Google Gemini Ultra: AI’s Next Frontier is Falling Short


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Gemini ultra in 2024 by outdoor tech lab

Ludington, MI – February 27, 2024

Google AI has taken a monumental leap forward with the unveiling of Gemini Ultra, it’s most powerful and versatile large language model (LLM) to date.

Built upon the revolutionary Gemini architecture, Ultra aims to redefine the ways AI interacts with and assists people in their daily lives, potentially transforming industries from education to customer service.

  • Beyond Search and Chatbots: Unlike previous LLMs, heavily focused on search engine-like tasks, Gemini Ultra excels at complex problem-solving, long-form content generation, and even creative tasks like writing different genres of poetry or helping with coding.
  • Seamless Multimodal Understanding: Gemini Ultra can understand and process not only text, but also code, images, video, and audio. This allows it to tackle requests that would baffle previous AI, like “Analyze this video, provide a summary of the key points, and create a musical soundtrack that matches the mood”.
  • Emphasis on Safety and Responsibility: Google’s AI research team has been keenly focused on safety and mitigating bias in its LLMs. Lead researchers on the Gemini project, state, “We’ve made significant progress in reducing harmful outputs and increasing factual accuracy. Gemini Ultra’s responses are carefully vetted through rigorous internal and external testing.”  https://blog.google/technology/ai/google-gemini-next-generation-model-february-2024/
  • Early Access and Impact: Gemini Ultra will be initially offered to select developers and industry partners, allowing Google to refine the model in real-world applications. Experts anticipate it could transform how businesses interact with customers, provide personalized education, or accelerate research in a variety of fields.

Major Gemini Shortcomings and Areas for Improvement

While powerful, Gemini is under development and has limitations to address:

  • Bias and Inaccurate Image Generation: 2024 versions of Gemini displayed racial bias in image generation. It will not create images of white people. It’s crucial to address these biases to ensure fair and representative outputs.
  • Limited Google Search and News Integration: Currently, Gemini can’t directly access and process information from other Google platforms like Google Search or News. This limits its ability to provide the most up-to-date and comprehensive information.
  • Accuracy and Factual Verification: Like any AI model, Gemini can generate inaccurate or misleading outputs. Users should critically evaluate the information it provides and verify facts through trusted sources.
  • Understanding Complex Queries: Gemini may struggle with understanding nuanced or multifaceted questions.

These are active areas of development, and Google is constantly working to improve Gemini’s capabilities and mitigate these limitations. As of now based on our testing this AI would not be recommended.

Additional Notes on Image Generation Bias

The specific concern about generating only images of brown people is a documented issue. Google addressed this by pausing image generation of people altogether while they implemented safeguards to prevent bias.

Google will need to evaluate their team and why it was programmed to show such racism towards white people.

Facts

  • Scale: Google reports Gemini Ultra is trained on a dataset significantly larger than previous LLMs, leading to enhanced performance.
  • Success on MMLU: Gemini Ultra achieved a score of 90.0% on MMLU, a benchmark for measuring AI’s problem-solving abilities across a range of subjects.
  • MMLU: https://blog.google/technology/ai/google-gemini-ai/

Gemini Ultra Expert Insights

  • Gemini Ultra signals a shift from AI as a tool to AI as a true collaborator!
  • Responsible development is key. Google’s focus on safety is encouraging, but with this power, ongoing scrutiny of the technology is essential, in our opinion at Outdoor Tech Lab.
Google Gemini Ultra image by Outdoor Tech Lab
Gemini Ultra image by Bing Image Creator as Gemini could not produce a quality image

 

Common Questions & Answers for Gemini Ultra

  • Q: How is Gemini Ultra different from search engines?

  • A: While Gemini Ultra can process and understand search queries, it goes far beyond that. It can analyze complex information, follow multi-step instructions, and generate different creative works, offering a much more dynamic interaction with information.

  • Q: Will Gemini Ultra be available to the public?

  • A: Google has announced limited developer and partner access initially. Wider availability will likely depend on real-world testing and refinement.

  • Q: Are there concerns about safety and bias with such a powerful AI?

  • A: Google emphasizes its commitment to mitigating harm, but the technology’s power raises valid concerns. Experts stress the importance of continued scrutiny and transparency as Gemini Ultra evolves.

Here’s a breakdown of responsible AI and how Google specifically approaches it within it’s products and research:

What is Responsible AI? Does Google Implement This?

Responsible AI centers on developing and deploying artificial intelligence technologies with a focus on these core principles:

  • Fairness & Bias Mitigation: Ensuring AI systems don’t perpetuate harmful biases or discrimination based on gender, race, ethnicity, or other factors. As of now Google has failed to do so.
  • Transparency & Explainability: Allowing users and stakeholders to understand how AI models arrive at decisions or outputs, fostering trust and accountability. 
  • Privacy and Security: Prioritizing the protection of user data and designing AI systems with robust security measures in place.
  • Accountability: Establishing clear lines of responsibility for the development, deployment, and monitoring of AI systems to address potential harms.
  • Social and Environmental Impact: Considering the broader societal and environmental consequences of AI technologies, striving for positive impact and minimizing potential negative externalities.

Google’s Approach to Responsible AI

Google recognizes the ethical implications of powerful AI and has instituted specific processes and initiatives to promote responsible AI practices but still shows racist outpout:

  • Google AI Principles: Google outlines seven core tenets that guide its AI development and implementation https://ai.google/principles/. These principles cover fairness, safety, accountability, transparency, privacy, human benefit, and scientific rigor.
  • Tools and Resources: Google provides a range of tools for developers and researchers to foster responsible AI, such as:
    • Explainable AI: Tools to help understand how AI models make decisions.
    • Model Cards: Documentation accompanying a model to provide clarity into its intended use, training data, and performance metrics.
    • TensorFlow Fairness Indicators: Libraries and tools to help identify and mitigate bias in datasets or models.
  • Cross-functional Review: Google has established review boards to scrutinize new technologies and products, especially in sensitive areas, ensuring alignment with the AI Principles.
  • Ongoing Research and Education: Google actively invests in responsible AI research, publishing work and collaborating with the wider community on addressing issues like fairness, transparency, and safety.

Challenges and Ongoing Efforts

Implementing responsible AI is not without challenges. Google, like other tech giants, has faced criticism and scrutiny in areas where their AI products potentially fell short of responsible AI goals.

This highlights that it’s an ongoing process with areas such as:

  • Algorithmic Bias: Even with rigorous testing, bias can still creep into datasets or models. Constant vigilance is needed.
  • Transparency Limits: Balancing transparency with intellectual property protection or security concerns can be difficult.
  • Evolving landscape: The rapid development of AI necessitates adaptable approaches and ongoing evaluation with new use cases.
  • Images of only brown people: The image function is 100% biased, as if nobody else exists.
Gemini ultra image by outdoortechlab.com
Gemini Ultra – Bing image creator image as Google AI could not produce this quality

 

Where to Learn More

Stay tuned! Google has announced plans for a series of developer workshops and early access opportunities for exploring Gemini Ultra’s potential.

Outdoor Tech Lab Gets Hands-On with Gemini Advanced

The team at Outdoor Tech Lab, known for putting the latest outdoor gear through rigorous field tests, is buzzing about the opportunity to test Google Gemini Advanced.

We’re always looking for ways technology can enhance our wilderness experiences at OTL!

Gemini ultra image done by gemini advanced outdoor tech lab test
Gemini Ultra image by Gemini Advanced

 

Here’s how Outdoor Tech Lab is putting Gemini Advanced to the test:

  • Trip Planning and Navigation: Asking Gemini Advanced to compare backpacking trails with current weather forecasts, campsite availability, and wildlife advisories, saving hours of online research.
  • Field Identification: Uploading photos of unfamiliar plants or animal tracks and asking Gemini Advanced to provide identification and safety information.
  • Emergency Preparedness: Generating scenarios like, “My friend is showing signs of heatstroke, and the nearest town is 5 miles away,” and analyzing Gemini’s emergency response guidance.
  • Creative Storytelling: Using Gemini Advanced to write campfire tales inspired by the day’s hike or even turn trip logs into a short poem.

We’re particularly interested in testing Gemini Advanced’s reliability in truly off-grid scenarios.

Also, it’s ability to process complex questions and provide accurate summaries, even without a constant internet connection, could be invaluable for any adventurer.

Stay tuned for the full Outdoor Tech Lab review of Google Gemini Advanced coming soon!

#GoogleAI #GeminiUltra #LLM #artificialintelligencenews

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