Introduction to Pre-Generative AI Era and VAEs (Variational Autoencoders)
Before the era of advanced generative AI models, life heavily depended on Google for information and assistance. Think of a time when you needed answers, and your first instinct was to type a query into the Google search bar. Back then, finding specific and nuanced information required sifting through search results, often clicking multiple links to piece together the details you were looking for.
It was like navigating a vast library with Google as your librarian, assisting you in locating the right shelves but leaving the task of interpreting and understanding the content largely to you. The dependency on Google was evident in everyday tasks, from seeking knowledge on diverse topics to solving problems and even generating ideas. However, the process was more manual, and the results were limited to the information available on the web at that moment.
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Over the past decade, artificial intelligence has made progress, with Generative AI standing out as a revolutionary force in shaping human language. The revolution in AI began with Variational Autoencoders (VAEs), introduced in 2013, which laid the foundation for deep generative modeling under encoder-decoder models, which encode unlabeled data, compress it, and then bring it back to its original form by decoding it.
Transformation with Transformers Language
Let us understand that language transformers come in three main types: models that focus only on encoding information (encoder-only models), solely decode information(decoder-only models), and models that do both encoding and decoding(encoder-decoder models.)
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In 2013, VAEs (Variational Autoencoders) opened the door to advanced generative modeling. This technology enables the creation of lifelike images and speech by learning from data without labels.
In 2017, transformers changed the game for language models by mixing the encoder-decoder setup with attention mechanisms. They process entire sentences at once, enabling parallel processing and efficient training. Transformers, known as foundation models, can be pre-trained on massive amounts of raw text, and then fine-tuned for specific tasks with minimal labeled data.
Encoder-only models like BERT surpass in tasks like search engines and customer-service chatbots.
In 2020, a big change happened in AI with the introduction of decoder-only models like GPT (Generative Pre-trained Transformer). GPT-3, in particular, with a massive 175 billion parameters, doesn’t have to encode information before figuring out the next word.
They’re really good at understanding context and can generate human-like language. GPT-3, released by OpenAI, is a game-changer because of its huge size and its ability to predict and create text without extra steps. It got a lot of attention and is being used in various ways across different fields.
Currently, the AI model is witnessing groundbreaking initiatives.
AI Initiatives in India
Reliance Jio, led by Akash Ambani, is introducing the “Bharat GPT” program in collaboration with IIT-Bombay. This initiative, part of Jio’s vision for the future called “Jio 2.0,” focuses on using advanced AI technologies to bring positive transformations to various sectors in India. Akash Ambani highlights the impact of AI in the coming decade, foreseeing its applications across different industries.
Another bid advancement is that Elon Musk’s xAI’s Grok AI is now accessible in India through X (formerly Twitter) for Premium+ subscribers. Priced at ₹2,299 per month or ₹22,900 annually on smartphones, it’s slightly more expensive than ChatGPT Plus. For desktop users, the cost is ₹1,300 monthly or ₹13,600 yearly.
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In early access and exclusive to the top-tier X subscription, Grok AI operates in two modes—fun and regular. The Premium+ subscription offers various perks, including an ad-free experience, blue check verification, monetization options, longer posts, high-res videos, post-editing, background video playback, and more. Grok AI stands out for its real-time data access from X, providing accurate responses to recent trends.
In the rapidly advancing field of language models, major players like Google, OpenAI, and Meta Llama (formerly Facebook) are engaged in intense competition. Google has recently unveiled Google Gemini, featuring three distinct models: Ultra, Pro, and Nano.
Google’s Gemini vs. OpenAI’s GPT-4
Gemini Ultra model tackles complex tasks with remarkable accuracy, while the Pro model is geared towards powering AI tools. On the other hand, the Nano model is optimized for mobile devices. Google Gemini Pro and Nano models are available for free use, and you can test the capabilities of the Gemini Pro model through Google Bard, assessing its advanced reasoning, mathematical prowess, and language understanding skills.
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However, the Ultra model is not yet publicly accessible, and information on its pricing is currently unavailable, pending safety checks. Meanwhile, OpenAI, renowned for its GPT series, has been making waves with the latest iterations, CHATGPT-3.5 and CHATGPT-4.
Let’s explore the features, abilities, and possible effects of AI models. We’ll compare Google’s Gemini with OpenAI’s GPT-4.
- Performance in Math and Reasoning
Let’s talk about Google’s Gemini model, which is doing exceptionally well in 30 out of 32 academic benchmarks used for large language models. These benchmarks help us see how good Gemini Ultra is at things like math and reasoning compared to other models like GPT-4. In tests like Big-Bench Hard, GSM8K, and MATH, Gemini Ultra outperforms GPT-4. The only exception is HellaSwag, which checks for common sense reasoning.
- Multimodal Performance
The concept of multimodality involves the ability of language models to handle various types of data, including text, code, visuals, audio, and video. It’s like having a multipurpose tool to analyze and process different forms of information.
When we focus on their performance in image, video, and audio tasks, we observe that the Gemini Ultra model consistently outshines GPT-4, boasting higher scores by up to 10%. This implies that when it comes to handling multimodal tasks, Gemini Ultra takes the lead, showcasing advanced capabilities and success compared to GPT-4.
- Coding Prowess
Google Gemini emerges as a game-changer, effortlessly tackling complicated coding with advanced reasoning and logic. What sets it apart is its ability to effortlessly integrate into advanced coding systems, flawlessly executing complex tasks with an accuracy rate.
Google Gemini Ultra model outshines GPT-4 in key benchmarks like HumanEval and Natural2Code, which are specifically designed to gauge coding performance. This translates to Google Gemini being a more adept player in coding and mathematics.
- Accessibility Comparison
GPT-4 and Gemini Nano and Pro models are out for people to use. However, the Gemini Ultra isn’t available to everyone just yet because they’re still working on making sure it’s really secure and reliable. So, at the moment, GPT-4 is easier to get your hands on compared to the Gemini Ultra.
Final Words
Whether prioritizing academic benchmarks, multimodal capabilities, coding proficiency, or accessibility, both models have distinct advantages. As advancements continue, users can expect further refinements and innovations from these major players in the field of language models.
