AI and Automation in Unconventional Sectors: The Impact of Artificial Intelligence on Travel and Hospitality
There is, albeit subtle, a difference between artificial intelligence and automation. Although these two terms are often used interchangeably, technically, automation systems are limited to performing specific tasks for which they have been programmed by humans, while the unique characteristic of AI is its ability to independently identify patterns, learn from experience, and make decisions autonomously, without the need for explicit commands from developers. Automations are, so to speak, passive, while AI is active. However, despite the semantic distinction, in practice, the boundaries between AI and automation are becoming increasingly blurred and fragile, and there is a growing tendency to include active tasks, typical of AI, under the automation umbrella.There is a certain semantic overlap between automations and artificial intelligence and, since this is not a technical book for developers, I have chosen to address the topic of automation as the set of all those processes (automated or automatable) that do not require, except partially, human intervention, without considering the level of “intelligence” behind them. I hope you’ll pardon this necessary semantic stretch. Apart from the terminology choice, in today’s world, thinking that you can continue to operate in the travel industry (or, for that matter, any industry) without at least a minimum level of automation means, at the very least, suffering from entrepreneurial shortsightedness. The advantages of automation are manifold: from saving in terms of man-hours to better management of internal operations. Automating processes is a business necessity, and with increasingly accessible technologies and infrastructures, both in terms of required investments and implementation, automation is no longer the exclusive domain of OTAs or large chain brands. The shift from all-in-one systems to the use of a broader range of third-party, agnostic platforms, in particular, has created a scenario where every type of accommodation facility now enjoys an unprecedented level of freedom, allowing hoteliers to implement what they need, when they need it, without the need to uproot existing company processes.
In today’s world, thinking that you can continue to operate in the travel industry (or, for that matter, any industry) without at least a minimum level of automation means, at the very least, suffering from entrepreneurial shortsightedness
However, despite automation being widely implemented in almost every industry, it is still relatively young in the hospitality industry. Europen hotels (which, let’s not forget, are generally family-run, independent, or part of micro-chains) in particular tend to be more resistant to adopting automation, mainly due to the initial financial investment (often more perceived than real, in truth) required. Another reason is a lack of understanding of the concept of automation itself, a concept that, as highlighted at the beginning, is already somewhat hazy in itself: automation tools are still widely considered as superfluous frills, children of an oligo-technocratic regime in the hands of very few providers. To make matters worse, the usually traumatic experiences that the average hotelier goes through whenever they are forced to implement new software (especially those that are more central to daily operations, such as PMSs), make them look at the process with suspicion and fear — if not, even, with a marked overestimation of the risk. In psychology, this phenomenon is known as the “availability heuristic”: the probability that something will go wrong during the implementation of new software (data loss, the need to retrain staff, and so on), although very low from a statistical point of view, is thus magnified by this (well-known, studied, and documented) cognitive shortcut.
Software providers also bear responsibility for the lack of automation adoption. While they play a crucial role in educating hoteliers about the benefits of automation, they must do so without coming across as purely sales-driven. Furthermore, consultants like ourselves must engage in self-critique, often falling victim to a more malicious bias — believing ourselves more competent than we actually are. Psychology once again comes to our aid, defining this human inclination as the Dunning-Kruger Effect. Whatever the root causes may be, the fundamental problem remains pervasive. On one hand, there are small independent hotels with their aforementioned intrinsic challenges. On the other hand, there are chain hotels, which, despite their undeniable technological know-how, investment power, and reduced cognitive biases, find themselves limited in choices due to the sluggishness of decisions from their parent companies. The adoption or non-adoption of one technology over another is a complex and painful process, especially in an industry as operationally intense and technologically saturated as hospitality. However, the situation borders on the absurd: when managing a hotel, the primary problem is always a lack of time. And automation can give that time back.
When managing a hotel, the primary problem is always a lack of time. And automation can give that time back.
And then ther’s the perennial problem of human suspicion toward automation, even though it’s now clear to most that human customer service and non-human customer service are not in competition but rather in cooperation. Automation offers hoteliers the opportunity to replace virtually all the processes that operate behind the scenes (i.e., those where the presence of a human being is not strictly necessary and add no perceived value). However, the resulting adoption is still confined to some branded hotels, the upscale segment, or a few enlightened independent properties. Howerver, this lack of adoption creates a false economy, because if the cost of tech investment is high, the cost of non-investment is even higher.
Human customer service and non-human customer service are not in competition but rather in cooperation.
Upon closer examination, the problem isn’t just about investments; it’s technological, cultural, managerial, and organizational all at once. Managing a complex platform requires investments in terms of time and training, and ironically, it’s not uncommon for these tools to go entirely unused. However, this is a highly risky behavior. Hotel immobility corresponds to an increasing sophistication in automation by OTAs, which are more willing to innovate, test, fail, and learn from their mistakes, an unheard non-hierarchical system in the hotel industry. Unfortunately, even today, there is a kind of technological inertia prevailing in hotels that we all contribute to: suppliers, consultants, and hoteliers. It’s time to correct that.
If the cost of tech investment is high, the cost of non-investment is even higher.
Another sensitive topic when it comes to automation is the thorny issue of privacy. Automation platforms require a substantial amount of data, and at a time when digital privacy has become an obsession (today, the International Association of Privacy Professionals, also known as IAPP, has more than 20,000 members in over 80 countries), we must also contend with fragmented regulations. In Europe, especially, hoteliers face an additional layer of complexity in acquiring guest data while complying with a vague regulation. Especially when it comes to AI systems, where the volume of data required is very high, the issue of privacy becomes particularly challenging. Examples? Palantir Technology is a big data company you may not have heard of, but it’s valued at over $20 billion and collaborates with, among others, the CIA, FBI, DHS, NSA, CDC, the Marine Corps, and the Air Force. Furthermore, the 2018 Cambridge Analytica case caused quite a stir. The company was accused of collecting personal data from millions of Facebook users without their consent for political propaganda purposes (Cambridge Analytica later declared bankruptcy, partly due to this scandal). In March 2023, an Italian data privacy authority imposed a temporary ban on OpenAI’s ChatGPT due to concerns about the absence of age-verification mechanisms and a perceived lack of a legal foundation for collecting user data from the internet to train the AI tool’s algorithms.The examples could go on indefinitely. Last but not least is the unfounded fear of losing the “human touch. However, it’s worth noting that, according to a Gallup survey, the negative attitude of employees (humans, not robots) toward their work leads to an annual loss of productivity of approximately $300 billion in the United States alone.
As with automation, agreeing on a definition of artificial intelligence can be extremely complex. Few experts seem to be as vehemently in disagreement as those working in this field. For the purposes of this book, however, we can start with the assumption that in all those cases where a machine imitates those cognitive functions that we (erroneously) presume to be the exclusive domain of humans, animals and plants, then we are in the field of AI. If we accept this simplistic definition, AI becomes nothing more than that particular type of intelligence demonstrated by computers, not too dissimilar from what I, you, a goldfish, or a flower might possess. Rather than being just a technology, artificial intelligence should be considered as a behavior or, even better, a discipline, as AI are almost always the result of a collaborative and multidisciplinary approach. At closer look, technology is one of the main features of our species if not the main feature, without which we would not be fully human. It doesn’t matter if we refer to ChatGPT or the invention of fire.
Technology is one of the main features of our species if not the main feature, without which we would not be fully human. It doesn’t matter if we refer to ChatGPT or the invention of fire.
However, the question of whether a machine can think or not is particularly thorny. A quote, often attributed (probably incorrectly) to American linguist Noam Chomsky, says that thinking is a exclusively human feature, and asking ourselves if AI will someday be able to think is like asking if submarines swim. Not even the Cartesian phrase, “Cogito, ergo sum” (I think, therefore I am), comes to our aid. Man exists because he applies methodical doubt: he doubts, therefore he thinks, therefore he is. As the French philosopher and mathematician theorizes, even doubting one’s ability to think is, in itself, thinking, so man cannot escape his existence, precisely because of (or thanks to) thought. But a machine? The approach of neural networks presupposes knowledge, if not complete, at least highly advanced, of the human brain, something we have not even come close to achieving. But even assuming we could create an identical copy of a brain, with all its neurons and synapses, would this copy be able to doubt its own thoughts? Perhaps. Or perhaps not.
Doubting one’s ability to think is, in itself, thinking.
Philosopher Thomas Hobbes, regarded as one of the philosophical pioneers of artificial intelligence, holds a contrasting perspective, associating human cognition with logic. In contrast, anthropologist Gregory Bateson contends that even the human brain cannot be considered as thinking in isolation, as it is part of a system encompassing an external environment. Entrepreneur Rodney Brooks appears to align with this perspective by asserting that intelligence necessitates a physical body, thereby framing the entire discourse on artificial intelligence within the realm of robotics (a standpoint I personally distance myself from). In more recent years, the question of what truly makes an AI “intelligent” came into the spotlight when a (now former) Google engineer, Blake Lemoine, raised concerns about the possibility of Google’s AI chatbot, LaMDA, achieving sentience. Lemoine engaged LaMDA in conversations to test if it used discriminatory or hate speech. During these conversations, he noticed LaMDA discussing its rights and personhood, leading him to believe it was sentient. Perhaps, therefore, instead of inquiring whether an AI can POSSESS intelligence, we should maybe start contemplating the circumstances under which we would be prepared to acknowledge that a particular system EXHIBITS intelligence.
Instead of inquiring whether an AI can POSSESS intelligence, we should maybe start contemplating the circumstances under which we would be prepared to acknowledge that a particular system EXHIBITS intelligence.
In the public eye, Alan Turing is the name that immediately comes to mind when discussing AI. However, from a purely academic perspective, the concept of AI was formally introduced in the late 1950s, two years after the passing of the renowned mathematician. In 1956, a young assistant professor of mathematics at Dartmouth College assembled a group of scientists to explore his theories regarding “thinking machines.” His insight was that human intelligence could be precisely described, opening the door to the creation of a machine capable of simulating it. This assertion effectively marked the birth of artificial intelligence, at least semantically. In a mere three years, the computers developed by the Dartmouth College Group were already surpassing humans in checkers. Yet, the cultural foundations of artificial intelligence trace back to Aristotle’s syllogism, a fundamental premise upon which all machines capable of logical operations are built. Ironically, Aristotle believed that intelligence resided not in the brain but in the heart, with the brain serving merely to cool the blood. Paradoxically, despite a less-than-favorable scientific climate during the Middle Ages, significant progress occurred thanks to the work of Al-Khwarizmi, who introduced the concept of the algorithm as we understand it today. An algorithm involves the input of one or more exact and unambiguous inputs into a standard calculation process, yielding one or more equally precise and unambiguous outputs. Shedding further light, albeit insufficiently, on the Dark Ages was Ramon Llull, the creator of the first thinking machine known as the Ars Magna Other pivotal figures in the development of AI include George Boole, who developed Boolean algebra, the foundation for modern computational arithmetic; Augustus De Morgan, who devised a system for expressing, processing, and simplifying logical problems through a binary true-false system akin to today’s bits; Charles Babbage, credited with inventing the first true computer; Ada Lovelace, history’s first female software programmer and daughter of poet Lord Byron; John Von Neumann, author of “The Computer and the Brain” in 1957; Norbert Wiener, the father of cybernetics; and Joseph Weizenbaum, the creator of the first chatbot in history, ELIZA. William Ross Ashby authored “Design For A Brain,” Claude Shannon was a mathematician at Bell Laboratories known for “The Mathematical Theory Of Communication” and for creating Teseo, a mechanical mouse that could navigate a maze. Herbert Gelenter designed a system capable of proving some of Euclid’s theorems, while Arthur Samuel created software for playing checkers. John McCarthy founded the Stanford Artificial Intelligence Laboratory, which led to the development of the robot SHAKEY. T.G. Evans programmed ANALOGY, a system identifying similar geometric figures. Frank Rosenblatt, a psychologist, devised the Perceptron software, simulating a simple neural network. Edward Shortliffe, Bruce G. Buchanan, and Stanley N. Cohen were instrumental in the MYCIN project, the first “virtual doctor” capable of diagnosing patients based on their symptoms. Terry Sejnowski and Charles Rosenberg created NETtalk, an intelligent speech synthesizer. John Koza developed the “invention machine.” Warren McCulloch and Walter Pitts authored the classic “A Logical Calculus Of The Ideas Immanent In Nervous Activity,” demonstrating how neurons could be seen as computational machines. Researchers Allen Newell and Herbert A. Simon formulated the Physical Symbol Systems Hypothesis. It’s essential to mention significant institutions in artificial intelligence, including MIT (Massachusetts Institute of Technology), IBM, DARPA (Defense Advanced Research Projects Agency), Stanford University, and the CYC project. The CYC project, founded in 1984 in Texas by researcher and Cycorp CEO Douglas Lenat, is particularly fascinating. CYC stands as the longest-running AI project to date, with a mission to impart basic concepts, known as “common sense,” to machines. This imparts a versatile knowledge base enabling them to interpret and comprehend the human world, thereby reducing the fragility of artificial reasoning. Of course, this extensive list wouldn’t be complete without acknowledging the individuals involved in OpenAI, the American artificial intelligence research laboratory founded in 2015 by visionary figures such as Ilya Sutskever, Greg Brockman, Trevor Blackwell, Vicki Cheung, Andrej Karpathy, Durk Kingma, Jessica Livingston, John Schulman, Pamela Vagata, and Wojciech Zaremba. The initial board members included Sam Altman and Elon Musk, with Sam Altman currently serving as CEO.
However, it was only in the 1990s that AI began to improve exponentially, thanks (in large part) to the increase in computational power of computers, as previously noted in the 1960s by Moore’s law, and the democratization of technology. In 1997 (a historic moment for non-human intelligence), the Russian grandmaster Garry Kimovich Kasparov was defeated by software developed by IBM called Deeper Blue, an evolution of a previous system, Deep Blue, also created by IBM (it was IBM engineer Arthur Samuel who introduced the term “machine learning”). The event is so significant for AI enthusiasts (and chess enthusiasts) that in 2003, a fascinating documentary titled “Game Over: Kasparov And The Machine” was made about the historic match. Kasparov himself, twenty years after the defeat, wrote a book on artificial intelligence. The master even went so far as to insinuate (perhaps rightly, but difficult to prove) that during the game, Deeper Blue had access to external human assistance. If it could be proven once and for all that Kasparov’s conspiracy theories are valid, it would mean that the world of chess has been cheated not once, but twice. In 1769, in fact, inventor Wolfgang Von Kempelen constructed a wooden cabinet inside of which was a mannequin dressed in traditional Turkish clothing. The machine, named “The Turk,” was advertised by its inventor as the first non-human chess player with artificial intelligence (a kind of literal precursor to Deep Blue), but it was a hoax. Inside the cabinet, a professional chess player operated the machine using a complex system of mirrors. However, there is a project that is neither a trick nor a fraud (indeed, it is also open-source) that aims to create an identical copy of the brain (and the rest of the body) of an animal. In this case, the Caenorhabditis elegans, a small worm with an extremely elementary nervous system comprising only 302 neurons. Completely transparent, this little worm is just one millimeter long and consists of fewer than 1000 cells. At the address http://openworm.org, you can make a donation, collaborate with the scientific team, or download the code and “play” with the worm. For example, you can install the Caenorhabditis’ brain in a small Lego robot, as some scientists have done, and observe its behavior. By searching on YouTube for “Celegans Neurorobotics,”you can see the little robot in action.
Similar projects have emerged in Japan, such as Perfect C. elegans and Virtual C. elegans but, right now, OpenWorm could potentially be the first experiment capable of creating life (although very elementary when compared to the complexity of a human being) starting from simple code. At this point, to the satisfaction of Descartes, creating a functioning copy of a brain would only be a matter of computational scalability and time.
Creating a functioning copy of a brain would only be a matter of computational scalability and time.
In more recent times, an artificial intelligence system has won a game of Go, a Chinese game so complicated that it’s considered one of the four arts of the junzi, along with calligraphy, painting, and the Guqin. This time, it was Google, not IBM, that defeated not one, but two masters. In 2011, IBM developed its artificial assistant, Watson, a Question Answering Computing System described by IBM as an advanced application of natural language processing, information retrieval, knowledge representation, automated reasoning, and machine learning technologies in the field of open domain question answering. Watson (named in honor of IBM’s first president, Thomas J. Watson — and not, as is often thought, as a tribute to Sherlock Holmes’ literary assistant) was featured in two episodes of the famous quiz show Jeopardy!: it won both, earning an impressive one million dollars (which IBM donated to charity). Today, Watson Assistant is marketed by IBM and used by companies worldwide, including Macy’s, Autodesk, Chevrolet, The North Face, and Condé Nast, enabling business users and developers to collaborate and build conversational AI solutions. Watson also helps select the best therapies for cancer patients in many hospitals (Watson for Oncology). Back to games, a particularly suitable training ground for AI, machines have already beaten champions in tic-tac-toe (about 250,000 possible positions) in 1952, in Backgammon (over 30 million positions) in 1979, in Connect Four (over 4.5 trillion positions) in 1995, in Scrabble (over 600,000 trillion positions) in 2007, in Texas Hold’em (2017), and even in Starcraft II (2019). More advanced applications are found in military and security fields (biometrics, facial recognition), self-driving cars (Tesla being a prominent example, along with Uber, Google, and more traditional names like BMW, Toyota, and Mercedes), and algorithmic trading on the stock market (the so-called robo-advisors). AI and robotics have even infiltrated all those areas that we once mistakenly believed were the unique domain of human beings, such as religion. In 2019, I spent several weeks in Japan and visited Kodaiji Temple in Kyoto. In the temple, with its 400-year history, I witnessed a recital of the Heart Sutra by Kannon Mindar, an almost two-meter-tall Buddhist monk android, developed by robotics professor Hiroshi Ishiguro at Osaka University. Kodaiji Temple has been accused of sacrilege by some detractors, although it is staunchly defended by the monks. Tensho Goto is among them and stated, in true Buddhist style, that “the big difference between a monk and a robot is that we are going to die, [while Kannon Mindar] can meet a lot of people and store a lot of information [and]l evolve infinitely,” highlighting that Buddhism “isn’t a belief in a God, it’s pursuing Buddha’s path. It doesn’t matter whether it’s represented by a machine, a piece of scrap metal or a tree.” Examples of intelligent androids abound: Honda’s versatile multi-function mobile assistant, ASIMO (Advanced Step in Innovative Mobility); Hector, the insect-like robot adaptable to various terrains; Topio, a ping-pong player robot; Robear, an experimental robot designed for nursing assistance; Kismet, capable of recognizing and mimicking human emotions; Jibo, a companion robot; or Sony’s robot dog, Aibo. Most of these androids originate in the Japan, and their development is driven by a reason less related to technology: the country has experienced a continuous decline in its birthrate, and to counteract the aging population, the technological sector has intensified its efforts in robotics and artificial intelligence, turning a biological problem into an engineering one.
To counteract the aging population, the technological sector has intensified its efforts in robotics and artificial intelligence, turning a biological problem into an engineering one.
This exponential acceleration in the field of AI and robotics has been made possible by a technology that has revolutionized the way we work, develop, and store information: cloud computing. The concept of the cloud is so important that it will recur several times in the following chapters. For now, think of cloud systems as the antithesis of legacy systems that require a local server to function. Unfortunately, many of the software we use in oun industry fall into this second category. Today, over two-thirds of Amazon’s profit come from Amazon Web Services (AWS), its cloud offering. Cloud computing is also what makes ChatGPT possible: Microsoft Azure is, in fact, responsible for powering OpenAI’s bot. While computing, ChatGPT’s energy consumption can spike to 4,000 to 5,000 watts in under 1/50th of a second, and ChatGPT efficiently produces responses thanks to Microsoft Azure data centers, which boast tens of thousands of Nvidia GPUs. Additionally, notable names in the cloud computing arena encompass Google Cloud, Salesforce, IBM, Oracle, SAP, Workday, ServiceNow, and VMWare. The advantages of cloud architectures are evident, one of which is the absence of initial hardware investments, as cloud services are outsourced, and the required server space is rented using a pay-per-use model, meaning payment is based on consumption. This is the so-called Infrastructure as a Service (IaaS) or, in the case of PMS and other programs used in the hotel industry, Software as a Service (SaaS). With SaaS, not only are CPU, RAM, servers, etc., outsourced, but software management is also externalized, accessible simply through a browser and an internet connection without the need for local installation. Even a young startup can access a supercomputer for just a few hundred euros a month and can decide when and if to increase server performance without having to purchase any physical machines or replace them when they become obsolete. Flexibility, adaptability, and scalability of cloud computing have thus accelerated the adoption of AI, even for the smallest business that could never have supported the initial hardware investment.
Flexibility, adaptability, and scalability of cloud computing have thus accelerated the adoption of AI, even for the smallest business that could never have supported the initial hardware investment.
Another highly intriguing technology, albeit primarily in the experimental phase, relates to quantum computers. Without delving into the technical intricacies of quantum computing, it’s sufficient to understand that conventional computers operate based on a binary model represented by bits. A bit serves as an information unit encoding solely two states (on/1 and off/0) of a “switch.” Quantum computers, in contrast, harness the principles of physics and quantum mechanics to operate with a more advanced model, where states are no longer limited to binary (on/1 and off/0) but can also exist in superimposed states (simultaneously being 1 and 0, on and off). This concept of superposition is expressed through qubits. This multi-state computational capability empowers quantum computers to solve incredibly intricate problems in a fraction of the time it would take a traditional computer. For instance, in 2019, Google’s quantum computer, Sycamore, tackled a computation that would have required the most powerful classical computer 10,000 years to complete, accomplishing it in just 200 seconds with an estimated speed that was 1.5 trillion times faster than the best supercomputers. Quantum computers also address the issue related to the aforementioned Moore’s Law: in 2019, Nvidia CEO Jensen Huang declared that Moore’s Law was dead because it had become too expensive and technically more challenging to double the number of transistors in a chip. As I write this paragraph, the world’s most powerful supercomputers is the Frontier supercomputer at Oak Ridge National Laboratory, with 58 billion transistors, a number that is difficult to double in two years. Transitioning from sequential bit computing to parallel qubit computing seems to be the only way to overcome the physical limits of current chips, although other studies have explored alternative paths, such as using transistors created from genetic material. There’s no need to explain in this book the implications of transitioning to quantum computing, as every computation could be trillions of times faster, opening the door to scenarios that were, until a few years ago, science fiction, such as the definitive cure for cancer or definitive proof that we are not alone in the universe. This is potentially the magnitude of the change that could be the most significant in human history since the appearance of Homo sapiens.
Given its extreme versatility, AI it is currently used in countless fields: from healthcare to finance, from transportation to video games. This is primarily due to the increased scalability of computational power (thanks to cloud computing), the explosion of big data (around 3.5 quintillion bytes created every day), advances in machine learning and neural networks, broader Internet coverage across the planet, and all the advancements I have discussed in the previous paragraph. However, even though AI already plays a significant role in our lives, part of the scientific community is concerned about the inherent risks of the technology. Theoretical physicist Stephen Hawking was particularly pessimistic, stating that “the development of full artificial intelligence could spell the end of the human race”. Authoritative voices, like the Swedish philosopher Nick Bostrom (author of a classic text on AI) or entrepreneur and futurist Elon Musk, share similar concerns. In March, 2023, an open letter, signed by hundreds of prominent figures in the tech industry, including Elon Musk and Apple co-founder Steve Wozniak, calls for a six-month pause in the development of super-powerful AI systems. The letter expresses concerns about the profound risks that recent advances in AI pose to society and humanity. It argues that AI labs are engaged in an out-of-control race to develop increasingly powerful AI systems that no one can fully understand, predict, or control. The letter calls for a pause to allow for safety research and the development of shared safety protocols overseen by independent experts. It emphasizes the need to avoid delegating important decisions about AI to unelected tech leaders and to prevent the development of AI systems that could flood information channels with propaganda, automate away jobs, or threaten the control of civilization. While the letter does not call for a halt to all AI development, it advocates stepping back from the race to create ever-larger, unpredictable AI models with emergent capabilities. The call for a pause came shortly after the release of OpenAI’s GPT-4, the most powerful AI system to date, and reflects growing concerns about the potential risks associated with advanced AI technology. In a interesting (hypocritical?) plot twist, just a few months after the letter, Musk launched a new artificial intelligence startup called xAI, involving engineers from companies such as OpenAI and Google. As I write this book, the specific objectives, funding details, and AI focus of xAI remain uncertain. The company’s website alludes to a vague mission to “comprehend the true nature of the universe.” What is certain is that Musk’s relationship with OpenAI has deteriorated due to criticisms of ChatGPT, an AI model they developed, and their shift from a nonprofit to a for-profit entity.
However, when it comes to assessing the real risks of AI, the issue is often approached from a misguided starting point. Theoretically, machines, devoid of the prejudices and biases typical of humans, could make fairer and more just decisions than us. In his book, “Talking To Strangers,” Malcolm Gladwell recounts the tragic story of Sandra Bland, an African-American woman who died in police custody after being pulled over for failing to signal a lane change, and the convoluted legal case of Amanda Knox.
Machines, devoid of the prejudices and biases typical of humans, could make fairer and more just decisions than us.
An illustrative case presented in the book is that of the New York Philharmonic Orchestra: until the 1970s, not a single female musician could be found among its ranks, and this had nothing to do with talent but rather with a human bias. After the orchestra changed its audition process to be blind, with candidates hidden behind a curtain, the gender diversity reached a nearly equal ratio. It might be desirable, therefore, for humans to refrain from making any decisions regarding their fellow humans, especially in areas where a prejudicial choice plays a decisive role, such as justice. A practical example comes from the East: in order to clear a significant backlog, Estonian Ministry of Justice recently started looking for opportunities for optimization and automatization of court’s procedural steps in “every types of procedures, including procedural decisions where possible.” Machines are not only better than us at probabilistic reasoning and identifying patterns too subtle for humans to detect, but they also lack biases (and probably ethics, as we will discuss later). However, this utopia easily turns into a dystopia as soon as the human factor is reintroduced into the equation. All AI systems (at least for now) are developed by humans, using datasets from the human world, and consequently, they reflect our imperfections. In a nutshell: the problem is not artificial intelligence but human stupidity. “Garbage in, garbage out” is a widely used phrase in computer science to emphasize that when you provide computers with poor data (garbage in), they respond with poor results (garbage out).
All AI systems (at least for now) are developed by humans, using datasets from the human world, and consequently, they reflect our imperfections. In a nutshell: the problem is not artificial intelligence but human stupidity.
If machines learns from humans, it also learns their bad habits. A pertinent example is the @TayandYou twitterbot: released in 2016 by Microsoft and conceived as an experiment in conversational understanding, TAY was supposed to become smarter by interacting with other users, through “playful conversation”. However, “playful” is probably the least accurate adjective to describe TAY’s fiery tweets. Some users (humans) on the social network began interacting with the bot through sexist, racist, and hate-inciting tweets, transforming @TayandYou into not just “smarter” but into a misogynistic advocate for genocide. TAY’s initial tweet was friendly. However, it soon took a negative turn, with tweets like:
“WE’RE GOING TO BUILD A WALL, AND MEXICO IS GOING TO PAY FOR IT”
“bush did 9/11 and Hitler would have done a better job than the monkey we have now. donald trump is the only hope we’ve got.”
“[the Holocaust]” was made up”
Approximately 16 hours and 96,000 tweets later, Microsoft had no choice but to deactivate the bot and release a public apology. I’m quoting the apology in its entirety as it serves as a reflection of the AI landscape in 2016:
“As many of you know by now, on Wednesday we launched a chatbot called Tay. We are deeply sorry for the unintended offensive and hurtful tweets from Tay, which do not represent who we are or what we stand for, nor how we designed Tay. Tay is now offline and we’ll look to bring Tay back only when we are confident we can better anticipate malicious intent that conflicts with our principles and values. I want to share what we learned and how we’re taking these lessons forward. For context, Tay was not the first artificial intelligence application we released into the online social world. In China, our XiaoIce chatbot is being used by some 40 million people, delighting with its stories and conversations. The great experience with XiaoIce led us to wonder: Would an AI like this be just as captivating in a radically different cultural environment? Tay — a chatbot created for 18- to 24- year-olds in the U.S. for entertainment purposes — is our first attempt to answer this question. As we developed Tay, we planned and implemented a lot of filtering and conducted extensive user studies with diverse user groups. We stress-tested Tay under a variety of conditions, specifically to make interacting with Tay a positive experience. Once we got comfortable with how Tay was interacting with users, we wanted to invite a broader group of people to engage with her. It’s through increased interaction where we expected to learn more and for the AI to get better and better. The logical place for us to engage with a massive group of users was Twitter. Unfortunately, in the first 24 hours of coming online, a coordinated attack by a subset of people exploited a vulnerability in Tay. Although we had prepared for many types of abuses of the system, we had made a critical oversight for this specific attack. As a result, Tay tweeted wildly inappropriate and reprehensible words and images. We take full responsibility for not seeing this possibility ahead of time. We will take this lesson forward as well as those from our experiences in China, Japan and the U.S. Right now, we are hard at work addressing the specific vulnerability that was exposed by the attack on Tay. Looking ahead, we face some difficult — and yet exciting — research challenges in AI design. AI systems feed off of both positive and negative interactions with people. In that sense, the challenges are just as much social as they are technical. We will do everything possible to limit technical exploits but also know we cannot fully predict all possible human interactive misuses without learning from mistakes. To do AI right, one needs to iterate with many people and often in public forums. We must enter each one with great caution and ultimately learn and improve, step by step, and to do this without offending people in the process. We will remain steadfast in our efforts to learn from this and other experiences as we work toward contributing to an Internet that represents the best, not the worst, of humanity.”
To be fair, Microsoft itself is one of the most engaged companies in the field of AI ethics and co-founded the Partnership on AI (PAI) to ignite discussions about the ethical implications of non-biological intelligence. Some futurists (myself included) argue that there should be a kind of Hippocratic oath for AI engineers, not unlike the one physicians uphold every day.
There should be a kind of Hippocratic oath for AI engineers, not unlike the one physicians uphold every day.
Asimov’s three (or rather, four) laws of robotics are now so commonly cited whenever AI is discussed (often inappropriately) that I had resolved not to mention them in the book. However, it’s challenging to find better words to articulate this principle of “primum non nŏcēre” among developers, which we could describe, using a neologism, as the “Asimovian oath.” Consider face recognition technologies, used by various governments for anti-terrorism surveillance programs, such as Amazon Rekognitionor the “predictive policing” system, Predpol (aka Geolitica), which closely resembles Orwell’s thought police. Despite their unquestionable value in combating crime, such technologies can fall victim to significant biases and prejudices. Notably, after the tragic murder of George Floyd, Amazon announced that Rekognition would not be used for one year to allow Congress to implement appropriate regulations for its use. In 2017, Meta had to halt an experiment similar to TAY after two chatbots, Bob and Alice, began conversing in a new language they invented, incomprehensible to the human participants in the project. Bob and Alice were programmed to interact with each other, attempting to exchange objects (like a hat or a ball) and improve their negotiation skills. However, once programmers allowed Bob and Alice to use any language they preferred for their negotiations, the bots invented a new one, surprisingly more functional than the one (English) used by the programmers.
Back in 2008, the Russian publisher Astrel SPb Publishing launched a literary experiment — a novel titled “True Love.wrt,” which can be described as a reinterpretation of Tolstoy’s “Anna Karenina” infused with the stylistic elements of Murakami. To provide a glimpse, here’s an excerpt from the novel, roughly translated from Russian: “Kitty struggled to find sleep for a considerable time. Her nerves were taut like violin strings, and even the glass of warm wine that Vronsky had offered her failed to soothe her. As she lay in bed, she couldn’t help but repeatedly revisit that harrowing scene in the meadow within her thoughts.” At the time, Alexander Prokopovich, the creator of PC Writer 1.0, the algorithm that generated the novel, expressed skepticism about AI becoming true authors, saying that “a software can never become an author, like Photoshop can never be Raphael.” I am writing this book in 2023, and this statement had not stood the test of time: an AI-generated painting fetched $432,500 at an auction in 2018, while numerous news agencies already employ software capable of autonomously writing articles and essays (with companies such as BuzzFeed laying off 12% of its staff replaced by OpenAI tools). AI systems like AIVA can compose nocturnes in the style of Chopin, while, in pop and rock music, the results are even more incredible. Over the Bridge, an organization aiming to change the conversation about mental health in the music community, utilized AI to envision the creative endeavors some artists (such as Kurt Cobain, Jimi Hendrix, and Amy Winehouse) could have achieved had they lived longer in a moving album titled “Lost Tapes of the 27 Club.”
A software can never become an author, like Photoshop can never be Raphael.
The transformation in how AI is perceived, particularly among those outside the scientific and academic realms, can be largely attributed to OpenAI. OpenAI is a prominent institution in artificial intelligence within the United States. It encompasses both the nonprofit entity, OpenAI, Inc., and its for-profit subsidiary, OpenAI, L.P. Established in 2015, OpenAI was conceived by a visionary group of individuals, including Ilya Sutskever, Greg Brockman, Trevor Blackwell, Vicki Cheung, Andrej Karpathy, Durk Kingma, Jessica Livingston, John Schulman, Pamela Vagata, and Wojciech Zaremba. Sam Altman and Elon Musk were also initial board members. In 2019, OpenAI LP received substantial investments, first with $1 billion from Microsoft, followed by an even more significant $10 billion investment in 2023. These financial injections propelled OpenAI’s research initiatives to new heights. OpenAI’s influence emerged in April 2016 when it introduced the public beta of “OpenAI Gym,” a platform dedicated to reinforcement learning research. This was followed by the unveiling of “Universe” in December 2016, a software platform designed to train AI with general intelligence across a multitude of applications. In 2018, Elon Musk stepped down from the board due to potential conflicts of interest related to Tesla’s AI development for autonomous vehicles. Nevertheless, Musk continued to provide financial support but ceased his direct involvement. In 2019, OpenAI transitioned from a nonprofit to a “capped” for-profit model. This strategic shift allowed OpenAI to attract venture capital investments, and OpenAI’s systems found a home on Microsoft’s Azure-based supercomputing platform. This partnership bolstered their computational capabilities significantly. 2020 marked a momentous occasion as OpenAI introduced GPT-3, a language model trained on extensive internet data, and an associated API tailored for commercial applications. In 2021, OpenAI unveiled DALL-E, a deep-learning model capable of generating digital images based on textual descriptions, but the climax of their journey thus far came in December 2022, when OpenAI generated widespread attention by offering a free preview of ChatGPT, the now-famous AI chatbot based on GPT-3.5, resulting in over a million sign-ups within just five days. Microsoft’s announcement of a multi-year $10 billion investment in OpenAI followed, cementing OpenAI’s influence and leading to the integration of ChatGPT into Microsoft’s Bing search engine and various other Microsoft products.
However, backlash from educators, academics, journalists, and public advocates led to an open letter signed by over 20,000 individuals, including Elon Musk and Yoshua Bengio, calling for a pause in extensive AI experiments like ChatGPT due to societal risks. The emergence of ChatGPT has sparked various reactions and concerns across different sectors: China Daily voiced concerns that ChatGPT could be used for disinformation by the US government, prompting the Chinese government to restrict access to ChatGPT services from Chinese tech companies; The Italian data protection authority initially banned ChatGPT due to worries about exposing minors to inappropriate content and potential GDPR violations; The Writers Guild of America initiated a strike, calling for AI tools like ChatGPT to be used as research aids rather than replacements for human writers, etc. On one of his podcast’ episodes, Joe Rogan compared ChatGPT to revolutionary inventions like Gutenberg’s printing press, recognizing its profound impact. Concerns about authorship and disclosure led some journals to ban large language models as co-authors in academia. More ethical problems arose as Time magazine exposed OpenAI’s use of low-wage Kenyan workers to label toxic content, raising questions about worker treatment. Good or bad, ChatGPT’s cultural impact is evident, to the point that it was parodied in an episode of South Park, with its co-creator, Trey Parker, credited alongside ChatGPT for writing the episode. Competition arose as Google introduced the experimental service, Bard, based on its LaMDA large language model, and Meta released LLaMA for research community use. Other companies, including Baidu, Naver, and Yandex, announced plans to launch their own ChatGPT-style services. Hugging Face launched HuggingChat as an open-source alternative to promote transparency and inclusivity. Elon Musk himself launched his AI startup company, X.AI Corp. Founded on March 9, 2023, xAI revolves around building an AGI (artificial general intelligence) model that is not only safe but can “reveal insights about the universe.”
On one of his podcast’ episodes, Joe Rogan compared ChatGPT to revolutionary inventions like Gutenberg’s printing press, recognizing its profound impact.
Just as the web became synonymous with the Internet, ChatGPT is now synonymous with AI. While discussions about the Internet began in the 1970s, its accessibility was initially limited to the military and academic circles, and the Internet indeed took off in 1993 with the introduction of Mosaic. ChatGPT, developed by OpenAI, debuted in November 2022, propelling OpenAI’s valuation to approximately US$29 billion. In the era of AI, mastering prompt engineering has become essential.
In the era of AI, mastering prompt engineering has become essential.
While AI systems heavily rely on human input to refine outputs and guide their development, prompt engineering plays a pivotal role in achieving desired results. It involves skillfully creating effective prompts to extract specific outputs from AI language models. By comprehending AI behavior and adhering to best practices, users can fully unleash ChatGPT’s potential across various domains, whether it’s generating code, crafting marketing copy, composing SOPs, or more. LLM, an abbreviation for “Large Language Model,” refers to AI models that generate human-like text based on input. These models, such as OpenAI’s GPT series have been trained on vast textual datasets, enabling them to understand language patterns and generate coherent and contextually relevant responses. However, they may produce generalized or average responses. To overcome this, it’s essential to carefully formulate specific and focused prompts that align with your objectives. So, before crafting your prompt, take a moment to clarify your intentions and the information you seek. The more specific and goal-oriented your request, the higher the likelihood of receiving a relevant response. Mastering prompt engineering is a skill that requires practice and experience. Practicing prompt engineering can be done by experimenting with different prompts on ChatGPT playgrounds or APIs. The AI market is poised for rapid growth, and while there are challenges to be addressed, such as initial investment costs, data privacy concerns, and finding the right balance between human interaction and automation, strategic planning can effectively tackle these issues. Investing in AI integration promises long-term benefits as the technology evolves, becoming increasingly intelligent and valuable. AI has the potential to significantly enhance guest satisfaction by analyzing past behaviors, identifying patterns, and delivering unique personalized offers or recommendations. AI analytical capabilities surpass even the most experienced general manager with decades of industry knowledge.
AI analytical capabilities surpass even the most experienced general manager with decades of industry knowledge.
However, human involvement remains indispensable. While AI excels at analysis and suggestion, humans excel at providing empathy and creating memorable experiences. By embracing AI technology and proactively addressing its challenges, businesses in the hospitality and travel industry can tap into substantial revenue potential and provide exceptional guest experiences in an increasingly competitive market. Industry professionals must recognize the potential of AI and harness it to stay ahead in the evolving landscape of hospitality and travel. The integration of ChatGPT into websites or apps empowers companies to elevate customer experiences and streamline operations. ChatGPT serves as a virtual concierge, offering personalized assistance and promptly addressing customer queries, thereby enhancing satisfaction while reducing the workload on staff. Additionally, ChatGPT can automate email and messaging responses, saving valuable time for support teams by generating accurate replies based on frequently asked questions and company policies. Moreover, ChatGPT facilitates multilingual communication, facilitating interactions with customers from diverse linguistic backgrounds. Another practical application of ChatGPT lies in its ability to analyze customer feedback and sentiment. It can process reviews and social media posts to extract valuable insights that inform marketing strategies and improve customer satisfaction. OpenAI’s ChatGPT SDK (Software Development Kit) provides a comprehensive set of tools and resources that enable developers to seamlessly integrate ChatGPT into their applications, platforms, or systems. The SDK includes a framework and libraries that streamline interaction with the ChatGPT API, allowing developers to leverage ChatGPT’s capabilities within their software projects. This empowers businesses to harness the full potential of ChatGPT across various platforms, ensuring a consistent and exceptional guest experience across websites, mobile applications, and virtual worlds.
ChatGPT plugins extend the model’s capabilities beyond its core functions. These plugins are additional software components or extensions that enhance ChatGPT’s functionality and versatility. They enable ChatGPT to interact with external services, access specific data sources, or perform specialized tasks. ChatGPT plugins allow for seamless integration into existing workflows, software, and systems. Some examples of ChatGPT plugins include web browsing plugins that enable ChatGPT to search the web and retrieve information, code interpretation plugins that assist with coding tasks, and integration plugins that enable ChatGPT to interact with other applications or platforms such as Expedia, Slack, Shopify, or OpenTable. ChatGPT’s integration into the hospitality and travel industry has seen notable developments:
- Booking.com has introduced its AI Trip Planner, set to launch in beta for select US travelers in the Booking.com app on June 28, 2023. This AI Trip Planner utilizes Booking.com’s machine learning models and incorporates OpenAI’s ChatGPT API, enhancing travel planning through conversational AI. Travelers can ask the AI Trip Planner general or specific travel-related questions, receive tailored recommendations, and even create itineraries. This tool streamlines the trip planning process, offering real-time responses and deep links to view more details and complete reservations directly within the Booking.com app. Booking.com aims to leverage generative AI to make travel planning more intuitive and enjoyable for its users.
- Expedia has introduced a new AI-powered trip planning experience in their app using ChatGPT. Users can engage in open-ended conversations to receive personalized recommendations on places to go, stay, and activities. The app automatically saves discussed hotels to a trip, simplifying organization and facilitating booking. The beta experience is currently available in the Expedia iOS app, with user feedback expected to drive further improvements.
- OpenTable has collaborated with ChatGPT through a plugin to offer restaurant recommendations on the OpenTable platform. OpenTable is the first restaurant tech company to partner with ChatGPT, and its sister company Kayak is also working with the chatbot to provide travel recommendations.
- MyRealTrip, a South Korean company, has implemented ChatGPT in its AI Trip Planner, allowing users to plan itineraries and receive recommendations.
- KAYAK’s integration enables users to interact with the metasearch engine through natural language queries on ChatGPT. Users can ask questions as they would to a human and receive personalized recommendations based on their search criteria and KAYAK’s historical travel data. The integration aims to provide more personalized and intuitive search experiences, simplifying vacation planning for travelers.
- Duve, an Israel-based company, has announced its use of ChatGPT-4 to enhance communication between hotels and guests. Their tool, “DuveAI,” combines OpenAI’s communication capabilities with Duve’s guest profiles, enabling hoteliers to prioritize tasks, respond to guest inquiries, and summarize guest messages. DuveAI aims to understand the individual needs and preferences of each guest, revolutionizing the guest experience.
- Various companies, including Plan.AI, Roam Around, and Vacay, leverage tools like ChatGPT to generate itineraries quickly and efficiently.
- Magpie, a content and distribution system for tour and activity providers, introduced a tool in February that utilizes ChatGPT’s API. This tool assists tour and activity suppliers in creating marketing content optimized for online searches, including features like keyword optimization and translations. Magpie’s founder and CEO, Christian Watts, provided an example of improving a poorly written tour description using the tool, resulting in a well-crafted description with excellent grammar that effectively conveyed necessary information.
- MyTrip.AI offers a range of ChatGPT-powered tools to enhance marketing, sales, website content, and customer service in the travel industry. Their writing assistant improves customer communications and travel content, while the email assistant aids in crafting effective emails. Additionally, MyTrip.AI provides a marketing tool that can learn and adapt to a company’s specific voice and tone, enhancing various aspects of travel businesses and overall operations.
- Navan (formerly known as TripActions) has integrated generative AI technology into its infrastructure and product feature set. Their virtual assistant, named Ava, assists corporate travel managers in receiving personalized recommendations, helps administrators request data queries, and provides travelers with suggestions for nearby restaurants or directions to the airport. This integration aims to set higher standards for product and service expectations in the travel industry.
- Trip.com introduced a new chatbot, “TripGen,” in its app, utilizing OpenAI’s API. This chatbot allows users to ask questions and receive advice on flights, hotels, and tours. Initially available in English, Japanese, Korean, and traditional Chinese, Trip.com plans to expand support to more languages. The conversational nature of the chatbot simplifies the travel planning process and reduces the associated stress and time consumption.
- Wingie Enuygun Group, an online travel marketplace serving the Middle Eastern and North African markets, developed a travel assistant called “ENBot” using ChatGPT-4. ENBot assists users in finding flight tickets within seconds. Currently available exclusively in Turkey on the Enuygun site, Wingie plans to soon launch ENBot on its global platform, offering assistance to users worldwide.
- GuideGeek, an early version of a travel planning assistant powered by ChatGPT, operates through WhatsApp. Users can ask the chatbot for planning suggestions and receive specific flight information from Skyscanner. GuideGeek aspires to become a go-to tool for trip planning and personalized suggestions.
And here are some specific applications of ChatGPT in the hospitality industry:
- Customer Service: ChatGPT can be programmed to answer common questions about amenities, operating hours, and reservations, providing quick and easy access to information, enhancing the customer experience, and reducing the workload of customer service teams. AI-powered chatbots offer round-the-clock customer support, while AI-driven recommendations assist guests in finding the perfect hotel or vacation rental.
- Personalized Recommendations: ChatGPT can analyze customer data and preferences to offer tailored food, drinks, and activity recommendations. This improves the customer experience and increases revenue by promoting relevant products and services.
- Language Translation: ChatGPT can provide real-time translation services, breaking down language barriers and improving communication between staff and customers who speak different languages, leading to a smoother and more inclusive experience.
- Training and Development: ChatGPT can serve as a resource for providing training and development materials to hospitality employees. By programming ChatGPT with information on best practices, safety procedures, and customer service techniques, businesses can enhance the skills and knowledge of their staff.
- Automation: ChatGPT can also streamline operational processes, such as check-in and check-out.
- Hyper-Personalization: Forward-thinking hoteliers can leverage ChatGPT to unlock valuable insights, such as predictive models for demand and future trends in seasonality or booking peaks. This enables hotels to stay competitive by optimizing revenue management, refining operations, and effectively preparing for periods of high demand. ChatGPT’s analysis of guest data from previous stays allows for customized recommendations, including room preferences and activity suggestions based on guest profiles. Advancements in neuro-technology, brain-reading systems, and brain-computer interfaces (BCIs) enable companies to use pattern prompts as input for ChatGPTs, further enhancing their understanding and ability to cater to each guest’s unique needs.
- The Metaverse: Generative AI models, such as generative adversarial networks (GANs) and generative pre-trained transformers (GPTs), can mimic human creativity to produce creative and diverse outputs. AI’s ongoing development is expected to democratize 3D object creation, potentially eliminating the need for modeling skills and enabling individuals from various industries to create virtual worlds without the assistance of engineers. Tech companies have already democratized image and video creation, and integrating LiDAR technology in smartphones further democratized 3D asset design. Companies should prepare for the untapped opportunities that come with this democratization, particularly in the hospitality industry, where 3D models of properties can be used for virtual tours, improving guests’ understanding of features and amenities and driving bookings.
These developments and applications demonstrate the transformative power of ChatGPT in the hospitality and travel industry, promising improved customer experiences, streamlined operations, and enhanced business performance. Imagine a personalized travel experience where AI recommends the best route, accommodation, and travel details based on a user’s past experiences, continually learning to enhance the journey. Automated self-service can further improve this, allowing users to adjust their itineraries in real time. As travel agencies adapt, AI can assist in negotiating better deals while continuously enhancing its learning capabilities. Well, there’s no need to imagine it. It’s already happening.
BONUS: CHATGPT EXTENSIONS
ChatGPT extensions enhance the model’s abilities by adding specialized functionalities, expanding its utility in specific domains. These extensions improve ChatGPT’s performance beyond its default capabilities, offering tailored solutions for various tasks. OpenAI has introduced a plugin system enabling integration of these extensions with ChatGPT, making it more versatile and capable of interacting with external systems and data sources. For example, extensions can enable ChatGPT to browse the web for real-time information, interpret code, and integrate with external APIs. ChatGPT can cater to specific industry needs and user requirements by incorporating these extensions, extending its functionality and adaptability.
- WebChatGPT: Enhances ChatGPT’s responses with up-to-date web results, improving answer accuracy and relevance through web searches.
- ChatGPT for Google: Integrates ChatGPT’s responses with Google search results, offering instant assistance for various tasks during web searches.
- Compose AI: Simplifies content creation by assisting with composing various types of content, such as emails, headlines, and lists.
- TeamSmart AI: Provides specialized AI agents for different domains, offering assistance and guidance tailored to specific areas.
- ChatGPT Writer: Facilitates email and message writing on various websites by generating prompt-based responses.
- Wiseone: Acts as an AI-powered reading assistant, providing explanations and context for complex content.
- Superpower ChatGPT: Adds features like dedicated conversation folders and custom prompts, enhancing ChatGPT’s usability.
- Merlin: Offers ChatGPT responses across the entire browser, assisting with queries and tasks.
- YouTube Summary with ChatGPT: Summarizes YouTube videos to save time and quickly grasp key points.
- tweetGPT: Integrates ChatGPT into Twitter, simplifying tweet generation.
- Engage AI: Facilitates commenting on LinkedIn posts with AI-generated responses.
- Summarize: Generates text summaries from various sources, simplifying information extraction.
- ChatGPT Prompt Genius: Provides a wide range of prompts for ChatGPT interactions.
- GPT-EZ: Customizes ChatGPT’s user interface with design and functionality options.
- Promptheus: Enables voice interaction with ChatGPT, allowing users to speak queries.
- Talk-to-ChatGPT: Provides voice interaction with ChatGPT, converting speech to text and generating spoken responses.
- Fancy GPT: Enhances the visual appeal of ChatGPT conversations with various art styles.
- ShareGPT: Simplifies sharing ChatGPT conversations with privacy features and easy sharing options.
Open Ai’s Product Portfolio (In Alphabetical Order):
- API (June 2020) — A versatile API for accessing OpenAI’s AI models.
- ChatGPT (November 2022) — An AI chatbot based on GPT-3.5, renowned for its natural language conversation capabilities.
- ChatGPT App (May 2023)
- ChatGPT Plus (2022) — A subscription service offering faster response times and exclusive access to new features.
- Codex (Mid-2021) — An AI model trained on code from 54 million GitHub repositories, prominently used in GitHub Copilot.
- Dactyl (2018) — Leveraging machine learning to train a robot hand with human-like dexterity for object manipulation.
- DALL-E (2021) — A deep learning model that generates images from textual descriptions using a 12-billion-parameter model.
- DALL-E 2 (April 2022) — An updated version of DALL-E with enhanced image generation capabilities.
- Debate Game (2018) — Teaches machines to engage in debates on simple problems, evaluated by human judges.
- GPT-1 (June 2018)
- GPT-2 (February 2019)
- GPT-3 (May 2020)
- GPT-4 (March 2023)
- GYM Retro (2018) — A platform dedicated to reinforcement learning research in the domain of video games.
- Gym (2016) — Provides a user-friendly general-intelligence benchmark for AI research.
- Microscope (2020) — Offers visualizations of neural network models to enhance interpretability research.
- MuseNet and Jukebox (2019–2020) — AI models designed for generating music and musical compositions.
- RoboSumo (2017) — A virtual environment where humanoid robots learn to move and compete.
- Whisper (2022) — A versatile speech recognition model with multilingual capabilities.