Page 34 - Security Today, September/October 2021
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Drivers and Implications Innovations in hardware have bolstered compute power
By Quang Trinh
Artificial Intelligence (AI) has been around since the 1950s when scientists and mathematicians essentially wanted to see if they could make machines think like humans. Since these early notions of AI, technology has advanced at a gradual rate. However, sig- nificant breakthroughs in AI have occurred within the last decade--accelerated by digi- talization, which has resulted in more data to analyze and improved outcomes.
It is fair to say that as technology con- tinues to advance, the impacts of AI will be experienced in every industry—par- ticularly in the security industry—and of- fer unprecedented opportunity to address real-world challenges.
Most recently, innovations in hardware have bolstered compute power and gener- ated more AI-related applications. Think about it: the transition from Central Pro- cessing Units (CPUs) to Graphics Pro- cessing Units (GPUs), and now Applica- tion Specific Integrated Circuits (ASICs), is well underway and rapidly evolving.
The shift from CPUs to GPUs re- sulted in efficiencies and advancements in parallel processing, and the transition to custom ASICs—specifically designed to accelerate AI techniques in Deep Learn- ing (DL)—has opened the door for on- premise and edge device solutions. As a result, many industries are now starting to realize the significance of both hardware and software when applying AI to more real-world use cases.
From CPUs, GPUs and ASICs to DL- PUs and SOCs (System on a Chip), AI is changing the way many device manufactur- ers are approaching future device design and functionality. Even though AI has been around for many decades, it’s recent ad- vancements that have allowed the tech com- munity to optimize the compute power re- quired for AI and its techniques, including: • Machine Learning (ML) the subset of AI,
which uses fundamental cognition and le- verages algorithms to solve basic problems
by identifying patterns to make highly confident predictions--resulting in decision making with minimal human interaction.
• Deep Learning (DL) the subset ML that utilizes algorithms based on simulated neural networks inspired by the way hu- mans learn (and trained on a massive amount of input data) in order to pro- vide more accurate outcomes.
• Neural Networks (NNET) or Artificial Neural Networks (ANNs) are the core of DL algorithms, whose structure is designed to simulate the way the human brain and its neurons operate in order to process and recognize relationships between data.
So what is the next step for AI? The com- mon goal is the commercialization of AI technology. The data required for AI be- gins at the edge with devices for collecting and processing that data into information.
Billions of devices interconnected in private and public networks are already in existence (and more are added to the net- work every day) which presents immense opportunity when it comes to the develop- ment of on-premise and edge-based com- mercial products. That said, in order to be successful, companies will need to adapt to the ever-evolving AI framework. The chal- lenge for most companies is how to apply AI into a real-world environment in order to solve a problem. Furthermore, the abil- ity to resolve real-world problems requires a lot of data—quality data.
The approach toward acquiring quality
data must be methodical and meaningful, so it’s a walk before you can run process. Accord- ingly, in its initial stages, it requires an expert who can examine a problem, ask the right questions and get to the root of a problem before properly designing a solution around an AI framework. Of course the visual data in IP cameras is essential for AI to learn from.
Once solid methodology is determined and quality visual data is collected, there is still a huge task to organize and label the data when applying ML and DL tech- niques. Compute power demands will in- crease especially when shifting from ML to DL techniques during the training pro- cess. Once a ML/DL model is trained, and ready for execution, compute power at the edge also plays an important role. Deep Learning Processing Units (DLPUs) in to- day’s high-performance cameras are pro- viding great advantages to the leap from Machine Learning to Deep Learning.
It is important to bear in mind that Ma- chine and Deep Learning require hun- dreds-of-thousands, if not millions of data sets to learn. Ultimately, the output in DL is only as good as the data that the algorithm is being taught. Training an AI model to correctly output an efficient re- sult is tedious and requires a lot of human interaction to test and retest the results. In fact, real-world situations are essential to training, so these exercises cannot be per- formed in a vacuum. Public safety cam-

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