ChatGPT4 vs. Gemini Advanced — 2024 Comparison
Picture this: In the vast and vibrant landscape of Artificial Intelligence, two giants are stepping into the ring, gearing up for an epic showdown that’s got everyone talking.
It’s like the ultimate battle of wits, starring OpenAI’s ChatGPT-4 and Google’s fresh face on the scene, Gemini Advanced.
Imagine them as the supercomputers of conversation, just like Deep Blue (from IBM) in the 90s, but now everyone can use them.
Each with their own quirks and talents, kind of like characters straight out of a sci-fi flick.
Here’s the lowdown for you, straight from the insider’s perspective: we’re about to dive deep into what makes these AI contenders tick, how they’re reshaping our daily grind and leisure time, and who might just snag the crown in this high-stakes AI popularity face-off.
ChatGPT-4? Oh, it’s the charismatic charmer of the AI world — think the effortlessly cool guy that everyone can’t help but gravitate towards.
It’s not just talk; this Generative AI can walk the walk, flipping between chatting, composing all sorts of written magic, and even dropping jokes that’ll have you doing a double-take.
Now, enter Gemini Advanced, the intriguing new kid on the block that’s got the rumor mill in overdrive.
Word on the street is, Gemini’s got a brainpower that could give ChatGPT-4 a run for its money, packing some next-level skills that we’re all dying to see in action. The buzz is real, and the anticipation? Palpable.
The first thing I tested is Word Count for standard Output. When you don’t specify any word count you expect to the AI, ChatGPT-4 averages ~900 words, while Gemini Advanced averages ~500 words.
The second thing to compare is the Speed. While ChatGPT-4 has improved its response time in the last months, Gemini Advanced excels here, so we can expect 1–2 seconds faster response in Gemini Advanced.
The third thing to test is Creativity. ChatGPT leads here and I will provide a very good sample where I asked both Generative AIs to imagine how Generative AI will evolve and definitely ChatGPT provided a better answer than Gemini Advanced.
My Testing Prompt:
Primer Step: Contextual Understanding
“Your task is to think step by step about the current trends in Generative AI technology.
You MUST consider the most recent advancements, their potential impact, and innovative applications.
Your role is an expert in technology trends, specifically in the field of Generative AI.”
Outline Step: Creativity Analysis
“Now, you will be penalized if you do not creatively speculate on future developments in Generative AI.
Imagine novel uses or advancements that could emerge in the next few years.
Your task is to outline these ideas in an engaging and imaginative manner.”
Optimization Step: Response Formatting
“Your task is now to organize the insights and speculative ideas you’ve generated into a tabular format.
The table should compare potential future developments in Generative AI.
You MUST ensure the table is easy to understand for the general public, using informal but technically accurate language.”
Final Step: Narrative Comparison
“Finally, you will write an ultra-detailed narrative for me, summarizing the insights from the table.
Your task is to compare the creativity of the ideas generated, highlighting key innovations and their possible impacts on society.
You MUST use an informal, technical, and casual tone, making the information accessible to the general public.”
ChatGPT-4’s Output:
Primer Step: Contextual Understanding
Generative AI technology has been making headlines for its rapid advancements and the broad spectrum of applications it’s influencing. From creating realistic images and videos to generating human-like text, the technology is pushing the boundaries of what machines can do. Recent developments have seen AI not just mimicking existing content but also creating novel, creative works in art, music, literature, and even code generation. Tools like DALL-E 2, GPT-4, and Codex are examples of this technology’s current capabilities, showcasing impressive levels of creativity and understanding.
The potential impact of these advancements is vast. In the creative industries, for example, artists and designers are using Generative AI to push the limits of their creativity, creating artworks and designs that were previously unimaginable. In the business world, companies are leveraging these technologies to generate marketing content, automate coding, and even innovate in product design. The educational sector is not left out, with AI being used to create personalized learning materials and interactive learning experiences.
Outline Step: Creativity Analysis
Imagining the future of Generative AI involves thinking about how these technologies could evolve and intersect with various aspects of human life and work. Here are some speculative ideas:
- Emotional Intelligence in AI: Future Generative AI could understand and replicate human emotions more accurately, leading to AI companions that can provide better emotional support or AI-driven therapists.
- AI as Co-Creators in Art and Design: Artists and designers might collaborate more closely with AI to create works that are a blend of human creativity and AI’s infinite capacity for variation and experimentation.
- Generative AI in Governance: AI could help in drafting laws or policies by generating proposals based on historical data, trends, and simulations of future scenarios.
- Personalized Education Content: AI could generate customized learning materials that adapt to each student’s learning pace, style, and interests, revolutionizing education.
- AI in Environmental Conservation: Generative AI could simulate complex environmental systems, helping in the creation of more effective conservation strategies.
Optimization Step: Response Formatting
Final Step: Narrative Comparison
Let’s dive into the creative pool of Generative AI’s future, shall we? Imagine a world where your AI buddy doesn’t just understand your words but gets your mood swings too. That’s the future we’re looking at with Emotional Intelligence in AI. It’s like having a friend who’s always there, ready to cheer you up or offer a shoulder to cry on, but with an off switch. The impact? A revolution in mental health support and user interfaces that feel more like talking to a human than tapping on a screen.
Now, picture collaborating with AI on your next art project. AI as Co-Creators isn’t about machines taking the artist’s job; it’s about pushing the envelope together. This partnership could lead to art and designs that are out of this world, literally. The blend of human emotion and AI’s endless imagination could create new art forms that we haven’t even dreamed of yet.
Generative AI in Governance sounds a bit like sci-fi, but it’s closer than you think. Imagine AI drafting policies by analyzing tons of data, predicting outcomes, and ensuring the best decisions for future generations. It’s like having a super-smart advisor that can crunch numbers and predict the future, making governance smarter and more efficient.
Education is getting a facelift with Personalized Education Content. This isn’t just about textbooks on a screen; it’s about learning materials that adapt to how you learn best, making education a tailor-made experience. It could change the game for students everywhere, making learning more engaging and effective.
Lastly, AI in Environmental Conservation could be our secret weapon against environmental challenges. By simulating complex ecosystems, AI can help us understand and protect our planet better. It’s like having a crystal ball that shows us the consequences of our actions on the environment, helping us make better choices for our planet’s future.
In this imaginative journey, the creativity of these ideas shines through, highlighting the potential of Generative AI to transform society. From emotional AI companions to AI-driven environmental conservation, the future looks not just smart but also heartwarming and green. The key innovation here is the blend of AI’s computational power with human creativity and empathy, leading to a future where technology and humanity go hand in hand.
Gemini Advanced’s Output:
Step 1: Contextual Understanding
Current Trends:
- Large Language Models (LLMs): Advancements like ChatGPT and others fueling conversation and text generation.
- Diffusion Models: Creating incredibly realistic images with DALL-E, Stable Diffusion, etc.
- Code Generation: Tools like GitHub’s Copilot that help programmers.
- Beyond 2D: Exploration into 3D object generation, video creation, and even the beginnings of generating and animating synthetic worlds.
Potential Impacts:
- Democratization of Creation: More people becoming content creators without traditional artistic or coding skills.
- Productivity Boosts: Speeding up drafting, brainstorming, and the creative process.
- Ethical Concerns: Potential for deepfakes, misinformation, and copyright issues.
Step 2: Creativity Analysis
Speculative Concepts:
- Hyper-personalized Experiences: AI tailors everything from product recommendations to educational materials on an unprecedented level.
- ‘Idea Engines’: AI collaborates with humans as a thought partner, sparking entirely new concepts and possibilities.
- Real-Time World Translation: AI instantly interprets a multitude of languages for truly live international conversation.
- AI-Generated Scientific Breakthroughs: Simulation and analysis beyond human limits could potentially uncover new discoveries in physics, medicine, and beyond.
Step 3: Response Formatting
Step 4: Narrative Comparison
All these ideas push the limits of Generative AI, but let’s focus on those top two lines on the chart:
- “Hyper-personalized AI” is a double-edged sword. The ultimate in customer service? Cool! But there’s potential creepiness where AI knows us too well. This might spark a huge fight around privacy and regulation.
- “AI as ‘Idea Engine’” sounds fantastic, right? It means artists/entrepreneurs don’t need to come up with EVERYTHING from scratch. But what changes then about original ideas? Do we value human brilliance differently if inspiration gets more AI-flavored?
The bottom two lines have less fuzzy ethics to worry about, yet more technical hurdles. Sure, “Real-Time World Translation” would change the world overnight, but the AI’s gotta master nuance across all those languages — that’s no small feat. It may arrive slower than these ‘softer’ developments.
And how about those “AI-Driven Science Discoveries?” If, hypothetically, an AI suggests a way to make nuclear fusion truly viable, who gets the Nobel Prize? This line might open the biggest philosophical can of worms!
Finally, I asked both Generative AIs to make an Illustration for this Post:
ChatGPT-4 (Using DALLE)
Gemini Advanced(Using BARD):
So that’s it for today’s comparison. As these Generative AIs evolve, let’s see what AI boundaries they push forward. Let me know in comments what are your thoughts and if you want me to test any specific prompt.
