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Google I/O 2021: What’s in it for Developers and Consumers this Year

7 minutes read

After a year-long hiatus due to the COVID-19 pandemic, Google’s developer conference, Google I/O returned in a virtual avatar this year with several new announcements. The event is hosted annually by the company to announce new products and services. While the search giant did not mention its upcoming Pixel devices, they announced upgrades expected on the phones. 

From AI in digital health, wearable technologies, a brand-new Android build, better security across websites via Google Chrome’s password manager, a new digital friend called LaMDA, a carbon-intelligent cloud computing platform, and more, there’s a lot in store for both developers and businesses this year, offered by Google. 

Sundar Pichai, CEO of Google and Alphabet, said on Google’s blog, “The last year has put a lot into perspective. At Google, it’s also given renewed purpose to our mission to organize the world’s information and make it universally accessible and useful. We continue to approach that mission with a singular goal: building a more helpful Google, for everyone. That means being helpful to people in the moments that matter and giving everyone the tools to increase their knowledge, success, health, and happiness.” 

Let’s take a look at what’s expected to make waves this year, categorized by their respective fields: 

Android 12

With brand-new privacy features and other useful experiences, like improved accessibility features for people with impaired vision, scrolling screenshots, conversation widgets, Android 12 focuses on building a secure operating system that adapts to you, and makes all your devices work better together. Google has described this update to its operating system, Android 12, as “the biggest design change in Android’s history”.

Android 12 will first be introduced on Pixel devices, and allow users to completely personalize their phones with a custom color palette and redesigned widgets. This Android build also unifies the entire software and hardware ecosystems under a single design language called Material You. 

It also introduced a new Privacy Dashboard offering a single view into your permissions settings as well as what data is being accessed, at what intervals, and by which apps. A new indicator to the top right of the status bar will tell the user when apps are accessing the phone’s microphone or camera.

Project Starline: A revolutionary 3D video conferencing 

The pandemic has led to a surge in video conferencing, video-based meets, webinars, and more. Google had previously announced it was working on a new video chat system that enables you to see the person you’re chatting to in 3D.

The project, titled Starline, aims to create uber-realistic projections for video chats. The future of videoconferencing will use 3D imaging to make video calls feel like you’re speaking with someone in person, just like you would in a pre-pandemic world. While other video conferencing apps including Zoom, Google Meet, Microsoft Teams, and others have allowed us to stay in touch with family, friends, colleagues, and peers even as we all stayed home, Project Starline’s introduction comes at a time when despite eased restrictions, the need for better remote conferencing tools might still be on a rise so the world stays effectively connected. 

LaMDA: Your new digital friend

LaMDA, a conversational language model built on Google’s neural network architecture called Transformer, is one of the most fascinating introductions at Google I/O 2021. Unlike other pre-existing language models which are trained to answer queries, LaMDA is being trained on dialogue to engage in free-flowing conversations on nearly any topic under the sun (or solar system). 

During the Keynote address, Google gave a demo of LaMDA acting as the planet Pluto at first, and a paper airplane thereafter, both very real. LaMDA, currently in its R&D phase, is likely to be used to power Google Assistant and other Google products in the future, including a key aspect for Google’s new Smart Home. 

Pushing the frontier of computing with TPU V4: 

TPUs, Google’s custom-built machine learning processes, enable advancements including Translation, image recognition, and voice recognition via LaMDA and multimodal models. TPU v4, which debuted at Google I/O 2021 is powered by the v4 chip and touted to be twice as fast as its previous generation. A single pod can deliver more than one exaflop, which is equivalent to the computing power of 10 million laptops combined. “This is the fastest system we’ve ever deployed, and a historic milestone for us. Previously to get to an exaflop, you needed to build a custom supercomputer. And we’ll soon have dozens of TPUv4 pods in our data centers, many of which will be operating at or near 90% carbon-free energy. They’ll be available to our Cloud customers later this year,” explained Google on their official blog. 

Google has opened a new state-of-the-art Quantum AI campus with their first quantum data center and quantum processor chip fabrication facilities, with a multi-year plan in the pipeline.  

No language barrier: Multitask Unified Model (MUM) 

MUM or the Multitask Unified Model comes as Google’s latest milestone to transfer knowledge without any language barriers for the user, thereby making it far more powerful than BERT, the Transformer AI model launched by Google in 2019. MUM can learn across 75 languages at the same time where most AI models train on one language at a time. It can also understand information across text, images, video, and other media.

“Every improvement to Google Search undergoes a rigorous evaluation process to ensure we’re providing more relevant, helpful results. Human raters, who follow our Search Quality Rater Guidelines, help us understand how well our results help people find information,” says Google on their blog. 

Digital Health: Google AI to help identify skin conditions

An AI tool by Google will be able to spot skin, hair, and nail conditions, based on images uploaded by patients. Expected to launch later this year, this ‘dermatology assist tool’ has been awarded a CE mark for use as a medical tool in Europe. 

The app took three years to develop and has been trained on a dataset of 65,000 images of diagnosed conditions, marks people were concerned about, and also pictures of healthy skin, in all shades and tones. It is said to be able to recognize 288 skin conditions but not designed to substitute medical diagnosis and treatment.

This app is based on previously developed tools for learning to spot the symptoms of certain types of cancers and tuberculosis. 

Digital Health & Lifestyle: Wear OS in collaboration with Samsung and Fitbit

Google’s Wear OS and Samsung’s Tizen are merging to form one super platform, Wear. It will likely lead to solid boosts in battery life, smoother running apps, and up to 30% faster app load times. Other updates such as a standalone version of Google Maps, offline Spotify and YouTube downloads, and a few of Fitbit’s best features will be a part of this platform. Wear OS will also be getting a fresh coat of Material You. 

AI for Lifestyle: A better shopping experience

Google has also announced that they are working with Shopify to aid merchants to feature their products across Google. From a customer’s POV, Google will be introducing a new feature in Chrome to help you continue shopping where you left off. On a new tab, Chrome will display all open shopping carts from across different shopping sites. 

On Android, on the other hand, Google Lens in Photos will soon be getting a “Search inside screenshot” button to help scan things like shoes, t-shirts, and other objects in a photo and suggest relevant products.

AI for Lifestyle: AI-driven Google Maps

Google Maps, powered by AI, will now be able to save users from “hard-breaking” moments by providing relevant information about the routes to avoid unnecessary roadblocks. “We’ll automatically recommend that route if the ETA is the same or the difference is minimal. We believe that these changes have the potential to eliminate 100 million hard-braking events in routes driven with Google Maps each year,” Google said on their official blog. The Live View tool will also get a renewed display with detailed information.

AI for Lifestyle: Curated albums on Google Photos

Google Photos will use AI to curate collections with similar images, landscape and more to share with the user, quite similar to Memories on both Apple and Facebook.

Google says they have taken people’s emotions regarding events and memories into consideration and avoid bringing forth anything they might want to get rid of. This new update allows users to control which photos they do or don’t see by letting them remove images, people or time periods. 

Another feature that will be introduced is called “little patterns” which will use AI to scan pictures and create albums based on similarities within them. 

Lastly, Google is also using machine learning to create “cinematic moments” which will analyze two or three pictures taken within moments of each other to create a moving image, akin to Apple’s Live Photos.

ARCore

ARCore, Google’s augmented reality platform, is gaining two new APIs namely, ARCore Raw Depth API and ARCore Recording and Playback API. ARCore Raw Depth API will enable developers to capture more detailed representations of surrounding objects. Additionally, the ARCore Recording and Playback API allow developers to capture video footage with AR metadata.  

Tensorflow.js: How’s it being used and what developers can expect? 

Google is releasing a new ML interface stack for Android to provide developers an integrated platform with a common set of tools and APIs to deliver a seamless ML experience across Android devices and other platforms. As part of this project, Google will also roll out TensorFlow Lite through Google Play Store, so developers don’t have to bundle it with their own apps, and thus can reduce the overall APK size.

This update will enable Machine learning to understand ethics, accessibility via Project Shuwa that’s being built to understand sign language and how it can solve everyday problems. An updated version of Face Mesh is also due for release which enables iris support and more robust tracking. Conversation Intent Detection, based on BERT architecture, identifies user intent along with related entities to fulfill said intent. 

The Google I/O 2021 also gave a close look into building cyber awareness through their Auto-Delete function, increased privacy, better camera features including a new selfie algorithm to make a more inclusive camera experience for everyone, TPU v4, the custom-built machine learning processes, and Multitask Unified Model (MUM) help make Google Search a lot smarter. 

What piqued your interest the most at this year’s Google I/O?

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The Rise of Domain-Specific AI Agents: How Enterprises Should Prepare

Generic AI is no longer enough. Domain-specific AI is the new enterprise advantage.

From hospitals to factories to insurance carriers, organizations are learning the hard way: horizontal AI platforms might be impressive, but they’re often blind to the realities of your industry.

Here’s the new playbook: intelligence that’s narrow, not general. Context-rich, not context-blind.
Welcome to the age of domain-specific AI agents— from underwriting co-pilots in insurance to care journey managers in hospitals.

Why Generalist LLMs Miss the Mark in Enterprise Use

Large language models (LLMs) like GPT or Claude are trained on the internet. That means they’re fluent in Wikipedia, Reddit, and research papers; basically, they are a jack-of-all-trades. But in high-stakes industries, that’s not good enough because they don’t speak insurance policy logic, ICD-10 coding, or assembly line telemetry.

This can lead to:

  • Hallucinations in compliance-heavy contexts
  • Poor integration with existing workflows
  • Generic insights instead of actionable outcomes

Generalist LLMs may misunderstand specific needs and lead to inefficiencies or even compliance risks. A generic co-pilot might just summarize emails or generate content. Whereas, a domain-trained AI agent can triage claims, recommend treatments, or optimize machine uptime. That’s a different league altogether.

What Makes an AI Agent “Domain-Specific”?

A domain-specific AI agent doesn’t just speak your language, it thinks in your logic—whether it’s insurance, healthcare, or manufacturing. 

Here’s how:

  • Context-awareness: It understands what “premium waiver rider”, “policy terms,” or “legal regulations” mean in your world—not just the internet’s.
  • Structured vocabularies: It’s trained on your industry’s specific terms—using taxonomies, ontologies, and glossaries that a generic model wouldn’t know.
  • Domain data models: Instead of just web data, it learns from your labeled, often proprietary datasets. It can reason over industry-specific schemas, codes (like ICD in healthcare), or even sensor data in manufacturing.
  • Reinforcement feedback: It improves over time using real feedback—fine-tuned with user corrections, and audit logs.

Think of it as moving from a generalist intern to a veteran team member—one who’s trained just for your business. 

Industry Examples: Domain Intelligence in Action

Insurance

AI agents are now co-pilots in underwriting, claims triage, and customer servicing. They:

  • Analyze complex policy documents
  • Apply rider logic across state-specific compliance rules
  • Highlight any inconsistencies or missing declarations

Healthcare

Clinical agents can:

  • Interpret clinical notes, ICD/CPT codes, and patient-specific test results.
  • Generate draft discharge summaries
  • Assist in care journey mapping or prior authorization

Manufacturing

Domain-trained models:

  • Translate sensor data into predictive maintenance alerts
  • Spot defects in supply chain inputs
  • Optimize plant floor workflows using real-time operational data

How to Build Domain Intelligence (And Not Just Buy It)

Domain-specific agents aren’t just “plug and play.” Here’s what it takes to build them right:

  1. Domain-focused training datasets: Clean, labeled, proprietary documents, case logs.
  1. Taxonomies & ontologies: Codify your internal knowledge systems and define relationships between domain concepts (e.g., policy → coverage → rider).
  2. Reinforcement loops: Capture feedback from users (engineers, doctors, underwriters) and reinforce learning to refine output.
  3. Control & Clarity: Ensure outputs are auditable and safe for decision-making

Choosing the Right Architecture: Wrapper or Ground-Up?

Not every use case needs to reinvent the wheel. Here’s how to evaluate your stack:

  • LLM Wrappers (e.g., LangChain, semantic RAG): Fast to prototype, good for lightweight tasks
  • Fine-tuned LLMs: Needed when the generic model misses nuance or accuracy
  • Custom-built frameworks: When performance, safety, and integration are mission-critical
Use CaseReasoning
Customer-facing chatbotOften low-stakes, fast-to-deploy use cases. Pre-trained LLMs with a wrapper (e.g., RAG, LangChain) usually suffice. No need for deep fine-tuning or custom infra.
Claims co-pilot (Insurance)Requires understanding domain-specific logic and terminology, so fine-tuning improves reliability. Wrappers can help with speed.
Treatment recommendation (Healthcare)High risk, domain-heavy use case. Needs fine-tuned clinical models and explainable custom frameworks (e.g., for FDA compliance).
Predictive maintenance (Manufacturing)Relies on structured telemetry data. Requires specialized data pipelines, model monitoring, and custom ML frameworks. Not text-heavy, so general LLMs don’t help much.

Strategic Roadmap: From Pilot to Platform

Enterprises typically start with a pilot project—usually an internal tool. But scaling requires more than a PoC. 

Here’s a simplified maturity model that most enterprises follow:

  1. Start Small (Pilot Agent): Use AI for a standalone, low-stakes use case—like summarizing documents or answering FAQs.
  1. Make It Useful (Departmental Agent): Integrate the agent into real team workflows. Example: triaging insurance claims or reviewing clinical notes.
  2. Scale It Up (Enterprise Platform): Connect AI to your key systems—like CRMs, EHRs, or ERPs—so it can automate across more processes. 
  1. Think Big (Federated Intelligence): Link agents across departments to share insights, reduce duplication, and make smarter decisions faster.

What to measure: Track how many tasks are completed with AI assistance versus manually. This shows real-world impact beyond just accuracy.

Closing Thoughts: Domain is the Differentiator

The next phase of AI isn’t about building smarter agents. It’s about building agents that know your world.

Whether you’re designing for underwriting or diagnostics, compliance or production—your agents need to understand your data, your language, and your context.

Ready to Build Your Domain-Native AI Agent? 

Talk to our platform engineering team about building custom-trained, domain-specific AI agents.

Further Reading: AI Code Assistants: Revolution Unveiled

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