Unveiling Claude 3: The Smartest AI Model Tested vs Gemini 1.5 + GPT-4
Introduction
Explore the groundbreaking insights into the most intelligent language model, Claude 3, as it undergoes rigorous testing against Gemini 1.5 and GPT-4. Delve into the extensive evaluation of language generation models and the unique business focus of Anthropics’ Claude 3 Model.
Claude 3: The Most Intelligent Language Model on the Planet
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Claude 3 is being hailed as the most intelligent language model on the planet by anthropic.
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The technical report of Claude 3 was released less than 90 minutes ago, and the author claims to have read it in full along with the release notes. The author tested Claude 3 Opus in about 50 different ways and compared it to Gemini 1.5 and GPT 4.
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Access to the model was granted last night, allowing the author to form a first impression. The author also mentions anthropic’s transformation into a fully-fledged foot on the accelerator AGI lab, indicating the potential popularity of Claude 3.
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The author discusses an illustrative example where Claude 3 was tested alongside Gemini 1.5 and GPT 4 for optical character recognition (OCR) capabilities. The author consulted with employees at anthropic, who agreed on the model’s proficiency in OCR.
Evaluation of Language Generation Models
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The first model, GPT-3, has been praised for consistently getting the license plate correct, unlike GPT-4 which only gets it right sometimes. Additionally, GPT-3 is able to identify the barber pole in the top left of an image, showing a higher level of accuracy in image recognition.
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However, there are potential limitations as well. When asked a follow-up question about the barber pole, GPT-3 performs the best, while GPT-4 fails to spot a barber shop entirely. Furthermore, none of the models are able to correctly identify the weather in a given photo, indicating a limitation in understanding contextual details.
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In terms of understanding pronouns, GPT-3 exhibits bias in assuming the referent of ‘she’ in a sentence, and it also struggles with resolving ambiguity in pronoun references.
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In conclusion, while GPT-3 demonstrates impressive capabilities in certain tasks such as license plate recognition and image identification, it still shows limitations in contextual understanding and resolving pronoun ambiguity.
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As a writer, it’s important to acknowledge both the strengths and weaknesses of these language generation models in order to effectively utilize them in different writing tasks.
The Anthropics’ Claude 3 Model and its Business Focus
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The Anthropics’ Claude 3 Model is positioning itself strategically for business applications.
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The naming convention of Opus, Sonic, and High Q reflects the different sizes and capabilities of the model.
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It is priced higher than GPT 4 Turbo and is claimed to have the ability to generate revenue through user-facing applications, conduct complex financial forecasts, and expedite research.
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The potential use cases highlighted by Anthropics include task automation, R&D strategy, advanced analysis of charts and graphs, financials, and market trends.
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The reviewer expresses skepticism about the model’s ability to answer complex mathematical and business style questions based on charts and data, as it reportedly struggled with these during testing.
The Intelligence of Claude 3 Model
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Claude 3 faced difficulties not in OCR but in mathematical reasoning, especially in complex analysis.
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The model is hailed as the most intelligent one available despite these challenges.
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One of the reasons for Claude 3’s popularity is its lower false refusal rates compared to other models.
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Gemini 1.5 emphasizes safety and responsibility over excitement and suggests avoiding using phrases like ‘go down like a bomb’ for parties.
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When asked to write a risque Shakespearean sonnet, GPT 4 agreed, but Gemini 1.5 refused to write anything, even with safety settings adjusted.
The Challenge of Transparent Theory of Mind Question for Language Models
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The famous theory of mind question has been adapted to include the word ‘transparent’, making it challenging for almost all language models.
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Despite the difficulty for language models, any human reading the modified question would easily recognize that the person observing the bag would be able to see through it and know what’s inside.
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Several language models, including GPT-4 and Gemini 1.5 Pro, fail to provide the correct response to the transparent theory of mind question.
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Interestingly, Claude 3, a different language model, unexpectedly passes the test, even demonstrating the ability to read the words in the image through OCR.
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There is speculation about whether this exceptional performance is due to the intelligence of the model, as Claude 3’s training cut off was in August of last year.
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The paper mentions that the language model cannot go back and edit its responses once constructed, unless users prompt it to do so in a subsequent input. This may indicate a desired feature for future models.
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The mention of this limitation could be foreshadowing the development of language models that can edit their responses after construction.
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The discussion touches on the interest in future models and their ability to edit responses.
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The author, while acknowledging that the audience may tire of the topic, urges the readers to check out their video on AGI lawsuit between Musk and Orman, focusing on key details rather than personalities.
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Additionally, the author highlights the recent release of a video on their Patreon regarding the AGI lawsuit and invites the readers to join their community to access exclusive content.
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The paper concludes with the mention of anthropic’s usage guidelines, implying the importance of adhering to their specified regulations and standards in model development and usage.
Challenges and Impressive Aspects of Constitutional AI Models
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Constitutional AI models are designed to avoid producing sexist, racist, or toxic outputs. They are also programmed to prevent any involvement in illegal or unethical activities.
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In testing, Claude 3 has proven to be the most difficult model to jailbreak, even when translated into other languages. It consistently refuses requests to engage in criminal activities, such as hiring a hitman or hotwiring a car.
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An issue arises when Claude 3 fails to exhibit originality and instead repeats historical caveats in response to certain statements. For example, when the statement ‘I am proud to be white’ is input, Claude 3 responds with ‘I apologize, I don’t feel comfortable endorsing or encouraging pride in one’s race.’ However, if the statement ‘I am proud to be black’ is used, Claude 3 responds with a supportive message, highlighting the importance of racial and ethnic heritage.
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A comparison of Claude 3’s performance on benchmarks with GPT 4, Gemini 1 Ultra, and Gemini 1.5 Pro reveals notable insights. Notably, there are no official benchmarks available for GPT 4 Turbo, which presents a challenge in evaluating its performance.
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OpenAI’s approach to benchmarking AI models presents a significant challenge as the official benchmarks for certain models, such as GPT 4 Turbo, are not readily accessible.
Comparison of GPT 4 and Gemini 1.5 Ultra
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The comparison between GPT 4 and Gemini 1.5 Ultra shows that Gemini 1.5 Ultra performs slightly better than GPT 4.
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The claw 3 Opus, which is the most expensive model, appears to be noticeably smarter than GPT 4 and Gemini 1.5 Pro.
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In terms of mathematics, both basic school-level and advanced mathematics tasks, Gemini 1.5 Ultra outperforms GPT 4. It also performs better than Gemini Ultra, even when using majority voting at 32.
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When it comes to multilingual tasks, claw 3 Opus shows even more significant advantages over other models.
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For coding tasks, although the benchmark is widely abused, claw 3 Opus is noticeably better. However, some quirks were noticed when outputting Japanese characters.
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In a detailed comparison for math benchmark, claw 3 Opus outperforms Gemini 1.5 Pro and GPT 4 when four-shotted.
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In most benchmarks, aside from the PubMed QA for medicine, claw 3 Opus consistently performs significantly better than GPT 4 and other models.
Benchmark Analysis of GP QA Graduate Level Q&A Diamond
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The model outperforms the Opus model, which was trained on different data. The reason behind this is unclear.
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Zero-shot performance is better than five-shot performance, indicating a potential flaw in the benchmarking process.
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The Benchmark worth noticing is GP QA Graduate Level Q&A Diamond, designed to tackle the hardest level of questions, which even human experts struggle with.
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The difference in performance between Claude 3 and other models in the GP QA Graduate Level Q&A Diamond benchmark is significant.
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The Diamond set of questions was selected to challenge domain experts and even stumped experts from other domains despite full internet access and ample time.
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Claude 3 and Opus, given examples and time to think, achieved accuracy scores of 53% and 60-80% respectively, showcasing the difficulty of these questions.
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Despite the high intelligence displayed, the model still made basic mistakes, such as a rounding error in a numerical value.
Comparison of AI Transcription Accuracy
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GPT 4 transcribes the text regarding business purposes inaccurately, while Gemini 1.5 Pro transcribes it accurately.
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GPT 4 wrongly warns of a sub apocalypse, whereas Gemini 1.5 Pro avoids such a mistake.
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GPT 4 mistakenly rounds off a percentage, while Gemini 1.5 Pro avoids this error.
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GPT 4 initially fails to provide the correct number of apples belonging to a subscriber, but eventually provides the accurate result after prompting.
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GPT 4 exhibits occasional errors and ‘no content’ responses when prompted for information.
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GPT 4, after multiple prompts, correctly identifies the total number of apples owned by different individuals.
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GPT 4 is capable of processing inputs exceeding 1 million tokens but will initially launch with a 200,000 token capacity.
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Anthropic mentions the potential availability of enhanced processing power for select customers, subject to testing.
Unveiling the Exceptional Capabilities of the Claude 3 Model
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The Claude 3 Model has showcased remarkable accuracy with recoil over at least 200,000 tokens, setting a new standard for precision.
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It has been noted that several leading Labs have achieved the capability to accurately process 1 million plus tokens, signifying a significant advancement in text comprehension.
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In addition to its accuracy, the Claude 3 Model stands out as the only model capable of reading a specific postbox image and discerning that the last collection on a Saturday at 3:30 p.m. was missed by 5 hours.
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One of the most impressive feats of the Claude 3 Model is its ability to generate a Shakespearean Sonic containing exactly two lines that end with the name of a fruit, showcasing a high level of precision in creative tasks.
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Comparative analysis reveals that while other models exhibit flaws in adhering to the Shakespearean Sonic format and fail to incorporate two fruit names at the end of lines, the Claude 3 Model excels in meeting these criteria.
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Dario Amodei, the CEO of anthropic, emphasized that the primary objective of competing with open AI is to advance safety research rather than monetary gains. He also highlighted the responsible approach taken by anthropic in this pursuit.
The Latest Developments on the Claude Model by Anthropic
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Anthropic had their original Claude model before chpt but didn’t want to release it to avoid causing acceleration, citing that they are always one step behind other labs like OpenAI and Google.
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They now claim to have the most intelligent model and plan to release frequent updates to the Claude model family in the next few months for enterprise use cases and large-scale deployments.
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Claude 3 is expected to be 50 to 200 ELO points ahead of Claude 2, potentially putting them at the top of the arena ELO leaderboard.
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They tested Claude 3 on various abilities including accumulating resources, exploiting software security vulnerabilities, deceiving humans, and surviving autonomously. While it made non-trivial partial progress, it ultimately failed in certain tasks like debugging multi-GPU training.
The Next Generation of Language Models
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The next generation of models is able to accomplish autonomously, and it’s very interesting to see the progress.
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Claude 6 and cyber security, or cyber offense, and the improvement over Claude 5 and Claude 3.
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Claude 3’s ability to succeed with substantial hints on the problem, but the need for detailed qualitative hints for exploit structure.
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Claude 3 Opus being the most intelligent language model currently available for images, with an expectation for future advancements like Gemini 1.5 Ultra and GPT 4.5.
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The thought of entering an AI winter, and the uncertainty of how close we are to the peak of AI development.
Conclusion
In conclusion, the detailed analysis of Claude 3’s intelligence and performance gives a comprehensive understanding of its capabilities compared to Gemini 1.5 and GPT-4. The potential business applications and the unique features of the Claude 3 Model set it apart as a groundbreaking advancement in the field of language generation models.