The AI wars have officially begun. Google has revealed its plan to dominate by announcing its Google Gemini AI project. According to Google CEO – Gemini AI was created from the ground up to be a Multimodal, highly efficient tool with API integrations built to enable future innovations like memory and planning.
Google aspires to create an AI that is unparalleled in processing. Gemini AI seeks to be an active collaborator and communicator. Generative Enhanced Multimodal Intelligent Network Interface. Yeah, I know that is a mouthful. Hopefully, this article can shed some light on what this means and help you, the reader, understand Google’s intention.
In essence, Gemini can interact with humans and other systems as if they were human, so whether you want to chat, seek assistance, or learn something new, Gemini is there to help you.
Gemini is a revolutionary AI model that DeepMind, the mastermind behind the pioneering Alpha Go, honed at Google.
For context, Google DeepMind is a versatile AI model primarily focusing on text prepossessing. However, It can handle data types like images, audio, and video. It learns from its experiences and the web and uses different data sources to enhance its capability.
Furthermore, Gemini is built on the foundations of reinforcement learning. If it makes mistakes, it learns not to repeat them; if it does well, it reinforces that behavior. It makes text processing more independent and adaptable, making it more creative and communicative in generating and understanding the text.
Gemini having these attributes as a foundation could mean that Gemini becomes one of the most potent generative AI tools yet.
Gemini promises to handle multiple types of data and tasks all at once. We’re talking images, video, audio text and even 3D models and graphs. The standout attribute that Gemini has is that it is not just one model but an entire network of models collaborating to generate outputs from prompts and inputs and multimodal inputs.
Let’s use the analogy simple of a battery. Chat GPT 3.5 is comparable to one battery that can be used for productivity and creativity. Now, hold that thought. Gemni AI is comparable to several batteries working together to give you an output from your input, and this output could be provided in a variety of formats of your preference.
Google has stated that Gemini AI is built to be more reliable, precise, original and engaging than its counterpart LLMs(large Language Models). Furthermore, Gemini uses experience to improve itself, gives reasons for its decisions and creates diverse content.
How Gemini Works
Here goes my attempt to explain the techie part. So, Gemini has a unique attribute that uses two primary components: a multimodal encoder and a multimodal decoder. The encoder’s job is to convert different data types into a common language that the decoder can understand.
Once the encoder finishes its job, the decoder takes over. It generates outputs in multiple modalities based on the inputs and tasks.
Gemini AI Compared to Other LLMs
What sets Gemini apart from other language models like GPT-4 is its ability to process new and unseen scenarios faster and more efficiently. It uses fewer computation resources and memory when compared to other mode LLM’s.
It uses a distributed training strategy, meaning it can make the most out of multiple devices and servers to speed up its learning process. Gemini can also learn from any domain or dataset without encountering any constraints of predefined categories.
What’s even more impressive is Gemini AI’s ability to scale to include large datasets and models without compromising quality.
As of May 2023, Gemini was still in training mode; at that point, it was already exhibiting multimodal capabilities never seen before in the models.
How important is Gemini to Google’s evolution:
The importance of the project to Google’s future is evident for most to see. For those who may not be aware, the insider scoop is that the Gemini AI project is so crucial to Google’s future that it has brought Google co-founder Sergi Brin out of semi-retirement.
At the time of writing this article, Google insiders revealed that Brin spends approximately four days a week working on the Gemini AI project alongside researchers while also using his clout to recruit the best AI research talent that the world has to offer.
In his letter to shareholders in 2018, yes, 2018. Brin mentioned how he believed that AI was the most significant computational breakthrough in his lifetime. This clearly shows that Brin has a personal interest in artificial intelligence. He is an AI crusader forging through obstacles to push the AI initiative and ensure that AI prevails.
Google Gemini Size and complexity
One of the things people look at to measure a large language model is its parameter count. In the simplest way to explain this, parameters are numerical variables that serve as the acquired knowledge breadth of the model. This Knowledge breadth enables the model to generate output and respond to inputs.
The more parameters there are, the more potential for learning and generating a more comprehensive range of accurate outputs. However, the more parameters a language model has, the more computational resources and memory it requires.
In comparing language models, GPT 3.5 has 175 Billion Parameters, and GPT4 has 1 trillion parameters. Google has said that Gemini AI comes in 4 sizes, Gecko Otter, Bison, and Unicorn, but it has yet to give the exact parameter count for each size.
Key Features of Gemini AI Features
Multimodal question answering,
This is when you ask a question involving multiple types of data, and the menu can combine its skills of combining text—and visuals to provide you with an answer.
Imagine having information containing different types of data like text and audio. You could summarise a video or podcast by generating a video Summary or an audio summary. Yep, Gemin can do this.
This is where information consists of multiple data types, like text and video. For example, let’s say we have a video course or a health and safety video for which you want to generate subtitles. Gemini can do this by combining its skills in textual and visual translation.
Multimodal generation is when you want to generate multiple types of data: text, video, audio and images. Why not. Again, Gemini can do this by combining its multimodal AI capabilities.
This uses information from multiple data types and tasks to make assumptions and draw conclusions. Let’s say you showed Gemini AI the Avengers Movie. You could ask Gemini to give you an interpretation of the movie and the significance of one of the characters in the movie. Gemini does those by synthesizing information from multiple modalities.
It ultimately gives you an interpretation of what the movie is really about. Watch out, film critics; AI may also threaten your jobs. Who would have seen that coming?
Gemini AI can do these things and a lot more, which would make this article longer than it needs to be. The critical point is that Google’s Gemini AI is powerful. It will change how we interact with AI, and its multimodal features will redefine our relationship with technology.
The Future of AI
I’m keen to see applications and solutions that use Gemini’s capabilities. I can’t fully comprehend what will be possible when Google makes the API accessible to the masses. However, here is something to think about: imagine having your own AI assistant, like Jarvis from Iron Man, respond to video, text, or audio questions. Then, imagine what that would look like if merged with augmented reality.
The name Google is ingrained into the minds of every digital citizen. It is comparable to brands like Coke-Cola and its associations with a brown sugary carbonated drink, and Hoover, its associations with the vacuum cleaner.
If Google does not keep up in the AI race, the digital behemoth could be dethroned from its lofty perch as ruler of the search engine kingdom. This idea is unfathomable and is a thought that Google will not permit to fester. That’s why the Gemini AI project is so important to Google.
Google should be concerned about the threat of GPT-5, which is coming, as is GPT-6. And these are just some of the front-running models that are challenging for Google’s throne.
Gemini AI is Google’s attempt to stay in the race and decimate its competition. Suppose Google’s Gemini AI delivers on its promise. In that case, we may see Goole spearheading the AI race for a long time.