Write, Plan, Learn, Decide and Do Much More with Gemini AI by Google

If you can remember, the AI war has witnessed severe domination by Open AI's ChatGPT, which smartly turned the table of attention towards it from Google's BARD.

Although previously Google wasn’t in the picture, the intended toe-to-toe competition of it just boomed the AI world and took center stage on December 6th, 2023 while announcing the latest AI model Gemini.

The motto, “The chance to make AI helpful for everyone, everywhere in the world”, crafted by the CEO Sundar Pichai, is the secret code of developing humanized AI Gemini. 

The rational purpose of building Gemini AI is that it can support both: Deep learning in generalist modality and understanding reasoning complexity. It means Gemini AI's knowledge, skill, and aptitude capability can serve every other domain as a whole.

I know, now you’re more excited to know the principle of Gemini, which has already created the buzz and demonstrated the revolutionary wave in the AI era. So, let me take you through a detailed guide of Google’s AI weapon Gemini while covering everything from architecture to surprising facts about it.

Google Gemini: An AI to build & blow AI

In Sundar Pichai’s words, “Gemini 1.0 is our most capable & general AI model yet. Built natively to be multimodal”.

Alphabet-owned techies giant Google trained Gemini’s infrastructure with the most advanced LLM (Large Language Model) and MMLU (Massive Multi-Language Understanding). 

For this reason, Gemini was extensively tested for four different capabilities: High-level object recognition, fine-grained transcription, chart understanding, and multimodal reasoning. 

So, Gemini’s maximum potential can cover everything including a wide range of multilingual communication, complex coding and reasoning, text completion, summarizing, and extracting info from images, audio, and video. Additionally, it can utterly imitate human behavior to upgrade and update itself according to the latest trends.

A great example of AI getting smarter with trends is nowadays, I've seen tattoo designers who create designs using AI-generated tattoos, and people like those tattoos.

The advanced triad architecture model of Gemini AI

Google’s Gemini primarily covers three different sizes: Ultra, Pro, and Nano, which have segmented purposes of addressing computational limitations alongside application requirements.

👉 Gemini Ultra: A most capable specialized model for high-complexity tasks.

👉 Gemini Pro: An optimized model covering performance, latency, and costs for diverse functions.

👉 Gemini Nano: An efficient 4-bit quantized model for on-device deployment.

Gemini AI: A deep & factual understanding that you can’t miss

“Every technology shift is an opportunity to advance scientific discovery, accelerate human progress, and improve lives”, said Sundar Pichai.

Is this similarly applicable to Gemini AI?

To have the answer, let’s have a walkthrough of astonishing facts about Gemini from here 👇

Gemini can smartly set academic benchmarks 

Is it true that Gemini AI can surpass the talent of text-based academic benchmarks?

Yeah! Gemini’s excellence extends to complex reasoning, reading comprehension, STEM (Science, Technology, Engineering, and Mathematics), and sophisticated coding. 

The surprising fact in this case is Gemini Ultra exceeded the accuracy of 90.04% while working really well in understanding 57 subjects with Chain-of-Thought (CoT) prompting. 

In layman language, Chain-of-Thought prompting works in a series of natural language processing which follows your input prompts step-by-step to lead to the final solution.

Well, there is more. Gemini can understand the data of your handwritten text and correct you if there’s anything wrong on your side. It focuses on the problem, analyzes it, and provides the depth of concept behind any scientific or mathematical formulas while harnessing its analytical capabilities.

Now, if I talk about Gemini AI’s forte in coding demonstration, then you need to see the video. In this, it’s explained how Gemini can set the bar of 75% of 200 benchmarking programs in decoding Python, and up to 45% on the PALM 2. Also, it can decode and generate high-quality codes in other programming languages as well such as JAVA, C++, and Go.

Let’s say, you’re in a situation of recovering and adjusting code, Gemini AI can solve your tasks with 90% accuracy while rechecking and repairing data. This simply means that you can run your codes smoother and in an error-free mode.

Gemini can optimize & generate images

When it comes to simultaneously working on a diverse range, Gemini sets a benchmark. It can impressively outperform in answering questions based on natural images and scanned documents, as well as understanding infographics, simple to complex charts, and scientific diagrams.

However, when another AI is solely dependent on Natural Language Processing to do the tasks, Gemini surprisingly doesn’t need an immediate reliance on natural language description to crack the nut of image interpretation. 

Additionally, the bottleneck of dependency on NLP can’t control Gemini’s image generation capability or competency in finding similarities or differences between two different images.

The Gemini Ultra version surpasses the pre-existing zero-shot concept for text-to-image generation similar to Merlin AI even for OCR (Optical Character Recognition) related image comprehension that is extensively used in digital images.

This altogether means that Gemini simply uses the image interwoven and text-sequencing method in a few-shot setting to arrange the data in an isolated manner while maintaining accuracy.

For example, as you can see in the image mentioned above, I requested Gemini to generate two different images: one of a cat and one of a dog, based on yarn ideas using blue and yellow colors. After receiving the prompt, Gemini, leveraging its capabilities in AI graphic design and understanding the intent of the request, suggested some images.

In another example, I asked Gemini to 'give me two ideas that I could do with these two colors—pink and green,' and it then created a green avocado from pink seeds and a cute green bunny with pink ears using the provided yarn.

Gemini can understand video sequence

Gemini’s prowess in understanding video frame by frame is exponentially evaluated across multiple standard benchmarks to identify whether it can understand a video series with framing sequences. 

To identify Gemini’s capability, it was fed a sample of 16 frames per video clip. Additionally, Gemini was given a video which is still available on YouTube to test whether Gemini can understand the information without prior training.

Guess what? In both cases, Gemini stood out on the expectations as it impressively did well in the few-shot video captioning as well as the zero-shot video question-answering task. 

It simply means that Gemini can classify text, analyze the sentiment in videos, and understand the language in it with limits or without prior hints.

To illustrate the video understanding capability of Gemini, the below-mentioned fact of the soccer-striking mechanism is given, by which a soccer player can improve his game. Here Gemini prompted well to provide step-by-step guidance while demonstrating its advanced adept in video apprehension.

Gemini can analyze audio data

Does Gemini handle the audio analytics to distinguish between vocals and spoken languages?

The answer is yes. While Gemini Nano-1 and Gemini Pro proved their competency in audio understanding as well when their forte was tested for ASR and AST mechanism, Gemini Ultra is yet to be evaluated. Additionally, Gemini's models were compared with USM, Whisper, and FLUERS to check whether it can beat another AI’s performance by meeting global standards.

Now, let me explain all the aforementioned technical terms to keep things simple for you.

ASR, the acronym for Automated Speed Recognition, is a technology used in transforming audio script into written text and making it accessible. AST stands for Automatic Speaker Tagging which focuses on identifying and segmenting speakers in an audio stream to structure the audio content. Whereas Universal Speech Model (USM), Whisper, and FLUERS are the benchmarks to test the ASR and AST proficiency.

Let’s suppose that if you give Gemini an audio script that is quite complicated for you to understand language or information within it, Gemini can transcribe any universal language into English without breaking contextual sequences. It can extract insight, understand human emotion, categorize vocals, and analyze audio data to give you the exact solution.

Gemini’s training for skillful technical capabilities

Taking Google into consideration, I must say Gemini AI’s training model is comparatively high as it uses TPUs (Tensor Processing Units), TPUv5e, and TPUv4. In this, Gemini Ultra is accelerating its fleet with TPUv4 to seamlessly work with multiple data centers. 

The new narrative of Google’s Gemini AI surpassed the pre-flagship model of Palm2 to address SDC (Silent Data Corruption) challenges to eliminate data redundancy at scale. 

Now, if you ask me how this can be helpful, let me tell you that the TPU model allows Gemini AI to run faster without compromising its workflow quality. It saves your time while maintaining accuracy, especially in large and complex data.

Additionally, the training with TPU’s skillful techniques of Gemini AI will serve you both a multimodal as well as multilingual functionalities in web documents, codes, books, and media data. As it uses SentencePiece Tokenizer, it can efficiently serve you in simple though high-quality vocabulary performance.

The real-time applications of Gemini AI

When other AIs are extensively trained to specialize in specific areas, for example, AI-driven social media tools can assist in creating engaging captions and content to captivate your audience. Similarly, AI writing tools offer capabilities such as storytelling, editing, phrase correction, language translation, summarization, and extensive research. Gemini AI has already started the ball rolling in multiple applications. Curious to know how? Let’s have a read here 👇

Gemini API + Google Cloud

Sundar Pichai Claimed, “Gemini would be highly efficient with tools and API integrations.”

Google’s Gemini AI is about setting a footprint in exceptional functionalities for developers especially when it collaborates with Vertex AI, the product of Google Cloud. Vertex AI will provide you with a low code deployment approach to developing impactful ML (Machine Learning) alongside strong control over privacy, responsibility, and safety. 

Additionally, Duet AI by Google Cloud and Gemini API can provide you with smart actions such as test generation and code explanation. Also, when it comes to cloud logging, log summarization, log queries for troubleshooting, and resolution for cybersecurity threats, the combo pack of this duo can save you in all these situations.

Gemini Pro + Google BARD Chatbot

You may be aware that Google’s experimental conversational AI chatbot Bard launched last year. It was previously powered by LaMDA (Language Model of Dialogue Applications) which can instantly reply to you with a personalized touch. Although it came to the market last year, it didn’t succeed in beating the competitive ChatGPT. 

However, fortune never goes away if there is consistency and that is proved by Google when it integrated BARD’s capabilities with the latest Gemini Pro model. Now, their combination has already beat GPT 3.5 while passing 6 out of 8 different industry benchmarks including MMLU (Massive Multitask Language understanding) and GSM8K. 

In simple language, these benchmarks are tested to prove whether the combination of Gemini Pro and BARD works smoothly in academic standards. Well, they together combine extensive knowledge sharing to provide you with information through prompt responses, simplifying the complexity of math and reasoning step by step, and engaging you in a worthwhile conversation.

Although it's now limited in geographical location as well as frequent uses, it will soon be accessible in territories worldwide in over 180 countries and with 38 global languages.

Gemini Nano + Pixel 8 Pro

Gemini Nano is launched in two different versions for on-device deployment, where Gemini Nano 1’s model size is 1.8B and Gemini 2’s model with 3.25 parameters respectively targeting low and high memories. Irrespective of Gemini Nano’s size, it brings forth flourishing features, likely, Summarize in Recorder and Smart Reply in Gboard.

Now, let me simplify for you how they work. Summarize in Recorder contains detailed insight from your recorded content such as interviews, meetings, presentations, and transcripts. Additionally, Smart Rely in Gboard saves you from burning crucial seconds while auto-suggesting high-quality responses with conversational awareness.

The other most important fact about this duo’s combination is that you don’t need to be concerned more about shaky or bloomy pictures and videos. As Nano’s deployment in Pixel 8 pro will provide you with computational photography, you will get crystal clear stunning pictures with adjusted colors, blur, light, shade, and stabilization. 

Also, it will provide you with noise cancellation to enhance audio-video quality and you can record any video in low light with high resolution.

Gemini’s ethical consideration for safety & quality assurance

I know, now your brain-picking thought is saying to you, despite being exceptionally proficient in every sphere, whether Gemini AI would ensure safety assurance to turn a new leaf in the AI era.

The reason is that the higher quality in reasoning and accuracy can become meaningless if you need to compromise with the appropriate privacy checking. Am I right?

Then let me tell you, Google claims a strong emphasis on safety control as Gemini is followed by Google DeepMind’s Responsibility and Safety Council (RSC) which covers ultimate key policies. Alongside, Gemini Ultra has undergone thorough trust and safety evaluation while it is refined with RLHF (Reinforcement Learning From Human Feedback).

How will it be helpful? While the multimodal AI can pose specific risks, Google said that it can mitigate the risk probabilities with Gemini AI in many places. The areas include accurate factual concern, child abuse, harmful virtual content, biorisk, and many more.

In simpler words, Gemini AI can evaluate multiple possibilities in one go to save you from virtual fears while maintaining alignment with Google’s AI principles.

The concern with Google Gemini AI’s hallucination

Is there any possibility of suffering from hallucinations by Gemini AI?

Umm, the answer can’t be straightforward with either yes or no. 

Why? As Google doesn’t give prompt replies, it’s taken factual consideration to reduce the frequency of hallucinations. Well, for the same reason, Google focused on instruction tuning efforts which cover mainly three aspects: Attribution, Closed-book response generation, and Hedging.

Now, let me give you a short glimpse of AI hallucinations. If you ask AI to provide prompt response in an area where it has insufficient data or hasn’t trained to provide subject matter expertise, then it will definitely mislead you with biased, incorrect, or incomplete information.

Imagine a scenario, where Gemini AI is trained with a dataset of medical images, especially in cancer. Now, you’re a cancer specialist who asks Gemini to provide you with curable solutions based on some cancer-scanned images. If those images are not pre-loaded with Gemini, it will simply predict faulty recognition while altering healthy tissues with cancerous ones.

Luckily, this is just a hypothetical situation to make you understand the impact of AI hallucinations which Google took into forbearance in the case of Gemini AI to avoid malfunctioning probability.

We’re in the Gemini era: The future of AI envision

In Sundar Pichai’s words, “The pace of innovation is extraordinary to see.”

This can be true in every inch, as Gemini already beat human intelligence while imitating human preferences. Not only that but it also can accurately forecast data with interleaved images and text, audio, infographics, charts, codes, and so much more.

Additionally, Gemini AI’s promising programs to automate and enhance cloud-edge operations will be beneficial in every domain from academics to business, healthcare to media, or any other key areas.

It’s 2024, and it is just the start of evaluation in the AI world by Google’s Gemini AI, there’s much more to see what will happen next in the upcoming days.

Well, for that, don’t forget to keep up with Unrola where every other AI tool and groundbreaking news with them will be covered. Just don’t miss it!