Welcome to “Reel Talk: The Customer Insights Show,” a podcast where we explore the latest strategies and techniques for gathering and leveraging customer insights. I’m your host, Jenn Vogel, and I’m excited to bring you interviews with some of the brightest minds in the market research and customer insights industry.
On this show, we share insights and strategies that professionals in market research, marketing, and anyone who wants to understand their customers better will find helpful. We’ll discuss various topics, including customer journey mapping, user experience research, data analysis, and more.
So join me on this journey as we dive deep into the world of customer insights and learn how to gather and use data to inform business decisions and drive results. Stay tuned for interviews with industry experts and practitioners who will share their insights and experiences on how to understand and connect with your customers.
That intro was written completely by AI through Open AI’s ChatGPT platform, so on today’s episode, I will interview an AI human to talk about AI in market research. All of the answers to today’s questions were generated by ChatGPT, which we then input into Synthesia to create a human-like expert that I’m interviewing today. Welcome to the show.
Question: OpenAI has been in the news with ChatGPT and Dall E 2 which create content based on instructions. ChatGPT does that through the written word and Dolly creates graphics based on spelled-out instructions. The company behind those services is OpenAI.
Let’s start by understanding the basics. Can you explain what OpenAI is, and also touch on some of the business applications for AI technologies?
Answer: OpenAI is a research organization that aims to promote and develop friendly artificial intelligence in a way that benefits humanity. The organization was founded in 2015 by a group of high-profile entrepreneurs with the goal of advancing and promoting research in the field.
There are many potential business applications for AI technologies, including the following:
The automating of processes and tasks. AI technologies can be used to automate a variety of processes and tasks, freeing up human workers to focus on more high-level tasks.
It improves decision-making. AI technologies can analyze large amounts of data and provide insights and recommendations to help businesses make better decisions.
It enhances customer experiences. AI technologies can be used to improve customer service by providing personalized recommendations and assistance.
AI can increase efficiency. AI technologies can help businesses operate more efficiently by identifying bottlenecks and inefficiencies in processes.
Question: What are some applications of AI in consumer research?
Answer: AI technologies have many potential applications in consumer research, including the following:
Sentiment analysis: AI technologies can be used to analyze consumer sentiment by processing large amounts of data from social media, surveys, and other sources.
Market segmentation: AI technologies can be used to identify patterns and trends in consumer behavior, allowing businesses to segment their market and tailor their products and services to specific groups of consumers.
Personalization: AI technologies can be used to personalize products and services for individual consumers, improving the customer experience.
Predictive modeling: AI technologies can be used to predict consumer behavior and preferences, helping businesses to anticipate and meet consumer demand.
Customer service: AI technologies can be used to improve customer service by providing personalized assistance and recommendations to consumers.
Opinion mining: AI technologies can be used to analyze consumer opinions and feedback, helping businesses to identify areas for improvement.
Question: Are there negatives to using AI in research?
Answer: There are both potential positives and negatives to using artificial intelligence in research. Here are some potential negatives to consider:
Bias: AI systems can reflect and amplify the biases that are present in the data they are trained on. This can lead to biased or unfair outcomes if the data used to train the AI is biased or unrepresentative.
Transparency: AI systems can be difficult to understand and interpret, making it challenging to determine how they arrived at their conclusions. This lack of transparency can make it difficult to trust the results of AI-powered research.
Ethical considerations: AI can raise ethical concerns when it is used to make decisions that affect people’s lives, such as in the criminal justice system or in hiring decisions.
Job displacement: The increasing use of AI in research may lead to job displacement as some tasks are automated.
Dependence on AI: Reliance on AI can lead to a lack of critical thinking and problem-solving skills, as people may become too reliant on AI to do their thinking for them.
Overall, it is important to carefully consider the potential impacts and limitations of using AI in research, and to ensure that appropriate safeguards are in place to mitigate any negative consequences.
Question: Talking about safeguards, how can we make AI be more transparent in research?
Answer: There are a few ways to make artificial intelligence and data more transparent in research:
Explainable AI: Developing AI systems that can provide explanations for their decisions or outputs can help to increase transparency and understanding of how the AI system arrived at its conclusions.
Data documentation: Ensuring that data used to train AI systems is well-documented and that the data sources are clearly identified can help to increase transparency and accountability.
Open data: Making data used in research openly available to others can help to increase transparency and facilitate the reproduction of research results.
Data governance: Establishing clear policies and guidelines for data use, storage, and sharing can help to ensure that data is used ethically and responsibly.
Peer review: Subjecting AI-powered research to the same level of peer review as other research can help to ensure that the results are accurate and transparent.
Auditing: Regularly auditing AI systems to ensure that they are functioning as intended and not introducing bias can help to increase transparency and accountability.
Question: In your opinion, who should lead AI in research in a company?
Answer: The leadership of artificial intelligence in research in a company will depend on the specific needs and goals of the company. Some options for leading AI in research might include:
Chief AI officer: A chief AI officer is a senior-level executive responsible for overseeing the development and implementation of AI strategy and initiatives within a company.They might be responsible for leading AI research within the company.
AI research team: A company might have a dedicated team of researchers focused on AI, led by a team leader or manager. This team would be responsible for conducting AI research and development within the company.
AI research department: Some larger companies may have a separate department focused on AI research, led by a department head or director. This department would be responsible for conducting AI research and development and working with other departments to integrate AI into the company’s products or services.
Ultimately, the leadership of AI in research in a company will depend on the company’s size, structure, and goals. It is important to ensure that AI research is aligned with the company’s overall strategy and that there is effective communication and collaboration between different teams and departments.
Question: What training is needed to be on an AI research team?
Answer: To be a member of an artificial intelligence research team, you will typically need to have a strong foundation in math, computer science, and machine learning. Depending on the specific focus of the research team, you may also need expertise in a related field, such as natural language processing, computer vision, or robotics.
Here are some specific skills and qualifications that may be helpful for a career in AI research:
A bachelor’s or master’s degree in a field such as computer science, electrical engineering, mathematics, or a related field.
Proficiency in programming languages such as Python, C++, or Java, as well as machine learning frameworks such as TensorFlow or PyTorch.
Strong problem-solving skills and the ability to think creatively and critically.
Familiarity with machine learning algorithms and techniques, such as supervised and unsupervised learning, neural networks, and deep learning.
Experience working with large data sets and using tools such as SQL and data visualization software.
Strong communication skills and the ability to work effectively in a team environment.
While these skills and qualifications can be helpful, they are not necessarily required for all AI research positions. The specific requirements will depend on the focus of the research team and the specific role you are applying for.
Question: Any advice for market researchers who want to learn more about AI?
Answer: If you are a market researcher and want to learn more about AI, here are some suggestions:
Read up on the basics: There are many resources available online that can help you learn about the basics of AI and how it works. This will give you a foundation of knowledge that you can build upon.
Learn a programming language: To work with AI technologies, you will need to have some programming skills. There are many programming languages that are commonly used for AI applications, including Python, R, and Java.
Take online courses: There are many online courses and tutorials that can help you learn about AI and how to apply it in a market research context. Some popular platforms for learning about AI include Coursera, edX, and Udemy.
Join a community: There are many online communities of AI enthusiasts and professionals where you can ask questions, share your knowledge, and learn from others. Some examples include the AI Subreddit, the Machine Learning Subreddit, and the AI Stack Exchange.
Attend conferences and workshops: Attending conferences and workshops on AI can be a great way to learn from experts in the field and network with other professionals.