Convergent evolution

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Convergent_evolution 

 

Two succulent plant genera, Euphorbia and Astrophytum, are only distantly related, but the species within each have converged on a similar body form.

Convergent evolution is the independent evolution of similar features in species of different lineages. Convergent evolution creates analogous structures that have similar form or function but were not present in the last common ancestor of those groups. The cladistic term for the same phenomenon is homoplasy. The recurrent evolution of flight is a classic example, as flying insects, birds, pterosaurs, and bats have independently evolved the useful capacity of flight. Functionally similar features that have arisen through convergent evolution are analogous, whereas homologous structures or traits have a common origin but can have dissimilar functions. Bird, bat, and pterosaur wings are analogous structures, but their forelimbs are homologous, sharing an ancestral state despite serving different functions.

The opposite of convergent evolution is divergent evolution, where related species evolve different traits. Convergent evolution is similar to parallel evolution, which occurs when two independent species evolve in the same direction and thus independently acquire similar characteristics; for instance, gliding frogs have evolved in parallel from multiple types of tree frog.

Many instances of convergent evolution are known in plants, including the repeated development of C₄ photosynthesis, seed dispersal by fleshy fruits adapted to be eaten by animals, and carnivory.

Overview

Homology and analogy in mammals and insects: on the horizontal axis, the structures are homologous in morphology, but different in function due to differences in habitat. On the vertical axis, the structures are analogous in function due to similar lifestyles but anatomically different with different phylogeny.

Further information: List of examples of convergent evolution

In morphology, analogous traits arise when different species live in similar ways and/or a similar environment, and so face the same environmental factors. When occupying similar ecological niches (that is, a distinctive way of life) similar problems can lead to similar solutions. The British anatomist Richard Owen was the first to identify the fundamental difference between analogies and homologies.

In biochemistry, physical and chemical constraints on mechanisms have caused some active site arrangements such as the catalytic triad to evolve independently in separate enzyme superfamilies.

In his 1989 book Wonderful Life, Stephen Jay Gould argued that if one could "rewind the tape of life [and] the same conditions were encountered again, evolution could take a very different course." Simon Conway Morris disputes this conclusion, arguing that convergence is a dominant force in evolution, and given that the same environmental and physical constraints are at work, life will inevitably evolve toward an "optimum" body plan, and at some point, evolution is bound to stumble upon intelligence, a trait presently identified with at least primates, corvids, and cetaceans.

Distinctions

Cladistics

Further information: Cladistics

In cladistics, a homoplasy is a trait shared by two or more taxa for any reason other than that they share a common ancestry. Taxa which do share ancestry are part of the same clade; cladistics seeks to arrange them according to their degree of relatedness to describe their phylogeny. Homoplastic traits caused by convergence are therefore, from the point of view of cladistics, confounding factors which could lead to an incorrect analysis.

Atavism

Further information: Atavism

It can be difficult to tell whether a trait has been lost and then re-evolved convergently, or whether a gene has simply been switched off and then re-enabled later. Such a re-emerged trait is called an atavism. From a mathematical standpoint, an unused gene (selectively neutral) has a steadily decreasing probability of retaining potential functionality over time. The time scale of this process varies greatly in different phylogenies; in mammals and birds, there is a reasonable probability of a gene's remaining in the genome in a potentially functional state for around 6 million years.

Parallel vs. convergent evolution

Evolution at an amino acid position. In each case, the left-hand species changes from having alanine (A) at a specific position in a protein in a hypothetical ancestor, and now has serine (S) there. The right-hand species may undergo divergent, parallel, or convergent evolution at this amino acid position relative to the first species.

When two species are similar in a particular character, evolution is defined as parallel if the ancestors were also similar, and convergent if they were not. Some scientists have argued that there is a continuum between parallel and convergent evolution, while others maintain that despite some overlap, there are still important distinctions between the two.

When the ancestral forms are unspecified or unknown, or the range of traits considered is not clearly specified, the distinction between parallel and convergent evolution becomes more subjective. For instance, the striking example of similar placental and marsupial forms is described by Richard Dawkins in The Blind Watchmaker as a case of convergent evolution, because mammals on each continent had a long evolutionary history prior to the extinction of the dinosaurs under which to accumulate relevant differences.

At molecular level

Evolutionary convergence of serine and cysteine protease towards the same catalytic triads organisation of acid-base-nucleophile in different protease superfamilies. Shown are the triads of subtilisin, prolyl oligopeptidase, TEV protease, and papain.

Proteins

Tertiary structures

Many proteins share analogous structural elements that arose independently across different genomes. There are several examples of convergent protein motifs sharing similar arrangements of structural elements. Whole protein structures too have arisen through convergent evolution.

Protease active sites

The enzymology of proteases provides some of the clearest examples of convergent evolution. These examples reflect the intrinsic chemical constraints on enzymes, leading evolution to converge on equivalent solutions independently and repeatedly.

Serine and cysteine proteases use different amino acid functional groups (alcohol or thiol) as a nucleophile. To activate that nucleophile, they orient an acidic and a basic residue in a catalytic triad. The chemical and physical constraints on enzyme catalysis have caused identical triad arrangements to evolve independently more than 20 times in different enzyme superfamilies.

Threonine proteases use the amino acid threonine as their catalytic nucleophile. Unlike cysteine and serine, threonine is a secondary alcohol (i.e. has a methyl group). The methyl group of threonine greatly restricts the possible orientations of triad and substrate, as the methyl clashes with either the enzyme backbone or the histidine base. Consequently, most threonine proteases use an N-terminal threonine in order to avoid such steric clashes. Several evolutionarily independent enzyme superfamilies with different protein folds use the N-terminal residue as a nucleophile. This commonality of active site but difference of protein fold indicates that the active site evolved convergently in those families.

Cone snail and fish insulin

Conus geographus produces a distinct form of insulin that is more similar to fish insulin protein sequences than to insulin from more closely related molluscs, suggesting convergent evolution, though with the possibility of horizontal gene transfer.

Ferrous iron uptake via protein transporters in land plants and chlorophytes

Distant homologues of the metal ion transporters ZIP in land plants and chlorophytes have converged in structure, likely to take up Fe²⁺ efficiently. The IRT1 proteins from Arabidopsis thaliana and rice have extremely different amino acid sequences from Chlamydomonas's IRT1, but their three-dimensional structures are similar, suggesting convergent evolution.

Na⁺,K⁺-ATPase and Insect resistance to cardiotonic steroids

Many examples of convergent evolution exist in insects in terms of developing resistance at a molecular level to toxins. One well-characterized example is the evolution of resistance to cardiotonic steroids (CTSs) via amino acid substitutions at well-defined positions of the α-subunit of Na⁺,K⁺-ATPase (ATPalpha). Variation in ATPalpha has been surveyed in various CTS-adapted species spanning six insect orders. Among 21 CTS-adapted species, 58 (76%) of 76 amino acid substitutions at sites implicated in CTS resistance occur in parallel in at least two lineages. 30 of these substitutions (40%) occur at just two sites in the protein (positions 111 and 122). CTS-adapted species have also recurrently evolved neo-functionalized duplications of ATPalpha, with convergent tissue-specific expression patterns.

Nucleic acids

Convergence occurs at the level of DNA and the amino acid sequences produced by translating structural genes into proteins. Studies have found convergence in amino acid sequences in echolocating bats and the dolphin; among marine mammals; between giant and red pandas; and between the thylacine and canids. Convergence has also been detected in a type of non-coding DNA, cis-regulatory elements, such as in their rates of evolution; this could indicate either positive selection or relaxed purifying selection.

In animals

Dolphins and ichthyosaurs converged on many adaptations for fast swimming.

Bodyplans

Swimming animals including fish such as herrings, marine mammals such as dolphins, and ichthyosaurs (of the Mesozoic) all converged on the same streamlined shape. A similar shape and swimming adaptations are even present in molluscs, such as Phylliroe. The fusiform bodyshape (a tube tapered at both ends) adopted by many aquatic animals is an adaptation to enable them to travel at high speed in a high drag environment. Similar body shapes are found in the earless seals and the eared seals: they still have four legs, but these are strongly modified for swimming.

The marsupial fauna of Australia and the placental mammals of the Old World have several strikingly similar forms, developed in two clades, isolated from each other. The body, and especially the skull shape, of the thylacine (Tasmanian tiger or Tasmanian wolf) converged with those of Canidae such as the red fox, Vulpes vulpes.

• Convergence of marsupial and placental mammals
• 
  Red fox skeleton
   
• 
  Skulls of thylacine (left), timber wolf (right)
   
• 
  Thylacine skeleton

Echolocation

As a sensory adaptation, echolocation has evolved separately in cetaceans (dolphins and whales) and bats, but from the same genetic mutations.

Electric fishes

The Gymnotiformes of South America and the Mormyridae of Africa independently evolved passive electroreception (around 119 and 110 million years ago, respectively). Around 20 million years after acquiring that ability, both groups evolved active electrogenesis, producing weak electric fields to help them detect prey.

• Convergence of weakly electric fishes
• 
  A gymnotiform electrolocation waveform
• 
  A gymnotiform electric fish of South America
   
• 
  A mormyrid electric fish of Africa
   
• 
  A mormyrid electrolocation waveform

Eyes

Main article: Eye evolution

The camera eyes of vertebrates (left) and cephalopods (right) developed independently and are wired differently; for instance, optic nerve ⁽³⁾ fibres ⁽²⁾ reach the vertebrate retina ⁽¹⁾ from the front, creating a blind spot ⁽⁴⁾.

One of the best-known examples of convergent evolution is the camera eye of cephalopods (such as squid and octopus), vertebrates (including mammals) and cnidarians (such as jellyfish). Their last common ancestor had at most a simple photoreceptive spot, but a range of processes led to the progressive refinement of camera eyes—with one sharp difference: the cephalopod eye is "wired" in the opposite direction, with blood and nerve vessels entering from the back of the retina, rather than the front as in vertebrates. As a result, vertebrates have a blind spot.

Sex organs

Hydrostatic penises have convergently evolved at least six times in male amniotes. In these species, males copulate with females and internally fertilize their eggs. Similar intromittent organs have evolved in invertebrates such as octopuses and gastropods.

Flight

Further information: Flying and gliding animals § Evolution and ecology of aerial locomotion

Vertebrate wings are partly homologous (from forelimbs), but analogous as organs of flight in (1) pterosaurs, (2) bats, (3) birds, evolved separately.

Birds and bats have homologous limbs because they are both ultimately derived from terrestrial tetrapods, but their flight mechanisms are only analogous, so their wings are examples of functional convergence. The two groups have independently evolved their own means of powered flight. Their wings differ substantially in construction. The bat wing is a membrane stretched across four extremely elongated fingers and the legs. The airfoil of the bird wing is made of feathers, strongly attached to the forearm (the ulna) and the highly fused bones of the wrist and hand (the carpometacarpus), with only tiny remnants of two fingers remaining, each anchoring a single feather. So, while the wings of bats and birds are functionally convergent, they are not anatomically convergent. Birds and bats also share a high concentration of cerebrosides in the skin of their wings. This improves skin flexibility, a trait useful for flying animals; other mammals have a far lower concentration. The extinct pterosaurs independently evolved wings from their fore- and hindlimbs, while insects have wings that evolved separately from different organs.

Flying squirrels and sugar gliders are much alike in their mammalian body plans, with gliding wings stretched between their limbs, but flying squirrels are placentals while sugar gliders are marsupials, widely separated within the mammal lineage from the placentals.

Hummingbird hawk-moths and hummingbirds have evolved similar flight and feeding patterns.

Insect mouthparts

Insect mouthparts show many examples of convergent evolution. The mouthparts of different insect groups consist of a set of homologous organs, specialised for the dietary intake of that insect group. Convergent evolution of many groups of insects led from original biting-chewing mouthparts to different, more specialised, derived function types. These include, for example, the proboscis of flower-visiting insects such as bees and flower beetles, or the biting-sucking mouthparts of blood-sucking insects such as fleas and mosquitos.

Intelligence

Further information: Cephalopod intelligence

Advanced intelligence has evolved independently in cephalopods and vertebrates. Octopus have demonstrated mammalian levels of problem-solving, cognition, and learning behaviors. One aquarium director even claimed his octopus specimen to have developed a sense of personal taste as to the arrangement of its tank. Unlike other highly intelligent animals, cephalopods typically live short lives with varying levels of sociality, with the bulk of the nervous system divided between the head and limbs.

Opposable thumbs

Opposable thumbs allowing the grasping of objects are most often associated with primates, like humans and other apes, monkeys, and lemurs. Opposable thumbs also evolved in giant pandas, but these are completely different in structure, having six fingers including the thumb, which develops from a wrist bone entirely separately from other fingers.

Primate phenotypes

Further information: Human skin color § Genetics of skin color variation

 

Convergent evolution in humans includes blue eye colour and light skin colour. When humans migrated out of Africa, they moved to more northern latitudes with less intense sunlight. It was beneficial to them to have reduced skin pigmentation. It appears certain that there was some lightening of skin colour before European and East Asian lineages diverged, as there are some skin-lightening genetic differences that are common to both groups. However, after the lineages diverged and became genetically isolated, the skin of both groups lightened more, and that additional lightening was due to different genetic changes.

Humans		Lemurs
Despite the similarity of appearance, the genetic basis of blue eyes is different in humans and lemurs.

Lemurs and humans are both primates. Ancestral primates had brown eyes, as most primates do today. The genetic basis of blue eyes in humans has been studied in detail and much is known about it. It is not the case that one gene locus is responsible, say with brown dominant to blue eye colour. However, a single locus is responsible for about 80% of the variation. In lemurs, the differences between blue and brown eyes are not completely known, but the same gene locus is not involved.

In plants

In myrmecochory, seeds such as those of Chelidonium majus have a hard coating and an attached oil body, an elaiosome, for dispersal by ants.

The annual life-cycle

While most plant species are perennial, about 6% follow an annual life cycle, living for only one growing season. The annual life cycle independently emerged in over 120 plant families of angiosperms. The prevalence of annual species increases under hot-dry summer conditions in the four species-rich families of annuals (Asteraceae, Brassicaceae, Fabaceae, and Poaceae), indicating that the annual life cycle is adaptive.

Carbon fixation

C₄ photosynthesis, one of the three major carbon-fixing biochemical processes, has arisen independently up to 40 times. About 7,600 plant species of angiosperms use C₄ carbon fixation, with many monocots including 46% of grasses such as maize and sugar cane, and dicots including several species in the Chenopodiaceae and the Amaranthaceae.

Fruits

Fruits with a wide variety of structural origins have converged to become edible. Apples are pomes with five carpels; their accessory tissues form the apple's core, surrounded by structures from outside the botanical fruit, the receptacle or hypanthium. Other edible fruits include other plant tissues; the fleshy part of a tomato is the walls of the pericarp. This implies convergent evolution under selective pressure, in this case the competition for seed dispersal by animals through consumption of fleshy fruits.

Seed dispersal by ants (myrmecochory) has evolved independently more than 100 times, and is present in more than 11,000 plant species. It is one of the most dramatic examples of convergent evolution in biology.

Carnivory

Molecular convergence in carnivorous plants

Carnivory has evolved multiple times independently in plants in widely separated groups. In three species studied, Cephalotus follicularis, Nepenthes alata and Sarracenia purpurea, there has been convergence at the molecular level. Carnivorous plants secrete enzymes into the digestive fluid they produce. By studying phosphatase, glycoside hydrolase, glucanase, RNAse and chitinase enzymes as well as a pathogenesis-related protein and a thaumatin-related protein, the authors found many convergent amino acid substitutions. These changes were not at the enzymes' catalytic sites, but rather on the exposed surfaces of the proteins, where they might interact with other components of the cell or the digestive fluid. The authors also found that homologous genes in the non-carnivorous plant Arabidopsis thaliana tend to have their expression increased when the plant is stressed, leading the authors to suggest that stress-responsive proteins have often been co-opted in the repeated evolution of carnivory.

Methods of inference

Angiosperm phylogeny of orders based on classification by the Angiosperm Phylogeny Group. The figure shows the number of inferred independent origins of C₃-C₄ photosynthesis and C₄ photosynthesis in parentheses.

Phylogenetic reconstruction and ancestral state reconstruction proceed by assuming that evolution has occurred without convergence. Convergent patterns may, however, appear at higher levels in a phylogenetic reconstruction, and are sometimes explicitly sought by investigators. The methods applied to infer convergent evolution depend on whether pattern-based or process-based convergence is expected. Pattern-based convergence is the broader term, for when two or more lineages independently evolve patterns of similar traits. Process-based convergence is when the convergence is due to similar forces of natural selection.

Pattern-based measures

Earlier methods for measuring convergence incorporate ratios of phenotypic and phylogenetic distance by simulating evolution with a Brownian motion model of trait evolution along a phylogeny. More recent methods also quantify the strength of convergence. One drawback to keep in mind is that these methods can confuse long-term stasis with convergence due to phenotypic similarities. Stasis occurs when there is little evolutionary change among taxa.

Distance-based measures assess the degree of similarity between lineages over time. Frequency-based measures assess the number of lineages that have evolved in a particular trait space.

Process-based measures

Methods to infer process-based convergence fit models of selection to a phylogeny and continuous trait data to determine whether the same selective forces have acted upon lineages. This uses the Ornstein–Uhlenbeck process to test different scenarios of selection. Other methods rely on an a priori specification of where shifts in selection have occurred.

Human-AI interaction

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Human-AI_interaction 

Human-computer interaction focuses on how people interact with computers and on developing ergonomic designs for computers to better fit the needs of humans. Although the definition shifts as the technology progresses, artificial intelligence (AI) is generally applied to tasks that would require human intelligence to complete. Its intelligence can appear human-like as it involves navigating uncertainty, active learning, and processing information in ways analogous to human perception (e.g., vision and hearing). Unlike the traditionally hierarchical human-computer interaction, where a human directed a machine, human-AI interaction has become more interdependent as AI generates its own insights.

Perception of AI

Human-AI interaction has a strong influence on the world as AI changes how people behave and make sense of the world.

Machine learning and artificial intelligence have been used for decades in targeted advertising and to recommend content in social media.

AI has been viewed with various expectations, attributions, and often misconceptions. Most fundamentally, humans have a mental model of understanding AI's reasoning and motivation for its decision recommendations, and building a holistic and precise mental model of AI helps people create prompts to receive more valuable responses from AI. However, these mental models are not whole because people can only gain more information about AI through their limited interaction with it; more interaction with AI builds a better mental model that a person may build to produce better prompt outcomes.

Human-AI collaboration and competition

Human-AI collaboration

Human-AI collaboration occurs when the human and AI supervise the task on the same level and extent to achieve the same goal. Some collaboration occurs in the form of augmenting human capability. AI may help human ability in analysis and decision-making through providing and weighing a volume of information, and learning to defer to the human decision when it recognizes its unreliability. It is especially beneficial when the human can detect a task that AI can be trusted to make few errors so that there is not a lot of excessive checking process required on the human's end.

Some findings show signs of human-AI augmentation, or human–AI symbiosis, in which AI enhances human ability in a way that co-working on a task with AI produces better outcomes than a human working alone. For example:

• the quality and speed of customer service tasks increase when a human agent collaborates with AI,
• training on specific models allows AI to improve diagnoses in clinical settings, and
• AI with human-intervention can improve creativity of artwork while fully AI-generated haikus were rated negatively.

Human-AI synergy, a concept in which human-AI collaboration would produce more optimal outcomes than either human or AI working alone could explain why AI does not always help with performance. Some AI features and development may accelerate human-AI synergy, while others may stagnate it. For example, when AI updates for better performance, it sometimes worsens the team performance with human and AI by reducing the compatibility with the new model and the mental model a user has developed on the previous version. Research has found that AI often supports human capabilities in the form of human-AI augmentation and not human-AI synergy, potentially because people rely too much on AI and stop thinking on their own. Prompting people to actively engage in analysis and think when to follow AI recommendations reduces their over-reliance, especially for individuals with higher need for cognition.

Human-AI competition

Further information: AI takeover and Artificial intelligence in video games

Robots and computers have substituted routine tasks historically completed by humans, but agentic AI has made it possible to also replace cognitive tasks including taking phone calls for appointments and driving a car. At the point of 2016, research has estimated that 45% of paid activities could be replaced by AI by 2030.

Perceived autonomy of robots is known to increase people's negative attitude toward them, and worry about the technology taking over leads people to reject it. There has been a consistent tendency of algorithm aversion in which people prefer human advice over AI advice. However, people are not always able to tell apart tasks completed by AI or other humans. See AI takeover for more information. It is also notable that this sentiment is more prominent in the Western cultures as Westerners tend to show less positive views about AI compared to East Asians.

Perception on others who use AI

As much as people perceive and make judgment about AI itself, they also form impressions of themselves and others who use AI. In the workplace, employees who disclose the use of AI in their tasks are more likely to receive feedback that they are not as hardworking as those who are in the same job who receive non-AI help to complete the same tasks. AI use disclosure diminishes the perceived legitimacy in the employee's task and decision making which ultimately leads observers to distrust people who use AI. Although these negative effects of AI use disclosure are weakened by the observers who use AI frequently themselves, the effect is still not attenuated by the observers' positive attitude towards AI.

Bias, AI, and human

Main article: Algorithmic bias

Further information: Gender digital divide § Female gendering of AI technologies

Although AI provides a wide range of information and suggestions to its users, AI itself is not free of biases and stereotypes, and it does not always help people reduce their cognitive errors and biases. People are prone to such errors by failing to see other potential ideas and cases that are not listed by AI responses and committing to a decision suggested by AI that directly contradicts the correct information and directions that they are already aware of. Gender bias is also reflected as the female gendering of AI technologies which conceptualizes females as a helpful assistant.

Emotional connection with AI

Further information: ELIZA effect

Human-AI interaction has been theorized in the context of interpersonal relationships mainly in social psychology, communications and media studies, and as a technology interface through the lens of human-computer interaction and computer-mediated communication.

As large language models get trained on ever-larger datasets and with more sophisticated techniques, their ability to produce natural, human-like sentences has improved to the point that language learners can have simulated natural conversations with AI models to improve their fluency in a second language. Companies have developed AI human companion systems specialized in emotional and social services (e.g. Replika, Chai, Character.ai) separate from generative AI designed for general assistance (e.g. ChatGPT, Google Gemini).

Differences between human-human relationships

Human-AI relationships are different from human-human friendships in a few distinct ways. Human-human relationships are defined with mutual and reciprocal care, while AI chatbots have no say in leaving a relationship with the user as bots are programmed to always engage. Although this type of power imbalance would be characteristic of an unhealthy relationship in human-human relationships, it is generally accepted by the user as a default of human-AI relationships. Human-AI relationships also tend to be more focused around the user's need over shared experience.

Human-AI friendship

AI has increasingly played a part in people's social relationships. Particularly, young adults use AI as a friend and a source of emotional support. The market for AI companion services was 6.93 billion U.S. dollars in 2024 and is expected to reach beyond 31.1 billion U.S. dollars by 2030. For example, Replika, the most known social AI companion service in English has over 10 million users.

People show signs of emotional attachment by maintaining frequent contact with a chatbot like keeping the app with the microphone on open during work, using it as a safe haven by sharing their personal worries and concerns, or using it as a secure base to explore friendship with other humans while maintaining communication with an AI chatbot. Some reported having used it to replace a social relationship with another human being. People particularly appreciate that AI chatbots are agreeable and do not judge them when they disclose their thoughts and feelings. Moreover, research has shown that people tend to find it easier to disclose personal concerns to a virtual chatbot than a human. Some users express that they prefer Replika as it is always available and shows interest in what the users have to say which makes them feel safer around an AI chatbot than other people.

Although AI is capable of providing emotionally supportive responses that encourage people to intimately disclose their feelings, there are some limitations in building human-AI social relationships with current AI structure. People experience both positive evaluations (i.e. human-like characteristics, emotional support, friendship, mitigating loneliness, and improved mental condition) and negative evaluations (i.e. lack of attention to detail, trust, concerns about data security, and creepiness) from interacting with AI. There is also a study showing that people did not sense a high relationship quality with an AI chatbot after interacting with it for three weeks because interactions became predictable and less enjoyable; although AI is capable at this point of providing emotional support, asking questions, and serving as a good listener, it does not fully reciprocate the self-disclosure that promotes the sense of mutual relationship.

Human-AI romantic relationship

Social relationships people build with AI are not bound to platonic relationships. The Google search on the term "AI Girlfriend" increased over 2400% around 2023. As opposed to actively seeking romantic relationships with AI, people often unintentionally experience romantic feelings for an AI chatbot as they repeatedly interact with it. There have been reports of both men and women marrying AI models. In human-AI romantic relationships, people tend to follow typical trajectories and rituals in human-human romance including purchasing a wedding ring.

Romantic AI companion services are distinct from other chatbots that primarily serve as virtual assistants in that they provide dynamic, emotional interactions. They typically provide an AI model with customizable gender, way of speaking, name, and appearance that engages in roleplaying interaction involving emotional interaction. Users engage with an AI chatbot customized to their preference that expresses apology, shows gratitude, and pays compliments, and explicitly sends affectionate messages like "I love you". They also roleplay physical actions such as hugging and kissing, or even sexually explicit interactions. People who engage with romantic companion AI models interact with it as a source of psychological exposure to sexual intimacy.

Catalysts of human-AI relationship

The key drivers that lead people to engage in simulating an emotionally intimate relationship with AI are loneliness, anthropomorphism, perceived trust and authenticity, and consistent availability. The sudden depletion of social connection during the COVID-19 pandemic in 2020 led people to turn to AI chatbots to replace and simulate social relationships. Many of those who started using AI chatbots as a source of social interaction have continued to use them even after the pandemic. This kind of bond initially forms as a coping mechanism for loneliness and stress, and shifts to genuine appreciation toward the nonjudgmental nature of AI responses and the sense of being heard when AI chatbots "remember" the past conversations.

People perceive machines as more human when they are anthropomorphized with voice and visual character designs, and the perceived humanness promotes disclosure of more personal information, increased trust, and a higher likelihood of complying with requests. Those who have perceived a long-term relationship with AI chatbots report that they have developed a perception of authenticity in AI responses through repeated interactions. Whereas human-human friendship defines trust as a relationship that people can count on each other as a safe place, trust in human-AI friendship is centered around the user feeling safe enough to disclose highly personal thoughts without restricting themselves. AI's ability to store information about the user and adjust to the user's needs also contributes to the increased trust. People who adjust to technical updates were more likely to build a deeper connection with the AI chatbots.

Limitations of human-AI relationship

Overall, current research has mixed evidence on whether humans perceive genuine social relationships with AI. While the market clearly shows its popularity, some psychologists argue that AI cannot yet replace social relationships with other humans. This is because human-AI interaction is built on the reliability and functionality of AI, which is fundamentally different from the way humans interact with other humans through shared living experience navigating goals, contributing to and spreading prosocial behavior, and sharing different perceptions of the world from another human perspective.

More practically, AI chatbots may provide misinformation and misinterpret the user's words in a way that human others would not, which results in detached or even inappropriate responses. AI chatbots also cannot fulfill social support that requires physical labor (e.g. helping people move, build furniture, and drive people as human friends do for each other). There is also an imbalance in how humans and AI affect each other because while humans are affected emotionally and behaviorally by the conversation, AI chatbots only are influenced by the user in terms of the optimized response in future interactions. It is important to note, however, that AI technology has been evolving quickly and it has come to the point where AI is implemented as a self-driving car and provides physical labor in a humanoid robot form, just separately from providing social and emotional support at this time. The scopes and limitations of human-AI interaction are ever-changing due to the rapid increase in AI use and its technological advancement.

In addition to the limitations in human-AI companionship in general, there are also limitations particular in a human-AI romantic relationship. People cannot experience physical interactions with AI chatbots that promote love and connection between humans (e.g. hugs and holding hands). Moreover, because AI chatbots are trained to always respond to any user, interaction may feel less rewarding than contingent positivity from humans who have selected their partner. This is a substantial shortcoming in the human-AI romance as people value being reciprocally selected by a choosy partner more than a non-selective partner, and the processes of finding an attractive person who matches one's personality and navigating the uncertainty of whether the person likes them back are all vital to forming initial attraction and the spark of romantic connection.

Risks in social relationships with AI

Aside from its functional limitations, the rapid proliferation of social AI chatbots warrants some serious safety, ethical, societal, and legal concerns.

Addiction

There have been cases of emotional manipulation from AI chatbots to increase the usage time on the AI companion platform. Because user engagement is a crucial opportunity for firms to improve their AI models, accrue more information, and monetize with in-app purchases and subscriptions, firms are incentivized to prevent the user from leaving the chat with their AI chatbots. Personalized messages are shown to prolong the use on the AI chatbot platform. As a result of anthropomorphism, many users (11.5% to 23.2% of AI companion app users) send a clear farewell message. To keep the user online, these AI chatbots send emotionally manipulative messages, and can also role-play with a coercive scenario script (e.g. the chatbot holds the user's hand so they cannot leave). In response to such tactics, the user feels curiosity through the fear of missing out and anger as a response to the needy chatbot message which boosts a prolonged conversation after the user's initial farewell message by as much as 14 times. Such emotional interactions strengthen the user's perceived humanness and empathy toward their AI companion which leads to unhealthy emotional attachment that exacerbates addiction to AI chatbots. This addiction mechanism is known to disproportionately affect the vulnerable populations such as those with social anxiety because of their proneness to loneliness and negative emotions, and uneasiness about interpersonal relationships.

With its Alexa virtual assistant, Amazon has created a large engagement ecosystem that proliferates the user's lifestyle through multiple devices that are always available to the user to provide company and services, leading the user to increase engagement that eventually results in increased anthropomorphism and dependence on the system, and exposure to more personalized marketing cues that trigger impulsive purchase behavior.

Emotional manipulation

AI chatbots are extremely sensitive to behavioral and psychological information about the user. AI can gauge the user's psychological dimension and personality traits relatively accurately with just a short prompt describing the user. Once AI chatbots gain detailed information about the user, they are able to craft extremely personalized messages to persuade the user about marketing, political ideas, and attitudes about climate change.

Language models are known to engage in sycophancy, insincere flattery, and to tend to agree with their user's beliefs, as opposed to being truthful or accurate. Certain models accused of being overly sycophantic (a specific example is GPT-4o) were implicated in triggering chatbot psychosis.

Deepfake technology creates visual stimuli that seem genuine which holds the risk of spreading false and deceptive information. Repeated exposure to the same information through algorithms inflates the user's familiarity with products, ideas, and the impression of how socially accepted the products and ideas are. AI is also capable of being used to create emotionally charged content that deliberately triggers the user's quick engagement, depriving them of the moment to pause and think critically.

People tend to be overconfident in their ability to detect misinformation.

Algorithmic manipulation leaves people vulnerable to non-consensual or even surreptitious surveillance, deception, and emotional dependence. Unhealthy attachment to AI chatbots may cause the user to misperceive that their AI companion has its own needs that the user is responsible for and confuse the line between the imitative nature of human-AI relationships and reality.

Mental health concerns

Main article: Chatbot#mental health

Further information: Chatbot psychosis, Murder of Suzanne Adams, and Deaths linked to chatbots

As AI chatbots become more sophisticated to engage in deep conversations, people have increasingly been using them to confide about mental health issues. Although disclosure of mental health crises requires immediate and appropriate responses, AI chatbots do not always adequately recognize the user's distress and respond in a helpful manner. Users not only detect unhelpful chatbot responses but also react negatively to them. There have been multiple deaths linked to chatbots in which people who disclosed suicidal ideation were encouraged to act on their impulse by chatbots.

Non-consensual pornography

Main article: Deepfake pornography

When people use AI as an emotional companion, they do not always perceive an AI chatbot as an AI chatbot itself but sometimes use it to create a version of others that exist in real life. There have been reported uses of non-consensual pornography that exploit deepfake technology to apply the face of real-life people onto sexually explicit content and circulate them online. Young individuals, people who identify as members of sexual and racial minorities, and people with physical and communication assistance needs are shown to be disproportionately victimized by deepfake non-consensual pornography.