In this article, we are going to explore some use cases and features that were found by some developers who got access to the latest Pro and Ultra models in their beta phase, long before it was released. We will discuss them in depth. So, let\u2019s get into it!<\/p>\n\n\n\n
How to Access Gemini Pro 1.5?<\/strong><\/h2>\n\n\n\nGemini\u2019s latest 1.5 Pro model has been released publicly as of now. The chatbot was removed from the waitlist queue and is now freely rolled out in Google\u2019s AI Studio Platform. <\/strong><\/p>\n\n\n\nHere\u2019s how you can access and try it for free:<\/p>\n\n\n\n
\n- Go to Google DeepMind\u2019s <\/a>Website.<\/li>\n\n\n\n
- Click Gemini 1.5 or scroll down till you see \u201cIntroducing Gemini 1.5\u201d<\/li>\n\n\n\n
- Click on \u201cTry Gemini 1.5\u201d and sign in with your Gmail account.<\/li>\n\n\n\n
- You will be taken to Google AI Studio. Click on the \u201cGet Started\u201d button.<\/li>\n\n\n\n
- You are now ready to use the latest Google Gemini 1.5 Pro model.<\/li>\n<\/ul>\n\n\n\n
Now that we know how to access it, let’s move to the main thing: its features.<\/p>\n\n\n\n
10 Amazing Features of the Gemini Pro 1.5 Models<\/strong><\/h2>\n\n\n\nHere are some of the best features that developers found when testing the new Gemini models:<\/p>\n\n\n\n
1) Summarization and Explanation<\/strong><\/h3>\n\n\n\nRadostin Cholakov, a Google Developer Researcher in Machine Learning, tried to get assistance from Gemini 1.5 Pro with some research work. He uploaded several PDFs<\/a> to Pro 1.5 and asked it to explain the topics in them, namely Contrastive Learning and its use cases.<\/p>\n\n\n\n![\"Gemini](\"https:\/\/favtutor.com\/articles\/wp-content\/uploads\/2024\/03\/Screenshot-415-1-1024x396.png\")
<\/figure>\n<\/div>\n\n\nGemini 1.5 Pro gave a detailed and informative summarization of the topic.<\/strong> It also managed to use mathematical notation to formulate a loss function. The summary was broad, well-defined, and explained properly in points. The only drawback was that the summary had a few inaccuracies.<\/p>\n\n\n\n![\"Gemini](\"https:\/\/favtutor.com\/articles\/wp-content\/uploads\/2024\/03\/WhatsApp-Image-2024-03-25-at-17.00.41_2e54cc72-1-721x1024.jpg\")
<\/figure>\n<\/div>\n\n\nThe key takeaway here is it’s zero-shot abilities. For long LLMs have been useful in long contextual understandings and documentation with RAG-based additional steps and human guidance. Gemini has deviated from this traditional approach with its zero-shot technique which doesn\u2019t require any additional human guidance at all.<\/p>\n\n\n\n
2) Understanding Related Concepts<\/strong><\/h3>\n\n\n\nRadostin wanted to put Gemini 1.5 Pro\u2019s understanding of related concepts to the test. So, he gave the chatbot two mathematical notations from different papers and asked it to unify them.<\/p>\n\n\n\n
The model was asked to produce a paragraph summarizing the ideas using notation akin to the original SupCon paper after uploading the TEX sources of the papers.<\/p>\n\n\n
\n![\"Understanding](\"https:\/\/favtutor.com\/articles\/wp-content\/uploads\/2024\/03\/Screenshot-416-1-1024x297.png\")
<\/figure>\n<\/div>\n\n\nThis was the prompt that it was given:<\/p>\n\n\n\n
\u201cUnify the notation of the SelfCon and SupCon paper.
Use the SupCon notation to define SelfCon by introducing necessary additions to the original SupCon formulation.
Provide latex code.\u201d<\/em><\/p>\n\n\n\nGemini did a perfect job in understanding the assignment and it got the idea of having two functions \\omega for the various sample views exactly right. However, a few key terms were missing in the equation.<\/strong><\/p>\n\n\n\n![\"\"](\"https:\/\/favtutor.com\/articles\/wp-content\/uploads\/2024\/03\/Screenshot-417-1.png\")
<\/figure>\n<\/div>\n\n\nBoth the use cases show that the long-context capabilities of Gemini 1.5 Pro represent a major advancement in the utility of LLMs.<\/p>\n\n\n\n
3) Analyzing differences from comparisons<\/strong><\/h3>\n\n\n\nHong Cheng, the founder of Ticker Tick, wanted to see how good Gemini 1.5 Pro\u2019s, 1 million context window is good at analyzing differences from comparisons. He uploaded two PDFs containing information about Meta\u2019s platform in 2022 and 2023. The documents had a token count of 115,272 and 131,757 tokens respectively.<\/p>\n\n\n\n
The summary of the differences was spot on. Not only did it show the comparisons, but it also made the comparisons in a sub-group manner, extracting relevant points and figures wherever possible to make the comparisons stronger and clearer.<\/strong><\/p>\n\n\n\nGemini 1.5 Pro's one million context window is impressive. I asked it to compare two Meta's 10-K filings and summarize the differences. The results are spot on. $GOOG<\/a> pic.twitter.com\/J57jMzJNEM<\/a><\/p>— Hongcheng (@hzhu_) March 24, 2024<\/a><\/blockquote>
Here\u2019s how you can access and try it for free:<\/p>\n\n\n\n
- \n
- Go to Google DeepMind\u2019s <\/a>Website.<\/li>\n\n\n\n
- Click Gemini 1.5 or scroll down till you see \u201cIntroducing Gemini 1.5\u201d<\/li>\n\n\n\n
- Click on \u201cTry Gemini 1.5\u201d and sign in with your Gmail account.<\/li>\n\n\n\n
- You will be taken to Google AI Studio. Click on the \u201cGet Started\u201d button.<\/li>\n\n\n\n
- You are now ready to use the latest Google Gemini 1.5 Pro model.<\/li>\n<\/ul>\n\n\n\n
Now that we know how to access it, let’s move to the main thing: its features.<\/p>\n\n\n\n
10 Amazing Features of the Gemini Pro 1.5 Models<\/strong><\/h2>\n\n\n\n
Here are some of the best features that developers found when testing the new Gemini models:<\/p>\n\n\n\n
1) Summarization and Explanation<\/strong><\/h3>\n\n\n\n
Radostin Cholakov, a Google Developer Researcher in Machine Learning, tried to get assistance from Gemini 1.5 Pro with some research work. He uploaded several PDFs<\/a> to Pro 1.5 and asked it to explain the topics in them, namely Contrastive Learning and its use cases.<\/p>\n\n\n
\n<\/figure>\n<\/div>\n\n\nGemini 1.5 Pro gave a detailed and informative summarization of the topic.<\/strong> It also managed to use mathematical notation to formulate a loss function. The summary was broad, well-defined, and explained properly in points. The only drawback was that the summary had a few inaccuracies.<\/p>\n\n\n
\n<\/figure>\n<\/div>\n\n\nThe key takeaway here is it’s zero-shot abilities. For long LLMs have been useful in long contextual understandings and documentation with RAG-based additional steps and human guidance. Gemini has deviated from this traditional approach with its zero-shot technique which doesn\u2019t require any additional human guidance at all.<\/p>\n\n\n\n
2) Understanding Related Concepts<\/strong><\/h3>\n\n\n\n
Radostin wanted to put Gemini 1.5 Pro\u2019s understanding of related concepts to the test. So, he gave the chatbot two mathematical notations from different papers and asked it to unify them.<\/p>\n\n\n\n
The model was asked to produce a paragraph summarizing the ideas using notation akin to the original SupCon paper after uploading the TEX sources of the papers.<\/p>\n\n\n
\n<\/figure>\n<\/div>\n\n\nThis was the prompt that it was given:<\/p>\n\n\n\n
\u201cUnify the notation of the SelfCon and SupCon paper.
Use the SupCon notation to define SelfCon by introducing necessary additions to the original SupCon formulation.
Provide latex code.\u201d<\/em><\/p>\n\n\n\nGemini did a perfect job in understanding the assignment and it got the idea of having two functions \\omega for the various sample views exactly right. However, a few key terms were missing in the equation.<\/strong><\/p>\n\n\n
\n<\/figure>\n<\/div>\n\n\nBoth the use cases show that the long-context capabilities of Gemini 1.5 Pro represent a major advancement in the utility of LLMs.<\/p>\n\n\n\n
3) Analyzing differences from comparisons<\/strong><\/h3>\n\n\n\n
Hong Cheng, the founder of Ticker Tick, wanted to see how good Gemini 1.5 Pro\u2019s, 1 million context window is good at analyzing differences from comparisons. He uploaded two PDFs containing information about Meta\u2019s platform in 2022 and 2023. The documents had a token count of 115,272 and 131,757 tokens respectively.<\/p>\n\n\n\n
The summary of the differences was spot on. Not only did it show the comparisons, but it also made the comparisons in a sub-group manner, extracting relevant points and figures wherever possible to make the comparisons stronger and clearer.<\/strong><\/p>\n\n\n\n
Gemini 1.5 Pro's one million context window is impressive. I asked it to compare two Meta's 10-K filings and summarize the differences. The results are spot on. $GOOG<\/a> pic.twitter.com\/J57jMzJNEM<\/a><\/p>— Hongcheng (@hzhu_) March 24, 2024<\/a><\/blockquote>