Artifical Intelligence of Things

Artifical Intelligence of Things (AIoT)

Transformation- Electronic Video Network Security

Overview:

The creation of the Internet is righfully seen as revoluntinary for its existensive transformation of the use and share of information around the world. Many now look to the Internet of Things (IoT) in a similar way. That is because – as a vast cybe-space of data from electrnoic sensors and other machine-generated sources- the IoT is now indexing the world with unprecendented graularity for remarkable new levels of visibility, efficency and decision support.

IoT includes everything from environmental gauges and telemetry from industrial machines, to wearable fitness monitors and inventory sensors on grocery shelves. Impressive as these and other examples may be, however, the IoT is the best understood less as a revolution in and of itself-and more as a transition toward an even greater revolution that we call the Artifical Intelligence of Things or AIoT.

This artcile will propose AIoT- the Artifical Intelligence (AI) and machine learning throughout the IoT ecosystem for new levels of automation and performance-is a revolution on a par with Internet itself or the sequencing of the human genome. In short, that AIoT is a paradigm shift where the digital capabilities develop a kind of consciousness; where digital intelligent systems distributed across the IoT become self-learning and self-decisioing. Against this backdrop, this article will examine in manner AIoT is redefining that is possible for electronic video network security.

Artifical Intelligence and Machine Learning

In the nearly two decades since the term “Internet of things” was coined, sensors, actuators and networked intelligence have made their way into every corner of society-from homes and cities to industry, energy exploration and environmental monitoring. A major IoT component-will make up to 10 percent of the entire digital universe by end of the decade.

The progress is nonstop, with eye-popping innovation examples from the early days (such as chip-enabled light bulbs for remote activation) qucikly becoming eclipsed by more advanced IoT applications (such as modern, smart LED street lights networked together to let municipal managers see, hear and sense conditions across an entire city).

However, the vast majority of IoT applications remain focused on gathering data and decision support. When the vast IoT network could be leveraged. The adavances in machine-learning(ML) and AI could be overlaid onto the IoT for distributed systems that become more predictive, self-learning and even self-decisioning. That is the definition of AIoT, and it is already happenining to some extent today.

The sharp setup has also developed an AIoT augmented kitchen that talks and consults with users about preferred cooking methods, and educates itself by leaning a family’s preferred cooking routines and food preferences. Neither of these examples would be possible just with AI or just with IoT alone. It is when the intelligence and self-learning of AI is combined with the connectivity as sensory power of IoT that the transformational capabilites of AIoT emerge. These and other advances in AIoT are tailor-made for some of the most daunting challenges faced by the security industry.

The challenge of Scale:

More than anything, the electronic video network security sector is suffering from a crisis of scale, as an avalanche of content outpaces the human ability to monitor all the data. Unfortuantely, the gold standard of one screen to one person is a fantasy for most cost-conscious security centers. And in fast moving environments like casions or nightclubs, a person can reach cognitive overload at just five screens. Against those human limitations is the exploding growth of data: Today, more than billions of hours of electrnoic security video network are recorded each day. The stark reality is that much of that footage is essentially ignored until something – same disaster or accident – occurs.

The IoT has made various “intelligent video network systems” possible, but the vast majority of such systems are not intelligent enough to contend with the deluge of data or get proactive enough to make a difference. Visual recognition, for instance, is a constant struggle. Part of the problem inovlves the complex and data- rich nature of network security video. To get a sense of this, just convert of information a human resources process visually into digital terms. According to Discover Magazine about 30 percent of cortex neurons in the brain are devoted to visual processing (compared with eight percent for touch and just three percent for hearing). When you consider a manner any electronic video network security system must approxmiate this level of performance, that we begin to understand this challenge.

Particulary, troublesome are the false alarms that might be triggered by something as simple as blowing wind or a camera terror. Unfortunately, as a decision support tool for operators, these limited IoT- driven systems trigger so many false alarms that the most frquent decision made by the operator is to simply turn the system off.

Thankfully, just as AIoT and ML have minimized the false alarms, AIoT can weed false alarms and add nuanced udnerstanding to both real-time and historical video security. It is just one of many advantages AIoT can bring to our industry through the ability to learn from experience and constantly improve performance with little to no human intervention.

Self-learning AIoT systems:

Now, take a closer look at AIoT and a manner it can address some of the electronic video network security sectors’s toughest challenges. Given how humans quickly get overloaded watching mulitple screens, AIoT can give a much-needed assit-detecting accurately, and in real-time, suspicious activity like unathuorized entry, physical violence, loitering and wall-scaling.

AI derives its power from alogrithms and processes that replicate human intelligence, judgment and learning; and perhaps the greatest AI approach is ML. Machine learning techniques can be applied to electronic video network security through what is known as a convolutional neural network (CNN) inloving advanced deep learning algorithms that work with learning cameras to handle object detection, image classification, visual tracking and action recognition.

Assuming IoT infrastructure,graphics and cloud capabilities are powerful enough, machine learning can analyze large amounts of visual information to learn from examples, programmed configuration and historical data. This self-learning happens as the computer examines many examples of behaviour and builds models to identify those behaviours more accurately and qucickly in the future.

The future of AIoT:

AIoT can revolutonize electronic video network security performance to operate more pro-actively at scale. But whatever the AIoT application may be, success relies on several important factors:

a) Data quantity and quality are crucial. ML models rely on the quanity and quality of the data flowing into them in order to deliver the fastest and most accurate performance. It helps if the system is optimzed from camera to cloud for comaptibility, so that performance is not affected by confusing anomalies and differences in a stream, image size, or other parameter that might negatively impact performance.

b) Processing power is another important ingredient. Consider the example of self-driving cars: Even if the key systems of perception, prediction and motion planning for automonous drive are well-designed, processing power can make the difference between the car being able to drive itself at 10km/hr versus 80km/hr. Similiarly, AIoT detection of events in real-time and at scale relies on high-speed processing systems that can support that level of performance. Indeed, processing power is often the difference between a proof of concept in a research laboratory and commercially avaiable systems for use in the real world.

c) Workforce expertise is a third ingredient to consider and finding the right people to design & operate AIoT systems may be harder than you think. Some estimates put the number of people with adequate AI skills and training at less than 10,000 worldwide, according to startup element AI Inc. That means top talent will be in high demand.