Gemini, Bard (rebranded as Gemini by Google), OpenAI, Anthropic, and Cohere are compared in terms of their AI offerings. The podcast also discusses the recent FCC decision to ban AI voices in robocalls and its implications for government involvement in AI in 2024.
The FCC has banned the use of AI voices in robocalls due to concerns about the spread of misinformation and deception, while allowing their use in other contexts such as customer service lines and voice assistants.
Google's Gemini offers AI models that compete with OpenAI's offerings, with the Gemini Advanced model rivaling GPT-4. Although initial tests indicate GPT-4 outperforms Gemini, Google is expected to improve their models and make them more versatile for chat interfaces, image generation, and natural text queries for analytics.
Deep dives
FCC Bans AI Voices in Robocalls
The FCC has ruled to ban the use of AI voices in robo calls after an incident where an AI voice clone of President Biden was used to spread misinformation. The ruling comes as a response to concerns about the misuse of AI voices in automated calls, which can deceive individuals into believing false information. While the ruling primarily targets robo calls, it does not restrict the use of synthesized voices in other contexts, such as customer service lines or voice assistants.
Gemini: Google's New AI Models
Google has released Gemini, its latest wave of AI models. Gemini includes the Gemini Pro model, which is equivalent to OpenAI's GPT-3.5, and the Gemini Advanced model, which competes with ChatGPT's GPT-4 model. Initial comparisons suggest that GPT-4 outperforms Gemini in various tests. However, Google's models are expected to improve over time as they address the current areas of roughness and refine their performance. Gemini offers capabilities such as chat interfaces, image generation, and natural text queries for analytics, signaling a trend of making AI models more versatile and usable in various contexts.
The Future of Analytics with Natural Language Queries
Emerging tools and frameworks are enabling the integration of natural language interfaces with analytics tasks, bridging the gap between traditional data science and generative AI models. The approach involves generating code or SQL queries from natural language prompts to analyze data. While large language models like OpenAI's ChatGPT are capable of generating code, many enterprise-focused models emphasize SQL generation for analytics queries. This hybrid approach combines the flexibility of generative AI with traditional data science methodologies, unlocking new possibilities for data analysis, forecasting, and other fields. As these tools improve, we can expect to see more widespread adoption and innovation in implementing AI-driven analytics workflows.
Improving Prompting Strategies for Multimodal Tasks
Prompt engineering is a key factor in achieving desired outcomes when working with AI models for multimodal tasks, data analysis, and other applications. The Prompt Engineering Guide by DARE AI provides a comprehensive resource on formulating prompts for different models, including ChatGPT, CodeLama, Gemini, and more. The guide covers strategies and techniques to maximize the effectiveness of prompts, helping users obtain better results and navigate the complexities of model interaction. Whether you are prompting for chatbots, code generation, or data analysis, this resource can assist in understanding and applying effective prompting practices.
Google has been releasing a ton of new GenAI functionality under the name “Gemini”, and they’ve officially rebranded Bard as Gemini. We take some time to talk through Gemini compared with offerings from OpenAI, Anthropic, Cohere, etc.
We also discuss the recent FCC decision to ban the use of AI voices in robocalls and what the decision might mean for government involvement in AI in 2024.
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