Autonomous technology labs
Where hardware meets software for AI system competency development with industry use cases
Autonomous technology lab demonstrates our commitment to people growth initiatives in artificial intelligence (AI). ICURO made significant AI hardware investment in equipping the Silicon Valley Autonomous Technology Lab in Santa Clara, California with enterprise grade sensors, edge computing processors, GPUs, and robots. It offers an unique hands-on AI system infrastructure and industry use cases for both software and hardware engineers to become AI system specialists. The AI systems competency development includes machine learning, machine vision, sensor fusion, edge intelligence, robotics, embedded processors, and cybersecurity.
The learning culture provides a strong foundation to learn directly from a cross-disciplined team of industry practitioners, hardware-software convergence experts, AI system specialists, and strategic partners like NVIDIA. Instead of taking online courses or bootcamps, learn by implementing industry relevant use cases on enterprise grade infrastructure. If you are passionate to get ahead and successfully land on AI roles, send your resume to email@example.com
Innovative business outcomes emerge when machines are powered by artificial intelligence at the core of an enterprise. Intelligent machines talking to other internet enabled devices become much aware of the world around them. In the emerging industry 4.0 ecosystem, there are several high value use cases where an autonomous mobility base with adaptable robotic arm can be applied. These autonomous mobility systems can recognize objects and handle it without human intervention while navigating shop floor stations, fulfillment centers, distribution centers, and warehouses.
The AI system competency is to develop 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. Industry 4.0 business use cases are defined and deployed with these autonomous systems. The technical competencies include semantic segmentation neural network, machine vision, sensor fusion with LiDAR, RGB-D camera, IMU, and robotic operating system.
In the AI driven era, augmented intelligence is being used to describe how AI is going to interact with people; not through replacing them, but through improving what they already know. 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 machines learning from human interaction creates even a powerful set of actionable intelligence for businesses to act upon.
The AI system competency development is to design and deploy augmented intelligence capabilities into products and services. The key competencies include autonomous system architecture for reinforcement learning neural network, edge computing processors, sensor fusion, and natural language processing. It begins with developing and deploying an autonomous edge stack for mobility, machines, and equipments that interact easily with 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.
The AI system competency development specializes 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 AI and digital health are addressed. The technical skills include anomaly detection using machine learning, edge computing processors, and secure gateway framework for autonomous systems.
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We value hardware and software partnerships to discover, develop, and deploy the future of autonomous systems
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