Google AI Products: A Complete List + What They Do
Here’s a complete list of all of the current Google AI products in operation right now, complete with explanations of what they do…
The AI arms race is officially on. OpenAI, Google and Meta are collectively investing tens of billions in what is now being described as Silicon Valley’s next, big gold rush. Even the markets are bullish about AI, following NVIDIA’s insane 18 months’ worth of bonkers growth.
But perhaps the really odd thing about all of this AI stuff, despite how quickly it has been adopted by the public, is that we’re still very much in the early days of what’s possible, capabilities-wise. The AI platforms of the not too distant future, and I mean like in 2024 or 2025, will make ChatGPT 4 look archaic.
Google has been researching and developing AI and machine learning projects and products for years now. In fact, Google’s AI division was pumping out innovations before OpenAI was even a twinkle in Sam Altman’s eye. But what is Google working on right now? What is open to the public and what isn’t? I decided to find out…
Google AI Products – A Complete List For 2023
Google now has a pretty comprehensive suite of AI products, solutions, and services under its belt. From data scientists and developers to businesses seeking to leverage AI’s transformative power, Google’s AI offerings cater to a wide range of use cases.
For instance, testing is now underway on Google’s AI notebook application, codenamed Project Tailwind. This innovative AI application will be trained on your Google Drive data and help you organise your notes and plan for things based on your own specific data. Basically, it sounds really freakin’ cool.
Here’s a full breakdown of Google’s other, more serious AI initiatives, tools, and current projects.
| Google AI Product and Description |
|---|
| AI for Data Scientists |
| Vertex AI: A unified machine learning platform for building, deploying, and scaling AI models. |
| Vertex AI Workbench: A single development environment for the entire data science workflow. |
| AI for Developers |
| AutoML: Enables the training of high-quality custom machine learning models with minimal effort and expertise. |
| Cloud Natural Language: Provides insights from unstructured text using Google’s machine learning algorithms. |
| Dialogflow: Allows the creation of conversational experiences across devices and platforms. |
| Media Translation (Beta): Adds real-time audio translation to content and applications. |
| Speech-to-Text: Accurately converts speech into text using Google’s AI technologies. |
| Text-to-Speech: Converts text into natural-sounding speech using Google’s AI technologies. |
| AI Infrastructure |
| Deep Learning Containers: Preconfigured and optimized containers for deep learning environments. |
| Deep Learning VM Image: Preconfigured virtual machines for deep learning applications. |
| GPUs: High-performance GPUs on Google Cloud for machine learning, scientific computing, and 3D visualization. |
| TensorFlow Enterprise: Provides reliability, performance, and enterprise-grade support for AI applications. |
| TPUs: Specialized hardware accelerators designed to train and run machine learning models faster. |
| Business Solutions |
| Contact Center AI: Transforms contact centers with AI technology, including virtual agents, agent assist, contact center AI platform, and insights. |
| Document AI: Offers solutions for managing unstructured documents, including pre-trained models for data extraction, custom model creation, and document search and storage. |
| Product Discovery: AI solutions that enhance personalized consumer experiences across channels. |
| Consulting Service |
| AI Readiness Program: A consulting service designed to accelerate value realization from AI efforts, including AI value benchmarking, capability assessment, and AI planning and roadmap development. |
How Much Does Google Spend on AI?
Developing and researching AI is an expensive business. According to reports, Google plans on investing around $800 billion in AI between now and 2028 – yes, you read that correctly: $800 billion. This investment will presumably be across its entire suite of AI products and is being done to cement its future as a leader in the field.
Google has plans to release more than 20 A.I. products and features this year, The New York Times has reported. The A.I. search engine features, which the company said would arrive soon, will try to distill complex information and multiple perspectives to give users a more conversational experience.
The company also plans to spread its underlying A.I. technology through partners so that they can build varied new applications.
Still, Meta is doing pretty much the same. It has already launched a seriously impressive sounding AI called I-JEPA that can think in a much more human-like way, unlike than traditional LLMs like ChatGPT. And, like Google and Apple, Meta has hundreds of billions to spend on AI too.
Essentially, what you have here is all the big players in Silicon Valley jostling to be the biggest players in the burgeoning AI market. Google clearly sees AI as the future, but so too does Meta and OpenAI. And there’s already been plenty of initial, teething problems for Google too.
Following the disastrous launch of its ChatGPT alternative, Bard, Google lost around $100 billion off its market cap, providing that, when it comes to launching and scaling AI products, even the slightest mistake could make or break a smaller company that doesn’t have Google’s resources.
Bard has improved a lot since and Google is now demoing its new AI-assisted search product, whereby Google uses its web-crawling abilities to provide answers direct in Google. Publishers of all shapes and sizes are not happy about this move, however, as it will dramatically affect traffic to blogs and websites. Expect plenty of litigation when it goes live.
Not that Google cares about any of that; it has money to burn and the best lawyers on the planet.
AI might sound like just another buzz word or fad to the uninitiated but the tech is here to stay and its wider affects will be significant, both for consumers, developers, and the global workforce at large.
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