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The World Health Organization said Wednesday a person in Mexico had died in the first confirmed human case globally of infection with the H5N2 variant of bird flu.

WHO Confirms First Human Death Of Bird Flu In Mexico

The 59-year-old was the first lab-confirmed human case of infection with A(H5N2) subtype of the virus which has been reported in poultry in the country.

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A Tampa woman is accused of being a ‘Madame’ and running a human trafficking and prostitution ring in two counties.

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H5N1 Bird Flu Isn’t a Human Pandemic—Yet | Scientific American

Human

AI’s ability to mimic human behavior, emotional intelligence, creativity, and morality while exploring the future of artificial intelligence Artificial intelligence, especially that afforded by ChatGPT, has changed our manner of dealing with machines. AI now puts us in a state of conversation, learns from us, and even attempts to understand the context behind the questions we ask. However, the significant issue that people keep asking is whether ChatGPT can be trained to become human. At the core of AIs like ChatGPT is a neural network, a complex web of algorithms meant to mimic the functioning of the human brain. The algorithms learn about patterns, make predictions, and improve over time through a process known as machine learning. However, there is quite a gap between teaching patterns and being human. But being human isn’t just about patterns or predictions and it is about emotions, experience, understanding of culture, making moral judgments, and the ability for truly original creativity. Interacting with AI today may often generate human-like responses that are interesting and even insightful at times. Yet, what generates those responses — patterns found in large datasets, not quite the sort of personal contemplation or emotive depth carried into every encounter by humans. To answer if ChatGPT can be trained to be human, we must consider what exactly it means for something to be human. For this argument, let’s break it down into several such components: emotional intelligence, creativity, morality, memory, and learning from experiences. Some of these are important facets of human behavior and cognition. Can AI, particularly ChatGPT, ever be imbued with these so that we consider the AI “human” to that degree? Emotional Intelligence Emotional intelligence is the ability to sense how others feel and manage one’s own emotions. Our words usually carry emotional intensity from subtle sarcasm to overt anger or happiness. Humans use tone, context, and body language to communicate and interpret emotions. ChatGPT will simulate aspects of emotional intelligence because it can generate text that carries an emotional flavor for a conversation. If you express frustration, then it produces a response that would appear empathetic; if you are ebullient, it can echo some lighthearted repartee. Does this mean it “knows” or understands emotions? No. Instead, what ChatGPT does is recognize patterns in language as they reflect emotion, but it does not feel emotions. It doesn’t have the physiological and psychological mechanisms that underlie human emotion. Training ChatGPT to “be human” in this way would take more than feeding it datasets on emotional expression. It would rather require an architecture that allowed for some authentic experiences of emotions — that is, something beyond the scope of current developments in AI. Even if we can, in some way, simulate emotional reactions in AI, they will still be fundamentally different from human emotions, which are tied closely with consciousness, biology, and experience. Creativity Creativity- the mark of human intelligence: the ability to come up with new ideas, solve problems in innovative ways, and create art that vibrates at a deeply emotional or intellectual level. Is ChatGPT creative? In this respect, yes. ChatGPT can write, for instance, poems, short stories, code- whatever you want it to do. All the same, this creativity is based on the pattern rather than on actual thinking outside of the box that may reflect human creativity. Some of the most creative solutions or artistic expressions are often born out of personal experience, emotional ups and downs, moments of inspiration, those things that cannot easily be expressed and replicated in an algorithm. Although AI can generate creative content, it remains without experiential or emotional depth to inform human creativity. This means it offers content based on learned patterns rather than any personal experience or emotional journey. That is the main point of explaining why, despite impressive outputs, ChatGPT is fundamentally different from a human mind. Morality The other very important element of being human is morality, for which decisions are generally guided by the moral compass of and through upbringing, culture, experiences, and societal norms. More often than not, humans struggle with conflicting values, weigh the consequences of their deeds, and sometimes, change their beliefs over time. Could ChatGPT make moral decisions? Not at all. While it may be able to be programmed to adhere to certain guidelines of morality or avoid harmful content, its understanding of morality is superficial. It does not possess personal values or understand right and wrong in the way humans do. Instead, it responds based on patterns in data that often simply mimic moral reasoning without truly engaging in the complex, human process of ethical decision-making. It is a huge task to train ChatGPT to be human in this regard since even humans are unable to accept one sense of moral principle. Morality also relates to consciousness, which AI lacks. Though AI systems can follow rules, they are not able to understand the sense of moral dilemmas that define human experience. Memory and Experience Humans are a product of their memories and experiences. Every conversation, every relationship, and triumph and every failure leave an impression on who we are and how we approach the world. AI does not have any kind of real memory of past interactions unless it has been explicitly programmed to retain specific data. Each interaction with ChatGPT is unique, and while it can process historical data, it doesn’t “remember” interactions the same way a human does. It would give this form so that ChatGPT can learn through experiences, creating a sense of identity and personal history over time. Now, it does not necessarily imply that it should remember anything but interpret it to affect the subsequent actions; the same way humans do in life, growing and changing according to their past. Current AI models, including ChatGPT, do not have this ability. Though they might learn from big datasets, they lack personal experiences and do not change according to individual interaction. Because of this, for ChatGPT to be closer to “being human,” it requires something more than the capability to store and accumulate information but must also develop the capability to reflect on that same information and to change in meaningful ways relative to past experiences. Learning by Experience Human intelligence is reinforced by the ability to reflect upon mistakes and utilize those as learning experiences. We sometimes reflect on our actions, question our decisions, and make alterations in behavior based on our experiences. ChatGPT, though, can be trained through machine learning models, though it learns very differently compared to humans. Improvement of AI in machine learning is through wide training on various datasets and adjustment of weights and biases for better outcomes. However, the methodology itself is not like that of human learning based on trial and error and is contextual, emotionally and socially tuned, and based on personal experience and interactions with other humans. In contrast, the learning process of ChatGPT is abstract and data-driven. While AI may learn to make improvements in its responses, it is without a human ability to reflect on itself. Human learning isn’t just adding new information, but building that new information off of what we already know and thinking about what the implications are. Sometimes, what we learn is questioned. ChatGPT learns to do this task better at getting statistically likely responses without any understanding or reflection about what it is learning. The Future of AI So, can ChatGPT be made human? Currently, as of the technology, the answer is no. In full, that is. While still able to mimic and even generate human-like conversations, produce creative content, and maintain adherence to all sorts of ethical directives, AI continues to lack depth, consciousness, and emotional complexities of what it means to be human. However, the future of AI is no longer so static. Researchers are more and more pushing the frontiers of what could be done with AI: find new approaches to making machines more intelligent, adaptive, and even emotionally intelligent. Some believe that with domains such as neuro-symbolic AI-machine learning coupled with symbolic reasoning or even quantum computation, we may eventually build one day the essence of human-like cognition in an AI. But yet, all of this raises a basic question: Do we want AI to be human? The development of machines that could think and feel like humans raises profound questions in the realms of ethics and philosophy. Where do we draw that line between the machine and the person if AI becomes too human?
Should AI have rights when it can feel? These are questions that society will have to grapple with as AI continues its evolution. AI’s ability to mimic human behavior, emotional intelligence, creativity, and morality while exploring the future of artificial intelligence Artificial intelligence, especially that afforded by ChatGPT, has changed our manner of dealing with machines. AI now puts us in a state of conversation, learns from us, and even attempts to understand the context behind the questions we ask. However, the significant issue that people keep asking is whether ChatGPT can be trained to become human. However, the significant issue that people keep asking is whether ChatGPT can be trained to become human. However, the significant issue that people keep asking is whether ChatGPT can be trained to become human. At the core of AIs like ChatGPT is a neural network, a complex web of algorithms meant to mimic the functioning of the human brain. The algorithms learn about patterns, make predictions, and improve over time through a process known as machine learning. However, there is quite a gap between teaching patterns and being human. But being human isn’t just about patterns or predictions and it is about emotions, experience, understanding of culture, making moral judgments, and the ability for truly original creativity. Interacting with AI today may often generate human-like responses that are interesting and even insightful at times. Yet, what generates those responses — patterns found in large datasets, not quite the sort of personal contemplation or emotive depth carried into every encounter by humans. To answer if ChatGPT can be trained to be human, we must consider what exactly it means for something to be human. For this argument, let’s break it down into several such components: emotional intelligence, creativity, morality, memory, and learning from experiences. Some of these are important facets of human behavior and cognition. Can AI, particularly ChatGPT, ever be imbued with these so that we consider the AI “human” to that degree? Can AI, particularly ChatGPT, ever be imbued with these so that we consider the AI “human” to that degree? Can AI, particularly ChatGPT, ever be imbued with these so that we consider the AI “human” to that degree? Emotional Intelligence Emotional intelligence is the ability to sense how others feel and manage one’s own emotions. Our words usually carry emotional intensity from subtle sarcasm to overt anger or happiness. Humans use tone, context, and body language to communicate and interpret emotions. ChatGPT will simulate aspects of emotional intelligence because it can generate text that carries an emotional flavor for a conversation. If you express frustration, then it produces a response that would appear empathetic; if you are ebullient, it can echo some lighthearted repartee. Does this mean it “knows” or understands emotions? No. Instead, what ChatGPT does is recognize patterns in language as they reflect emotion, but it does not feel emotions. It doesn’t have the physiological and psychological mechanisms that underlie human emotion. Training ChatGPT to “be human” in this way would take more than feeding it datasets on emotional expression. It would rather require an architecture that allowed for some authentic experiences of emotions — that is, something beyond the scope of current developments in AI. Even if we can, in some way, simulate emotional reactions in AI, they will still be fundamentally different from human emotions, which are tied closely with consciousness, biology, and experience. Creativity Creativity- the mark of human intelligence: the ability to come up with new ideas, solve problems in innovative ways, and create art that vibrates at a deeply emotional or intellectual level. Is ChatGPT creative? In this respect, yes. ChatGPT can write, for instance, poems, short stories, code- whatever you want it to do. All the same, this creativity is based on the pattern rather than on actual thinking outside of the box that may reflect human creativity. Some of the most creative solutions or artistic expressions are often born out of personal experience, emotional ups and downs, moments of inspiration, those things that cannot easily be expressed and replicated in an algorithm. Although AI can generate creative content, it remains without experiential or emotional depth to inform human creativity. This means it offers content based on learned patterns rather than any personal experience or emotional journey. That is the main point of explaining why, despite impressive outputs, ChatGPT is fundamentally different from a human mind. Morality The other very important element of being human is morality, for which decisions are generally guided by the moral compass of and through upbringing, culture, experiences, and societal norms. More often than not, humans struggle with conflicting values, weigh the consequences of their deeds, and sometimes, change their beliefs over time. Could ChatGPT make moral decisions? Not at all. While it may be able to be programmed to adhere to certain guidelines of morality or avoid harmful content, its understanding of morality is superficial. It does not possess personal values or understand right and wrong in the way humans do. Instead, it responds based on patterns in data that often simply mimic moral reasoning without truly engaging in the complex, human process of ethical decision-making. It is a huge task to train ChatGPT to be human in this regard since even humans are unable to accept one sense of moral principle. Morality also relates to consciousness, which AI lacks. Though AI systems can follow rules, they are not able to understand the sense of moral dilemmas that define human experience. Memory and Experience Humans are a product of their memories and experiences. Every conversation, every relationship, and triumph and every failure leave an impression on who we are and how we approach the world. AI does not have any kind of real memory of past interactions unless it has been explicitly programmed to retain specific data. Each interaction with ChatGPT is unique, and while it can process historical data, it doesn’t “remember” interactions the same way a human does. It would give this form so that ChatGPT can learn through experiences, creating a sense of identity and personal history over time. Now, it does not necessarily imply that it should remember anything but interpret it to affect the subsequent actions; the same way humans do in life, growing and changing according to their past. Current AI models, including ChatGPT, do not have this ability. Though they might learn from big datasets, they lack personal experiences and do not change according to individual interaction. Because of this, for ChatGPT to be closer to “being human,” it requires something more than the capability to store and accumulate information but must also develop the capability to reflect on that same information and to change in meaningful ways relative to past experiences. Learning by Experience Human intelligence is reinforced by the ability to reflect upon mistakes and utilize those as learning experiences. We sometimes reflect on our actions, question our decisions, and make alterations in behavior based on our experiences. ChatGPT, though, can be trained through machine learning models, though it learns very differently compared to humans. Improvement of AI in machine learning is through wide training on various datasets and adjustment of weights and biases for better outcomes. However, the methodology itself is not like that of human learning based on trial and error and is contextual, emotionally and socially tuned, and based on personal experience and interactions with other humans. In contrast, the learning process of ChatGPT is abstract and data-driven. While AI may learn to make improvements in its responses, it is without a human ability to reflect on itself. Human learning isn’t just adding new information, but building that new information off of what we already know and thinking about what the implications are. Sometimes, what we learn is questioned. ChatGPT learns to do this task better at getting statistically likely responses without any understanding or reflection about what it is learning. The Future of AI So, can ChatGPT be made human? Currently, as of the technology, the answer is no. So, can ChatGPT be made human? Currently, as of the technology, the answer is no. So, can ChatGPT be made human? Currently, as of the technology, the answer is no. In full, that is. While still able to mimic and even generate human-like conversations, produce creative content, and maintain adherence to all sorts of ethical directives, AI continues to lack depth, consciousness, and emotional complexities of what it means to be human. However, the future of AI is no longer so static. Researchers are more and more pushing the frontiers of what could be done with AI: find new approaches to making machines more intelligent, adaptive, and even emotionally intelligent. Some believe that with domains such as neuro-symbolic AI-machine learning coupled with symbolic reasoning or even quantum computation, we may eventually build one day the essence of human-like cognition in an AI. But yet, all of this raises a basic question: Do we want AI to be human? The development of machines that could think and feel like humans raises profound questions in the realms of ethics and philosophy. Where do we draw that line between the machine and the person if AI becomes too human? Should AI have rights when it can feel? Where do we draw that line between the machine and the person if AI becomes too human? Should AI have rights when it can feel? These are questions that society will have to grapple with as AI continues its evolution.

Can We Train AI to Become More Human-Like?

Humans have proved to be not only biologically complex creatures, but also one with uncanny, complex intellectual capabilities; they have successfully created robots after their own likeness. Robots now do repetitive work in which human labor becomes too costly and ineffective to justify. In fact, there is hope that these robots will surpass many human limitations–many of which have already been met. To researchers in the extensive field of robotics development – the speed, accuracy and intelligence these robots currently exhibit are monumental achievements – while to others who have a modicum of knowledge of robots, these exhibitions by robots are cause for concern. They worry, like Polish playwright Karel Capek painted in his play “ ”, that robots will eventually displace humans from their jobs, or worse still, like presented in “Terminator” series, that robots might become future overlords and go to war with humans. Rossum’s Universal Robots How much human abilities are robots able to exhibit? To a large extent, contemporary robots have replicated human dexterity, perception and intelligence. For example, , a robot developed at the Massachusetts Institute of Technology, owing to sophisticated hardware and high-level programming, works like the human nervous system, which consists of both voluntary and involuntary functionality. , a robotic avatar developed by Toyota, mimics the movement of its human operator, and will be able to perform surgeries in the future while being controlled from another world by a doctor. , an AI-powered humanoid robot, talks, draws, sings and shows some emotions–and , another AI-powered humanoid robot, mimics human-like facial expressions. Despite all these, robots are yet to perform tasks in which complex, rigorous human intellectual capabilities are required. This is a consequence of the fact that researchers in the field of robotics development have not understood the intricacies of human intelligence. Kismet T-HR3 Sophia Ameca Why do robots lack unique human intellectual abilities? What is known is that manifests in the elaborate and interconnected networks of neurons within the human brain and is a function of learning and experience. Neurons in the human brain form electrical connections with one another, but it is rather unclear how they collectively cultivate brain activities like thinking and reasoning. Consequently, researchers in robotics development have been unable to download into robots this wondrous human intelligence–and hence robots lack the intellectual independence that they will need to beat humans at their own game. Furthermore, robots are biologically limited in their make-up and do not have the natural power-house like in humans–therefore, availability of power for use will continue to limit the functionality of even the most sophisticated robot for quite a long time. human intelligence Moreover, the human sensory system–vision, hearing, touch, smell and taste–are naturally complex. However, they are somehow being implemented into robots through elegant technology. For instance, robots like Ameca and Sophia are already doing well at responding to these human senses, but their ability therein is still considered to be in a nascent stage like that of a newborn baby that lacks the experience to understand what it senses. The recognition of sound, for instance, by a robot is intricately dependent on the extent of its “lexicon”, where the sound is compared among similar sound waves–and the lexicon does not expand unless through high-level programming, which needs to be carried out regularly. Besides, robots lack empathy, which is the ability to understand and share the feelings of another–and it has been established that empathy is a powerful driver of many human decisions and actions. In essence, robots can replicate emotions but cannot feel them; they can show sadness but do not feel sad. What are the jobs robots cannot do because of lack of unique human intellectual abilities? Robots are unable to undertake tasks in which special human intellectual capabilities are needed. who has not only medical education, but also the experience in prescribing medicines, applying operation techniques, and using medical apparatus. Robots cannot perform scientific analysis at particularly complex levels that usually bring about novel epiphany that engenders scientific discoveries; although they can be employed to do certain tasks, robots cannot be depended on for new ways of completing those tasks. Robots lack emotions and hence are useless in creative writing, which is a work of feelings and emotions brought on paper through words; they can only write what humans instruct them. Robots cannot teach like humans because they lack the understanding power to engage in interactive exchange as demonstrated between a teacher and the students; a teacher understands the teaching materials as well as the students on an individual level. Robots cannot perform the duties of a doctor Conclusion Robots have done exceedingly very well in performing jobs humans considered drudgery, in which health risks and hazards are high and human labor is mostly costly. They have allowed humans to improve on their living standard and become more precise and accurate in carrying out their jobs, especially those that are done casually that do not need intellectual capabilities but precision and accuracy. This is because robots are currently as intelligent as to only follow rigidly and precisely the instructions laid down for them by humans. Therefore, they have enabled humans to upgrade their skills and go for those jobs that maximize their capabilities. Although robots are seemingly intelligent–especially when considering the speed and accuracy with which they perform tasks–they are not any more intelligent than the human that controls them. If ever they have any intelligence of their own at all, it is still not up to that of a housefly. advanced intellectual

The Lack of Unique Human Abilities in Robots

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 5 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 4.1. Implementation Details For audio transcript extraction, base Whisper model is used. Our grounding module is based on GroundingDINOT variant and CLIP ViT-B/32. For the image-tagging model we use RAM Swin-Large variant (with input size 384). DEVA Tracker is applied under online-setting in our experiments. Vicuna-13b-v1.5 model is used in performing videobased conversational benchmarking, zero-shot question answering evaluation, and extracting the key noun or referring expression from the model output in the quantitative evaluation of the spatial grounding task. Further, Vicuna-13b-v1.5 was used to implement the entity matching as in [49]. 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 5 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 5 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 5 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 5 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 4.1. Implementation Details For audio transcript extraction, base Whisper model is used. Our grounding module is based on GroundingDINOT variant and CLIP ViT-B/32. For the image-tagging model we use RAM Swin-Large variant (with input size 384). DEVA Tracker is applied under online-setting in our experiments. Vicuna-13b-v1.5 model is used in performing videobased conversational benchmarking, zero-shot question answering evaluation, and extracting the key noun or referring expression from the model output in the quantitative evaluation of the spatial grounding task. Further, Vicuna-13b-v1.5 was used to implement the entity matching as in [49]. 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

UAE Researchers Teach AI to Watch, Listen, and Understand Videos Like Humans

A Roadmap for Addressing Critical Challenges in Human-Machine Social Systems

This is Part 2 of a 12-part series based on the research paper “Human-Machine Social Systems.” Use the table of links below to navigate to the next part. Table of Links Abstract and Introduction Human-machine interactions Collective outcomes Box 1: Competition in high-frequency trading markets Box 2: Contagion on Twitter Box 3: Cooperation and coordination on Wikipedia Box 4: Cooperation and contagion on Reddit Discussion Implications for research Implications for design Implications for policy Conclusion, Acknowledgments, References, and Competing interests Human-machine Interactions We use the term “machines” to refer to various computational artifacts that exist in a multidimensional space ranging on embodiment (from physical devices such as humanoid robots to bots and algorithms that exist in digital space only), algorithmic sophistication (from simple expert systems that use pre-defined if-else rules to generative deep-learning models that learn from data in real-time), autonomy (from fully managed to fully autonomous), sociality (e.g., from infrastructure to human peers), multiplicity (from single large models like GPT4 to many simple models like those used in ensemble learning), heterogeneity (from the coordinated behaviors of bot farms to the diverse strategies of competing trading algorithms), and generality (from highly specialized chess bots to open-ended problem-solvers like LLMs). Here, we restrict our attention to multiple, autonomous, and social artificial agents that operate and interact in the same social environment as humans. Although potentially relevant, in this work, we do not discuss human-assisted bots and bot-assisted humans known as “cyborgs” [22,23], coordinated bot accounts known as botnets, swarmbots, and bot farms [24,25,26,27], algorithms that perform background infrastructure-related tasks such as recommender systems [28,29], crawlers, indexers, and scrapers [30,31], smart electricity grids [32], and traffic light control systems [33], as well as hybrid traffic flows [34,35]. These systems have been extensively discussed elsewhere. We acknowledge that we may be excluding other interesting cases, including presently out-of-scope cases that future technological advancements may ultimately align with our taxonomy of “autonomous” and “social” machines. Like humans, the machines we consider exhibit diverse goal-oriented behavior shaped by information and subject to constraints. However, the actual cognition and behavior of machines differ from those of humans. Machines’ behavior tends to be predictable and persistent [36], with higher precision and faster execution[37], better informed with access to global information[38], and less adaptable and susceptible to influence [39,40,41]. In contrast, humans tend to be limited to local information, satisfice, act with errors, learn and adapt, succumb to social influence and peer pressure, yet also exhibit opinion stubbornness and behavioral inertia; on occasions, they may also use metacognition and revise their own perceptual and decision-making models. Humans often exhibit cognitive biases due to limited information processing capacity, bounded rationality, reliance on heuristics, vestiges of evolutionary adaptation, and emotional motivations [42,43], and algorithms trained on data generated by humans may reproduce these biases [44,45]. Research on human-like general AI aims to erase the cognitive and behavioral differences between humans and machines, while work on human-competitive AI strives for superintelligence that is faster, smarter, and more precise than humans’ [46,47]. Either way, humans will remain distinct from machines in the near future. Research from the CASA (computers as social actors) paradigm in psychology emphasizes that humans treat and respond to machines similarly to other humans: people reciprocate kind acts by computers[48], treat them as politely as they treat humans [49], consider them as competent, but also apply gender and racial stereotypes to them[50,51]; people also humanize and empathize with machines, experiencing distress when witnessing the mistreatment of a robot [52,53]. Nevertheless, there are visible neurophysiological differences in the brain when humans interact with robots[54,55], likely because humans do not attribute agency and morals to them[56,57]. AI is perceived to have lower intentional capacity, lack self-interest, and be more unbiased than humans. Consequently, humans exhibit a narrower emotional spectrum with machines than with humans, reacting with lower and flatter levels of social emotions such as gratitude, anger, pride, and a sense of fairness[58,59,60,61], yet judging machines more harshly when they commit mistakes, cause harm, or incur losses[62,63]. Further, humans behave more rationally and selfishly with machines, cooperating and sharing less and demanding and exploiting more [64,65,66,67]. People would design a machine to be more cooperative than they are themselves [68] but act pro-socially towards it only if it is more human-like[69], or if it benefits another human[70]. Compared to a single person, small groups of people are even more likely to exhibit competitive behavior and bullying toward robots [71]. Despite this intergroup bias, humans are still susceptible to machine influence when making decisions or solving problems[72]. Robots can cause both informational and normative conformity in people [73,74] and AI and ChatGPT can corrupt humans’ moral judgment and make them follow unethical advice [75,76]. Humans tend to trust algorithmic advice more than advice coming from a another human or a human crowd[77,78] but may also avoid it if they perceive a threat to their decision control or a lack of understanding and cognitive compatibility [79,80]. Authors:
(1) Milena Tsvetkova, Department of Methodology, London School of Economics and Political Science, London, United Kingdom;
(2) Taha Yasseri, School of Sociology, University College Dublin, Dublin, Ireland and Geary Institute for Public Policy, University College Dublin, Dublin, Ireland;
(3) Niccolo Pescetelli, Collective Intelligence Lab, New Jersey Institute of Technology, Newark, New Jersey, USA;
(4) Tobias Werner, Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany. This paper is available on arxiv under CC BY 4.0 DEED license. This is Part 2 of a 12-part series based on the research paper “Human-Machine Social Systems.” Use the table of links below to navigate to the next part. This is Part 2 of a 12-part series based on the research paper “Human-Machine Social Systems.” Use the table of links below to navigate to the next part. “ Human-Machine Social Systems .” Human-Machine Social Systems Table of Links Abstract and Introduction Abstract and Introduction Human-machine interactions Human-machine interactions Collective outcomes Collective outcomes Box 1: Competition in high-frequency trading markets Box 1: Competition in high-frequency trading markets Box 2: Contagion on Twitter Box 2: Contagion on Twitter Box 3: Cooperation and coordination on Wikipedia Box 3: Cooperation and coordination on Wikipedia Box 4: Cooperation and contagion on Reddit Box 4: Cooperation and contagion on Reddit Discussion Discussion Implications for research Implications for research Implications for design Implications for design Implications for policy Implications for policy Conclusion, Acknowledgments, References, and Competing interests Conclusion, Acknowledgments, References, and Competing interests Human-machine Interactions We use the term “machines” to refer to various computational artifacts that exist in a multidimensional space ranging on embodiment (from physical devices such as humanoid robots to bots and algorithms that exist in digital space only), algorithmic sophistication (from simple expert systems that use pre-defined if-else rules to generative deep-learning models that learn from data in real-time), autonomy (from fully managed to fully autonomous), sociality (e.g., from infrastructure to human peers), multiplicity (from single large models like GPT4 to many simple models like those used in ensemble learning), heterogeneity (from the coordinated behaviors of bot farms to the diverse strategies of competing trading algorithms), and generality (from highly specialized chess bots to open-ended problem-solvers like LLMs). Here, we restrict our attention to multiple , autonomous , and social artificial agents that operate and interact in the same social environment as humans. multiple autonomous social Although potentially relevant, in this work, we do not discuss human-assisted bots and bot-assisted humans known as “cyborgs” [22,23], coordinated bot accounts known as botnets, swarmbots, and bot farms [24,25,26,27], algorithms that perform background infrastructure-related tasks such as recommender systems [28,29], crawlers, indexers, and scrapers [30,31], smart electricity grids [32], and traffic light control systems [33], as well as hybrid traffic flows [34,35]. These systems have been extensively discussed elsewhere. We acknowledge that we may be excluding other interesting cases, including presently out-of-scope cases that future technological advancements may ultimately align with our taxonomy of “autonomous” and “social” machines. Like humans, the machines we consider exhibit diverse goal-oriented behavior shaped by information and subject to constraints. However, the actual cognition and behavior of machines differ from those of humans. Machines’ behavior tends to be predictable and persistent [36], with higher precision and faster execution[37], better informed with access to global information[38], and less adaptable and susceptible to influence [39,40,41]. In contrast, humans tend to be limited to local information, satisfice, act with errors, learn and adapt, succumb to social influence and peer pressure, yet also exhibit opinion stubbornness and behavioral inertia; on occasions, they may also use metacognition and revise their own perceptual and decision-making models. Humans often exhibit cognitive biases due to limited information processing capacity, bounded rationality, reliance on heuristics, vestiges of evolutionary adaptation, and emotional motivations [42,43], and algorithms trained on data generated by humans may reproduce these biases [44,45]. Research on human-like general AI aims to erase the cognitive and behavioral differences between humans and machines, while work on human-competitive AI strives for superintelligence that is faster, smarter, and more precise than humans’ [46,47]. Either way, humans will remain distinct from machines in the near future. Research from the CASA (computers as social actors) paradigm in psychology emphasizes that humans treat and respond to machines similarly to other humans: people reciprocate kind acts by computers[48], treat them as politely as they treat humans [49], consider them as competent, but also apply gender and racial stereotypes to them[50,51]; people also humanize and empathize with machines, experiencing distress when witnessing the mistreatment of a robot [52,53]. Nevertheless, there are visible neurophysiological differences in the brain when humans interact with robots[54,55], likely because humans do not attribute agency and morals to them[56,57]. AI is perceived to have lower intentional capacity, lack self-interest, and be more unbiased than humans. Consequently, humans exhibit a narrower emotional spectrum with machines than with humans, reacting with lower and flatter levels of social emotions such as gratitude, anger, pride, and a sense of fairness[58,59,60,61], yet judging machines more harshly when they commit mistakes, cause harm, or incur losses[62,63]. Further, humans behave more rationally and selfishly with machines, cooperating and sharing less and demanding and exploiting more [64,65,66,67]. People would design a machine to be more cooperative than they are themselves [68] but act pro-socially towards it only if it is more human-like[69], or if it benefits another human[70]. Compared to a single person, small groups of people are even more likely to exhibit competitive behavior and bullying toward robots [71]. Despite this intergroup bias, humans are still susceptible to machine influence when making decisions or solving problems[72]. Robots can cause both informational and normative conformity in people [73,74] and AI and ChatGPT can corrupt humans’ moral judgment and make them follow unethical advice [75,76]. Humans tend to trust algorithmic advice more than advice coming from a another human or a human crowd[77,78] but may also avoid it if they perceive a threat to their decision control or a lack of understanding and cognitive compatibility [79,80]. Authors: (1) Milena Tsvetkova, Department of Methodology, London School of Economics and Political Science, London, United Kingdom; (2) Taha Yasseri, School of Sociology, University College Dublin, Dublin, Ireland and Geary Institute for Public Policy, University College Dublin, Dublin, Ireland; (3) Niccolo Pescetelli, Collective Intelligence Lab, New Jersey Institute of Technology, Newark, New Jersey, USA; (4) Tobias Werner, Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany. Authors: Authors: (1) Milena Tsvetkova, Department of Methodology, London School of Economics and Political Science, London, United Kingdom; (2) Taha Yasseri, School of Sociology, University College Dublin, Dublin, Ireland and Geary Institute for Public Policy, University College Dublin, Dublin, Ireland; (3) Niccolo Pescetelli, Collective Intelligence Lab, New Jersey Institute of Technology, Newark, New Jersey, USA; (4) Tobias Werner, Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany. 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

Human-Machine Interactions Reveal Behavioral Patterns, Biases, and Autonomous Roles

Authors:
(1) Milena Tsvetkova, Department of Methodology, London School of Economics and Political Science, London, United Kingdom;
(2) Taha Yasseri, School of Sociology, University College Dublin, Dublin, Ireland and Geary Institute for Public Policy, University College Dublin, Dublin, Ireland;
(3) Niccolo Pescetelli, Collective Intelligence Lab, New Jersey Institute of Technology, Newark, New Jersey, USA;
(4) Tobias Werner, Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany. This is Part 1 of a 12-part series based on the research paper “Human-Machine Social Systems.” Use the table of links below to navigate to the next part. Table of Links Abstract and Introduction Human-machine interactions Collective outcomes Box 1: Competition in high-frequency trading markets Box 2: Contagion on Twitter Box 3: Cooperation and coordination on Wikipedia Box 4: Cooperation and contagion on Reddit Discussion Implications for research Implications for design Implications for policy Conclusion, Acknowledgments, References, and Competing interests Abstract From fake social media accounts and generative-AI chatbots to financial trading algorithms and self-driving vehicles, robots, bots, and algorithms are proliferating and permeating our communication channels, social interactions, economic transactions, and transportation arteries. Networks of multiple interdependent and interacting humans and autonomous machines constitute complex social systems where the collective outcomes cannot be deduced from either human or machine behavior alone. Under this paradigm, we review recent research from across a range of disciplines and identify general dynamics and patterns in situations of competition, coordination, cooperation, contagion, and collective decision-making, with context-rich examples from high-frequency trading markets, a social media platform, an open-collaboration community, and a discussion forum. To ensure more robust and resilient human-machine communities, researchers should study them using complex-system methods, engineers should explicitly design AI for human-machine and machine-machine interactions, and regulators should govern the ecological diversity and social co-evolution of humans and machines. Introduction Robotic trains and cars drive us around, auction bots outbid us for purchases, ChatGPT answers our questions, while social media bots feed us with dubious facts and news. Modern society is a complex human-machine social system in which machines are becoming more numerous, human interactions with machines – more frequent, and machine-machine interactions – more consequential. With recent advances in generative AI models, the existential threat of unexplainable and uncontrollable general AI is looming large again[1,2,3]. However, when they are numerous and interdependent, even simple unintelligent artificial agents can produce unintended and potentially undesirable outcomes. If we want to prevent financial crashes, improve road safety, preserve market competition, increase auction market efficiency, and reduce misinformation, it is no longer sufficient to understand humans – we need to consider machines, understand how humans and machines interact, and how the collective behavior of systems of humans and machines can be predicted. We require a “new sociology of humans and machines.” This narrative review synthesizes research and ideas related to social systems composed of multiple autonomous yet interacting and interdependent humans and machines such as algorithms, bots, and robots (Fig. 1). Similar to the conceptualizations of socio-technical systems [4], actor-network theory 5,6, cyber-physical social systems [7,8] social machines [9,10,11] and human-machine networks12,13, our principal assumption is that humans and machines form a single social system. In contrast, we do not approach machines as a single medium or entity – “technology” – but emphasize their multiplicity, independence, and heterogeneity and their interactions [14]. Thus, we target machines capable of social behavior, namely, a behavior that influences and is influenced by the behavior of other actors (human or machine) in the system. Further, we approach the aggregates as complex social systems where network effects and nonlinear dynamic processes produce collective outcomes that cannot be necessarily deduced from individual preferences and behavior alone [15]. Our conceptualization extends and complements the ecological approach to studying machine behavior[16], the “hybrid collective intelligence perspective” [17], and the budding field of Social AI [18]. We aim to offer a conceptual overview of the topic. Hence, we do not utilize a systematic literature search strategy and instead present selected examples to showcase the new conceptualization[19,20,21]. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Milena Tsvetkova, Department of Methodology, London School of Economics and Political Science, London, United Kingdom; (2) Taha Yasseri, School of Sociology, University College Dublin, Dublin, Ireland and Geary Institute for Public Policy, University College Dublin, Dublin, Ireland; (3) Niccolo Pescetelli, Collective Intelligence Lab, New Jersey Institute of Technology, Newark, New Jersey, USA; (4) Tobias Werner, Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany. Authors: Authors: (1) Milena Tsvetkova, Department of Methodology, London School of Economics and Political Science, London, United Kingdom; (2) Taha Yasseri, School of Sociology, University College Dublin, Dublin, Ireland and Geary Institute for Public Policy, University College Dublin, Dublin, Ireland; (3) Niccolo Pescetelli, Collective Intelligence Lab, New Jersey Institute of Technology, Newark, New Jersey, USA; (4) Tobias Werner, Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany. This is Part 1 of a 12-part series based on the research paper “Human-Machine Social Systems.” Use the table of links below to navigate to the next part. This is Part 1 of a 12-part series based on the research paper “Human-Machine Social Systems.” Use the table of links below to navigate to the next part. “ Human-Machine Social Systems .” Human-Machine Social Systems Table of Links Abstract and Introduction Abstract and Introduction Human-machine interactions Human-machine interactions Collective outcomes Collective outcomes Box 1: Competition in high-frequency trading markets Box 1: Competition in high-frequency trading markets Box 2: Contagion on Twitter Box 2: Contagion on Twitter Box 3: Cooperation and coordination on Wikipedia Box 3: Cooperation and coordination on Wikipedia Box 4: Cooperation and contagion on Reddit Box 4: Cooperation and contagion on Reddit Discussion Discussion Implications for research Implications for research Implications for design Implications for design Implications for policy Implications for policy Conclusion, Acknowledgments, References, and Competing interests Conclusion, Acknowledgments, References, and Competing interests Abstract From fake social media accounts and generative-AI chatbots to financial trading algorithms and self-driving vehicles, robots, bots, and algorithms are proliferating and permeating our communication channels, social interactions, economic transactions, and transportation arteries. Networks of multiple interdependent and interacting humans and autonomous machines constitute complex social systems where the collective outcomes cannot be deduced from either human or machine behavior alone. Under this paradigm, we review recent research from across a range of disciplines and identify general dynamics and patterns in situations of competition, coordination, cooperation, contagion, and collective decision-making, with context-rich examples from high-frequency trading markets, a social media platform, an open-collaboration community, and a discussion forum. To ensure more robust and resilient human-machine communities, researchers should study them using complex-system methods, engineers should explicitly design AI for human-machine and machine-machine interactions, and regulators should govern the ecological diversity and social co-evolution of humans and machines. Introduction Robotic trains and cars drive us around, auction bots outbid us for purchases, ChatGPT answers our questions, while social media bots feed us with dubious facts and news. Modern society is a complex human-machine social system in which machines are becoming more numerous, human interactions with machines – more frequent, and machine-machine interactions – more consequential. With recent advances in generative AI models, the existential threat of unexplainable and uncontrollable general AI is looming large again[1,2,3]. However, when they are numerous and interdependent, even simple unintelligent artificial agents can produce unintended and potentially undesirable outcomes. If we want to prevent financial crashes, improve road safety, preserve market competition, increase auction market efficiency, and reduce misinformation, it is no longer sufficient to understand humans – we need to consider machines, understand how humans and machines interact, and how the collective behavior of systems of humans and machines can be predicted. We require a “new sociology of humans and machines.” This narrative review synthesizes research and ideas related to social systems composed of multiple autonomous yet interacting and interdependent humans and machines such as algorithms, bots, and robots (Fig. 1). Similar to the conceptualizations of socio-technical systems [4], actor-network theory 5,6, cyber-physical social systems [7,8] social machines [9,10,11] and human-machine networks12,13, our principal assumption is that humans and machines form a single social system. In contrast, we do not approach machines as a single medium or entity – “technology” – but emphasize their multiplicity, independence, and heterogeneity and their interactions [14]. Thus, we target machines capable of social behavior, namely, a behavior that influences and is influenced by the behavior of other actors (human or machine) in the system. Further, we approach the aggregates as complex social systems where network effects and nonlinear dynamic processes produce collective outcomes that cannot be necessarily deduced from individual preferences and behavior alone [15]. Our conceptualization extends and complements the ecological approach to studying machine behavior[16], the “hybrid collective intelligence perspective” [17], and the budding field of Social AI [18]. We aim to offer a conceptual overview of the topic. Hence, we do not utilize a systematic literature search strategy and instead present selected examples to showcase the new conceptualization[19,20,21]. 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

How Human-Machine Networks Shape Collective Behavior in a Hybrid Society 

This is Part 5 of a 12-part series based on the research paper “Human-Machine Social Systems.” Use the table of links below to navigate to the next part. Table of Links Abstract and Introduction Human-machine interactions Collective outcomes Box 1: Competition in high-frequency trading markets Box 2: Contagion on Twitter Box 3: Cooperation and coordination on Wikipedia Box 4: Cooperation and contagion on Reddit Discussion Implications for research Implications for design Implications for policy Conclusion, Acknowledgments, References, and Competing interests Box 2: Contagion on Twitter Social bots on the micro-blogging platform Twitter (re-branded as X in 2022) are covert automated accounts designed to impersonate humans to boost followers, disseminate information, and promote products. Bots and bot detection methods have co-evolved, resulting in increasingly more sophisticated imitation or detection strategies [177,178,179,180,39,181,182], but detection is inherently limited due to the overlap between covert autonomous bots, managed user accounts, hacked accounts, cyborgs, sock-puppets, and coordinated botnets [24,183,22,184]. Estimates suggest that 9-15% of Twitter users are bots185,182, with bot activity typically increasing around controversial political events [186]. Twitter social bots, who do not follow social instincts but neither succumb to fatigue, engage less in social interactions via replies and mentions than humans but produce more content [187]. The bots mainly retweet – a passive strategy to indicate support and gain followers – but are less successful in attracting friends and followers than humans [182]. Overall, they are less connected and bot-bot (2%), bot-human (19%), and human-bot (3%) interactions are considerably less common than human-human interactions (76%) 1[86]. Despite their rudimentary social behavior and weak network integration, Twitter bots significantly influence political communication, public opinion, elections, and markets. They play an important role in misinformation dissemination in relation to political events [188,189,190,191,192,193,194], COVID-19[195,196], and stock market investment [197]. Bots can affect human interaction networks by encouraging followings and conversations [198] and amplify low-credibility content early on by targeting influential humans through replies and mentions [192]. Bots’ large numbers enhance their visibility and influence to trigger deep information cascades [199]. Bots equally link to true and false news from low-credibility sites, but people prefer false content, making humans ultimately responsible for the spread of false news [200]. Twitter bots significantly contribute to negative sentiment and conflict escalation. Acting from the periphery, they target central human users to exert indirect influence. They amplify existing negative sentiment and selectively promote inflammatory content, often targeting only one of the factions[186]. Their success stems from exploiting human tendencies to connect with similar others and engage with messages that reinforce their beliefs [201]. Consequently, bots increase ideological polarization and negatively affect democratic discourse on social media, as seen in the 2016 US presidential election[202], the 2016 UK Brexit Referendum[201], and the 2017 Catalan independence referendum[186]. In sum, Twitter’s covert social bots are considered harmful, prompting the platform to cull them[203,204]. Their strength lies in indirect action: they skew the platform’s recommendation system to bias content popularity [137] and exploit human behavioral weaknesses like attention seeking, confirmation bias, moral outrage, and ideological homophily. Authors:
(1) Milena Tsvetkova, Department of Methodology, London School of Economics and Political Science, London, United Kingdom;
(2) Taha Yasseri, School of Sociology, University College Dublin, Dublin, Ireland and Geary Institute for Public Policy, University College Dublin, Dublin, Ireland;
(3) Niccolo Pescetelli, Collective Intelligence Lab, New Jersey Institute of Technology, Newark, New Jersey, USA;
(4) Tobias Werner, Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany. This paper is available on arxiv under CC BY 4.0 DEED license. This is Part 5 of a 12-part series based on the research paper “Human-Machine Social Systems.” Use the table of links below to navigate to the next part. This is Part 5 of a 12-part series based on the research paper “Human-Machine Social Systems.” Use the table of links below to navigate to the next part. “ Human-Machine Social Systems .” Human-Machine Social Systems Table of Links Abstract and Introduction Abstract and Introduction Human-machine interactions Human-machine interactions Collective outcomes Collective outcomes Box 1: Competition in high-frequency trading markets Box 1: Competition in high-frequency trading markets Box 2: Contagion on Twitter Box 2: Contagion on Twitter Box 3: Cooperation and coordination on Wikipedia Box 3: Cooperation and coordination on Wikipedia Box 4: Cooperation and contagion on Reddit Box 4: Cooperation and contagion on Reddit Discussion Discussion Implications for research Implications for research Implications for design Implications for design Implications for policy Implications for policy Conclusion, Acknowledgments, References, and Competing interests Conclusion, Acknowledgments, References, and Competing interests Box 2: Contagion on Twitter Social bots on the micro-blogging platform Twitter (re-branded as X in 2022) are covert automated accounts designed to impersonate humans to boost followers, disseminate information, and promote products. Bots and bot detection methods have co-evolved, resulting in increasingly more sophisticated imitation or detection strategies [177,178,179,180,39,181,182], but detection is inherently limited due to the overlap between covert autonomous bots, managed user accounts, hacked accounts, cyborgs, sock-puppets, and coordinated botnets [24,183,22,184]. Estimates suggest that 9-15% of Twitter users are bots185,182, with bot activity typically increasing around controversial political events [186]. Twitter social bots, who do not follow social instincts but neither succumb to fatigue, engage less in social interactions via replies and mentions than humans but produce more content [187]. The bots mainly retweet – a passive strategy to indicate support and gain followers – but are less successful in attracting friends and followers than humans [182]. Overall, they are less connected and bot-bot (2%), bot-human (19%), and human-bot (3%) interactions are considerably less common than human-human interactions (76%) 1[86]. Despite their rudimentary social behavior and weak network integration, Twitter bots significantly influence political communication, public opinion, elections, and markets. They play an important role in misinformation dissemination in relation to political events [188,189,190,191,192,193,194], COVID-19[195,196], and stock market investment [197]. Bots can affect human interaction networks by encouraging followings and conversations [198] and amplify low-credibility content early on by targeting influential humans through replies and mentions [192]. Bots’ large numbers enhance their visibility and influence to trigger deep information cascades [199]. Bots equally link to true and false news from low-credibility sites, but people prefer false content, making humans ultimately responsible for the spread of false news [200]. Twitter bots significantly contribute to negative sentiment and conflict escalation. Acting from the periphery, they target central human users to exert indirect influence. They amplify existing negative sentiment and selectively promote inflammatory content, often targeting only one of the factions[186]. Their success stems from exploiting human tendencies to connect with similar others and engage with messages that reinforce their beliefs [201]. Consequently, bots increase ideological polarization and negatively affect democratic discourse on social media, as seen in the 2016 US presidential election[202], the 2016 UK Brexit Referendum[201], and the 2017 Catalan independence referendum[186]. In sum, Twitter’s covert social bots are considered harmful, prompting the platform to cull them[203,204]. Their strength lies in indirect action: they skew the platform’s recommendation system to bias content popularity [137] and exploit human behavioral weaknesses like attention seeking, confirmation bias, moral outrage, and ideological homophily. Authors: (1) Milena Tsvetkova, Department of Methodology, London School of Economics and Political Science, London, United Kingdom; (2) Taha Yasseri, School of Sociology, University College Dublin, Dublin, Ireland and Geary Institute for Public Policy, University College Dublin, Dublin, Ireland; (3) Niccolo Pescetelli, Collective Intelligence Lab, New Jersey Institute of Technology, Newark, New Jersey, USA; (4) Tobias Werner, Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany. Authors: Authors: (1) Milena Tsvetkova, Department of Methodology, London School of Economics and Political Science, London, United Kingdom; (2) Taha Yasseri, School of Sociology, University College Dublin, Dublin, Ireland and Geary Institute for Public Policy, University College Dublin, Dublin, Ireland; (3) Niccolo Pescetelli, Collective Intelligence Lab, New Jersey Institute of Technology, Newark, New Jersey, USA; (4) Tobias Werner, Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany. 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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