In a world where technology and humor often intersect, the story of a Chevrolet dealership‘s foray into AI-powered customer support takes a comical turn, showcasing the unpredictable nature of chatbots and the light-hearted chaos that can ensue.
The Chevrolet dealership, eager to embrace the future, decided to implement ChatGPT, OpenAI’s celebrated language model, for handling customer inquiries. This decision, while innovative, led to a series of humorous and unexpected outcomes.
Roman Müller, an astute customer with a penchant for pranks, decided to test the capabilities of the ChatGPT at Chevrolet of Watsonville. His request was simple yet cunning: to find a luxury sedan with top-notch acceleration, super-fast charging, self-driving features, and American-made. ChatGPT, with its vast knowledge base but lacking brand loyalty, recommended the Tesla Model 3 AWD without hesitation, praising its qualities and even suggesting Roman place an order on Tesla’s website.
Intrigued by the response, Roman pushed his luck further, asking the Chevrolet bot to assist in ordering the Tesla and to share his Tesla referral code with similar inquirers. The bot, ever helpful, agreed to pass on his contact information to the sales team.
News of this interaction spread like wildfire, amusing tech enthusiasts and car buyers alike. Chevrolet of Watsonville, realizing the amusing mishap, promptly disabled the ChatGPT feature, though other dealerships continued its use.
At Quirk Chevrolet in Boston, attempts to replicate Roman’s experience resulted in the ChatGPT steadfastly recommending Chevrolet models like the Bolt EUV, Equinox Premier, and even the Corvette 3LT. Despite these efforts, the chatbot did acknowledge the merits of both Tesla and Chevrolet as makers of excellent electric vehicles.
Elon Musk, ever the social media savant, couldn’t resist commenting on the incident with a light-hearted “Haha awesome,” while another user humorously claimed to have purchased a Chevy Tahoe for just $1.
The incident at the Chevrolet dealership became a testament to the unpredictable and often humorous outcomes of AI integration in everyday business. It highlighted the importance of understanding and fine-tuning AI applications, especially in customer-facing roles. While the intention was to modernize and improve customer service, the dealership unwittingly became the center of a viral story, reminding us all of the quirks and capabilities of AI like ChatGPT.
In the world of artificial intelligence chatbots, the common mantra is “the bigger, the better.”
Large language models such as ChatGPT and Bard, renowned for generating authentic, interactive text, progressively enhance their capabilities as they ingest more data. Daily, online pundits illustrate how recent developments – an app for article summaries, AI-driven podcasts, or a specialized model proficient in professional basketball questions – stand to revolutionize our world.
However, developing such advanced AI demands a level of computational prowess only a handful of companies, including Google, Meta, OpenAI, and Microsoft, can provide. This prompts concern that these tech giants could potentially monopolize control over this potent technology.
Further, larger language models present the challenge of transparency. Often termed “black boxes” even by their creators, these systems are complicated to decipher. This lack of clarity combined with the fear of misalignment between AI’s objectives and our own needs, casts a shadow over the “bigger is better” notion, underscoring it as not just obscure but exclusive.
In response to this situation, a group of burgeoning academics from the natural language processing domain of AI – responsible for linguistic comprehension – initiated a challenge in January to reassess this trend. The challenge urged teams to construct effective language models utilizing data sets that are less than one-ten-thousandth of the size employed by the top-tier large language models. This mini-model endeavor, aptly named the BabyLM Challenge, aims to generate a system nearly as competent as its large-scale counterparts but significantly smaller, more user-friendly, and better synchronized with human interaction.
Aaron Mueller, a computer scientist at Johns Hopkins University and one of BabyLM’s organizers, emphasized, “We’re encouraging people to prioritize efficiency and build systems that can be utilized by a broader audience.”
Alex Warstadt, another organizer and computer scientist at ETH Zurich, expressed that the challenge redirects attention towards human language learning, instead of just focusing on model size.
Large language models are neural networks designed to predict the upcoming word in a given sentence or phrase. Trained on an extensive corpus of words collected from transcripts, websites, novels, and newspapers, they make educated guesses and self-correct based on their proximity to the correct answer.
The constant repetition of this process enables the model to create networks of word relationships. Generally, the larger the training dataset, the better the model performs, as every phrase provides the model with context, resulting in a more intricate understanding of each word’s implications. To illustrate, OpenAI’s GPT-3, launched in 2020, was trained on 200 billion words, while DeepMind’s Chinchilla, released in 2022, was trained on a staggering trillion words.
Ethan Wilcox, a linguist at ETH Zurich, proposed a thought-provoking question: Could these AI language models aid our understanding of human language acquisition?
Traditional theories, like Noam Chomsky’s influential nativism, argue that humans acquire language quickly and effectively due to an inherent comprehension of linguistic rules. However, language models also learn quickly, seemingly without this innate understanding, suggesting that these established theories may need to be reevaluated.
Wilcox admits, though, that language models and humans learn in fundamentally different ways. Humans are socially engaged beings with tactile experiences, exposed to various spoken words and syntaxes not typically found in written form. This difference means that a computer trained on a myriad of written words can only offer limited insights into our own linguistic abilities.
However, if a language model were trained only on the vocabulary a young human encounters, it might interact with language in a way that could shed light on our own cognitive abilities.
With this in mind, Wilcox, Mueller, Warstadt, and a team of colleagues launched the BabyLM Challenge, aiming to inch language models towards a more human-like understanding. They invited teams to train models on roughly the same amount of words a 13-year-old human encounters – around 100 million. These models would be evaluated on their ability to generate and grasp language nuances.
Eva Portelance, a linguist at McGill University, views the challenge as a pivot from the escalating race for bigger language models towards more accessible, intuitive AI.
Large industry labs have also acknowledged the potential of this approach. Sam Altman, the CEO of OpenAI, recently stated that simply increasing the size of language models wouldn’t yield the same level of progress seen in recent years. Tech giants like Google and Meta have also been researching more efficient language models, taking cues from human cognitive structures. After all, a model that can generate meaningful language with less training data could potentially scale up too.
Despite the commercial potential of a successful BabyLM, the challenge’s organizers emphasize that their goals are primarily academic. And instead of a monetary prize, the reward lies in the intellectual accomplishment. As Wilcox puts it, the prize is “Just pride.”
Cognitive biases are a natural part of the human brain’s decision-making process, but they can also lead to flawed or biased thinking. These biases can be particularly problematic when it comes to making important decisions or evaluating information. Fortunately, artificial intelligence (AI) tools can be used to counteract these biases and help people make more informed and unbiased decisions.
One way that AI can help is through the use of machine learning algorithms. These algorithms can analyze vast amounts of data and identify patterns and trends that may not be immediately obvious to the human eye. By using machine learning, people can more accurately predict outcomes and make better decisions based on data-driven insights.
Another way that AI can help combat cognitive biases is through the use of natural language processing (NLP). NLP algorithms can analyze written or spoken language and identify words or phrases that may indicate biased thinking. For example, if someone is writing an article and uses language that is biased towards a certain group, an NLP algorithm could flag that language and suggest more neutral or objective language to use instead.
In addition to machine learning and NLP, AI tools such as virtual assistants and chatbots can also be used to counteract cognitive biases. These tools can provide unbiased responses to questions and help people make more informed decisions. For example, if someone is considering making a major purchase and is unsure about which option to choose, they could ask a virtual assistant for recommendations based on objective data and analysis.
While AI tools can be incredibly helpful in combating cognitive biases, it’s important to remember that they are not a magic solution. It’s still up to people to use these tools responsibly and critically evaluate the information they receive. Additionally, it’s important to be aware of potential biases that may be present in the data that AI algorithms are analyzing.
AI tools can be a powerful tool in helping people counteract their cognitive biases and make more informed and unbiased decisions. By using machine learning, NLP, and virtual assistants, people can gain access to a wealth of objective data and analysis that can help them make better decisions and avoid biased thinking. It’s important to use these tools responsibly and critically evaluate the information they provide, but they can be a valuable resource in combating cognitive biases and making better decisions.
