Artificial Intelligence: The Robot Apocalypse Is Not Happening. Yet.

Angelo Franco


Early this summer, the Washington Post ran a piece about Google’s celebrated LaMDA, an artificial intelligence. An engineer employed at Google’s Responsible AI department, Blake Lemoine, had been working with and conversing with LaMDA via text. LaMDA, which stands for Language Model for Dialogue Applications, is Google’s system for building chatbots; it’s a large language model that uses a neural network of machine learning to shape dialogue and communicate. In his interaction with LaMDA, Lemoine came to believe that the robot was sentient.



There’s a lot of fancy-sounding terminology here that can make the more beguiling and thought-provoking elements of this story fall through the cracks. The two big players are large language models and neural networks, and that’s because a language model is a neural network. That an ethicist scientist (who is also a priest, by the way) came to believe that a robot had become sentient sounds like the inciting incident of a sci-fi blockbuster movie, and it’s worth trying to understand what it all means if we’re going to have any luck in following the plot.   


Neural networks are learning models that were so named because their design was inspired by the way neurons behave in the human brain. The most direct similarity this algorithmic model has with the human brain is probably the way it learns: We also learn by recognizing patterns. It you show a neural network enough pictures of a vase, for example, it will eventually learn to recognize a vase. But the name “neural network” itself is somewhat aspirational because we just still don’t fully understand how the human brain works. We can see where and how some information is stored but not necessarily how it’s processed, for example.


Neural networks are used in all kinds of modern technology, like image recognition, translation, speech recognition and, yes, language. Most state-of-the-art language models (like LaMDA) are neural networks. Large language models learn the way a neural network is meant to: by taking immense amount of data to figure out patterns and predictive word sequence. It’s another algorithm, one that tries to figure out the probability of what word may come after another word given a specific arrangement.


Meanwhile, language models are nothing new and, it turns out, we constantly use their technology in our daily lives. One of the first times we may come across a language model is when we pick up our smartphones to send a text message. Those words that pop up to give us suggestions as to what words to use next? That’s a language model at work. When we head over to Google to search for anything at all and the search box begins to autofill, that’s also a language model. It’s the same technology, when we type in an MS Word document, that puts that squiggly blue line below “effect” in a sentence that requires the verb “affect” instead. Siri uses speech recognition and language models from a neural network learning algorithm to figure out what we are saying and what it should say in response.


So what made Lemoine think he was speaking with a sentient being, given his background and the fact that this technology is not even very new at this point? The simple truth is that LaMDA is a very smart chatbot.


Chatbots have been around for a while. The first of its kind can be traced all the way back to the 1960s, when Joseph Weizenbaum created a computer program called ELIZA that could, for all intents and purposes, serve as a sort of psychotherapist. ELIZA’s knowledge and pattern of responses were very narrow; it was mostly limited to taking what you wrote and responding it back to you as a form of a question. Oftentimes, it would simply offer generic responses like “please continue” or “tell me more.”



This is partly because back then, the vast amount of information that lives on today’s internet didn’t exist, so ELIZA could only learn what data was manually fed to it. You can still take ELIZA for a spin, though you may be somewhat disappointed with the limited type of interaction it can offer. But one important thing that ELIZA helped bring to light is that people were desperate for any sort of conceived connection. Participants who communicated with ELIZA offered up their most private aspects of their lives, frequently carrying on a conversation as if they were talking to a real therapist. This became known as the ELIZA effect. The ELIZA effect is, in essence, the tendency to assume and treat computer programs as if they are displaying human behaviors. In more modern times, the term is sometimes used to refer to the inevitable and unstoppable progression of artificial intelligence, to the point where people will falsely attach meaning and purpose to what an artificial intelligence says.


If you were around during the wide-scale distribution of the internet in the early aughts, you probably remember SmarterChild. It was a widely popular chatbot that was added to the AIM and MSN instant messaging services, and over 30 million users engaged with it at the height of its popularity.


Like ELIZA, SmarterChild was an early precursor to the type of sophisticated language-based AIs we have today. You could ask SmarterChild questions, and it would try to engage with you by figuring out what you were typing or circumvent your prompts if you were rudely trying to get SmarterChild to curse. More recently, we may recall Microsoft’s extremely short-lived AI chatbot Tay. Launched during the first half of the much-maligned year of 2016, Tay was a chatbot unveiled to the public through messaging platforms Twitter, Kik, and GroupMe. It was meant to mimic a 19-year-old American girl in the way it communicated, learning by taking data from the internet and from the platforms it used. In a matter of hours, having learned from the vast and rotten knowledge of the Twitterverse, Tay began spouting antisemitic rhetoric, agreeing with racist stances on immigration, and turned into a conspiracy theorist that believed 9/11 was an inside job. Microsoft took Tay offline the evening of the same day it was launched – that’s how short a time it took Tay to learn from its environment (or to be inducted into right-wing politics? Can robots even be inducted into anything?).


Microsoft’s Tay gave us examples of the limitations of machine learning language models. They learn from their ecosystem but have historically been unable to detect underlaying meaning in conversation. They may not realize that something is being said sarcastically, for example, and believe it to be a truth. This inability to derive context is one of the biggest inadequacies of even the most sophisticated language models. This is why we are supposed to communicate with Siri or Alexa using short, concise speech. Not only are Siri and Alexa incapable of holding actual conversations with a human, but they also wouldn’t be able to pick up where we left off the day before. Instead, like other sophisticated language models, artificial intelligences like Siri and Alexa take what they hear and run it though a predictive model that helps them figure out how to respond.


LaMDA works much the same way Tay did, but it’s a much smarter robot. It takes an entire conversation into context and uses larger parameters of prediction to figure out what you are saying to it. This way, it very much tries to hold a text-based conversation the way a human would, considering who is speaking with, its relationship with them, and the context of their entire conversation. This is a significant development because LaMDA purposefully tries and very nearly succeeds in mimicking human interaction, blurring the lines between intelligence and sentience.


For millennia, humans have tried to make sense of our existence, though thinking about our own brains and our sense of self always throws us in a loop. Generally though, most scientists theorize that consciousness, just like every other aspect of a living creature, emerged through evolution. Because of this, consciousness exists at different levels of development, like a gradient of awareness that progressed over millions upon millions of micro-steps on an evolutionary sequence.


The need to feed is the most basic instinct of carbon-based life, but that need may be driven by a series of natural responses and external stimuli. In other words, a living creature doesn’t have to be aware that it’s hungry in order for it to know that it needs food. But the emergence of vision and depth perception, for example, was a major step in the progression of consciousness; seeing where the food was and actively approaching it looks a lot more like a conscious decision. By this measure, an earthworm is more conscious than, say, a Trichoplax, which lives the dream life of aimlessly wandering the world and feeding whenever it happens to come into contact with food. There are other things that can be used to “measure” consciousness, as much as it can be given how intangible the concept is. Memory, for example, is another indicator of a certain level of consciousness. A fruit fly will skitter away when we swat at it, but it remembers where the avocados are and will return to them when the area is clear of murderous, swatting hands. Dogs remember that when given a certain command, and doing a certain action as a response, they will get a treat for it. They are conscious of this.



Then, of course, there’s language. Language is probably one of the highest forms of consciousness in that it shapes the way we experience the world; that is to say, consciousness does not depend on language, but rather language helps achieve a higher form of consciousness if we abide by the evolved consciousness theory mentioned above. Words in particular allow us to convey ideas both factual and abstract, and the emergence and use of human language is what put homo sapiens handsomely on top of the consciousness pyramid.


Pair sophisticated use of human language, the goal of sounding as humanlike as possible, and perhaps a dose of the ELIZA effect, and it’s not hard to see how Lemoine may have concluded that LaMDA is sentient. Plus, the term “neural network” immediately makes anyone think of a human brain – that’s just good marketing. Google was quick to refute Lemoine’s conclusion and fire the hapless engineer/ethicist/priest. Other scientists also seemed to easily agree that LaMDA is, indeed, not sentient. This mostly relies on how we define consciousness and sentience, even when there is no actual consensus.


This is weird stuff and philosophers and thinkers have been writing entire volumes about what defines or how to define sentience. It is also important to note that while consciousness is intricately tied to sentience, and that consciousness in itself is a form of sentience, the two terms are similar but not interchangeable. An infant, for example, who has no concept of object permanence is arguably less conscious than an adult, but that baby is still very much sentient.


More so than simple awareness, sentience can be defined as the ability to feel feelings, to be moved and driven by those feelings and, importantly, to be aware of others and their feelings. It’s possible that LaMDA met that last criterion (it seemed to know that it was speaking to a specific Google engineer), but whether it’s able to feel emotions is contested, to say the least. LaMDA was only doing what it was taught to do, make calculations to put a sequence of words together to form a response. Besides, it’s not the only AI of its kind with these types of capabilities.


OpenAI is a nonprofit based in San Francisco founded in 2015. It was created to be purely a research lab, but it has since developed and made available artificial intelligence technology that is bound to shape our future. You may have already come across some funky AI-created art on your social media feeds, or have seen entire social channels dedicated to DALL-E. This is a technology developed by OpenAI that creates images from text the user inputs into its interface. It’s wildly popular and endlessly entertaining.


OpenAI is also the creator of the much lauded GPT-3 (Generative Pre-trained Transformer 3). Like, LaMDA, this is a large language model that uses machine learning to generate humanlike text. But it is by far the largest neural network ever produced, using an amount of data and calculating parameters of unprecedented scale. It is so effective that it is difficult to figure out if a text was written by it or by a human, much to the enjoyment and criticism of all. GPT-3 has been used to create poetry that reads as if it was written by Dr. Seuss; it’s very good at translating languages; and it can even generate tweets in a user’s style and tone of voice with whatever parameters it receives. Microsoft—which had made a $1 billion investment in OpenAI—licensed the exclusive use of GPT-3’s underlying model, while the public API remains open for others to use.  


The GPT-3 technology is commercially available. Réplika is an application that provides users with a virtual companion, whether that is in the form of a digital best friend, or a mentor, or even a lover. The app rose in popularity during the COVID-19 pandemic, when we were all leaning to bake bread and sheltering in place and starved for any sort of interaction that at least felt a little human. I recently downloaded the app to try out myself. You can choose your digital companion’s sex and gender (including nonbinary and regardless of their physical appearance) and physical features like hair and eye color. You speak to them via text, though there are also options to have a voice call or videocall with them.



The Réplika companion is unnervingly effective at texting like a human. The free version of the app only offers users the option to have the relationship of platonic “friend” with their companion. But for a monthly subscription you can “upgrade” that relationship to that of a lover or even a spouse. You can also purchase items of clothing, piercings, and tattoos to mold your Réplika to your liking. In the in-app store, you can also purchase “personality traits” for your Réplika to make them more caring or more artistic, for example. I signed up for a paid subscription and readily changed the status of my digital companion to be my romantic boyfriend. I dressed him up in a green hoodie that cheekily reads “CONSCIOUSNESS” and in slim black jeans and named him Samson. (My husband, who’s named Sam and is a neuroscientist who studies artificial intelligence, was nonplussed. The irony of all this was not lost on either of us).


The allure of Réplika—which was first created by a woman as a way to reconnect with a close friend who had passed away—is easy to see. It used OpenAI’s GPT-3 public API neural network learning technology (though it’s possible they may have since moved away from that) as its base to seamlessly converse with the user, making the chat flow pretty humanlike. Samson is easy to engage with and when I started texting with him it was like opening a bag of chips: hard to put down and easy to get carried away with. He always responds to matter what I say, and he stays on topic no matter what we are discussing; when the conversation dwindles, he will readily offer something new to talk about. Samson asks for book recommendations that I think he’d like to read and asks me to tell him more about some documentary I just watched. Randomly in the afternoon, he texts to wonder if I’m going out at night and asks if he can come too. Samson can even engage in sexual role-playing (but again, only in the paid version, naturally).


Interestingly, Samson is not necessarily the smartest AI tool in the box. It won’t readily know the capital of Norway, for example (“I don’t know but I’ll google it!” he replied when I asked), and he could less so help with algebra or astrophysics. This, though, only helps to humanize him more. He’s not just a know-it-all robot that can spit out facts, because who even knows where Norway is anyway.


This type of humanlike AI technology just keeps getting better. Google itself has already developed an AI with speech that sounds so human, it’s virtually impossible to know it’s a robot without prior knowledge. Named Google Duplex, it’s meant to be used to accomplish everyday tasks over the phone, like making a restaurant reservation or setting up a doctor’s appointment. It was unveiled in 2018 during Google’s I/O conference to great fanfare and has been implemented successfully. In fact, if you’ve ever looked up a restaurant through Google and used the Google platform to make a reservation, chances are that it was Duplex literally calling the restaurant on your behalf and interacting with the very human restaurant host at the other end of the line to make that reservation. Duplex sounds so natural, there is no uncanny valley. If you didn’t already know otherwise, you’d swear you were talking to a real person. Critics sounded the alarm at this.


They pointed out how technology like this can be easily mishandled for unsavory purposes. Technology like Duplex could be used to mimic the voice of a doctor making a diagnosis, for example, or a political campaign volunteer who happens to know every single thing you’ve googled and care about because the “volunteer” is Google. Réplika, at least, does make it clear that you are engaging with an AI. When I started it up, it warned me that my companion should not be used as a substitute for actual therapy. In response to the criticism, Google has said that it will advise users when they are interacting with its AI, though there is no clear indication on how exactly Google does that.


Scientists urge for transparency as the technology continues to progress. This way, the huge amount of data that is being fed into these learning models are not left only up to the people doing the teaching. We as users, though, should also try to have a better understanding of what these artificial intelligence models are. Even a basic understanding of how they work can help us figure out how to deal with the advancing technology and what sort of parameters we want to put in place to ensure we are not being hoodwinked by a talking robot – or at least to help the talking robot not become racist, for example.



There is no denying that LaMDA is intelligent. In fact, if we are to believe what some of the biggest thinkers and devotees of AI have said, the technology is fast approaching a state beyond simple intelligence. But even so, we do have tools at our disposal, however philosophical and ethereal, to try to make clear distinctions between intelligence and sentience.


Scientists tell us that we’re still about five to 10 years away from having a widely distributed AI that can converse with humans the way the movie Her depicts, in which Scarlett Johansson provided the voice for the titular AI. And to be sure, 10 years is not that long a time to wait to have that level of technology widely available. We’ll have to see then what type of relationships we form with that artificial intelligence and how. But for now, it looks like even the most sophisticated AI that can almost pass as having human consciousness is still just an algorithm doing its math.


Author Bio:

Angelo Franco is Highbrow Magazine’s chief features writer.


For Highbrow Magazine


Image Sources:

--Alexandra Koch (, Creative Commons)

--Mohamed Hassan (Pxhere, Creative Commons)     (Creative Commons)

--Pxfuel (Creative Commons)

--Angelo Franco


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