The Marketing AI Show
ChatGPT Code Interpreter, the Misuse of AI in Content and Media, and Why Investors Are Betting on Generative AI
This episode covers various topics related to data analysis, AI-generated content, generative AI companies, language models, ethics in artificial intelligence, recent developments, and upcoming events. It explores the capabilities of Chat GPT's code interpreter feature, a data analysis tool for marketers, the impact of AI-generated content on media outlets, advancements in language models and privacy concerns, recent developments in the field, and the upcoming Marketing AI Conference. The episode also discusses the ethics of artificial intelligence, including the Vatican's handbook on the subject. Overall, it provides insights into the current state and future prospects of AI in various industries.
Code Interpreter for Data Analysis
Chat GPT's code interpreter feature allows users to run code, analyze data, create charts, and perform sophisticated math within Chat GPT. It provides data analysis capabilities for marketers and businesses, complementing the work of data analysts.
AI-Generated Content and Media Trust
The use of AI-generated content can damage trust in media outlets and lead to downsizing of human writing teams. Brands and media companies should be cautious about the output of their content to avoid damaging their brand reputation.
Generative AI Companies and Investments
Generative AI companies like Inflection AI are attracting significant investments due to their potential value in customer operations, marketing and sales, software engineering, and R&D. Marketers and business leaders should carefully consider where to allocate time and budget in generative AI companies.
Ethics in Artificial Intelligence
The Vatican has released a handbook on the ethics of artificial intelligence, providing guidance for tech companies grappling with ethical questions. The guidelines emphasize principles such as respect for human dignity and rights, transparency, and explainability.
Recent Developments and Lawsuits
- Chat GPT's Code Interpreter Feature
- Data Analysis Tool for Marketers
- AI in Data Analysis and Content Generation
- Impact of AI-Generated Content on Media Outlets
- Inflection AI and Generative AI Companies
- Language Models and Synthetic Data
- Ethics of Artificial Intelligence
- Advancements in Language Models and Privacy
- Recent Developments and Lawsuits
- Ethics in Artificial Intelligence
- Meta's Threads and Marketing AI Conference
- Upcoming Marketing AI Conference
Chat GPT's Code Interpreter Feature
00:00 - 07:20
- Chat GPT's code interpreter feature is now available to all Chat GPT plus users.
- Code interpreter allows users to run code, analyze data, create charts, and perform sophisticated math within Chat GPT.
- Examples of using code interpreter include segmenting customer lists, gathering and visualizing data, and performing data analysis.
- Ethan Molick considers code interpreter a powerful AI tool for marketers and businesses.
- Code interpreter is not a replacement for data analysts but can complement their work.
- It operates at the level of a good grad student in most data analysis tasks.
- Code interpreter provides data analysis capabilities where there may not be dedicated data analysts on staff.
Data Analysis Tool for Marketers
07:01 - 13:21
- The podcast discusses the capabilities of a tool that provides data analysis capabilities for those who don't have data analysts on staff.
- The tool allows users to analyze data from various sources, such as email campaigns, and identify interesting patterns and anomalies.
- The host shares an example of using the tool to create an AI talent index by analyzing LinkedIn Sales Navigator data.
- The tool analyzes the dataset and identifies the top job titles and industries for targeting AI education and events.
- Visualizations, including bar charts, are created to help tell the story of the data and market opportunities.
- Suggestions are given to enrich the dataset with additional factors like current AI usage, technological readiness, and regulatory environment.
- The tool provides insightful questions that can lead to strategic insights when analyzing the dataset.
- Various creative ways of visualizing the data are suggested, including heat maps, bubble charts, tree maps, box and whisker plots, stacked part charts, and work graphs.
- The host expresses excitement about the potential uses of this tool for marketers in terms of analyzing churn, growth, audience building, target markets, etc.
- While not a replacement for data scientists or analysts, this tool adds value by providing access to technology and capabilities that may not be available on a marketer's team.
- The host imagines how useful it would be if this capability was integrated into platforms like HubSpot for easy access to data analysis and chart building.
- The host reflects on their experience in the agency world and how this tool would have been valuable for their own business as well as clients' businesses.
AI in Data Analysis and Content Generation
13:03 - 19:45
- State of the industry survey closing soon, data analysis to be done.
- Using AI to analyze survey data and find correlations.
- AI can automate tasks like building pivot tables and creating charts.
- AI can help visualize data in creative ways.
- Misuse of AI in content generation and media.
- Content farms using AI to generate low-quality articles for ad revenue.
- Legitimate media sites also experimenting with AI-generated content with problematic results.
- German tabloid planning to replace over 100 jobs with AI.
- Concerns about trust and credibility when using AI-assisted content.
Impact of AI-Generated Content on Media Outlets
19:27 - 26:26
- The use of AI-generated content can destroy trust in media outlets and lead to a collapse of companies.
- Media outlets may feel financial pressure to integrate AI technology and drive efficiency, leading to downsizing of human writing teams.
- Gizmodo and other outlets claim that AI-generated content is additional rather than a replacement for human writers, but the motivation to generate revenue through ads and clicks may lead to a preference for low-quality AI content.
- Brands and media companies should care about the output of their content to avoid damaging their brand reputation.
- The appearance of major brands alongside AI-generated content could pose an ad-related problem that Google needs to address.
- Content farms using AI may not be sustainable in the long run, similar to previous attempts with human-written low-quality content.
- Generative AI has the potential to add trillions of dollars in value to the global economy, with customer operations, marketing and sales, software engineering, and R&D being key areas.
- Investors are responding to the market impact of generative AI by investing significant amounts in leading generative AI companies like Inflection AI. Inflection AI raised $1.3 billion in funding from Microsoft, Reid Hoffman, Bill Gates, and NVIDIA.
Inflection AI and Generative AI Companies
26:03 - 32:46
- Inflection AI raised $1.3 billion in a fundraising round led by Microsoft, Reid Hoffman, Bill Gates, and NVIDIA.
- The company has built one of the world's most sophisticated large language models.
- Inflection AI is building the largest AI cluster in the world with 22,000 NVIDIA H100 tensor core GPUs.
- Mustafa Suleiman, CEO and co-founder of Inflection AI, also co-founded DeepMind which was acquired by Google.
- Runway announced a $141 million extension to its series C funding round with participation from Google, Nvidia, Salesforce Ventures, and other investors.
- Investors are writing enormous checks for generative AI companies because they are building foundational models for the tech stack.
- These companies invest heavily in infrastructure like chips and GPUs to build and train massive models.
- Generative AI companies are seen as the best bets for future development on top of foundational models.
- Inflection AI's personal AI assistant product called Pi is designed to be more conversational and iterative compared to GPT-like models.
- Mustafa Suleiman believes that solving hallucination in large language models is solvable and actively working on it.
- Marketers and business leaders should carefully consider where to allocate time and budget in generative AI companies given the competitive space.
Language Models and Synthetic Data
32:26 - 39:01
- The buying process for an LLM or for the applications is not yet defined.
- For big enterprises, starting with Microsoft as the core tech step is recommended due to procurement and regulatory issues.
- Financial services companies may opt for application layer companies like Jasper or Writer in the near term, while considering long-term strategies with language model companies.
- Synthetic data could lead to expanding a model's knowledge beyond human data and self-improvement without human intervention.
- The extent of human knowledge limits the ability to provide new knowledge to models that approach the performance of the best humans in a field.
- Conversations with AI agents can become training data for future LLMs, but there is debate about whether it adds new information or is just empty calories.
Ethics of Artificial Intelligence
38:40 - 45:13
- Synthetic data is a critical topic for the future of AI models.
- Autonomous AI agents are systems that can take actions online on behalf of users.
- OpenAI's concept of 'world of bits' explores the idea of machines taking actions on our behalf.
- OpenAI announced their intent to build a team to align superintelligence with human intent.
- Superintelligence, AI systems smarter than humans, could arrive in the next decade.
- OpenAI aims to solve the problem of ensuring superintelligence follows human intent within four years.
- The power and capabilities of superintelligence pose both opportunities and dangers for humanity.
Advancements in Language Models and Privacy
45:02 - 52:08
- Google DeepMind CEO, Demis Hissabas, is working on a system called Gemini that combines techniques from AlphaGo and Chatsh EPT to create a more capable language model.
- Gemini aims to give the system new capabilities such as planning and problem-solving.
- Google is launching a machine unlearning challenge to protect privacy and erase inaccurate or harmful information from AI models.
- Machine unlearning can be used to make AI systems safer and more responsible.
- Extracting harmful or unwanted information from AI models is currently not possible, but Google's challenge aims to address this issue.
- The future of AI involves figuring out how to remove knowledge from these models, which poses a grand challenge.
- Removing copyrighted or restricted data from trained models could be facilitated by machine unlearning.
Recent Developments and Lawsuits
51:38 - 58:41
- The US House of Representatives has issued a memo stating that offices can only use the paid version of Chat GPT due to privacy concerns.
- OpenAI is facing a class action lawsuit claiming that Chat GPT was trained on books without permission from authors.
- The outcome of these lawsuits is uncertain, but it may lead to penalties and impact future foundational models.
- LinkedIn has changed its algorithm to prioritize posts with meaningful content and crack down on clickbait-style content.
- The Vatican has released a handbook on the ethics of artificial intelligence.
Ethics in Artificial Intelligence
58:16 - 1:04:54
- The Vatican released a handbook on the ethics of artificial intelligence called 'Ethics in the Age of Disruptive Technologies, an operational roadmap'.
- The guidelines and suggestions aim to guide the tech industry through ethics in AI, machine learning, encryption, data usage, and tracking.
- The Vatican hopes to provide guidance for people within tech companies who are already grappling with difficult ethical questions.
- The guidelines include principles such as respect for human dignity and rights, promotion of transparency and explainability.
- While some find it interesting, others question whether the Vatican is the appropriate source for ethical guidelines in business.
Meta's Threads and Marketing AI Conference
1:04:34 - 1:06:12
- Meta has released Threads as a competitor to Twitter, which has already reached 100 million users.
- Threads aims to address perceived weaknesses of Twitter but lacks functionality like lists and newsfeed customization.
- Some journalists have started using Threads but many are skeptical about its usefulness and prefer Twitter's functionality.
- The Marketing AI Conference (Mekon) is happening in Cleveland from July 26th to July 28th.
- There is an amazing agenda with a final keynote yet to be announced.
- Discount code 'AIPOD100' can be used for $100 off registration.
Upcoming Marketing AI Conference
1:04:34 - 1:06:12
- An amazing final keynote is being added to the agenda.
- The event is trending towards selling out, so get your tickets soon.
- Mike and I are teaching workshops on day one.
- There will be three tracks and a variety of general sessions.
- Check out makecon.a for more information.
- Paul and the host have covered a lot of material in the past two weeks.