释放生成式AI在市场研究中的潜力

AI Essentialsfor Market ResearchersTransforming Market Research with Generative Intelligence Contents AI? research implementation hallucinations What is AI? Artificial Intelligence (AI) enables machines to mimic humanintelligence. In this section, we'll explore AI fundamentals,the impact of generative AI, and what AI excels at today—essential knowledge for leveraging AI in market research. what is ai What is AI? them the ability to simulate aspects of human cognition andbehavior. It’s applied in many areas, from automating repetitivetasks to enabling more complex functions like voice assistants orpredictive analytics. AI doesn’t necessarily have to be“generative”—it can involve anything from simple automation toadvanced decision-making processes.AI has a long history of application in market research, and most Artificial Intelligence (AI) is a broad field ofcomputer science focused on creating systemscapable of performing tasks that typically requirehuman intelligence. These tasks include learningfrom experience, understanding and processinglanguage, recognizing patterns, solvingproblems, and making decisions.AI systems can range from simple rule-based advanced analysis techniques can be thought of as being AI(e.g., regression, cluster analysis).The AI that has caught the attention of the world today is aspecial form of AI called generative AI, which is AI that can systems to more complex machine learningmodels that can adapt and improve over timebased on the data they encounter. create high quality content.The two paragraphs at the beginning of the of this section werewritten by ChatGPT as was the image to the right. what is ai The importance of generative AI advance in technology, on par with the steam engine, thetelephone, computers, and the internet.Despite the name “generative AI” the magic isn’t that the computers are generating content. Computers have beengenerating content for many years. For example, the various ToyStories movies back in the 1990s.What’s magic about generative AI is that computers can now quickly generate high quality content with minimal instructions.The first Toy Story movie took four years to make, with 27animators, 22 technical directors, 61 filmmakers, and more than25,000 storyboards.However, by asking a generative AI, "Can you please create animage with the characters in Toy Story 1?" in a matter of seconds, you can get back something like this image. what is ai What AI is good at (no, it’snot reasoning or creativity) “create,” such descriptions are overly simplistic and ultimatelymisleading.AI doesn’t reason like a human. It makes many very un-human errors. As discussed below, AI is overconfident to a level beyondthe most optimistic narcist. Similarly, while generative AIimpresses in that it can create anything, it’s impressive in the waythat a dog playing a piano is impressive. People aren’t queuing toview generative AI art, nor are they listening to AI music. Theymay one day. But not today. Question answering: the most immediately visible skill of moderngenerative AI tools like ChatGPT and Perplexity is their ability toanswer both general and obscure questions. Surprisingly (to thisauthor anyway), the question-answering skill is a consequence ofthe AI being trained to predict the next word in a sequence,where the sequence is all the world’s information.Language tagging: recognizing and labeling key bits of Prediction and classification prediction and classification. Common examples include:Segmentation: dividing things into parts. For example,Ò Á Image segmentation: recognizing people and other entities inphotos and handwriting into letters and characters.Ò information in spoken and written words. For example, productslike Gong are used by sales teams around the world to analyzesales conversations, such as identifying names, key reasons forpurchasing, problematic sales techniques.Diagnosis: such as medical diagnosis. While this sounds like Á Clustering of data: grouping similar objects into groups (e.g.,market segmentation).Optical character recognition: classifying written symbols as reasoning, it’s in practice just a combination of pattern matchingand chatting, where the app continually asks for information untilthe required information is obtained. different letters of the alphabet and other symbols.Forecasting: predicting future values in sequences of numbers, based on past values and related data.Anomaly detection Recommendation systems,products to buy and Facebook feeds. Predicting the next word(s)in a sequence Embedding/encoding:Converting text to numbers.The easiestexample ofthis is sentiment analysis, where text is represented asnumbers, with positive numbers indicating positive sentiment andnegative numbers indicating negative sentiment. “AI handles thetedious stuff, soyou can focuson the insightsthat count.” Transformation decade, is transformation: converting some things into others.Some examples:Compression: transforming