In continuation of the last two days the IO event became more detail oriented with deep dive technical sessions covering various aspects of the improvements in Google products and enhanced capabilities that developer can access.

Here is our summary of some important discussions.

1.Improved sign-in, payments and forms in Apps

Google is trying to tackle the challenges of critical flows like login, payments and forms by using new APIs. Android now has Autofill, Google Smart Lock, and Backup and Restore APIs for your apps. These new APIs will help users

1) The login and payment experience,

2) Seamlessly syncing logins between your website and mobile app, and

3) Preventing users from getting locked out when they switch devices.

Watch the complete video here Link

2. Android meets TensorFlow

TensorFlow is powering google AI. A detailed session on AI technology for production Android apps was conducted. One of the main benefits of TensorFlow is Portability. You can easily move the neural network model to Android and run the prediction inside mobile phones, to do many AI tricks things like image recognition, motion recognition and etc.

Google provided the tips and tricks to overcome the challenges of the model size and CPU consumption for neural network prediction.

Watch the complete video here Link

3. Performance and Memory improvements in Android Run Time

Android Run Time (ART) is getting major improvements like the new concurrent copying garbage collector (GC) based on read barriers, and improvements to the ahead-of-time (AOT) and just-in-time (JIT) compiler. The new GC will reduce pause times and heap sizes compared to its predecessor.

Watch full video here Link

4. Kotlin

Kotlin is now an officially supported language for Android. Kotlin is a language that runs on the JVM (Java Virtual Machine), and it’s already possible to use Kotlin and many other JVM languages for Android development. This is the Link https://youtu.be/X1RVYt2QKQE to know the tips for developers to get started with it.

5. Machine Learning APIs

Google has introduced new machine earning APIs that provide access to pre-trained machine learning models with a single API call. Now you can make use of Google’s machine learning expertise to power your applications. Google Cloud Platform (GCP) offers five APIs that provide : Google Cloud Vision API, Cloud Speech API, Cloud Natural Language API, Cloud Translation API and Cloud Video API. Using these APIs, you can focus on adding new features to your app rather than building and training your own custom models.

watch full video here Link

The Million-Dollar AI Mistake: What 80% of Enterprises Get Wrong

By : Sampada

Published on Nov 26, 2024

AI Artificial Intelligence Data Science

When we hear million-dollar AI mistakes, the first thought is: What could it be? Was it a massive investment in the wrong technology? Did a critical AI application go up in flames? Or was it an overhyped solution that failed to deliver on its promises? Spoiler alert: it’s often all of these—and more. From overlooked data science issues to misaligned business goals and poorly defined AI projects, failures are a mix of preventable errors.

Remember Blockbuster? They had multiple chances to embrace advanced technology like streaming but stuck to their old model, ignoring the shifting landscape. The result? Netflix became a giant while Blockbuster faded into history. AI failures follow a similar pattern—when businesses fail to adapt their processes, even the most innovative AI tools turn into liabilities. Gartner reports nearly 80% of AI projects fail, costing millions. How do companies, with all their resources and brainpower manage to bungle something as transformative as AI?

1. Investing Without a Clear Goal

Enterprises often treat artificial intelligence as a must-have accessory rather than a strategic tool. “If our competitors have it, we need it too!” they exclaim, rushing into adoption without asking why. The result? Expensive systems that yield no measurable business outcomes. Without aligning AI’s capabilities—like natural language processing or generative AI solutions—with goals such as boosting customer experience or driving operational efficiency, AI becomes just another line item in the budget.

2. Data Woes

AI is only as smart as the data it’s fed. Yet, many enterprises underestimate the importance of clean, structured, and unbiased data. They plug in inconsistent or incomplete data and expect groundbreaking insights. The result? AI models that churn out unreliable or even harmful outcomes.

Case in Point: A faulty ATS filtered for outdated AngularJS skills, rejecting all applicants, including a manager’s fake CV. The error, unnoticed due to blind reliance on AI, cost the HR team their jobs—a stark reminder that human oversight is critical in AI systems.

3. Underestimating the Human Element

AI might be powerful, but it does not replace human judgment. Whether it’s an AI assistant like Claude AI or OpenAI’s ChatGPT API, Enterprises often overlook the need for human oversight and fail to train employees on how to interact with AI systems. What you get is either blind trust in algorithms or complete resistance from employees, both of which spell trouble.

4. Stuck in Experiment Mode

AI adoption often stagnates when businesses fixate on piloting instead of scaling. Tools like DALL-E or MidJourney may excel in proofs of concept but lack enterprise-wide integration. This leaves companies in an endless cycle of testing AI applications, wasting resources without realizing full-scale business value.

5. Ignoring Change Management

Transitioning to AI technology is as much about organizational culture as it is about deploying AI models. Mismanagement, such as overlooking ethical AI considerations or failing to explain AI’s impact on roles, leads to resistance. Whether it’s a small chatbot AI tool or full-scale AI automation, fostering employee buy-in is critical.

How to Avoid These Pitfalls

Start with Strategy: Define clear objectives for adopting artificial intelligence programs.
Invest in Data: Build a robust data infrastructure. Clean, unbiased, and relevant data is the foundation of any successful AI initiative.
Prioritize Education and Oversight: Train teams to work with AI and establish clear guidelines for human-AI collaboration.
Think Big, but Scale Smart: Start with pilots but plan to expand AI in finance, healthcare, operations or other areas from day one.
Focus on Change Management: Communicate the value of tools like AI robots or AI-driven insights to teams at all levels.

Graph of AI adoption across different countries

Mantra Labs is Your AI Partner for Success

At Mantra Labs, we don’t just offer AI solutions—we provide a comprehensive, end-to-end strategy to help businesses adopt the complex process of AI implementation. While implementing AI can lead to transformative outcomes, it’s not a one-size-fits-all solution. True success lies in aligning the right technology with your unique business needs, and that’s where we excel. Whether you’re leveraging AI in healthcare with tools like poly AI or exploring AI trading platforms, we craft custom solutions tailored to your needs.

By addressing challenges like biased AI algorithms or misaligned AI strategies, we ensure you sidestep costly pitfalls. Our approach not only simplifies AI adoption but transforms it into a competitive advantage. Ready to avoid the million-dollar mistake and unlock AI’s full potential? Let’s make it happen—together.

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