Google的Gemini让GPT-4显得像个玩具

Google的Gemini让GPT-4显得像个玩具

Table of Contents:

1. Introduction
2. The Great AI War of 2023
3. GPT-4: Capturing the Zeitgeist
4. Google's Gemini Model 4.1 Multimodal Capabilities 4.2 Real-time Video Recognition 4.3 Multilingual Functionality 4.4 Ongoing Video Feed Tracking 4.5 Connect the Dots and Other Outputs 4.6 Logic and Spatial Reasoning
5. Alpha Code 2: The Programmer's Nightmare
6. Is Google's Presentation Just a Marketing Trick?
7. Understanding the Different Versions of Gemini
8. Benchmark Results: Gemini Pro vs. Gemini Ultra
9. Surpassing Human Experts: Multitask Language Understanding
10. Gemini's Performance on Hella Swag Benchmark
11. Training the Beast: Version 5 Tensor Processing Units
12. The Massive Scale of Gemini Ultra
13. The Training Data Set: Filtering and Reinforcement Learning
14. Disappointments and Delays: Availability of Nano, Pro, and Ultra Models
15. Conclusion

The Great AI War of 2023

In the battle for supremacy in the world of artificial intelligence, Google suffered a devastating blow at the hands of Microsoft's blitzk attack. The year 2023 saw the emergence of GPT-4, a revolutionary AI model that captured the spirit of the age we had just entered. Soon enough, the tides turned so drastically that people began unironically using Bing as their preferred search engine. However, the war was far from over, as Google had a formidable weapon in its arsenal: the highly anticipated Gemini model.

Google's Gemini Model

Gemini, which was first introduced to the public at google.io, is a multimodal large language model designed to surpass its predecessor, GPT-4. Unlike previous models, Gemini is not only trained on text but also on sound, images, and video. Its capabilities are nothing short of mind-blowing.

Multimodal Capabilities

Gemini's ability to comprehend and respond in real-time to video feeds is surreal. For example, it can accurately identify objects in a video, such as recognizing a duck when someone draws it. Even as the cups in a game of "find the ball" are shuffled, Gemini can effortlessly track the ball's position. Moreover, Gemini excels at connect the dots, rendering a 5-year-old's ability obsolete. It can generate images on the fly, Compose music Based on a prompt, and even convert images to audio.

Logic and Spatial Reasoning

Gemini also boasts impressive logic and spatial reasoning skills. By analyzing the aerodynamics of different vehicles, it can predict which car will go faster. This has significant implications for civil engineers who can now take a picture of land and have Gemini Instantly generate blueprints for a bridge. It's not just software engineers who stand to be impacted by Gemini; other types of engineers may also face obsolescence.

Alpha Code 2: The Programmer's Nightmare

In another blow to programmers, Google unveiled Alpha code 2, a model that outperforms 90% of competitive programmers, even on highly complex abstract problems. With techniques like dynamic programming, Alpha code 2 efficiently breaks down problems into smaller components. While these demonstrations are undoubtedly impressive, skeptics wonder if Google's presentation is merely a marketing ploy.

Understanding the Different Versions of Gemini

Gemini comes in three sizes: Tall, Grande, and Ventti. The smallest version is designed to be embedded on devices, while the Pro version serves as a general-purpose model. The Ultra version, however, is the crown jewel of the Gemini family, blowing everyone's minds. Although it is not yet available due to ongoing safety testing and the need to meet Hell Woke Benchmark standards, Gemini Pro is already accessible in the United States through The Bard chatbot.

Benchmark Results: Gemini Pro vs. Gemini Ultra

In most situations, Gemini Pro falls short of GPT-4's performance in benchmark tests. However, Gemini Ultra outperforms GPT-4 in nearly every category. It is the first model ever to surpass human experts in massive multitask language understanding. On the downside, Gemini Ultra struggles with the Hell Swag Benchmark, which evaluates common Sense natural language.

Training the Beast: Version 5 Tensor Processing Units

To train Gemini, Google harnesses the power of its newly unveiled version 5 tensor processing units (TPUs). These TPUs are deployed in super PODS, each consisting of 4,096 chips. With a dedicated optical switch and the ability to reconfigure into 3D topologies, these PODS enable Parallel training. Such is the scale of Gemini Ultra that communication between multiple data centers was necessary.

The Massive Scale of Gemini Ultra

The sheer size of Gemini Ultra necessitates communication between multiple data centers. To reduce latency, the super PODS can Shape-shift into donut-like formations. This architectural feat allows for faster data transfer and training.

The Training Data Set: Filtering and Reinforcement Learning

Gemini's training data set includes vast amounts of information crawled from the internet, ranging from web pages and YouTube videos to scientific papers and books. After filtering for quality, Google fine-tuned Gemini using reinforcement learning, incorporating human feedback. This process ensured that the model avoided hallucinations and improved overall quality.

Disappointments and Delays: Availability of Nano, Pro, and Ultra Models

While Gemini Nano and Pro models are set to be available on Google Cloud from December 13th, Gemini Ultra Pro Max will only be released next year after completing additional safety tests and achieving a perfect score on the Hell Woke Benchmark.

Conclusion

Google's Gemini model signifies a significant advancement in AI capabilities, particularly in the realm of multimodal language understanding. Although it faces challenges and delays, Gemini has the potential to transform various industries. However, only time will reveal its true impact.