Undoubtedly, Artificial Intelligence is the next big thing of the high-tech industry. The research and innovation led by the top technology companies are influencing industry verticals including healthcare, automobile, finance, manufacturing and retail. Though technology has always been an important factor for these domains, AI is making technology the core of the business. From critical life-saving medical equipment to self-driving vehicles, AI will be infused into almost every application and device.
Platform companies such as Amazon, Apple, Facebook, Google, IBM and Microsoft are investing in the research and development of AI. They are working towards making AI more accessible to businesses.
Three essential aspects are accelerating the pace of innovation in the field of Machine Learning and Artificial Intelligence.
Next-generation computing architecture
Traditional microprocessors and CPUs are not designed to deal with Machine Learning. Even the fastest CPU may not be the ideal choice for training a complex ML model. For training and inferencing ML models that deliver intelligence to applications, CPUs must be complemented by a new breed of processors.
Thanks to the rise of AI, Graphics Processing Unit (GPU) are in demand. What was once considered to be a part of high-end gaming PCs and workstations is now the most sought-after processor in the public cloud. Unlike CPUs, GPUs come with thousands of cores that speed up the ML training process. Even for running a trained model for inferencing, GPUs are becoming essential. Going forward, some form of a GPU will be there wherever there is a CPU. From consumer devices to virtual machines in the public cloud, GPUs are the key for AI.
The next innovation comes in the form of Field Programmable Gate Array or FPGA. These processors are programmable and customizable for a specific type of workload. Traditional CPUs are designed for general-purpose computing while FPGAs can be programmed in the field after they are manufactured. FPGA devices are chosen for niche computing tasks such as training ML models. Public cloud vendors are exploiting FPGAs to deliver highly optimized and customized infrastructure for AI.
Finally, the availability of bare metal servers in the public cloud is attracting researchers and scientists to run high-performance computing jobs in the cloud. These dedicated, single-tenant servers deliver best in class performance. Virtual machines suffer from the noisy neighbor problems due to the shared and multi-tenant infrastructure. Cloud infrastructure services including Amazon EC2 and IBM Cloud are offering bare metal servers.
These innovations will fuel the adoption of AI in fields such as Aerospace, medical, image processing, automotive and manufacturing.
Access to historical datasets
Before cloud became mainstream, storing and accessing data was expensive. Thanks to the cloud - businesses, academia and governments are unlocking the data that was once confined to the tape cartridges and magnetic disks.
Data scientists need access to large, historical datasets to train ML models that can predict with increased accuracy. The efficiency of an ML model is directly proportional to the quality and size of the dataset. To solve complex problems like detecting cancer or predicting rainfall, researchers need large datasets with diverse data points.
With data storage and retrieval becoming cheaper, government agencies, medical institutions and universities are making unstructured data available to the research community. From medical imaging to historical rainfall trend, researchers now have access to rich datasets. This factor alone significantly impacts AI research.
Abundant data combined with high-performance computing devices will drive next-generation AI solutions.
Advances in Deep Neural Networks
The third and the most critical factor in AI research in the advancement in deep learning and artificial neural networks.
Artificial Neural Networks (ANN) are replacing traditional Machine Learning models to evolve precise and accurate models. Convolutional Neural Networks (CNN) brings the power of deep learning to computer vision. Some of the recent advancements in computer vision such as Single Shot Multibox Detector (SSD) and Generative Adversarial Networks (GAN) are revolutionizing image processing. For example, using some of these techniques, images and videos that are shot in low light and low resolution can be enhanced to HD quality. The ongoing research in computer vision will become the base for image processing in healthcare, defense, transportation and other domains.
Some of the emerging ML techniques such as Capsule Neural Networks (CapsNet) and Transfer Learning will fundamentally change the way ML models are trained and deployed. They will be able to generate models that predict with accuracy even when trained with limited data.
Facebook, Google, IBM and Microsoft are leading the AI research. They are investing billions of dollars to make AI applicable across diverse industry verticals.
The availability of rich datasets combined with next-generation computing architectures is enabling researchers and data scientists to innovate at a rapid pace. These factors will make AI an integral part of applications and devices.
The article was originally published here