What can we learn from new transformational technologies like autonomous cars? I’m a big believer in cross-industry convergence and innovation for businesses. The technologies from a scenario in one industry most likely with some imagination and adopted deployment approach has major potential in other verticals.
Clearly, cloud-driven digital transformation with Artificial Intelligence (AI) and Internet of Things (IoT) are emerging as major change drivers across nearly every industry. Cloud and AI-first verticals like automotive, public safety, retail, transportation, healthcare, hospitality, and manufacturing will see massive implications on business models, workflow management, systems, and people.
To utilize the full potential of the biggest digital transformation in modern history we just launched a new company, Cognize Inc. to explore the wonders of AI cognitive learning by adopting sensor fusion technologies, including those used from autonomous cars.
The intent with Cognitive Intelligence, which is the next stage of machine learning, is to design and analyze unstructured data, video, images, voice, environment and human language to provide situational awareness and understand patterns of behaviors to increase value and mitigate risk.
At its core, cognitive computing uses hardware and software to simulate human intelligence, using all the processes of human intelligent behavior to run a cognitive system of ambient intelligence presented in a virtual reality. The combination of cloud computing, big data, neural networks and mobile computing makes it possible to understand the context in which its users operate.
“I believe the automotive industry will change more the next 5-10 years then it has in the past 50” - Mary Barra, Chairman & CEO of General Motors
Autonomy is the mother of all AI projects
Apple’s CEO Tim Cook described the innovation initiative of autonomous cars as “the mother of all AI projects.” It literally combines all the latest technologies and machine learning algorithms into a complete situational awareness system. Using input from three main sensors, cameras, radar, and LiDAR (light direction and ranging), and cognitive computing, autonomous cars have situational context for driving events. What does this mean? It means the cars react in the same way a human would, but quicker and without accidents. Is a ball rolling into the street? Is the road slippery from rain? In an on-coming car swerving into the car’s lane? With the fusion of multi-dimensional sensors, the cars synthesize the input and quickly take action.
“From our point of view, autonomy is the mother of all A.I. projects, and the autonomous systems can be used in a variety of ways, and a vehicle is only one. But there are many different areas of it.” -Tim Cook, CEO of Apple
The camera is the optical aspect, which enables an autonomous vehicle to visualize its surroundings. Cameras are very efficient at the classification of texture interpretation, widely available, and affordable. The radar is a very relevant technology that can define speed, distance, people count detection and doesn’t need a set line of sight since radio waves are reflective. LiDAR technologies use lasers and light to determine the distance between the vehicle and an object. LiDAR is capable of scanning over 100 meters in all directions, giving it the ability to generate an intricate 3D map of its surroundings.
Cross-deployed in different physical spaces
Obviously, autonomous vehicles are super advanced and packed with sensors to monitor their surroundings, detect oncoming obstacles, and determine the car’s course of action. We are now exploring how we can apply this into physical spaces. This situational awareness learning system can effectively be cross-deployed in different user scenarios in crowd management, building spaces and into public spaces to improve safety, optimize business operations and improve customer experience/service. Adapting existing technologies into different use cases is key to realize the full innovation and growth potential for today’s businesses.
Talking about a company in transformation; originally a computer game chip company, Nvidia, a partner of Cognize™, is now one of the leading tech firms in the autonomous driving field. Its Drive PX family of AI car computers enable automakers, suppliers and startups to accelerate production of autonomous and automated vehicles.
As an example of the processing power, on which AI is dependent, the Nvidia AI edge optimized processor can compute 320 trillion deep-learning operations per second. In a unit roughly the size of a license plate, it has the AI performance of a 100-server data center and makes Nvidia the first tech company to offer a complete A/V stack highest level of autonomous driving. With this edge-based technology deployed in other industry use cases, you can start adding cloud-based big data aggregations to scale business insights and security systems beyond anything existing today.
The reality is that investments, innovation, industry change, and opportunities are huge. Incremental updates on well-known strategy frameworks in old world companies will not be enough to maintain your competitiveness or even remain relevant in your market. This might be the time to start thinking bold and reinvent.
Cognitive Computing In Situational Awareness Is Transforming The World Of Business Value Creation And Risk Mitigation
Today’s video and IoT connected systems are made primarily for surveillance, forensics, and “after the fact” static observations. However, organizations and public services are challenged to both analyze IoT data and to deliver predictive and prescriptive insights into action.
The world is wide open for those who understand the scope and potential of AI, cloud, IoT and digital transformation. Opportunities will be in demand for providing new game-changing services, installing and integrating solutions to visualize, as well as to manage and verify it all.
Cognize™ is redefining the situational awareness systems category. We leverage technologies for cloud, autonomous cars, video, IoT, and business analytics to move beyond space optimization and advanced security protocols and into value creation.
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