the tools available on the market to assist with conducting your literature review. During the COVID period, tools like Research Rabbit and Zotero were a bit more rudimentary and easier to use, and they proved invaluable in my first year. However, this realisation prompted me to share insights on how to effectively utilise such tools. This short blog was inspired after watching the potential of AI tools showcased in “ Write A Masterpiece Systematic Literature Review With AI [Next Level Strategies ]” by Andy Stapleton, Please note that the advice and approaches discussed in this blog reflect my personal experience and are not professional academic advice. In this blog, I will introduce the Triple T Approach — Tactics, Techniques, and Tools — to streamline literature reviews. By integrating strategic planning, methodological execution, and AI tools like ChatGPT, Elicit, and Litmap, this approach ensures a comprehensive and efficient review process. Write A Masterpiece Systematic Literature Review With AI [Next Level Strategies Be Aware of the Texas sharpshooter! Be Aware of the Texas sharpshooter! Before diving into the Triple T framework, it’s crucial to heed the advice of being wary of the Texas sharpshooter fallacy . that refers to a scenario where a person, lacking actual shooting skills, randomly fires bullets at a barn wall. Subsequently, they paint a bull’s-eye around a fortuitous cluster of bullet holes and proclaim themselves a sharpshooter. Falling into this trap can be tempting, particularly with tools that produce seemingly strong evidence, which may obscure their biases. Texas sharpshooter fallacy This is why it’s essential to question everything produced by AI, by adopting a philosophical skepticism mindset. It’s crucial to question everything produced by AI to provoke awareness of any bias existing in the data set and challenge our assumptions while evaluating evidence generated by GenAI tools and exploring different perspectives and their implications on our research direction. This logical pitfall warns against being drawn to tools that appear to provide strong evidence, potentially concealing biases. Utilizing these tools effectively requires critical thinking skills; in this context, these skills act as your shooting skills for the Texas sharpshooter fallacy! This is why I always recommend reading more about this topic, as it introduces essential life skills to help not only grow your research but also grow as a researcher. This approach ensures a comprehensive and efficient review process while also keeping in mind the limitations of current AI tools and their biases. When choosing each tactic, try to link it with appropriate tools and techniques while considering how they align together and the implications of such an approach on the limitations of AI tools. I have only focused on the bias challenge, but of course, there are many limitations awaiting AI in academia. Always remember that you are leading the tools, not the other way around. you are leading What is the Triple T Approach? What is the Triple T Approach? The Triple T framework breaks down into Tactics, Techniques, and Tools. Tactics involve strategic decisions in structuring the review, conducting thorough literature searches, and synthesizing information. Techniques encompass methods such as AI-assisted structuring and analytical reading, while Tools consist of resources like ChatGPT and Mendeley for efficient literature management. strategic decisions methods resources Applying the Triple T Approach to Your Literature Review Workflow: Applying the Triple T Approach to Your Literature Review Workflow: Here’s a quick walkthrough of some of the concepts and strategies applied in the approach to ensure a methodical and tool-driven process, enhancing the quality and depth of your review. 1. Tactics: 1. Tactics: • Structuring the Review : Develop a clear, organized structure for your literature review. Structuring the Review • Comprehensive Literature Search : Systematically search for and select relevant literature. Comprehensive Literature Search • Synthesizing Information : Integrating findings from various sources into a cohesive narrative. Synthesizing Information 2. Techniques: 2. Techniques: • AI-Assisted Structuring : Using AI to generate a preliminary structure for your review. AI-Assisted Structuring • Targeted Literature Discovery : Employing specific queries based on your review structure to find relevant literature. Targeted Literature Discovery • Analytical Reading and Organization : Critically reading and categorizing literature for easy reference and synthesis. Analytical Reading and Organization 3. Tools: 3. Tools: • ChatGPT and Perplexity : For generating the initial structure of your literature review. ChatGPT and Perplexity • PaperPal and Jenni AI : Tools to assist with proofreading your initial drafts and refining your literature structure. PaperPal and Jenni AI • Elicit or Scispace : To search for and summarize seed papers based on your structured queries. Elicit or Scispace • Litmap and Connected Papers : For discovering related literature reviews. Litmap and Connected Papers • Mendeley or Zetro : To manage and organize your references and PDFs. Mendeley or Zetro • Docanalyzer.AI : For analyzing documents and aligning them with your review structure. Docanalyzer.AI By aligning your research with this approach, you’ll streamline your workflow and lay a robust foundation for your literature review paper. Of course, this approach is flexible. For example, when you start structuring the plan for your literature review, you could use tools like Perplexity and then draft and proofread the initial paper structure using PaperPal. However, you could also gather information before you do any planning. Thus, a tactic like synthesizing information will need some techniques for targeted literature discovery through Elicit. So, please add or update any section of this approach that you feel fits your needs! structuring synthesizing information targeted literature discovery

AI Literature Reviews Are So Much Better With the Triple T Method

SINGAPORE, December 5, 2024 – Leading crypto exchange MEXC officially announced a comprehensive upgrade to its referral commission program, delivering enhanced benefits for users worldwide. Under the updated policy, referral commission rates for trading fees have increased to 40%, with select regions enjoying rates as high as 60%, setting a new benchmark for user incentives. As part of this upgrade, referrers can now earn up to 60 USDT in Futures bonuses for each valid invitee, while every valid invitee will receive a 20 USDT Futures bonus. This upgrade not only boosts referrer earnings but also ensures mutual benefits for both referrers and their invitees. With no increase in referral requirements, users can enjoy significantly higher rewards under the updated policy, reflecting MEXC's dedication to enhancing user benefits. By inviting friends, users can also access these additional perks: Trending Token Airdrops: Users can invite friends to participate in the Token Airdrop events and complete tasks to earn a share of token rewards from premium projects.
Reward Multipliers: Users can boost their earnings by inviting friends to participate in MEXC's Kickstarter events. Referring more valid invitees increases the reward multiplier, resulting in a larger share of token rewards. This upgrade underscores MEXC’s mission to empower its users while strengthening its community ecosystem. By enabling users to share in the platform's success, MEXC reaffirms its commitment to providing exceptional value. Looking ahead, MEXC will continue to innovate, enhance user experiences, and introduce more rewarding programs for its global community. About MEXC Founded in 2018, MEXC is committed to being "Your Easiest Way to Crypto." Serving over 30 million users across 170+ countries, MEXC is known for its broad selection of trending tokens, frequent airdrop opportunities, and low trading fees. Our user-friendly platform is designed to support both new traders and experienced investors, offering secure and efficient access to digital assets. MEXC prioritizes simplicity and innovation, making crypto trading more accessible and rewarding. Official Website｜X｜Telegram｜Invite Friends｜How to Invite Friends Disclaimer: Cryptocurrency investments carry inherent risks. The information provided does not constitute financial advice. It is important to fully understand the associated risks and proceed with caution when investing. Users are encouraged to conduct thorough research and assess their individual risk tolerance. This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here SINGAPORE, December 5, 2024 – Leading crypto exchange MEXC officially announced a comprehensive upgrade to its referral commission program, delivering enhanced benefits for users worldwide. Under the updated policy, referral commission rates for trading fees have increased to 40%, with select regions enjoying rates as high as 60%, setting a new benchmark for user incentives. As part of this upgrade, referrers can now earn up to 60 USDT in Futures bonuses for each valid invitee, while every valid invitee will receive a 20 USDT Futures bonus. This upgrade not only boosts referrer earnings but also ensures mutual benefits for both referrers and their invitees. With no increase in referral requirements, users can enjoy significantly higher rewards under the updated policy, reflecting MEXC's dedication to enhancing user benefits. By inviting friends, users can also access these additional perks: Trending Token Airdrops: Users can invite friends to participate in the Token Airdrop events and complete tasks to earn a share of token rewards from premium projects. Reward Multipliers: Users can boost their earnings by inviting friends to participate in MEXC's Kickstarter events. Referring more valid invitees increases the reward multiplier, resulting in a larger share of token rewards. Trending Token Airdrops: Users can invite friends to participate in the Token Airdrop events and complete tasks to earn a share of token rewards from premium projects. Token Airdrop Token Airdrop Reward Multipliers: Users can boost their earnings by inviting friends to participate in MEXC's Kickstarter events . Referring more valid invitees increases the reward multiplier, resulting in a larger share of token rewards. MEXC's Kickstarter events MEXC's Kickstarter events This upgrade underscores MEXC’s mission to empower its users while strengthening its community ecosystem. By enabling users to share in the platform's success, MEXC reaffirms its commitment to providing exceptional value. Looking ahead, MEXC will continue to innovate, enhance user experiences, and introduce more rewarding programs for its global community. About MEXC Founded in 2018, MEXC is committed to being "Your Easiest Way to Crypto." Serving over 30 million users across 170+ countries, MEXC is known for its broad selection of trending tokens, frequent airdrop opportunities, and low trading fees. Our user-friendly platform is designed to support both new traders and experienced investors, offering secure and efficient access to digital assets. MEXC prioritizes simplicity and innovation, making crypto trading more accessible and rewarding. Official Website｜ X ｜ Telegram ｜ Invite Friends ｜ How to Invite Friends Official Website ｜ Official Website X ｜ Telegram ｜ Invite Friends ｜ How to Invite Friends Disclaimer: Cryptocurrency investments carry inherent risks. The information provided does not constitute financial advice. It is important to fully understand the associated risks and proceed with caution when investing. Users are encouraged to conduct thorough research and assess their individual risk tolerance. This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

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Authors:
(1) Limeng Zhang, Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia;
(2) M. Ali Babar, Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia. Table of Links Abstract and 1 Introduction 1.1 Configuration Parameter Tuning Challenges and 1.2 Contributions 2 Tuning Objectives 3 Overview of Tuning Framework 4 Workload Characterization and 4.1 Query-level Characterization 4.2 Runtime-based Characterization 5 Feature Pruning and 5.1 Workload-level Pruning 5.2 Configuration-level Pruning 5.3 Summary 6 Knowledge from Experience 7 Configuration Recommendation and 7.1 Bayesian Optimization 7.2 Neural Network 7.3 Reinforcement Learning 7.4 Search-based Solutions 8 Experimental Setting 9 Related Work 10 Discussion and Conclusion, and References 5 FEATURE PRUNING Given the diversity of workloads and the high-dimensional configurable parameters of DBMSs, both data collection and configuration space search can be time-intensive. Hence, employing strategies to mitigate workload execution time and streamline the search space becomes imperative. In this section, we present pruning strategies at two levels: workload-level pruning to optimize workload execution time and configuration-level pruning for enhancing search space efficiency. Table 2 provides a technical comparison of strategies on feature pruning (in Section 5) and knowledge extraction (in Section 6) from existing tuning methods. 5.1 Workload-level Pruning Workload-level pruning strategies primarily contribute to two aspects: data collection and model training. Query-level filtering aims to remove unimportant queries from a workload, thereby reducing the execution time of each workload and accelerating both training data collection and model validation. Workload Feature Selection aims to reduce the dimensionality of each workload feature, thereby decreasing the training time for autotuning models. 5.1.1 Query-level Filtering Considering that each application consists of a number of queries, the execution time of a workload can be decreased through removing some unimportant queries. Query filtering aims to removing the unimportant queries which are not sensitive to the configuration parameters. • Coefficient of Variation (CV): is a statistical measure that provides insights into the relative variability of a set of values. It is calculated as the ratio of the standard deviation to the mean, expressed as a percentage. LOCAT [7] utilizes CV to identify and eliminate configuration-insensitive queries whose performance (query execution time) is not affected by configuration changes within an application. It first constructs a matrix of the execution time for each query under different Spark SQL configurations (30 configuration instances) and then calculates the CV to filter queries with smaller CV values, categorizing them as ’configuration-insensitive’. 5.1.2 Workload Feature Selection Workload feature selection aims to reduce the dimensionality of workload features. Existing strategies applied in the automatic parameter tuning on DBMSs include: • k-means: is a popular unsupervised machine learning algorithm used for partitioning a dataset into clusters based on similarity. The primary goal of kmeans is to group data points in such a way that points within the same cluster are more similar to each other than to points in other clusters. In Ottertune [8], each workload is represented with runtime metrics. When conducting workload feature selection, factor analysis (FA) is first applied to transform the (potentially) high-dimensional DBMS metric data into lower-dimensional features. Then k-means, to cluster this lower dimensional data into meaningful clusters (for reducing noise data) and select a single metric for each cluster, namely, the one closest to the cluster center. During the process, number of clusters is determined through a heuristic model proposed in the work [25]. • Factor analysis (FA): is a statistical technique used for identifying underlying latent factors that explain the observed relationships among a set of variables. The key assumption in factor analysis is that observed variables are linear combinations of unobservable latent factors and random error terms. Ottertune [8] utilizes it to transform the (potentially) high dimensional DBMS metric embedding vector into lower-dimensional features. • Principal Component Analysis (PCA): is a popular linear dimension reduction method that transforms correlated variables into uncorrelated ones, known as principal components. It does so by calculating the covariance matrix, performing eigendecomposition, and selecting a subset of principal components based on their variance. HUNTER [13] applies PCA to complete the feature reduction on its collected workload runtime metrics for a workload, which including number of lush statements executed, number of threads activated, etc. • Autoencoder: is a neural network architecture meticulously devised to achieve precise reconstruction of its input. Comprising two integral components, namely the encoder and the decoder, it operates by first directing the input through the encoder to produce a latent code. This latent code subsequently serves as the foundation for the decoder to generate the output. Thus, the latent code assumes a pivotal role in the identification and disentanglement of various sources of variation inherent within the input data. Through the reduction of the latent code’s dimension, the autoencoder facilitates effective dimension reduction, thereby yielding a more condensed and meaningful representation of the input data. MMOT [22] utilizes the autoencoder architecture to extract inherent patterns among workloads and then uses these patterns to identify the model from similar workloads to generate configurations. This paper is available on arxiv under CC BY 4.0 DEED. Authors: (1) Limeng Zhang, Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia; (2) M. Ali Babar, Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia. Authors: Authors: (1) Limeng Zhang, Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia; (2) M. Ali Babar, Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 1.1 Configuration Parameter Tuning Challenges and 1.2 Contributions 1.1 Configuration Parameter Tuning Challenges and 1.2 Contributions 2 Tuning Objectives 2 Tuning Objectives 3 Overview of Tuning Framework 3 Overview of Tuning Framework 4 Workload Characterization and 4.1 Query-level Characterization 4 Workload Characterization and 4.1 Query-level Characterization 4.2 Runtime-based Characterization 4.2 Runtime-based Characterization 5 Feature Pruning and 5.1 Workload-level Pruning 5 Feature Pruning and 5.1 Workload-level Pruning 5.2 Configuration-level Pruning 5.2 Configuration-level Pruning 5.3 Summary 5.3 Summary 6 Knowledge from Experience 6 Knowledge from Experience 7 Configuration Recommendation and 7.1 Bayesian Optimization 7 Configuration Recommendation and 7.1 Bayesian Optimization 7.2 Neural Network 7.2 Neural Network 7.3 Reinforcement Learning 7.3 Reinforcement Learning 7.4 Search-based Solutions 7.4 Search-based Solutions 8 Experimental Setting 8 Experimental Setting 9 Related Work 9 Related Work 10 Discussion and Conclusion, and References 10 Discussion and Conclusion, and References 5 FEATURE PRUNING Given the diversity of workloads and the high-dimensional configurable parameters of DBMSs, both data collection and configuration space search can be time-intensive. Hence, employing strategies to mitigate workload execution time and streamline the search space becomes imperative. In this section, we present pruning strategies at two levels: workload-level pruning to optimize workload execution time and configuration-level pruning for enhancing search space efficiency. Table 2 provides a technical comparison of strategies on feature pruning (in Section 5) and knowledge extraction (in Section 6) from existing tuning methods. 5.1 Workload-level Pruning Workload-level pruning strategies primarily contribute to two aspects: data collection and model training. Query-level filtering aims to remove unimportant queries from a workload, thereby reducing the execution time of each workload and accelerating both training data collection and model validation. Workload Feature Selection aims to reduce the dimensionality of each workload feature, thereby decreasing the training time for autotuning models. 5.1.1 Query-level Filtering 5.1.1 Query-level Filtering Considering that each application consists of a number of queries, the execution time of a workload can be decreased through removing some unimportant queries. Query filtering aims to removing the unimportant queries which are not sensitive to the configuration parameters. • Coefficient of Variation (CV): is a statistical measure that provides insights into the relative variability of a set of values. It is calculated as the ratio of the standard deviation to the mean, expressed as a percentage. LOCAT [7] utilizes CV to identify and eliminate configuration-insensitive queries whose performance (query execution time) is not affected by configuration changes within an application. It first constructs a matrix of the execution time for each query under different Spark SQL configurations (30 configuration instances) and then calculates the CV to filter queries with smaller CV values, categorizing them as ’configuration-insensitive’. 5.1.2 Workload Feature Selection 5.1.2 Workload Feature Selection Workload feature selection aims to reduce the dimensionality of workload features. Existing strategies applied in the automatic parameter tuning on DBMSs include: • k-means: is a popular unsupervised machine learning algorithm used for partitioning a dataset into clusters based on similarity. The primary goal of kmeans is to group data points in such a way that points within the same cluster are more similar to each other than to points in other clusters. In Ottertune [8], each workload is represented with runtime metrics. When conducting workload feature selection, factor analysis (FA) is first applied to transform the (potentially) high-dimensional DBMS metric data into lower-dimensional features. Then k-means, to cluster this lower dimensional data into meaningful clusters (for reducing noise data) and select a single metric for each cluster, namely, the one closest to the cluster center. During the process, number of clusters is determined through a heuristic model proposed in the work [25]. • Factor analysis (FA): is a statistical technique used for identifying underlying latent factors that explain the observed relationships among a set of variables. The key assumption in factor analysis is that observed variables are linear combinations of unobservable latent factors and random error terms. Ottertune [8] utilizes it to transform the (potentially) high dimensional DBMS metric embedding vector into lower-dimensional features. • Principal Component Analysis (PCA): is a popular linear dimension reduction method that transforms correlated variables into uncorrelated ones, known as principal components. It does so by calculating the covariance matrix, performing eigendecomposition, and selecting a subset of principal components based on their variance. HUNTER [13] applies PCA to complete the feature reduction on its collected workload runtime metrics for a workload, which including number of lush statements executed, number of threads activated, etc. • Autoencoder: is a neural network architecture meticulously devised to achieve precise reconstruction of its input. Comprising two integral components, namely the encoder and the decoder, it operates by first directing the input through the encoder to produce a latent code. This latent code subsequently serves as the foundation for the decoder to generate the output. Thus, the latent code assumes a pivotal role in the identification and disentanglement of various sources of variation inherent within the input data. Through the reduction of the latent code’s dimension, the autoencoder facilitates effective dimension reduction, thereby yielding a more condensed and meaningful representation of the input data. MMOT [22] utilizes the autoencoder architecture to extract inherent patterns among workloads and then uses these patterns to identify the model from similar workloads to generate configurations. This paper is available on arxiv under CC BY 4.0 DEED. This paper is available on arxiv under CC BY 4.0 DEED. available on arxiv

Prune Your Way to Better Database Management System Performance

Author:
(1) David M. Markowitz, Department of Communication, Michigan State University, East Lansing, MI 48824. Editor's note: This is part 7 of 10 of a paper evaluating the effectiveness of using generative AI to simplify science communication and enhance public trust in science. The rest of the paper can be accessed via the table of links below. Table of Links Abstract
The Benefits of Simple Writing
The Current Work
Study 1a: Method
Study 1a: Results
Study 1b: Method
Study 1b: Results
Study 2: Method
Study 2: Results
General Discussion, Acknowledgements, and References Study 1b: Results Distributions of the comparisons in this study are reflected in Figure 1. Indeed, GPT significance statements were written in a simpler manner than PNAS significance statements for the simplicity index, Welch’s t(1492.1) = 11.55, p < .001, Cohen’s d = 0.58, 95% CI [0.47, 0.69]. Specifically, GPT significance statements (M = 75.53%, SD = 5.57%) contained more common words than PNAS significance statements (M = 69.84%, SD = 7.45%), Welch’s t(1478.7) = 17.31, p < .001, Cohen’s d = 0.87, 95% CI [0.76, 0.97]. GPT significance statements (M = 17.59, SD = 11.15) were also more readable than PNAS significance statements (M = 12.86, SD = 14.27), Welch’s t(1510) = 7.39, p < .001, Cohen’s d = 0.37, 95% CI [0.27, 0.47]. However, GPT significance statements (M = 92.73, SD = 6.89) had a statistically equivalent analytic style as PNAS significance statements (M = 92.32, SD = 7.48), Welch’s t(1587.7) = 1.16, p = .246, Cohen’s d = 0.06, 95% CI [-0.04, 0.16]. All results were maintained when comparing GPT significance statements to PNAS abstracts and PNAS significance statements as well. Alternative Explanations One possible explanation for the Study 1b results is that there are content differences across the PNAS and GPT texts explaining or impacting such differences across groups. This concern was addressed in two ways. First, PNAS has various sections that authors submit to, and LIWC has categories to approximate words associated with such sections. For example, the LIWC category for political speech would approximate papers submitted the Social Science section, specifically Political Sciences. Several linguistic covariates were therefore examined to account for content-related differences across GPT and PNAS texts. After including overall affect/emotion and cognition (to control for topics within the Psychological Sciences section of PNAS), political speech (to control for topics within the Political Science section of PNAS), and physical references to the multivariate models (to control for topics within the Biological Sciences section of PNAS), all results were maintained except for Analytic writing, where GPT texts were more analytic than PNAS texts, which is also consistent with prior work (42). Please see the online supplement for additional LIWC differences across these text types. Content effects were also evaluated in a bottom-up manner using the Meaning Extraction Method to measure dominant themes across the GPT and PNAS texts (43, 44). The evidence in the online supplement states there were 8 themes reliably extracted from the data, ranging from basic methodological and research information to gene expression and cancer science. Controlling for these themes, including the prior LIWC content dimensions, revealed consistent results as well (see supplement). Therefore, Study 1b evidence is robust to content. Altogether, human authors write simpler for lay audiences than for scientific audiences (Study 1a), but Study 1b demonstrated artificial intelligence and large language models can do so more effectively (e.g., the effect size differences between GPT significance statements and PNAS significance statements was larger than human in Study 1a). The findings thus far are correlational and therefore need causal evidence to demonstrate the impact of these effects on human perceptions. In Study 2, participants were randomly assigned to read a GPT significance statement or PNAS significance statement from pairs of texts that appeared in the previous studies. Participants made perceptions about the author (e.g., intelligence, credibility, trustworthiness), judged the complexity of each text, and they rated how much they believed the author of each text was human or artificial intelligence. Only perceptions of the author were made because prior work suggests people generally report consistent ratings when asked about both scientists and their science in similar studies (9). This paper is available on arxiv under CC BY 4.0 DEED license. Author: (1) David M. Markowitz, Department of Communication, Michigan State University, East Lansing, MI 48824. Author: Author: (1) David M. Markowitz, Department of Communication, Michigan State University, East Lansing, MI 48824. Editor's note: This is part 7 of 10 of a paper evaluating the effectiveness of using generative AI to simplify science communication and enhance public trust in science. The rest of the paper can be accessed via the table of links below. Editor's note: This is part 7 of 10 of a paper evaluating the effectiveness of using generative AI to simplify science communication and enhance public trust in science. The rest of the paper can be accessed via the table of links below. Editor's note: This is part 7 of 10 of a paper evaluating the effectiveness of using generative AI to simplify science communication and enhance public trust in science. The rest of the paper can be accessed via the table of links below. Table of Links Abstract The Benefits of Simple Writing The Current Work Study 1a: Method Study 1a: Results Study 1b: Method Study 1b: Results Study 2: Method Study 2: Results General Discussion, Acknowledgements, and References Abstract Abstract The Benefits of Simple Writing The Benefits of Simple Writing The Current Work The Current Work Study 1a: Method Study 1a: Method Study 1a: Results Study 1a: Results Study 1b: Method Study 1b: Method Study 1b: Results Study 1b: Results Study 2: Method Study 2: Method Study 2: Results Study 2: Results General Discussion, Acknowledgements, and References General Discussion, Acknowledgements, and References Study 1b: Results Distributions of the comparisons in this study are reflected in Figure 1. Indeed, GPT significance statements were written in a simpler manner than PNAS significance statements for the simplicity index, Welch’s t(1492.1) = 11.55, p < .001, Cohen’s d = 0.58, 95% CI [0.47, 0.69]. Specifically, GPT significance statements (M = 75.53%, SD = 5.57%) contained more common words than PNAS significance statements (M = 69.84%, SD = 7.45%), Welch’s t(1478.7) = 17.31, p < .001, Cohen’s d = 0.87, 95% CI [0.76, 0.97]. GPT significance statements (M = 17.59, SD = 11.15) were also more readable than PNAS significance statements (M = 12.86, SD = 14.27), Welch’s t(1510) = 7.39, p < .001, Cohen’s d = 0.37, 95% CI [0.27, 0.47]. However, GPT significance statements (M = 92.73, SD = 6.89) had a statistically equivalent analytic style as PNAS significance statements (M = 92.32, SD = 7.48), Welch’s t(1587.7) = 1.16, p = .246, Cohen’s d = 0.06, 95% CI [-0.04, 0.16]. All results were maintained when comparing GPT significance statements to PNAS abstracts and PNAS significance statements as well. Alternative Explanations Alternative Explanations One possible explanation for the Study 1b results is that there are content differences across the PNAS and GPT texts explaining or impacting such differences across groups. This concern was addressed in two ways. First, PNAS has various sections that authors submit to, and LIWC has categories to approximate words associated with such sections. For example, the LIWC category for political speech would approximate papers submitted the Social Science section, specifically Political Sciences. Several linguistic covariates were therefore examined to account for content-related differences across GPT and PNAS texts. After including overall affect/emotion and cognition (to control for topics within the Psychological Sciences section of PNAS), political speech (to control for topics within the Political Science section of PNAS), and physical references to the multivariate models (to control for topics within the Biological Sciences section of PNAS), all results were maintained except for Analytic writing, where GPT texts were more analytic than PNAS texts, which is also consistent with prior work (42). Please see the online supplement for additional LIWC differences across these text types. Content effects were also evaluated in a bottom-up manner using the Meaning Extraction Method to measure dominant themes across the GPT and PNAS texts (43, 44). The evidence in the online supplement states there were 8 themes reliably extracted from the data, ranging from basic methodological and research information to gene expression and cancer science. Controlling for these themes, including the prior LIWC content dimensions, revealed consistent results as well (see supplement). Therefore, Study 1b evidence is robust to content. Altogether, human authors write simpler for lay audiences than for scientific audiences (Study 1a), but Study 1b demonstrated artificial intelligence and large language models can do so more effectively (e.g., the effect size differences between GPT significance statements and PNAS significance statements was larger than human in Study 1a). The findings thus far are correlational and therefore need causal evidence to demonstrate the impact of these effects on human perceptions. In Study 2, participants were randomly assigned to read a GPT significance statement or PNAS significance statement from pairs of texts that appeared in the previous studies. Participants made perceptions about the author (e.g., intelligence, credibility, trustworthiness), judged the complexity of each text, and they rated how much they believed the author of each text was human or artificial intelligence. Only perceptions of the author were made because prior work suggests people generally report consistent ratings when asked about both scientists and their science in similar studies (9). This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

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