Autonomous technology labs
Hands-on mentoring with industry use cases
Autonomous Technology Labs is the foundation for our people growth initiative that offers an unique hand-holding process for professionals to adapt into the new reality of autonomous systems. This learning culture provides an unique framework to learn directly from a cross-disciplined team of industry practitioners, hardware-software convergence experts, technology platform specialists, and strategic partners. When you or your company sign up, you are mentored hands-on to tackle multiple industry use cases in the intersection of hardware and software. Everyone goes through specific hands-on delivery tracks and its defined outcomes. You are learning by implementing real business use cases on enterprise-grade hardware and software platforms. We invite you to transform your career and work with iconic companies. If you are passionate to lead the new era of intelligence, join us to make a difference!
Innovative business outcomes emerge when machines are powered by artificial intelligence at the core of an enterprise. These smart machines talking to other internet enabled devices become much aware of the world around them. In a smart manufacturing factory floor, autonomous vehicles communicate with robotic arms or a conveyor to undertake loading and unloading instructions without human intervention. These autonomous vehicles can recognize objects or people while navigating shop floor stations, fulfillment centers, distribution centers, and warehouses.
This autonomous technology lab track is to architect a fully autonomous system stack by unifying the power of sensors with artificial intelligence. Both deep learning at the cloud and machine learning optimization at the edge are implemented. Smart manufacturing industry 4.0 business use cases are defined and deployed with these autonomous systems. The technical skills include machine vision, sensor fusion with LiDAR, robotic operating system, edge computing stack, and machine learning.
Augmented intelligence incorporates machine learning to supplement and support human analysis. While the system can learn without human intervention, augmenting it with the user’s decision-making process and capabilities by providing deep insights which are otherwise hidden or inaccessible. This level of intelligence from smart machines learning from human interaction creates even a powerful set of actionable intelligence for businesses to act upon. We have reached the point where the synthesis of machine learning and human curation has the potential to completely reshape businesses and ecosystems.
The purpose of this lab track is to design and deploy augmented intelligence capabilities into products and services. Artificial intelligence techniques such as natural language processing, spatial navigation, machine vision, cognitive analytics, and machine learning are applied. It begins with developing edge intelligent systems for collaborative robots that work alongside humans in multiple industry domains.
With people, robots, machines, and devices interconnected via internet, the more “things” that are online, the more entry points there will be to access and disrupt a system. Hackers constantly find new ways to attack cyber-physical systems and protocols. Augmenting the expertise of cyber professionals, artificial intelligence systems are learning how to monitor unstructured data to detect risks before they emerge. As they continue to learn, these intelligence systems will be more adept at detecting the difference between a computer glitch and a malicious attack, alleviating the need for security analysts to waste their valuable time on wild goose chases.
This autonomous technology lab track helps to specialize in cyber security and edge node security by embedding adaptive intelligence-enabled cyber threat detection and remediation solutions beginning with edge devices. The use cases from industrial internet of things are addressed. The technical skills include anomaly detection analytics, machine learning, blockchain, and secure gateway framework for autonomous systems.
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