It's here... GEMINI!

Alright, so here's the big news. Google has finally released Gemini, the AI model everyone's been waiting for since GPT4 came out, and it was a total surprise. In this video we'll look at the technical aspects, what Gemini is capable of, and how it stacks up against GPT4. So let's get into it. Alright, so Gemini is Google's new AI, a super smart tool that understands text, images, sounds, videos and more all at once. Launched on December 6, 2023, it's part of Google's big push into AI, making it a key feature in their products. There are three versions of Nano for personal use, Pro for professional work and Ultra for advanced research. These run on Google's special tensor processing units that make AI tasks efficient and cost effective. Gemini is challenging OpenAI's GPT4, boasting higher efficiency and versatility by outperforming it in multiple areas. This AI model stands out because it can work with different types of data like text, images, sounds, videos and code all at the same time. This makes it super versatile in solving complex tasks. Now Gemini Ultra is the most powerful version of Gemini and it is designed for training and fine tuning large and complex deep learning models that feature many matrix calculations such as building large language models. It has achieved human expert level performance on the MMLU Exam Benchmark, which is a test that covers 57 tasks including elementary mathematics, US history, computer science, law and more. Gemini Ultra scored 86.5% on average while GPT4 scored 70%. Gemini Ultra also excels at multimodal reasoning tasks such as answering questions based on images, videos or graphs, or generating summaries or reviews based on multimodal inputs. Gemini Pro and Nano are the smaller and cheaper versions of Gemini and they are designed for specific applications and use cases. Gemini Pro is ideal for a variety of use cases such as chatbots, code generation, media content generation, synthetic speech, vision services, recommendation engines, personalization models, among others. Gemini Nano is ideal for personal and small scale use such as education, entertainment, gaming, hobby and social media. Both of these smaller models can leverage the pre trained models from Gemini Ultra or fine tune them for their own purposes. Now let's see how Gemini performs compared to GPT4 on different benchmarks and tasks. One of the most widely used and comprehensive benchmarks for natural language understanding is Superglue, which stands for General Language Understanding Evaluation. It's a tough test that checks how well an AI can understand language by making it do things like reading and answering questions. Gemini Ultra got a score of 92.3 here, beating GPT4's 89.8. This means Gemini did better at reading and understanding stuff in six out of eight tests. Then there's MM Fusion, which is about how good the AI is at handling different types of data like text, pictures and videos. Gemini Ultra scored 81.7 here, which is better than GPT4's 76.4. This shows Gemini is really good at working with a mix of information, like answering questions about a picture or a video. We also have AlphaCode 2, a coding challenge. It's about writing, fixing and improving computer code. Out of 100 coding tasks, Gemini Ultra scored 94.6 higher than GPT4's 88.2. It actually outperformed GPT4 on 82 out of 100 coding tasks and tied with GPT4 on the remaining 18. Gemini showed a significant advantage over GPT4 in tasks that involve writing and running code in Python, Java and C, as well as in tasks that involve using advanced programming concepts such as recursion loops, functions and classes. It also showed a slight edge over GPT4 in tasks that involve writing and running code in HTML, CSS and JavaScript, as well as in tasks that involve using basic programming concepts such as variables, operators and conditionals. Now when we talk about integration of the model, it is actually designed to make Google's products like Google Search, Google Workspace and Google Bard better. Like it helps Google Search give better answers and summaries, and it makes Google Workspace tools like Help Me Write and Smart Canvas more productive and creative. Also, Gemini is useful for Google Cloud, offering advanced AI features for things like recognizing speech and language or writing code. It's making Google Bard more engaging by giving more natural responses and working with different languages. For Google's devices like Pixel and Nest, Gemini adds features like voice control and recognizing faces and objects. It's the same for Google Ads, Google's tool for online advertising. Right now it uses AI to make and improve ads based on what the advertiser wants. But Gemini is going to take this to the next level by adding things like audio and video to the ads. Alright now Gemini is built on a transformer model, which is a type of neural network that's really good at understanding relationships between words and sentences. For training, it uses self supervised learning, which means it learns from large amounts of data without needing humans to label it. This type of learning is great because it can use the data itself to get better, and it complements supervised learning where data is labeled and more specific. Gemini's training uses big data sets like conceptual captions audioset, YouTube 8M and GitHub and it has different goals like understanding masked language or images, aligning different types of data, or generating one type of data from another. It actually uses various techniques to make the model smaller and faster without losing quality. This includes quantization, which reduces the precision of numbers in the model, and pruning, which gets rid of parts that aren't really needed. There's also distillation, where a big complex model teaches a smaller, simpler one, and the sparsification, which makes the model less dense. Google's Tensor processing units, or TPUs also help make Gemini more efficient. These are specialized AI accelerators designed to be faster and more energy efficient than general purpose processors. Google's massive infrastructure, including data centers and networks also plays a big part in supporting Gemini. Now, since this new AI model is really powerful, it comes with big responsibilities. It can impact society and the environment in huge ways, and there's a chance it could cause problems like bias, misinformation and privacy issues. So it's really important that Gemini is used in a way that's good for everyone. Google makes sure of this by using their Responsible AI framework. This framework guides them in making Gemini fair, private, secure, safe, accountable, environmentally friendly and beneficial for society. For fairness, Google works to ensure Gemini treats everyone equally and doesn't discriminate, especially against those who are already disadvantaged. When it comes to privacy, Google protects personal information using techniques like encryption. They also focus on making Geminis secure against cyberattacks, Gemini and ensuring it doesn't harm people physically or mentally. Accountability's a big deal too. Google wants to be clear about how Gemini works and be responsible for its outcomes. They do things like audits and reports to stay transparent and for the planet. They try to minimize Gemini's environmental impact, making it as eco friendly as possible. Lastly, they want Gemini to contribute to society positively, like supporting health and education. So there you have it. Gemini is a pretty big deal in the AI world, and it looks like it's doing better than GPT4 on paper and in tests. But what do you think? Will Gemini be better than GPT4 in real life too? Drop your thoughts in the comments, and if you're into AI and tech stuff, don't forget to hit the like and subscribe button. Thanks for tuning in and I'll catch you in the next video.

Sam.