The first day of the Google I/O 2018 consisted over 5,000 developers, designers, and managers gathered at Shoreline, Amphitheatre to witness the opening day with a keynote by Google CEO Sundar Pichai.

Let’s dive into the key announcements that were made!

Google I/O: Android P

Google launched Android P, the latest version of the operating system that runs on Android devices. The Android P beta is available for certain devices such as the Essential Phone, Google Pixel 2, Nokia 7 plus, Sony Xperia XZ2, Vivo and Xiaomi Mi Mix 2S. Users of these devices can update to Android P beta immediately. There are a bunch of features that Google added to Android P.

Shush: This feature ensures that there are no notifications coming in from your apps when your phone is turned face down.
App Dashboard: This is a dashboard that shows how much time you spend on your apps each day. This allows you to know which apps you spend so much time on.
App Timer: With the app timer, you’d get a notification when you spend more than the allocated time specified for engaging with an app.
Slices and Actions: A slice is a piece of app content and action that can be surfaced outside of the app without opening the app itself. They are UI templates that can display rich and interactive content from your app within the Google Search app.

Google I/O: AI in Google

Google AI

Google has worked hard over the last few years on improving every aspect of their products with AI and Machine Learning. This year they made some key announcements about new products and also the existing products that are improved with AI. Google announced that its research division has been rebranded to Google AI. This rebranding came as a result of Google’s continued focus on Computer vision, Natural language processing, and neural networks.

Google Assistant

The Google Assistant has been greatly improved. It will be available in 6 new voices including the voice of popular musician, John Legend. Furthermore, Google announced a new technology called Duplex, it is an AI system for natural conversations that’ll enable your Google Assistant to make conversations on your behalf like a lunch reservation at a restaurant, and setting up a meeting.

Until now every time we need the Google Assistant to do something, we usually start every conversation with Hey, Google! Now, we don’t have to do that anymore because the Google Assistant now has support for continued conversations. Google Assistant also has a new feature called Pretty Please. This feature was added to help train kids to avoid being commanding when asking for favors. With the new Pretty Please feature, kids can be taught to always make polite requests.

Google Lens

The Google Lens is also updated with some amazing new features. Now, Google Lens allows you copy-paste text from a photo in the real world to your phone. It also provides the ability to take a photo and instantly provide information about the objects and landmarks in the photo. Google Lens introduced Style Match. With Style Match, you could take a photo of a fashion item such as a shirt, blouse, shoe, or a fancy lamp. Once captured, a blue dot appears on the photo. Tap the dot, and Google Lens will provide lists of items similar to it. What a time to be alive!

Google Maps

Google announced better navigation for Google Maps aided by AI. The Google Maps was difficult for users that are not familiar with the North, South, West, East form of directions. The new Google Maps provides a street view with a very obvious direction sign. With Google Assistant in navigation in Google Maps, it provides users a better description of routes. It also adds a navigation animal that allows the map user quickly identify the route to take.

Google News

Google is keen on getting users a seamless way of catching up with news all over the world. Google is rolling out a new AI improved version of the Google News product that provides an great way to catch up with events around the world. This improved product replaces Google Play Newsstand and Google News app. It will be available on Android, iOS and the web in 127 countries by next week.

Stay tuned for more updated from Google I/O 2018!!

The Future-Ready Factory: The Power of Predictive Analytics in Manufacturing

By : Sampada S M

Published on Mar 6, 2025

Manufacturing Predictive Analytics

In 1989, a missing $0.50 bolt led to the mid-air explosion of United Airlines Flight 232. The smallest oversight in manufacturing can set off a chain reaction of failures. Now, imagine a factory floor where thousands of components must function flawlessly—what happens if one critical part is about to fail but goes unnoticed? Predictive analytics in manufacturing ensures these unseen risks don’t turn into catastrophic failures by providing foresight into potential breakdowns, supply chain risk analytics, and demand fluctuations—allowing manufacturers to act before issues escalate into costly problems.

Industrial predictive analytics involves using data analysis and machine learning in manufacturing to identify patterns and predict future events related to production processes. By combining historical data, machine learning, and statistical models, manufacturers can derive valuable insights that help them take proactive measures before problems arise.

Beyond just improving efficiency, predictive maintenance in manufacturing is the foundation of proactive risk management, helping manufacturers prevent costly downtime, safety hazards, and supply chain disruptions. By leveraging vast amounts of data, predictive analytics enables manufacturers to anticipate machine failures, optimize production schedules, and enhance overall operational resilience.

But here’s the catch, models that predict failures today might not be necessarily effective tomorrow. And that’s where the real challenge begins.

Why Predictive Analytics Models Need Retraining?

Predictive analytics in manufacturing relies on historical data and machine learning to foresee potential failures. However, manufacturing environments are dynamic, machines degrade, processes evolve, supply chains shift, and external forces such as weather and geopolitics play a bigger role than ever before.

Without continuous model retraining, predictive models lose their accuracy. A recent study found that 91% of data-driven manufacturing models degrade over time due to data drift, requiring periodic updates to remain effective. Manufacturers relying on outdated models risk making decisions based on obsolete insights, potentially leading to catastrophic failures.

The key is in retraining models with the right data, data that reflects not just what has happened but what could happen next. This is where integrating external data sources becomes crucial.

Is Integrating External Data Sources Crucial?

Traditional smart manufacturing solutions primarily analyze in-house data: machine performance metrics, maintenance logs, and operational statistics. While valuable, this approach is limited. The real breakthroughs happen when manufacturers incorporate external data sources into their predictive models:

Weather Patterns: Extreme weather conditions have caused billions in manufacturing risk management losses. For example, the 2021 Texas power crisis disrupted semiconductor production globally. By integrating weather data, manufacturers can anticipate environmental impacts and adjust operations accordingly.
Market Trends: Consumer demand fluctuations impact inventory and supply chains. By leveraging market data, manufacturers can avoid overproduction or stock shortages, optimizing costs and efficiency.
Geopolitical Insights: Trade wars, regulatory shifts, and regional conflicts directly impact supply chains. Supply chain risk analytics combined with geopolitical intelligence helps manufacturers foresee disruptions and diversify sourcing strategies proactively.

One such instance is how Mantra Labs helped a telecom company optimize its network by integrating both external and internal data sources. By leveraging external data such as radio site conditions and traffic patterns along with internal performance reports, the company was able to predict future traffic growth and ensure seamless network performance.

The Role of Edge Computing and Real-Time AI

Having the right data is one thing; acting on it in real-time is another. Edge computing in manufacturing processes, data at the source, within the factory floor, eliminating delays and enabling instant decision-making. This is particularly critical for:

Hazardous Material Monitoring: Factories dealing with volatile chemicals can detect leaks instantly, preventing disasters.
Supply Chain Optimization: Real-time AI can reroute shipments based on live geopolitical updates, avoiding costly delays.
Energy Efficiency: Smart grids can dynamically adjust power consumption based on market demand, reducing waste.

Conclusion:

As crucial as predictive analytics is in manufacturing, its true power lies in continuous evolution. A model that predicts failures today might be outdated tomorrow. To stay ahead, manufacturers must adopt a dynamic approach—refining predictive models, integrating external intelligence, and leveraging real-time AI to anticipate and prevent risks before they escalate.

The future of smart manufacturing solutions isn’t just about using predictive analytics—it’s about continuously evolving it. The real question isn’t whether predictive models can help, but whether manufacturers are adapting fast enough to outpace risks in an unpredictable world.

At Mantra Labs, we specialize in building intelligent predictive models that help businesses optimize operations and mitigate risks effectively. From enhancing efficiency to driving innovation, our solutions empower manufacturers to stay ahead of uncertainties. Ready to future-proof your factory? Let’s talk.

In the manufacturing industry, predictive analytics plays an important role, providing predictions on what will happen and how to do things. But then the question is, are these predictions accurate? And if they are, how accurate are these predictions? Does it consider all the factors, or is it obsolete?

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