One of the most powerful capabilities of the new OpenAI's GPT-3 model, known as GPT-3.5 or GPT-3 Turbo, is its advanced image reasoning abilities. This feature, called "Thinking with Images," allows the model to analyze images in remarkable detail and provide insightful reasoning about what it observes.

Unlike traditional image recognition models, GPT-3.5 doesn't just identify objects or features in an image. It goes a step further, dissecting the image and reasoning about the various elements it sees. The model can zoom in on specific details, analyze the scene's composition, and even cross-reference the information it gathers with its internal knowledge base to provide a comprehensive understanding of the image.

This advanced image reasoning capability opens up a wide range of use cases. For example, you can use it to solve puzzles, such as "Where's Waldo" challenges, by having the model carefully examine the image and provide the location of the elusive character. Additionally, the model's ability to reason about images can be leveraged for tasks like location identification, where it can analyze visual cues to determine the geographic location depicted in the image.

One particularly impressive demonstration of this capability is the model's ability to solve mazes. By treating the maze as an image and applying its reasoning skills, GPT-3.5 can quickly identify the optimal path through the maze, even in complex, 200x200 grid layouts.

Beyond these more playful applications, the advanced image reasoning feature can also be valuable in business and analytical contexts. For instance, you can use it to analyze data visualizations or financial reports, allowing the model to extract insights and trends that might not be immediately apparent to the human eye.

Overall, the advanced image reasoning capabilities of GPT-3.5 represent a significant advancement in the field of artificial intelligence, blending visual processing with language-based reasoning to unlock a new level of understanding and problem-solving. As you explore the use cases and push the boundaries of this technology, be mindful of the model's limitations, such as its tendency to hallucinate or make mistakes in certain tasks. Nonetheless, the potential of this feature is truly exciting and holds promise for a wide range of applications.

One of the most powerful use cases for the GPT-3 model is its ability to conduct deep research and provide comprehensive business analysis. By leveraging the model's advanced reasoning capabilities, you can quickly generate detailed insights and projections to drive your business forward.

In this example, I was provided with synthetic data for a Shopify store selling vintage clothing online over the past 3 years. I asked the model to analyze the data, identify any trends, and provide future projections along with actionable recommendations.

The model first imported and analyzed the data, identifying a compound annual growth rate of around 1.3% - positive but not substantial. It then delved deeper, noting seasonal trends with revenue peaks in March, June, and September, as well as dips in January, February, and midsummer.

Based on this analysis, the model provided a 24-month revenue projection, showcasing the gradual, stable growth of the business. Importantly, it also offered several tactical recommendations to help drive performance, such as:

• Set a concrete 10% year-over-year growth target and track progress
• Focus ad spend on the 6 weeks before peak seasons to capitalize on demand
• Implement tactics to increase average order value, like live shopping events

The level of detail and actionable insights provided by the model is truly remarkable. It not only analyzed the historical data, but also leveraged its broader knowledge to contextualize the findings and provide tailored, data-driven recommendations.

This use case demonstrates how the GPT-3 model can supercharge your business analysis, delivering comprehensive insights in a fraction of the time it would take a human researcher. By combining the model's deep research capabilities with its reasoning and problem-solving skills, you can make informed, data-driven decisions to propel your business forward.

Surprisingly, OpenAI's GPT-3 model, known as 03, has demonstrated exceptional performance in the Creative Writing V3 benchmark, which measures the emotional intelligence of language models. Despite being primarily designed as an agentic model capable of utilizing various tools, 03 has managed to secure the top spot in this creative writing assessment.

This unexpected capability suggests that 03's reasoning abilities and language understanding may lend themselves well to creative writing tasks. The model's ability to infuse emotional depth and nuance into its written output could make it a valuable asset for those seeking to enhance their creative writing skills.

However, it's important to note that 03 is an incredibly expensive model to use, and there are more cost-effective options available for creative writing, such as GPT-4.5 or DeepSeeK R1. While 03's performance in this benchmark is impressive, it may not be the most practical choice for everyday creative writing needs.

Nonetheless, the fact that 03 has excelled in this domain is a testament to the model's versatility and the ongoing advancements in language AI. As the field of artificial intelligence continues to evolve, we may see even more surprising and innovative applications of these powerful models emerge.

While the capabilities of OpenAI's GPT-3 model are impressive, it is important to be aware of its limitations, particularly when it comes to the risk of hallucination. The research paper on GPT-3 and GPT-4 mini models has revealed that these reasoning-focused models have a higher tendency to hallucinate, with GPT-4 mini hallucinating 48% of the time and GPT-3 hallucinating 33% of the time, compared to GPT-1 at 16%.

This means that users must be vigilant in double-checking the outputs of these models, as they may provide inaccurate or fabricated information. For example, the model's inability to accurately count the number of fingers in an emoji demonstrates its limitations in visual reasoning and perception.

To mitigate the risks of hallucination, it is crucial to:

1. Fact-check outputs: Always verify the information provided by the model, especially for critical applications where accuracy is paramount. Cross-reference the model's responses with reliable sources to ensure the validity of the information.

2. Understand the model's limitations: Be aware of the specific areas where the model may struggle, such as counting or precise visual analysis. Adjust your prompts and expectations accordingly.

3. Use the model judiciously: Ration the use of the more expensive GPT-3 model, and consider using more cost-effective options like GPT-4 or DeepSeeK R1 for tasks that do not require the advanced reasoning capabilities of GPT-3.

4. Implement safeguards: Develop processes and systems to monitor the model's outputs and flag potential hallucinations or inconsistencies. This can help catch errors before they have significant consequences.

By being mindful of these limitations and taking proactive steps to mitigate the risks, users can leverage the powerful capabilities of GPT-3 while maintaining the necessary level of accuracy and reliability in their applications.

The future is indeed agentic, and OpenAI's GPT-3 model is a testament to the advancements in AI technology. The model's ability to reason with images, analyze business data, and even excel at creative writing tasks is truly remarkable.

However, it's important to note the limitations of the model, such as its tendency to hallucinate and the low weekly message limit, even with a paid account. Users must be diligent in fact-checking the model's outputs to ensure accuracy, especially in critical applications.

Despite these caveats, the use cases presented in this video showcase the immense potential of GPT-3 and the agentic models that are emerging. From solving mazes to generating insightful business reports, the model's versatility is undeniable. As the technology continues to evolve, the possibilities for its application in various industries are endless.

Ultimately, the future belongs to those who embrace the power of agentic AI and leverage it to their advantage. By understanding the strengths and limitations of these models, users can harness their capabilities to drive innovation and efficiency in their respective fields.