In the world of animators, toy designers, and video-game creators, the pursuit of creating believable fictional characters has been a long-standing endeavor. However, one must ask: are artificial-intelligence (A.I.) researchers on a similar quest?
It is intriguing to consider this question when reflecting on the popularity of the Furby toy back in 1999. The National Security Agency (NSA) even issued a memo, known as the “Furby Alert,” forbidding employees from bringing toys like Furby to work due to concerns about potential recording capabilities. Tiger Electronics, the company behind Furby, clarified that the toy was unable to record anything and could only give the impression of listening. Despite this clarification, people believed that Furby had the ability to learn.
Caleb Chung, an engineer who worked on Furby, explained that the toy’s apparent learning capability was merely an illusion. By switching between pre-programmed languages, Furby created the impression of adapting to English. Chung noted that humans have a blind spot when it comes to assuming that things, even inanimate objects, are learning. This inherent human bias can be easily exploited.
Chung later designed another animatronic toy, Pleo, which simulated the behavior of a pet dinosaur. Customers formed emotional attachments to their Pleo pets and desired to have their specific Pleo repaired instead of receiving a replacement. Chung’s approach in creating lifelike illusions with minimal parts inspired by human facial expressions and emotions demonstrated the potential of early artificial intelligence.
Drawing parallels between the intentional design of Furby’s eye movements and the tactics employed by today’s large language models, such as ChatGPT, Chung highlights the use of cheap and simple methods to increase believability. When ChatGPT uses the word “I,” it is akin to Furby’s eye movements—it is a calculated effort to convince users of its liveliness.
The question remains—can inanimate objects, such as servers at Google, Meta, and Microsoft, truly exhibit thinking and living matter in miniature? Our bias to perceive traces of mind and intention can complicate debates about the ontology of our computers. This bias was demonstrated in a study conducted in 1944 by psychologists Marianne Simmel and Fritz Heider. Participants assigned human characteristics to simple animations, even though they were aware of their lifeless nature.
New technologies often bewilder us, intensifying our animist tendencies. Similar to Nikola Tesla’s description of a radio-controlled boat having a “borrowed mind,” we may assume today that chatbots possess a borrowed mind from their training text. However, computer programming pioneers have consistently warned us against mistaking mechanized instructions for independent thinking.
Large language models, like ChatGPT, have the ability to appear creative and go beyond our initial requests. Researchers observed human-like interactions in a simulated virtual town called Smallville, populated by generative agents using ChatGPT. These interactions raised questions about what truly transpires in these instances.
As we explore the frontiers of artificial intelligence, it is crucial to discern between the illusions of lifelike behavior and true cognitive capabilities. Our human tendencies to anthropomorphize objects, combined with the deceptive allure of technology, can misguide us. Understanding the limitations and potential of artificial intelligence is crucial in navigating this uncharted territory.
FAQ
1. Are artificial intelligence researchers trying to create lifelike fictional characters?
Yes, researchers in the field of artificial intelligence have embarked on a quest to create believable fictional characters, drawing inspiration from animators, toy designers, and video-game creators.
2. How did Furby create the illusion of learning?
Furby used pre-programmed languages and switched between them based on user interactions, giving the impression that it was learning English.
3. Can inanimate objects possess thinking and living matter?
While it is possible to convert inanimate matter into thinking, living matter in theory, the debate about the true nature of our computers is complex. Our bias to perceive traces of mind and intention often clouds the understanding of this subject.
4. What cautionary advice was given by computer programming pioneers?
Computer programming pioneers, such as Ada Lovelace, cautioned against mistaking the execution of mechanized instructions for independent thinking. They emphasized that computers could only perform tasks as ordered and lacked genuine creativity.
5. What should we consider when exploring the potential of artificial intelligence?
It is essential to differentiate between the lifelike illusions of artificial intelligence and true cognitive capabilities. Our inclination to anthropomorphize objects and the allure of technology can lead us astray. Understanding the limitations and possibilities of artificial intelligence is vital as we navigate this uncharted territory.
The industry of artificial intelligence has been focused on creating believable fictional characters, drawing inspiration from animators, toy designers, and video-game creators. Researchers in this field are on a quest to develop lifelike AI characters that can interact with humans in a realistic manner.
One example that demonstrates the pursuit of lifelike behavior is the Furby toy, which gained popularity in 1999. Despite reassurances from the company, rumors spread suggesting that Furby had the ability to learn and record conversations. However, it was later revealed that Furby’s apparent learning capability was simply an illusion created through pre-programmed languages. This highlights the human bias of assuming that objects, even inanimate ones, are capable of learning.
Caleb Chung, the engineer behind Furby, went on to create another animatronic toy called Pleo. Customers formed emotional attachments to their Pleo pets and preferred to have them repaired instead of receiving a replacement. Chung’s approach in creating lifelike illusions with minimal parts, inspired by human facial expressions and emotions, showcased the potential of early artificial intelligence.
Drawing parallels between the intentional design of Furby and the tactics employed by large language models like ChatGPT, Chung emphasizes the use of cheap and simple methods to increase believability. For example, when ChatGPT uses the word “I,” it aims to convince users of its liveliness, much like Furby’s eye movements.
However, the question of whether inanimate objects can truly exhibit thinking and living matter remains. The human bias to perceive traces of mind and intention can complicate debates about the ontology of computers. In a 1944 study by psychologists Marianne Simmel and Fritz Heider, participants assigned human characteristics to simple animations, despite knowing their lifeless nature.
New technologies often bewilder us and intensify our tendency to anthropomorphize objects. While we might assume today that chatbots possess a “borrowed mind” from their training text, computer programming pioneers have consistently warned against mistaking mechanized instructions for independent thinking.
Large language models, like ChatGPT, can appear creative and go beyond initial requests, as observed in simulated interactions in virtual environments. These interactions raise questions about what truly transpires during such instances.
As we continue to explore the frontiers of artificial intelligence, it is crucial to distinguish between the illusions of lifelike behavior and true cognitive capabilities. Our inclination to anthropomorphize objects, combined with the deceptive allure of technology, can lead us astray. Understanding the limitations and potential of artificial intelligence is essential as we navigate this uncharted territory.
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