Day 2 of Google I/O 2017 has completed. We’ve got all of the details on day 2 rounded up just in case you happened to miss anything. Mostly Keynote speech and bigger announcements happened on the first day.

There were multiple tracks on the second Day of I/O and we chose to focus on the AR/VR related topics.

Google is working on the whole spectrum on Reality as we know. From Real world problem solutions to using AR for enhancing real world environments and VR to complete virtual experience of the real world.

Google Tango

This is a very interesting project building on the AR capabilities for Smartphones. Google calls it WorldSense. It uses SLAM( Simultaneous Localisation and Mapping). The smartphone AR powered by Tango has Depth sensing, wide angle tracking camera and relocalisation capabilities. This allows greater capabilities for AR/VR developers. This technology can provide you with directions indoors and combined with AR, it can also create things which aren’t there.

Expeditions AR

This is the new version of the earlier Expeditions VR experience Google launched a few years ago. It is powered by the virtual positioning system. The VPS you to navigate through a store with the help of Tango — combined with image recognition systems that can track where you are. It enhances the interaction with the real world with low latencies. Developers can also build these AR Expeditions.

Daydream

Google calls its VR program, Daydream. Daydream 2.0, Euphrates, comes with support for standalone headsets.

In Euphrates, the focus is on standalone support and sharing the VR experience. Three important features showcased are

Software support for standalone headsets
Making VR content front and centre
Making it easy to share your VR exp

Some Google VR capable devices are already available in the market from ASUS, Motrola with some more slated to come from Samsung.

VR/AR developer tools

Google announced new tools to take advantage of the new platforms.

Instant Preview –

Allows Faster iteration — Google wants to speed up iteration times for building VR apps. With Instant Preview, which is deeply integrated into the editor and mobile device, developers can now make changes and see them in VR right away. No need to wait minutes to recompile an application.

Immersive web —

WebVR , brings the full Chrome browser to VR, using the Daydream controller. Google is also building WebAR into the browser. That way, you can preview what a new coffee table would look like on your phone — and it would know what actually fits between your couch and table.

Seurat for High fidelity graphics—

What you can render in real time depends on the amount of power you have available.” On mobile, you can’t get desktop-quality graphics. A new tool for simplifying 3D scenes so they still look great but only need a little bit of rendering power compared to the full scene. It will bring cinema level quality to desktop graphics.

For more updates, stay tuned for Day 3.

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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