Gemini's data-analyzing abilities aren't as good as Google claims
Google's Gemini 1.5 Pro and 1.5 Flash AI models face scrutiny for poor data analysis performance, struggling with large datasets and complex tasks. Research questions Google's marketing claims, highlighting the need for improved model evaluation.
Read original article
Gemini's data-analyzing abilities, as claimed by Google for its flagship generative AI models, Gemini 1.5 Pro and 1.5 Flash, have been called into question by new research. Studies revealed that these models struggle to accurately answer questions about large datasets, with accuracy rates as low as 40%-50% in some cases. Despite Google's marketing highlighting the models' long-context capabilities, researchers found that the models often fail to understand content and struggle with complex reasoning tasks. Additionally, Gemini's performance in evaluating true/false statements about fiction books and reasoning over videos was found to be subpar. The research suggests that Google may have overpromised the capabilities of Gemini, with other models also failing to perform well in similar tests. The studies emphasize the need for better benchmarks and third-party critique in evaluating the true capabilities of generative AI models, as businesses and investors express concerns about the technology's limitations and potential for errors.
Related
Testing Generative AI for Circuit Board Design
A study tested Large Language Models (LLMs) like GPT-4o, Claude 3 Opus, and Gemini 1.5 for circuit board design tasks. Results showed varied performance, with Claude 3 Opus excelling in specific questions, while others struggled with complexity. Gemini 1.5 showed promise in parsing datasheet information accurately. The study emphasized the potential and limitations of using AI models in circuit board design.
Lessons About the Human Mind from Artificial Intelligence
In 2022, a Google engineer claimed AI chatbot LaMDA was self-aware, but further scrutiny revealed it mimicked human-like responses without true understanding. This incident underscores AI limitations in comprehension and originality.
Francois Chollet – LLMs won't lead to AGI – $1M Prize to find solution [video]
The video discusses limitations of large language models in AI, emphasizing genuine understanding and problem-solving skills. A prize incentivizes AI systems showcasing these abilities. Adaptability and knowledge acquisition are highlighted as crucial for true intelligence.
AI can beat real university students in exams, study suggests
A study from the University of Reading reveals AI outperforms real students in exams. AI-generated answers scored higher, raising concerns about cheating. Researchers urge educators to address AI's impact on assessments.
Large Language Models are not a search engine
Large Language Models (LLMs) from Google and Meta generate algorithmic content, causing nonsensical "hallucinations." Companies struggle to manage errors post-generation due to factors like training data and temperature settings. LLMs aim to improve user interactions but raise skepticism about delivering factual information.
1 commentsPrices tend to be set based on whatever the market will bear --- and so is the level of bullsh!t. Both tend to get moderated over time as reality settles in and consumer awareness starts to develop.
Where AI/LLMs are concerned, we are currently near peak bullsh!t.
Related
Testing Generative AI for Circuit Board Design
A study tested Large Language Models (LLMs) like GPT-4o, Claude 3 Opus, and Gemini 1.5 for circuit board design tasks. Results showed varied performance, with Claude 3 Opus excelling in specific questions, while others struggled with complexity. Gemini 1.5 showed promise in parsing datasheet information accurately. The study emphasized the potential and limitations of using AI models in circuit board design.
Lessons About the Human Mind from Artificial Intelligence
In 2022, a Google engineer claimed AI chatbot LaMDA was self-aware, but further scrutiny revealed it mimicked human-like responses without true understanding. This incident underscores AI limitations in comprehension and originality.
Francois Chollet – LLMs won't lead to AGI – $1M Prize to find solution [video]
The video discusses limitations of large language models in AI, emphasizing genuine understanding and problem-solving skills. A prize incentivizes AI systems showcasing these abilities. Adaptability and knowledge acquisition are highlighted as crucial for true intelligence.
AI can beat real university students in exams, study suggests
A study from the University of Reading reveals AI outperforms real students in exams. AI-generated answers scored higher, raising concerns about cheating. Researchers urge educators to address AI's impact on assessments.
Large Language Models are not a search engine
Large Language Models (LLMs) from Google and Meta generate algorithmic content, causing nonsensical "hallucinations." Companies struggle to manage errors post-generation due to factors like training data and temperature settings. LLMs aim to improve user interactions but raise skepticism about delivering factual information.