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Is GPT Powerful Enough to Analyze the Emotions of Memes?: Discussion and References

Authors:
(1) Jingjing Wang, School of Computing Clemson University Clemson, South Carolina, USA;
(2) Joshua Luo, The Westminster Schools Atlanta, Georgia, USA;
(3) Grace Yang, South Windsor High School South Windsor, Connecticut, USA;
(4) Allen Hong, D.W. Daniel High School Clemson, South Carolina, USA;
(5) Feng Luo, School of Computing Clemson University Clemson, South Carolina, USA. Table of Links Abstract & Introduction Related Work Methodology Experiment Result Discussion and References IV. EXPERIMENT RESULTS In this section, we conducted an in-depth investigation by using the two datasets aforementioned in section 3 to explore the effectiveness of GPT in analyzing sentiments in memes. After carefully evaluating each meme based on the prompts designed, we meticulously collated and analyzed the results. Here is the comprehensive analysis of our findings. Case 1: The results of Facebook Hateful Memes dataset Our initial investigation involved the evaluation of GPT’s proficiency in identifying hateful content within memes using Facebook’s Hate/Non-Hate Classification Dataset. Here we repeat the experiments four times with different prompts. Hateful Content Identification: The task of identifying hateful memes posed a significant challenge. It required the AI to understand not only the content of the image and the accompanying text but also their nuanced interplay. The results showed an accuracy rate of 39% in detecting hateful content with a relatively high standard deviation of 6.87%. It can be seen that the performance of GPT is far less accurate in classifying the hateful memes, and the different prompts would affect the decision of GPT considerably. To accurately flag hateful memes, a model needs to be adept at grasping not only the overt message but also the undertones, context, and subtext. The 39% accuracy rate thus provides us with a baseline for improvement and future model refinements. Non-Hateful Content Identification: On a brighter note, GPT’s performance in recognizing non-hateful memes was much stronger, with an impressive accuracy rate of 80% and much lower standard deviation of 2.69%. The high accuracy suggests that GPT is quite adept at discerning memes that are not hateful, and the different prompts used had minimal impact on its decision-making process. Case 2: Memotion analysis dataset Overall Sentiment Classification: the first task in this series was classifying the overall sentiment of a meme as positive, negative, or neutral. In this regard, GPT achieved an accuracy rate of 79% for positive and 35% for negative. It can be observed that this result is consistent with the performance in the classification of hateful memes in the Facebook dataset, which shows confidence in understanding the positive sentiment in a meme, based on both the visual content and the accompanying text. For negative (for example, offensive in the later paragraph) content detection, the low accuracy still suggests the challenge. Humor recognition: GPT achieved a 60% accuracy rate in the task of recognizing humor. This outcome is notably remarkable, considering the intricate nature of humor as a multifaceted human emotion, often subject to cultural, social, and individual perspectives. The considerable level of accuracy exhibited by GPT indicates its ability to comprehend the humorous aspects present in a diverse range of memes. This result also implies that the GPT has acquired the ability to recognize various indicators of humor to a certain degree. These indicators might be obvious, such as punchlines or visual gags, or implicit, such as irony or absurdity. Sarcasm recognition: the model’s performance in the domain of sarcasm recognition was notably lower, registering an accuracy of 45% as illustrated in Table 2. Though lower, this accuracy level is acceptable given the inherent difficulties of sarcasm detection. Sarcasm often involves expressing the opposite of what is meant, requiring an understanding of context, tone, and frequently shared cultural knowledge. This observation helps to emphasize the limits of the approach in accurately detecting more subtle emotional cues and contextual subtleties. The lower accuracy rate observed in sarcasm recognition highlights the need for further study and model enhancement in this area. Offensive recognition: as seen in Table 2, GPT’s accuracy stood at 37% in recognizing offensive content, presenting the weakest performance. As aforementioned in overall sentiment classification, GPT meets the huge challenge in offensive content recognition. The consistency of this finding illustrates the difficulty associated with detecting offensive content. Comparable to hateful meme classification, offensive content in memes may exhibit subtlety, sometimes concealed within humor or sarcasm, or manifest through a complex interplay of textual and visual elements that can be hard to interpret for an AI. This paper is available on arxiv under CC 4.0 license. Authors: (1) Jingjing Wang, School of Computing Clemson University Clemson, South Carolina, USA; (2) Joshua Luo, The Westminster Schools Atlanta, Georgia, USA; (3) Grace Yang, South Windsor High School South Windsor, Connecticut, USA; (4) Allen Hong, D.W. Daniel High School Clemson, South Carolina, USA; (5) Feng Luo, School of Computing Clemson University Clemson, South Carolina, USA. Authors: (1) Jingjing Wang, School of Computing Clemson University Clemson, South Carolina, USA; (2) Joshua Luo, The Westminster Schools Atlanta, Georgia, USA; (3) Grace Yang, South Windsor High School South Windsor, Connecticut, USA; (4) Allen Hong, D.W. Daniel High School Clemson, South Carolina, USA; (5) Feng Luo, School of Computing Clemson University Clemson, South Carolina, USA. Table of Links Abstract & Introduction Abstract & Introduction Related Work Related Work Methodology Methodology Experiment Result Experiment Result Discussion and References Discussion and References IV. EXPERIMENT RESULTS In this section, we conducted an in-depth investigation by using the two datasets aforementioned in section 3 to explore the effectiveness of GPT in analyzing sentiments in memes. After carefully evaluating each meme based on the prompts designed, we meticulously collated and analyzed the results. Here is the comprehensive analysis of our findings. Case 1: The results of Facebook Hateful Memes dataset Case 1: The results of Facebook Hateful Memes dataset Our initial investigation involved the evaluation of GPT’s proficiency in identifying hateful content within memes using Facebook’s Hate/Non-Hate Classification Dataset. Here we repeat the experiments four times with different prompts. Hateful Content Identification: The task of identifying hateful memes posed a significant challenge. It required the AI to understand not only the content of the image and the accompanying text but also their nuanced interplay. The results showed an accuracy rate of 39% in detecting hateful content with a relatively high standard deviation of 6.87%. It can be seen that the performance of GPT is far less accurate in classifying the hateful memes, and the different prompts would affect the decision of GPT considerably. To accurately flag hateful memes, a model needs to be adept at grasping not only the overt message but also the undertones, context, and subtext. The 39% accuracy rate thus provides us with a baseline for improvement and future model refinements. Non-Hateful Content Identification: On a brighter note, GPT’s performance in recognizing non-hateful memes was much stronger, with an impressive accuracy rate of 80% and much lower standard deviation of 2.69%. The high accuracy suggests that GPT is quite adept at discerning memes that are not hateful, and the different prompts used had minimal impact on its decision-making process. Case 2: Memotion analysis dataset Case 2: Memotion analysis dataset Overall Sentiment Classification: the first task in this series was classifying the overall sentiment of a meme as positive, negative, or neutral. In this regard, GPT achieved an accuracy rate of 79% for positive and 35% for negative. It can be observed that this result is consistent with the performance in the classification of hateful memes in the Facebook dataset, which shows confidence in understanding the positive sentiment in a meme, based on both the visual content and the accompanying text. For negative (for example, offensive in the later paragraph) content detection, the low accuracy still suggests the challenge. Humor recognition: GPT achieved a 60% accuracy rate in the task of recognizing humor. This outcome is notably remarkable, considering the intricate nature of humor as a multifaceted human emotion, often subject to cultural, social, and individual perspectives. The considerable level of accuracy exhibited by GPT indicates its ability to comprehend the humorous aspects present in a diverse range of memes. This result also implies that the GPT has acquired the ability to recognize various indicators of humor to a certain degree. These indicators might be obvious, such as punchlines or visual gags, or implicit, such as irony or absurdity. Sarcasm recognition: the model’s performance in the domain of sarcasm recognition was notably lower, registering an accuracy of 45% as illustrated in Table 2. Though lower, this accuracy level is acceptable given the inherent difficulties of sarcasm detection. Sarcasm often involves expressing the opposite of what is meant, requiring an understanding of context, tone, and frequently shared cultural knowledge. This observation helps to emphasize the limits of the approach in accurately detecting more subtle emotional cues and contextual subtleties. The lower accuracy rate observed in sarcasm recognition highlights the need for further study and model enhancement in this area. Offensive recognition: as seen in Table 2, GPT’s accuracy stood at 37% in recognizing offensive content, presenting the weakest performance. As aforementioned in overall sentiment classification, GPT meets the huge challenge in offensive content recognition. The consistency of this finding illustrates the difficulty associated with detecting offensive content. Comparable to hateful meme classification, offensive content in memes may exhibit subtlety, sometimes concealed within humor or sarcasm, or manifest through a complex interplay of textual and visual elements that can be hard to interpret for an AI. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Is GPT Powerful Enough to Analyze the Emotions of Memes?: Experiment Result

Authors:
(1) Jingjing Wang, School of Computing Clemson University Clemson, South Carolina, USA;
(2) Joshua Luo, The Westminster Schools Atlanta, Georgia, USA;
(3) Grace Yang, South Windsor High School South Windsor, Connecticut, USA;
(4) Allen Hong, D.W. Daniel High School Clemson, South Carolina, USA;
(5) Feng Luo, School of Computing Clemson University Clemson, South Carolina, USA. Table of Links Abstract & Introduction Related Work Methodology Experiment Result Discussion and References Abstract Large Language Models (LLMs), representing a significant achievement in artificial intelligence (AI) research, have demonstrated their ability in a multitude of tasks. This project aims to explore the capabilities of GPT-3.5, a leading example of LLMs, in processing the sentiment analysis of Internet memes. Memes, which include both verbal and visual aspects, act as a powerful yet complex tool for expressing ideas and sentiments, demanding an understanding of societal norms and cultural contexts. Notably, the detection and moderation of hateful memes pose a significant challenge due to their implicit offensive nature. This project investigates GPT’s proficiency in such subjective tasks, revealing its strengths and potential limitations. The tasks include the classification of meme sentiment, determination of humor type, and detection of implicit hate in memes. The performance evaluation, using datasets from SemEval-2020 Task 8 and Facebook hateful memes, offers a comparative understanding of GPT responses against human annotations. Despite GPT’s remarkable progress, our findings underscore the challenges faced by these models in handling subjective tasks, which are rooted in their inherent limitations including contextual understanding, interpretation of implicit meanings, and data biases. This research contributes to the broader discourse on the applicability of AI in handling complex, context-dependent tasks, and offers valuable insights for future advancements. Index Terms—memotion analysis, hateful memes detection, GPT model I. INTRODUCTION Large Language Models (LLMs), representing a significant achievement in artificial intelligence (AI) research, have recently gathered substantial interest from both academia and industry. Due to their exceptional capacity to comprehend, generate, and engage in complex linguistic tasks, these models are revolutionizing the development and application of AI algorithms. Equipped with sophisticated algorithms and vast training datasets, LLMs have demonstrated advanced conversational capabilities, processing textual input and output to participate in detailed and meaningful dialogues [6], [10], [11]. OpenAI’s ChatGPT [12] is a prominent representative among these LLMs. It is an AI chatbot backed by the generative pretrained transformer (GPT) model and has attracted a broad societal interest. The proficiency of ChatGPT is particularly noticeable in the realm of question answering, spanning multiple sectors including, but not limited to, healthcare [7] and finance [8]. This model uses its linguistic capabilities to understand questions and generate human-like answers, marking a crucial milestone in AI-driven communication. Nevertheless, despite these impressive feats, LLMs, including GPT, confront considerable challenges when addressing subjective tasks, one such task being the interpretation and annotation of internet memes. Internet memes, modern digital artifacts, encompass a variety of cultural and sociological attitude, and serve as a type of social shorthand among online communities [1], [2]. These memes blend verbal and graphic elements to express complex concepts or feelings in a concise manner. However, the multimodal nature of memes, which combine various images with informal, often humorous or ironic text, introduces an intricate layer of complexity. To comprehend and analyze memes, one must manage the subtle interplay of visual clues, linguistic content, cultural allusions, and common community knowledge. This complexity, along with how subjective memes can be, makes it really tough for LLMs to accurately interpret memes and analyze their sentiment. Sentiment Analysis (SA) primarily concentrate on interpreting user sentiments from textual content. However, due to the proliferation and pervasiveness of multimodal social media data, such as online memes, SA must diversify to handle multimodal data sources. This diversification becomes even more complex when considering ’hateful memes’, which cleverly combine text and images to spread offensive or harmful messages under the guise of humor [3]–[5]. Detecting and moderating such content not only requires understanding the text-image interaction but also a grasp of the cultural context and, in many cases, specific sub-cultural expertise. These challenges call for ongoing research and development in the field, pushing the boundaries and exploring the potential of LLMs like GPT. In this project, we focus on the capabilities and limitations of LLMs like GPT in dealing with subjective and nuanced tasks, particularly internet meme sentiment analysis. This exploration forms the crux of this research, contributing to the broader discussion on the evolving potential and limitations of AI. In this project, we delve into the intricacies of subjectivity, assessing GPT’s performance in tasks necessitating an understanding of societal norms and cultural contexts. Our investigation primarily revolves around two research questions: 1. Can GPT accurately detect implicitly hateful memes given a specific prompt? 2. Can GPT effectively conduct sentiment analysis of memes, including classifying the sentiment of each meme as positive or negative, and categorizing the type of humor into three sub-classes: sarcastic, humorous, and offensive? We draw upon datasets from Facebook’s hateful memes [3] and SemEval-2020 Task 8: Multimodal Memotion Analysis [9] and for this project. The experimental section of the study will present a comparative analysis highlighting the constraints and limitations of GPT’s responses. This paper is available on arxiv under CC 4.0 license. Authors: (1) Jingjing Wang, School of Computing Clemson University Clemson, South Carolina, USA; (2) Joshua Luo, The Westminster Schools Atlanta, Georgia, USA; (3) Grace Yang, South Windsor High School South Windsor, Connecticut, USA; (4) Allen Hong, D.W. Daniel High School Clemson, South Carolina, USA; (5) Feng Luo, School of Computing Clemson University Clemson, South Carolina, USA. Authors: (1) Jingjing Wang, School of Computing Clemson University Clemson, South Carolina, USA; (2) Joshua Luo, The Westminster Schools Atlanta, Georgia, USA; (3) Grace Yang, South Windsor High School South Windsor, Connecticut, USA; (4) Allen Hong, D.W. Daniel High School Clemson, South Carolina, USA; (5) Feng Luo, School of Computing Clemson University Clemson, South Carolina, USA. Table of Links Abstract & Introduction Abstract & Introduction Related Work Related Work Methodology Methodology Experiment Result Experiment Result Discussion and References Discussion and References Abstract Large Language Models (LLMs), representing a significant achievement in artificial intelligence (AI) research, have demonstrated their ability in a multitude of tasks. This project aims to explore the capabilities of GPT-3.5, a leading example of LLMs, in processing the sentiment analysis of Internet memes. Memes, which include both verbal and visual aspects, act as a powerful yet complex tool for expressing ideas and sentiments, demanding an understanding of societal norms and cultural contexts. Notably, the detection and moderation of hateful memes pose a significant challenge due to their implicit offensive nature. This project investigates GPT’s proficiency in such subjective tasks, revealing its strengths and potential limitations. The tasks include the classification of meme sentiment, determination of humor type, and detection of implicit hate in memes. The performance evaluation, using datasets from SemEval-2020 Task 8 and Facebook hateful memes, offers a comparative understanding of GPT responses against human annotations. Despite GPT’s remarkable progress, our findings underscore the challenges faced by these models in handling subjective tasks, which are rooted in their inherent limitations including contextual understanding, interpretation of implicit meanings, and data biases. This research contributes to the broader discourse on the applicability of AI in handling complex, context-dependent tasks, and offers valuable insights for future advancements. Index Terms —memotion analysis, hateful memes detection, GPT model Index Terms I. INTRODUCTION Large Language Models (LLMs), representing a significant achievement in artificial intelligence (AI) research, have recently gathered substantial interest from both academia and industry. Due to their exceptional capacity to comprehend, generate, and engage in complex linguistic tasks, these models are revolutionizing the development and application of AI algorithms. Equipped with sophisticated algorithms and vast training datasets, LLMs have demonstrated advanced conversational capabilities, processing textual input and output to participate in detailed and meaningful dialogues [6], [10], [11]. OpenAI’s ChatGPT [12] is a prominent representative among these LLMs. It is an AI chatbot backed by the generative pretrained transformer (GPT) model and has attracted a broad societal interest. The proficiency of ChatGPT is particularly noticeable in the realm of question answering, spanning multiple sectors including, but not limited to, healthcare [7] and finance [8]. This model uses its linguistic capabilities to understand questions and generate human-like answers, marking a crucial milestone in AI-driven communication. Nevertheless, despite these impressive feats, LLMs, including GPT, confront considerable challenges when addressing subjective tasks, one such task being the interpretation and annotation of internet memes. Internet memes, modern digital artifacts, encompass a variety of cultural and sociological attitude, and serve as a type of social shorthand among online communities [1], [2]. These memes blend verbal and graphic elements to express complex concepts or feelings in a concise manner. However, the multimodal nature of memes, which combine various images with informal, often humorous or ironic text, introduces an intricate layer of complexity. To comprehend and analyze memes, one must manage the subtle interplay of visual clues, linguistic content, cultural allusions, and common community knowledge. This complexity, along with how subjective memes can be, makes it really tough for LLMs to accurately interpret memes and analyze their sentiment. Sentiment Analysis (SA) primarily concentrate on interpreting user sentiments from textual content. However, due to the proliferation and pervasiveness of multimodal social media data, such as online memes, SA must diversify to handle multimodal data sources. This diversification becomes even more complex when considering ’hateful memes’, which cleverly combine text and images to spread offensive or harmful messages under the guise of humor [3]–[5]. Detecting and moderating such content not only requires understanding the text-image interaction but also a grasp of the cultural context and, in many cases, specific sub-cultural expertise. These challenges call for ongoing research and development in the field, pushing the boundaries and exploring the potential of LLMs like GPT. In this project, we focus on the capabilities and limitations of LLMs like GPT in dealing with subjective and nuanced tasks, particularly internet meme sentiment analysis. This exploration forms the crux of this research, contributing to the broader discussion on the evolving potential and limitations of AI. In this project, we delve into the intricacies of subjectivity, assessing GPT’s performance in tasks necessitating an understanding of societal norms and cultural contexts. Our investigation primarily revolves around two research questions: 1. Can GPT accurately detect implicitly hateful memes given a specific prompt? 2. Can GPT effectively conduct sentiment analysis of memes, including classifying the sentiment of each meme as positive or negative, and categorizing the type of humor into three sub-classes: sarcastic, humorous, and offensive? We draw upon datasets from Facebook’s hateful memes [3] and SemEval-2020 Task 8: Multimodal Memotion Analysis [9] and for this project. The experimental section of the study will present a comparative analysis highlighting the constraints and limitations of GPT’s responses. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Is GPT Powerful Enough to Analyze the Emotions of Memes?

Authors:
(1) Maria Rigaki, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic and maria.rigaki@fel.cvut.cz;
(2) Sebastian Garcia, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic and sebastian.garcia@agents.fel.cvut.cz. Table of Links Abstract & Introduction Threat Model Background and Related Work Methodology Experiments Setup Results Discussion Conclusion, Acknowledgments, and References Appendix Abstract Due to the proliferation of malware, defenders are increasingly turning to automation and machine learning as part of the malware detection toolchain. However, machine learning models are susceptible to adversarial attacks, requiring the testing of model and product robustness. Meanwhile, attackers also seek to automate malware generation and evasion of antivirus systems, and defenders try to gain insight into their methods. This work proposes a new algorithm that combines Malware Evasion and Model Extraction (MEME) attacks. MEME uses model-based reinforcement learning to adversarially modify Windows executable binary samples while simultaneously training a surrogate model with a high agreement with the target model to evade. To evaluate this method, we compare it with two state-of-the-art attacks in adversarial malware creation, using three well-known published models and one antivirus product as targets. Results show that MEME outperforms the state-of-the-art methods in terms of evasion capabilities in almost all cases, producing evasive malware with an evasion rate in the range of 32-73%. It also produces surrogate models with a prediction label agreement with the respective target models between 97-99%. The surrogate could be used to fine-tune and improve the evasion rate in the future. Keywords: adversarial malware · reinforcement learning · model extraction · model stealing 1 Introduction As machine learning models are more commonly used in malware detection, there is a growing need for detection tools to combat evasive malware. Understanding attackers’ motives is vital in defending against malware, often created for profit. Malware-as-a-Service operations are used to automate the obfuscation and evasiveness of existing malware, and it is safe to assume that attackers will continue to improve their automation and try to create adversarial malware as efficiently as possible [45]. In this work, we are interested in the problem of automating the generation of evasive Windows malware executables primarily against machine learning static detection models. Creating evasive malicious binaries that preserve their functionality has been the subject of several works until now. Most works use a set of pre-defined actions that alter the Windows binary file by, e.g., adding benign sections and strings, modifying section names, and other ”non-destructive” alterations. The selection of the most appropriate set of actions is learned through reinforcement learning or similar approaches. To our knowledge, all prior work relies on the assumption that the target model (or system) is available to perform an unlimited number of checks or queries to verify whether a malicious binary has evaded the target. However, from the attacker’s perspective, fewer queries can a) generally mean less time to produce malware, b) lead to lower detection probabilities, and c) leak less information about the adversarial techniques. Therefore, assuming an attacker can do unlimited queries to a target model to modify their malware may not be realistic in evasive malware creation. We propose an algorithm that combines malware evasion with model extraction (MEME) based on model-based reinforcement learning. The goal of MEME is to learn a reinforcement learning policy that selects the appropriate modifications to a malicious Windows binary file in order for it to evade a target detection model while using a limited amount of interactions with the target. The core idea is to use observations and labels collected during the interaction with the reinforcement learning environment and use them with an auxiliary dataset to train a surrogate model of the target. Then the policy is trained to learn to evade the surrogate and evaluated on the original target. We test MEME using three malware detection models that are released and publicly available and on an antivirus installed by default in all Windows operating systems. MEME is compared with two baseline methods (a random policy and a PPO-based policy [35]) and well as with two state-of-the art methods (MAB [38] and GAMMA [9]). Using only 2,048 queries to the target during the training phase, MEME learns a policy that evades the targets with an evasion rate of 32-73%. MEME outperforms all baselines and state-of-the-art methods in all but one target. The algorithm also learns a surrogate model for each target with 97-99% label agreement with a much lower query budget than previously reported. The main contributions of this work are: – A novel combination of two attacks, malware evasion and model extraction in one algorithm (MEME). – An efficient generation of adversarial malware using model-based reinforcement learning while maintaining better evasion rates than state-of-the-art methods in most targets. – An efficient surrogate creation method that uses the adversarial samples produced during the training and evaluation of the reinforcement learning agent. The surrogates achieve high label agreement with the targets using minimal interaction with the target models. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Maria Rigaki, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic and maria.rigaki@fel.cvut.cz; (2) Sebastian Garcia, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic and sebastian.garcia@agents.fel.cvut.cz. Authors: Authors: (1) Maria Rigaki, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic and maria.rigaki@fel.cvut.cz; (2) Sebastian Garcia, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic and sebastian.garcia@agents.fel.cvut.cz. Table of Links Abstract & Introduction Abstract & Introduction Threat Model Threat Model Background and Related Work Background and Related Work Methodology Methodology Experiments Setup Experiments Setup Results Results Discussion Discussion Conclusion, Acknowledgments, and References Conclusion, Acknowledgments, and References Appendix Appendix Abstract Due to the proliferation of malware, defenders are increasingly turning to automation and machine learning as part of the malware detection toolchain. However, machine learning models are susceptible to adversarial attacks, requiring the testing of model and product robustness. Meanwhile, attackers also seek to automate malware generation and evasion of antivirus systems, and defenders try to gain insight into their methods. This work proposes a new algorithm that combines Malware Evasion and Model Extraction (MEME) attacks. MEME uses model-based reinforcement learning to adversarially modify Windows executable binary samples while simultaneously training a surrogate model with a high agreement with the target model to evade. To evaluate this method, we compare it with two state-of-the-art attacks in adversarial malware creation, using three well-known published models and one antivirus product as targets. Results show that MEME outperforms the state-of-the-art methods in terms of evasion capabilities in almost all cases, producing evasive malware with an evasion rate in the range of 32-73%. It also produces surrogate models with a prediction label agreement with the respective target models between 97-99%. The surrogate could be used to fine-tune and improve the evasion rate in the future. Keywords: adversarial malware · reinforcement learning · model extraction · model stealing Keywords: 1 Introduction As machine learning models are more commonly used in malware detection, there is a growing need for detection tools to combat evasive malware. Understanding attackers’ motives is vital in defending against malware, often created for profit. Malware-as-a-Service operations are used to automate the obfuscation and evasiveness of existing malware, and it is safe to assume that attackers will continue to improve their automation and try to create adversarial malware as efficiently as possible [45]. In this work, we are interested in the problem of automating the generation of evasive Windows malware executables primarily against machine learning static detection models. Creating evasive malicious binaries that preserve their functionality has been the subject of several works until now. Most works use a set of pre-defined actions that alter the Windows binary file by, e.g., adding benign sections and strings, modifying section names, and other ”non-destructive” alterations. The selection of the most appropriate set of actions is learned through reinforcement learning or similar approaches. To our knowledge, all prior work relies on the assumption that the target model (or system) is available to perform an unlimited number of checks or queries to verify whether a malicious binary has evaded the target. However, from the attacker’s perspective, fewer queries can a) generally mean less time to produce malware, b) lead to lower detection probabilities, and c) leak less information about the adversarial techniques. Therefore, assuming an attacker can do unlimited queries to a target model to modify their malware may not be realistic in evasive malware creation. We propose an algorithm that combines malware evasion with model extraction (MEME) based on model-based reinforcement learning. The goal of MEME is to learn a reinforcement learning policy that selects the appropriate modifications to a malicious Windows binary file in order for it to evade a target detection model while using a limited amount of interactions with the target. The core idea is to use observations and labels collected during the interaction with the reinforcement learning environment and use them with an auxiliary dataset to train a surrogate model of the target. Then the policy is trained to learn to evade the surrogate and evaluated on the original target. We test MEME using three malware detection models that are released and publicly available and on an antivirus installed by default in all Windows operating systems. MEME is compared with two baseline methods (a random policy and a PPO-based policy [35]) and well as with two state-of-the art methods (MAB [38] and GAMMA [9]). Using only 2,048 queries to the target during the training phase, MEME learns a policy that evades the targets with an evasion rate of 32-73%. MEME outperforms all baselines and state-of-the-art methods in all but one target. The algorithm also learns a surrogate model for each target with 97-99% label agreement with a much lower query budget than previously reported. The main contributions of this work are: – A novel combination of two attacks, malware evasion and model extraction in one algorithm (MEME). – An efficient generation of adversarial malware using model-based reinforcement learning while maintaining better evasion rates than state-of-the-art methods in most targets. – An efficient surrogate creation method that uses the adversarial samples produced during the training and evaluation of the reinforcement learning agent. The surrogates achieve high label agreement with the targets using minimal interaction with the target models. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

The Power of MEME: Adversarial Malware Creation with Model-Based Reinforcement Learning

Super Charge Your Email Marketing Campaigns With the Power of AI!!

Welcome to "Power Leader," an exploration of leadership and innovative technologies reshaping various industries. This series is designed to bring to light the pivotal roles that leaders play in driving their organizations towards groundbreaking achievements, especially through the lens of technological advancements and strategic foresight. The leaders featured in "Power Leader" are selected because they exemplify what it means to steer companies through the challenges of modern business landscapes. They are pioneers, thinkers, and strategists making notable impacts within their sectors. By focusing on their achievements and the specifics of the technologies they develop or adopt, it provides a comprehensive look at what makes their approaches successful and influential. Why is this important? Understanding the nexus between effective leadership and innovation is more crucial than ever. These stories not only highlight successful strategies and cutting-edge solutions but also serve as a source of inspiration and learning for our readers. They offer insights into managing complex transformations and leveraging technological advancements to not only survive but thrive in competitive markets. Next up is Zach Taylor, CEO of Anvl and a noted SaaS advisor. Anvl is a leader in enhancing workplace safety and productivity through innovative technology. How does Anvl's platform specifically cater to the unique needs of different businesses? Anvl’s AI Connected Worker Platform is the sole digital layer for industrial enterprises to digitally transform to enhance frontline work experiences, making work safer, improving quality, and delivering trustworthy data in real-time to leadership for continuous improvement. Can you describe some real-world scenarios where your platform has improved workplace safety and productivity? One compelling example of our platform's impact comes from our work with Constellation. Initially, their Near Miss program relied heavily on manual, paper-based processes. Technicians submitted nearly 12,000 Near Miss forms annually, which were then transcribed into spreadsheets. This cumbersome process resulted in thousands of hours of administrative work, offering minimal insight into safety trends or reporting capabilities. Recognizing the need for a more efficient solution, Constellation implemented the Anvl Mobile App. This transition to a mobile-first system enabled the immediate deployment of a fully customized Near Miss program alongside digital workflows for various safety audits and permits. The app's adoption by around 400 technicians and supervisors facilitated tens of thousands of digital workflows, significantly enhancing the identification of hazards and risks directly in the field. The results were transformative. The app's adoption led to a 95% compliance rate across Constellation's safety programs, with thousands of workflows completed yearly. The app's offline functionality ensured uninterrupted productivity, while its features, such as voice-to-text, annotated photos, and video, enabled the collection of reliable data. This shift not only allowed for real-time issue identification and risk notification but also provided leadership with insights into trending hazards and operational challenges faced by the crew. Through the aggregation of thousands of safety-related data points, Anvl delivered scalable insights that were previously unattainable. Constellation could proactively identify the top issues their teams faced each quarter, including significant operational "Materials" issues, thereby reducing unnecessary expenses and optimizing material usage and time. Moreover, Anvl's introduction of proprietary metrics such as the Strength Score & Power Score enabled a nuanced assessment of safety performance across various levels of the organization. This holistic approach to safety management and continuous improvement has not only elevated compliance to an all-time high but also resulted in significant cost savings and increased work efficiency for Constellation What makes Anvl's software different from other connected worker solutions available on the market? Anvl’s Mobilize product leverages AI to automatically transform your company’s specific procedures and compliance documentation in seconds into dynamic logic workflows made available through our fully internationalized mobile app (iOS, Android, and Windows). How does the customization process work for a new client? Can you walk us through the highlights  from initial contact to full implementation? This is where Anvl’s AI shines, implementation takes minutes rather than months. Upload your company's forms or  let Mobilize generate OSHA and ISO compliant programs and documentation in seconds. These are then made immediately available through our SOC2 compliant mobile app available through iOS, Android, and Windows. In terms of data privacy and security, what measures does Anvl take to protect its users? Anvl leverages modern, best-in-class cloud and security architecture and protocols, and maintains SOC2 compliance. Looking ahead, how do you see Anvl evolving to meet future workplace safety and productivity needs? Anvl is positioned at the forefront of a significant transformation in workplace safety and productivity. Our vision is grounded in the continuous innovation and enhancement of our platform to not only meet but anticipate the evolving needs of the modern workforce. One key area of focus is the further integration of artificial intelligence and machine learning capabilities into our software. This will enable us to provide even more predictive analytics, offering businesses foresight into potential safety risks before they occur. By analyzing patterns and trends from the extensive data collected through our platform, we can identify probable hazards and advise on preventive measures, thereby shifting from reactive to proactive safety management. As the tenured labor force with vast institutional knowledge starts retiring en masse and a new generation, accustomed to smartphones and digital apps take their place , Anvl intends to enhance connectivity and real-time communication features. This will ensure that regardless of location, employees can access critical safety information, report incidents immediately, and receive timely support. Our goal is to create a seamless, integrated experience that prioritizes employee well-being and promotes a culture of safety, regardless of geographical boundaries. We are committed to making our platform even more user-friendly and accessible. By simplifying the user interface and enhancing the mobile experience, we aim to encourage higher engagement levels among frontline workers. Their direct input and feedback are invaluable for building a comprehensive safety culture. Collaboration with industry partners and regulatory bodies will also be a priority to ensure our solutions not only comply with but also influence future safety standards and practices. By staying ahead of regulatory changes and industry trends, Anvl can provide businesses with the tools they need to remain compliant and competitive. Lastly, sustainability and environmental health will play a larger role in our platform's evolution. Recognizing the intersection between environmental practices and worker safety, Anvl will expand its capabilities to support organizations in their pursuit of sustainability goals, ensuring a safe, healthy, and sustainable workplace for all. Our commitment to innovation, coupled with a deep understanding of our clients' needs, positions us to lead the way in creating safer, more efficient, and more resilient workplaces worldwide. How does Anvl stay ahead of changes in workplace regulations and ensure its platform remains compliant? Anvl’s AI ensures we are constantly evolving with the changing requirements and allows for real-time edits and customizations by clients. What advice would you give to businesses considering implementing a connected worker platform? Ensure you implement a platform over a point solution; obsess over frontline worker usability and adoption (does it work offline?) and information integrity (how does the platform ensure data parity and data integrity while simultaneously solving for bespoke experiences for different areas and/or sites. Looking at the current landscape of workplace technology, where do you see the biggest gaps or opportunities for innovation? Innovation is happening at a rapid clip, what the broader Industrial SaaS world needs is more integration and interoperability. Larger, established players should be willing and wanting to optimize end-user experiences and data enablement via simple integrations. Welcome to "Power Leader," an exploration of leadership and innovative technologies reshaping various industries. This series is designed to bring to light the pivotal roles that leaders play in driving their organizations towards groundbreaking achievements, especially through the lens of technological advancements and strategic foresight. The leaders featured in "Power Leader" are selected because they exemplify what it means to steer companies through the challenges of modern business landscapes. They are pioneers, thinkers, and strategists making notable impacts within their sectors. By focusing on their achievements and the specifics of the technologies they develop or adopt, it provides a comprehensive look at what makes their approaches successful and influential. Why is this important? Understanding the nexus between effective leadership and innovation is more crucial than ever. These stories not only highlight successful strategies and cutting-edge solutions but also serve as a source of inspiration and learning for our readers. They offer insights into managing complex transformations and leveraging technological advancements to not only survive but thrive in competitive markets. Next up is Zach Taylor, CEO of Anvl and a noted SaaS advisor. Anvl is a leader in enhancing workplace safety and productivity through innovative technology. Zach Taylor, Anvl How does Anvl's platform specifically cater to the unique needs of different businesses? How does Anvl's platform specifically cater to the unique needs of different businesses? Anvl’s AI Connected Worker Platform is the sole digital layer for industrial enterprises to digitally transform to enhance frontline work experiences, making work safer, improving quality, and delivering trustworthy data in real-time to leadership for continuous improvement. Can you describe some real-world scenarios where your platform has improved workplace safety and productivity? Can you describe some real-world scenarios where your platform has improved workplace safety and productivity? One compelling example of our platform's impact comes from our work with Constellation. Initially, their Near Miss program relied heavily on manual, paper-based processes. Technicians submitted nearly 12,000 Near Miss forms annually, which were then transcribed into spreadsheets. This cumbersome process resulted in thousands of hours of administrative work, offering minimal insight into safety trends or reporting capabilities. Recognizing the need for a more efficient solution, Constellation implemented the Anvl Mobile App. This transition to a mobile-first system enabled the immediate deployment of a fully customized Near Miss program alongside digital workflows for various safety audits and permits. The app's adoption by around 400 technicians and supervisors facilitated tens of thousands of digital workflows, significantly enhancing the identification of hazards and risks directly in the field. The results were transformative. The app's adoption led to a 95% compliance rate across Constellation's safety programs, with thousands of workflows completed yearly. The app's offline functionality ensured uninterrupted productivity, while its features, such as voice-to-text, annotated photos, and video, enabled the collection of reliable data. This shift not only allowed for real-time issue identification and risk notification but also provided leadership with insights into trending hazards and operational challenges faced by the crew. Through the aggregation of thousands of safety-related data points, Anvl delivered scalable insights that were previously unattainable. Constellation could proactively identify the top issues their teams faced each quarter, including significant operational "Materials" issues, thereby reducing unnecessary expenses and optimizing material usage and time. Through the aggregation of thousands of safety-related data points, Anvl delivered scalable insights that were previously unattainable. Constellation could proactively identify the top issues their teams faced each quarter, including significant operational "Materials" issues, thereby reducing unnecessary expenses and optimizing material usage and time. Moreover, Anvl's introduction of proprietary metrics such as the Strength Score & Power Score enabled a nuanced assessment of safety performance across various levels of the organization. This holistic approach to safety management and continuous improvement has not only elevated compliance to an all-time high but also resulted in significant cost savings and increased work efficiency for Constellation What makes Anvl's software different from other connected worker solutions available on the market? What makes Anvl's software different from other connected worker solutions available on the market? Anvl’s Mobilize product leverages AI to automatically transform your company’s specific procedures and compliance documentation in seconds into dynamic logic workflows made available through our fully internationalized mobile app (iOS, Android, and Windows). How does the customization process work for a new client? Can you walk us through the highlights  from initial contact to full implementation? How does the customization process work for a new client? Can you walk us through the highlights  from initial contact to full implementation? This is where Anvl’s AI shines, implementation takes minutes rather than months. Upload your company's forms or  let Mobilize generate OSHA and ISO compliant programs and documentation in seconds. These are then made immediately available through our SOC2 compliant mobile app available through iOS, Android, and Windows. This is where Anvl’s AI shines, implementation takes minutes rather than months. Upload your company's forms or  let Mobilize generate OSHA and ISO compliant programs and documentation in seconds. These are then made immediately available through our SOC2 compliant mobile app available through iOS, Android, and Windows. In terms of data privacy and security, what measures does Anvl take to protect its users? In terms of data privacy and security, what measures does Anvl take to protect its users? Anvl leverages modern, best-in-class cloud and security architecture and protocols, and maintains SOC2 compliance. Looking ahead, how do you see Anvl evolving to meet future workplace safety and productivity needs? Looking ahead, how do you see Anvl evolving to meet future workplace safety and productivity needs? Anvl is positioned at the forefront of a significant transformation in workplace safety and productivity. Our vision is grounded in the continuous innovation and enhancement of our platform to not only meet but anticipate the evolving needs of the modern workforce. One key area of focus is the further integration of artificial intelligence and machine learning capabilities into our software. This will enable us to provide even more predictive analytics, offering businesses foresight into potential safety risks before they occur. By analyzing patterns and trends from the extensive data collected through our platform, we can identify probable hazards and advise on preventive measures, thereby shifting from reactive to proactive safety management. As the tenured labor force with vast institutional knowledge starts retiring en masse and a new generation, accustomed to smartphones and digital apps take their place , Anvl intends to enhance connectivity and real-time communication features. This will ensure that regardless of location, employees can access critical safety information, report incidents immediately, and receive timely support. Our goal is to create a seamless, integrated experience that prioritizes employee well-being and promotes a culture of safety, regardless of geographical boundaries. We are committed to making our platform even more user-friendly and accessible. By simplifying the user interface and enhancing the mobile experience, we aim to encourage higher engagement levels among frontline workers. Their direct input and feedback are invaluable for building a comprehensive safety culture. Collaboration with industry partners and regulatory bodies will also be a priority to ensure our solutions not only comply with but also influence future safety standards and practices. By staying ahead of regulatory changes and industry trends, Anvl can provide businesses with the tools they need to remain compliant and competitive. Collaboration with industry partners and regulatory bodies will also be a priority to ensure our solutions not only comply with but also influence future safety standards and practices. By staying ahead of regulatory changes and industry trends, Anvl can provide businesses with the tools they need to remain compliant and competitive. Lastly, sustainability and environmental health will play a larger role in our platform's evolution. Recognizing the intersection between environmental practices and worker safety, Anvl will expand its capabilities to support organizations in their pursuit of sustainability goals, ensuring a safe, healthy, and sustainable workplace for all. Our commitment to innovation, coupled with a deep understanding of our clients' needs, positions us to lead the way in creating safer, more efficient, and more resilient workplaces worldwide. How does Anvl stay ahead of changes in workplace regulations and ensure its platform remains compliant? How does Anvl stay ahead of changes in workplace regulations and ensure its platform remains compliant? Anvl’s AI ensures we are constantly evolving with the changing requirements and allows for real-time edits and customizations by clients. What advice would you give to businesses considering implementing a connected worker platform? What advice would you give to businesses considering implementing a connected worker platform? Ensure you implement a platform over a point solution; obsess over frontline worker usability and adoption (does it work offline?) and information integrity (how does the platform ensure data parity and data integrity while simultaneously solving for bespoke experiences for different areas and/or sites. Looking at the current landscape of workplace technology, where do you see the biggest gaps or opportunities for innovation? Looking at the current landscape of workplace technology, where do you see the biggest gaps or opportunities for innovation? Innovation is happening at a rapid clip, what the broader Industrial SaaS world needs is more integration and interoperability. Larger, established players should be willing and wanting to optimize end-user experiences and data enablement via simple integrations.

POWER LEADER: Anvl's Zach Taylor on the Connected Worker Platform for Safety and Productivity

Intro How often have you heard that the golden era for VC startups is coming to an end? More and more voices, from Steve Blank to VC investors and accelerator representatives, are emphasizing that startup founders need to show financial performance rather than simply relying on groundbreaking ideas and technological innovations. As a result, founders are faced with a crucial question: do they have the skills to model and utilize unit economics? To gain a deeper understanding of this framework, I had a conversation with Daniil Khanin, a Barcelona (Spain) - based entrepreneur known as the guru of Unit Economics and the Data-Driven Decisions approach. Daniil is the founder and CEO of the startup ueCalc and the author of the book "Unit Economics: Data-Driven Decisions for Business and Startups" featuring a foreword by Ash Maurya, bestselling author of "Running Lean" and "Scaling Lean". Over the years, we've collaborated with startups through The Internet Initiatives Development Fund accelerator, and as part of my special project, "Behind the Startup: Conversations with Founders & VC Visionaries", I extended an invitation to Daniil to participate in an interview. Unit economics offers a rapid and accessible means to answer critical questions about the effectiveness of a chosen business model, the required investment for a startup and its duration, the projected depletion of funds, and the prioritization of business processes. Q&A Victor Markov: "Let's begin with the most common question: what is unit economics, and why is it so crucial?" Daniil Khanin: "Unit economics serves as a universal framework that integrates the financial aspects of a business with the product competencies of the team driving that business. In the early stages of its journey, an innovative startup operates in a realm of complete uncertainty. It's unclear who the customer is, what their true needs are, how much time it will take to develop a sought-after product, and the associated costs for the team. Nonetheless, decisions regarding product creation must be made. While various frameworks exist for this purpose, such as Lean Canvas developed by Ash Maurya, unit economics stands out as a tool for evaluating finances at the outset. Additionally, this tool aids in identifying growth opportunities within an already expanding startup." Victor Markov: "What makes unit economics so indispensable?" Daniil Khanin: "The significance of unit economics lies in its ability to conserve the scarce resources of a startup. It guides startups in correctly allocating resources over time, directing them toward the most crucial processes. Furthermore, it can lead to the unpopular decision of not launching a startup at all if it lacks economic viability. While this may seem counterintuitive - who can predict the future? - unit economics is not solely about forecasting; it's about assessing the team's readiness to shape that future." Victor Markov: "I recall numerous instances where teams opted to abandon their business models and shut down their startups after analyzing their financial models and unit economics. Can you share your experiences working with such teams?" Daniil Khanin: "While I can't disclose specific details due to confidentiality, I can provide a general overview. It began when a team set out to develop IT solutions for large public facilities like stadiums and major shopping centers. The team harbored expectations regarding the project's profitability. However, upon assembling their financial model, they were dismayed by the dismal financial outlook. It became evident that the projected returns were insufficient to justify pursuing the venture. At this juncture, conventional wisdom might dictate tweaking the model parameters to yield favorable outcomes. However, with a unit economics-based model, altering several key parameters implies adjusting corresponding aspects of the business model, each overseen by a team member. This realization prompted the team to acknowledge their lack of requisite competencies to enter the market and navigate subsequent business development." Victor Markov: "Why were the teams unable to recognize this themselves?" Daniil Khanin: "Often, our unwavering belief in an idea blinds us to rational and critical evaluation. No set criteria can circumvent this tendency. I've made similar mistakes countless times. The beauty of unit economics lies in its transparency. By constructing a financial model based on unit economics, you control a limited set of parameters, each directly linked to your team and its expertise. You can deceive yourself all you want, but the numbers lay bare any underlying issues, illuminating the true cost involved." Victor Markov: "To what extent does this approach align with traditional methods? Financial modeling has been a staple of business schools and online courses for quite some time." Daniil Khanin: "This is a pivotal question. Formally, a financial model crafted using my templates mirrors one crafted by seasoned financiers. However, the challenge lies in finding someone within a startup capable of creating such a document and adeptly managing all the parameters therein. My approach represents a fundamental shift - a new philosophy of document creation. I encapsulate it as 'being accountable only for what you can control.'" Victor Markov: "Intriguing. Can you go into more detail about this philosophy?" Daniil Khanin: "It all circles back to unit economics. As unit economics intertwines business processes with financial metrics, it facilitates the construction of financial documents like profit and loss statements, cash flows, and balance sheets. However, the model's author only manages a select set of metrics comprising the unit economics model. Thus, to achieve specified targets - say, EBITDA or Revenue - the model's author adjusts unit economics metrics, which, in turn, directly influence business processes. Whether the team possesses the means to impact these processes becomes readily apparent. Most importantly, this approach is easily verifiable. If a team claims a 15% conversion rate significantly higher than the market average, they must elucidate their strategy for achieving this feat. Do they possess relevant experience or a concrete plan? If not, surpassing the market average seems improbable." Victor Markov: "It seems like a win-win situation, benefiting both startup founders and potential investors. You recently published a book on this topic. Could you give more details about it?" Daniil Khanin: "Certainly. While advocating for unit economics, I fielded inquiries about further reading on the subject. Surprisingly, I couldn't identify any books despite accumulating a wealth of material. Consequently, I compiled a book elucidating the approach, philosophy, and underscoring how competencies influence business's financial performance. Essentially, I distilled my decade-long experience with unit economics, examining myriad examples of its application in addressing various business inquiries - from identifying growth opportunities to crafting financial models." Victor Markov: "Okay, while startups leverage unit economics for modeling, those at later funding stages with impressive revenue figures also employ this framework for decision-making. How is this approach utilized in such scenarios?" Daniil Khanin: "Indeed, unit economics alone proves insufficient. While it offers insight into the current state of a business, informed decision-making requires identifying processes ripe for improvement, thereby enhancing business performance while minimizing resource expenditure. This task isn't straightforward, but it represents the essence of business focus. By pinpointing the process that currently offers the most potential for growth, you can achieve substantial growth with minimal resource outlay - a critical consideration for resource-constrained startups." Victor Markov: "That sounds intriguing. Can you shed light on how startups can identify these growth opportunities? What tools or solutions are required?" Daniil Khanin: "You can start with just a napkin and pen, but effective tools are indispensable. While you can construct a model using any spreadsheet editor or even pen and paper, pinpointing growth opportunities and crafting financial models is no easy feat. It all boils down to cohorts - the bedrock of unit economics data. Managing and analyzing cohorts isn't straightforward, even with Excel. Hence, I developed ueCalc. It streamlines the creation of unit economics models for most business models and facilitates the rapid construction of corresponding financial models. The service handles the heavy lifting, requiring the team merely to delineate their fundamental capabilities in influencing their model's unit economics metrics. Even though creating a high-quality model necessitates some adjustments and a serious commitment, the process remains far more streamlined and expedited than Excel-based modeling." Outro Cohort analysis in unit economics presents a multifaceted challenge deserving of a dedicated article. In this interview, Daniil Khanin touched upon critical issues in today's world and the state of the venture capital market, such as profitability, resource conservation, and decision-making based on data-driven decisions. While implementing the framework requires time, the return on investment is swift. Crucially, all the necessary elements are present: market demand, accessible solutions, and a plethora of articles, books, and resources. Such models yield benefits not only for startups and investors but also for the market at large. Intro How often have you heard that the golden era for VC startups is coming to an end? More and more voices, from Steve Blank to VC investors and accelerator representatives, are emphasizing that startup founders need to show financial performance rather than simply relying on groundbreaking ideas and technological innovations. As a result, founders are faced with a crucial question: do they have the skills to model and utilize unit economics? Steve Blank To gain a deeper understanding of this framework, I had a conversation with Daniil Khanin , a Barcelona (Spain) - based entrepreneur known as the guru of Unit Economics and the Data-Driven Decisions approach. Daniil is the founder and CEO of the startup ueCalc and the author of the book "Unit Economics: Data-Driven Decisions for Business and Startups" featuring a foreword by Ash Maurya , bestselling author of "Running Lean" and "Scaling Lean". Daniil Khanin Barcelona (Spain) ueCalc "Unit Economics: Data-Driven Decisions for Business and Startups" Ash Maurya Over the years, we've collaborated with startups through The Internet Initiatives Development Fund accelerator , and as part of my special project, "Behind the Startup: Conversations with Founders & VC Visionaries" , I extended an invitation to Daniil to participate in an interview. The Internet Initiatives Development Fund accelerator "Behind the Startup: Conversations with Founders & VC Visionaries" Unit economics offers a rapid and accessible means to answer critical questions about the effectiveness of a chosen business model, the required investment for a startup and its duration, the projected depletion of funds, and the prioritization of business processes. Q&A Victor Markov: "Let's begin with the most common question: what is unit economics, and why is it so crucial?" Victor Markov: Daniil Khanin: "Unit economics serves as a universal framework that integrates the financial aspects of a business with the product competencies of the team driving that business. In the early stages of its journey, an innovative startup operates in a realm of complete uncertainty. It's unclear who the customer is, what their true needs are, how much time it will take to develop a sought-after product, and the associated costs for the team. Nonetheless, decisions regarding product creation must be made. While various frameworks exist for this purpose, such as Lean Canvas developed by Ash Maurya , unit economics stands out as a tool for evaluating finances at the outset. Additionally, this tool aids in identifying growth opportunities within an already expanding startup." Daniil Khanin: Ash Maurya Victor Markov: "What makes unit economics so indispensable?" Victor Markov: Daniil Khanin: "The significance of unit economics lies in its ability to conserve the scarce resources of a startup. It guides startups in correctly allocating resources over time, directing them toward the most crucial processes. Furthermore, it can lead to the unpopular decision of not launching a startup at all if it lacks economic viability. While this may seem counterintuitive - who can predict the future? - unit economics is not solely about forecasting; it's about assessing the team's readiness to shape that future." Daniil Khanin: Victor Markov: "I recall numerous instances where teams opted to abandon their business models and shut down their startups after analyzing their financial models and unit economics. Can you share your experiences working with such teams?" Victor Markov: Daniil Khanin: "While I can't disclose specific details due to confidentiality, I can provide a general overview. It began when a team set out to develop IT solutions for large public facilities like stadiums and major shopping centers. The team harbored expectations regarding the project's profitability. However, upon assembling their financial model, they were dismayed by the dismal financial outlook. It became evident that the projected returns were insufficient to justify pursuing the venture. Daniil Khanin: At this juncture, conventional wisdom might dictate tweaking the model parameters to yield favorable outcomes. However, with a unit economics-based model, altering several key parameters implies adjusting corresponding aspects of the business model, each overseen by a team member. This realization prompted the team to acknowledge their lack of requisite competencies to enter the market and navigate subsequent business development." Victor Markov: "Why were the teams unable to recognize this themselves?" Victor Markov: Daniil Khanin: "Often, our unwavering belief in an idea blinds us to rational and critical evaluation. No set criteria can circumvent this tendency. I've made similar mistakes countless times. The beauty of unit economics lies in its transparency. By constructing a financial model based on unit economics, you control a limited set of parameters, each directly linked to your team and its expertise. You can deceive yourself all you want, but the numbers lay bare any underlying issues, illuminating the true cost involved." Daniil Khanin: Victor Markov: "To what extent does this approach align with traditional methods? Financial modeling has been a staple of business schools and online courses for quite some time." Victor Markov: Daniil Khanin: "This is a pivotal question. Formally, a financial model crafted using my templates mirrors one crafted by seasoned financiers. However, the challenge lies in finding someone within a startup capable of creating such a document and adeptly managing all the parameters therein. My approach represents a fundamental shift - a new philosophy of document creation. I encapsulate it as 'being accountable only for what you can control.'" Daniil Khanin: Victor Markov: "Intriguing. Can you go into more detail about this philosophy?" Victor Markov: Daniil Khanin: "It all circles back to unit economics. As unit economics intertwines business processes with financial metrics, it facilitates the construction of financial documents like profit and loss statements, cash flows, and balance sheets. However, the model's author only manages a select set of metrics comprising the unit economics model. Thus, to achieve specified targets - say, EBITDA or Revenue - the model's author adjusts unit economics metrics, which, in turn, directly influence business processes. Daniil Khanin: Whether the team possesses the means to impact these processes becomes readily apparent. Most importantly, this approach is easily verifiable. If a team claims a 15% conversion rate significantly higher than the market average, they must elucidate their strategy for achieving this feat. Do they possess relevant experience or a concrete plan? If not, surpassing the market average seems improbable." Victor Markov: "It seems like a win-win situation, benefiting both startup founders and potential investors. You recently published a book on this topic. Could you give more details about it?" Victor Markov: Daniil Khanin: "Certainly. While advocating for unit economics, I fielded inquiries about further reading on the subject. Surprisingly, I couldn't identify any books despite accumulating a wealth of material. Consequently, I compiled a book elucidating the approach, philosophy, and underscoring how competencies influence business's financial performance. Essentially, I distilled my decade-long experience with unit economics, examining myriad examples of its application in addressing various business inquiries - from identifying growth opportunities to crafting financial models." Daniil Khanin: Victor Markov: "Okay, while startups leverage unit economics for modeling, those at later funding stages with impressive revenue figures also employ this framework for decision-making. How is this approach utilized in such scenarios?" Victor Markov: Daniil Khanin: "Indeed, unit economics alone proves insufficient. While it offers insight into the current state of a business, informed decision-making requires identifying processes ripe for improvement, thereby enhancing business performance while minimizing resource expenditure. This task isn't straightforward, but it represents the essence of business focus. By pinpointing the process that currently offers the most potential for growth, you can achieve substantial growth with minimal resource outlay - a critical consideration for resource-constrained startups." Daniil Khanin: Victor Markov: "That sounds intriguing. Can you shed light on how startups can identify these growth opportunities? What tools or solutions are required?" Victor Markov: Daniil Khanin: "You can start with just a napkin and pen, but effective tools are indispensable. While you can construct a model using any spreadsheet editor or even pen and paper, pinpointing growth opportunities and crafting financial models is no easy feat. It all boils down to cohorts - the bedrock of unit economics data. Managing and analyzing cohorts isn't straightforward, even with Excel. Daniil Khanin: Hence, I developed ueCalc . It streamlines the creation of unit economics models for most business models and facilitates the rapid construction of corresponding financial models. The service handles the heavy lifting, requiring the team merely to delineate their fundamental capabilities in influencing their model's unit economics metrics. Even though creating a high-quality model necessitates some adjustments and a serious commitment, the process remains far more streamlined and expedited than Excel-based modeling." ueCalc Outro Outro Cohort analysis in unit economics presents a multifaceted challenge deserving of a dedicated article. In this interview, Daniil Khanin touched upon critical issues in today's world and the state of the venture capital market, such as profitability, resource conservation, and decision-making based on data-driven decisions. Daniil Khanin While implementing the framework requires time, the return on investment is swift. Crucially, all the necessary elements are present: market demand, accessible solutions, and a plethora of articles, books, and resources. Such models yield benefits not only for startups and investors but also for the market at large.

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