Tech Surprises

“The Magic of Generative AI” is still my favorite talk I’ve ever given, hands down. I loved collaborating with Google’s top AI minds on the story and the visuals, building demos that showed how Vertex AI helps marketers like me, and connecting with fellow AI enthusiasts in awesome places like LA and Rome.

But the best part was diving deep into how large language models (LLMs) actually work, reading those mind-bending research papers, and piecing together the “magic” they create. Preparing this talk was like living Google’s innovation mantra: stay curious, experiment, build something useful.

In this newsletter, I’m sharing my reflections on the magic of Gen AI and how Google’s unique innovation culture was key to making these incredible tools a reality.

Innovation💡 = Curiosity🧐 + Experimentation🧪 + Application 🚀

Curiosity, experimentation, and application: This is the heart of how Google is driving the generative AI revolution. It’s the same formula behind some of our biggest breakthroughs, like Google Search, Translate, and Vertex AI.

Here’s how it works:

• Curiosity: This is where it all starts – that burning question of “what if?” or “why not?” Curiosity is what drives us to explore the unknown and challenge the status quo.
• Experimentation: Curiosity without action is just daydreaming. Experimentation is where we get our hands dirty, trying new things, making mistakes, and learning from them. It’s the messy but essential part of the process.
• Application: The ultimate goal of innovation is to create something that makes a real difference in the world. Application takes those wild ideas and experiments and turns them into practical solutions that people can use and benefit from.

This isn’t just a theory; it’s the blueprint behind Google’s most groundbreaking AI tools.

Embeddings in Google Search: Grasp query intent beyond exact keywords

In 2013, Google researchers authored the seminal paper “Efficient Estimation of Word Representations in Vector Space“. This paper unveiled a revolutionary method for creating Word Embeddings, mathematical representations of words capturing both their meaning (semantics) and relationships (semantic similarity). Here’s the Google’s innovation formula in action:

• Curiosity: Dissatisfied with existing word organizational methods, such as dictionaries ordering words by lexicographical order, researchers were curious if a better approach could capture word semantics and organize them by semantic meaning.
• Experimentation: They explored various neural network types, training objectives and relationship representations. Through experimentation, they discovered how to automatically create a word embedding. A name to be remembered, an embedding is a mathematical representation for each word that captures their semantic meaning in the form of a vector of 768 numbers.
• Application: Way before it was applied in gen AI, word embeddings found a magical application in semantic search, enabling Google Search 🔍 to grasp query intent beyond exact keywords. For example, a search for “cars that are good on gas” now returns results for fuel-efficient cars, even if the word “gas” doesn’t appear in the options returned.

Transformer in Google Translate: More accurate translations

In 2017, Google researchers presented “Attention is All You Need” introducing the Transformer architecture, built on decision-making and attention-span concepts. It empowers the language models to understand context and relationships within word sequences. Curiosity, experimentation and application were again vital:

• Curiosity: In the search to improve the quality of language translation, researchers sought ways to model relationships among words in a sentence.
• Experimentation: They experimented with various mechanisms, relationship representations and training methods, discovering that much could be extracted by simply paying attention to the relationship between each word and every other word in a sentence. They discovered that these interdependencies could be achieved through parallel computations, which accelerated time to result, and found that representations through embeddings could capture long-range dependencies between words in fluent, grammatically correct text. Voilà! The Transformer architecture was born, introducing a huge breakthrough in science.
• Application: The transformer revolutionized Google Translate 🌐. The Transformer’s attention mechanisms are excellent at understanding the relationships between words in a sentence, leading to more accurate translations.

Let’s see this in action by translating this sentence from English to Italian: “The cat didn’t cross the street because it was too wide.

Gen AI in Enterprise Search: New way of working

Fast forward to 2023, Google Cloud researchers set to simultaneously tackle two common challenges for many organizations:

• How to organize enterprises information scattered across many internal systems
• How to make this information accessible and useful for enterprises, and seamlessly available and actionable in applications such as customer service bots, document summarizations or as part of steps in automated workflows.

Not surprisingly, Google Cloud researchers followed the proven innovation framework:

• Curiosity: While Google Search was designed to scale to organize the world’s information, researchers started exploring whether the technology could be descaled and made available to enterprises to organize their information in a way that could be easily accessible and useful to them, and only to them.
• Experimentation: Intrigued by the potential to bring together several cutting-edge technologies, researchers used the ability to crawl web-sites to discover content on internal websites and structured content, and Optical Character Recognition (OCR) to discover content from all sorts of semi-structured and unstructured documents, creating a wealth of knowledge about the enterprise. The researchers then used embeddings to extract and organize the semantic meaning of all of this data. Once enterprise’s data has been semantically organized in embeddings, the full power of Generative AI can be applied to it and leveraged across the Vertex AI platform.
• Application: First launched in March 2023, Google Cloud Vertex AI Search 🔍 quickly became “the killer enterprise app”. A killer application, often abbreviated as killer app, is a software application that is so necessary or desirable that it proves the core value of some larger technology, such as its video game console, software platform, or in this case of gen AI in the enterprise context. Killer apps are the pinnacle of innovation: well-designed, easy to use and solving a real problem for users. Enterprise Search is the killer enterprise app as it unlocks unprecedented levels of productivity and efficiency.

These transformative breakthroughs exemplify Google’s dedication to AI innovation, with continued explorations on the horizon.

Ready to experience the magic of Gen AI? Explore Gemini today: https://gemini.google.com/

#GoogleAI #GenerativeAI #Innovation #ArtificialIntelligence #SemanticSearch #NLP #AIInnovation #TransformerArchitecture #ApplicationsOfAI

Tags: ai, artificial-intelligence, machine-learning, nlp, technology