For the last five or so years, Qualcomm has bet big on bringing more artificial intelligence to smartphones, laptops, vehicles, smart infrastructure and other devices in the field--or what the company calls the "connected intelligent edge." It's Don McGuire's job to tell Qualcomm's technology and artificial intelligence story in a way that's not scary. Recently, that's been harder to do. Last fall's launch of ChatGPT--a generative AI chatbot that answers prompts with polished essays, poetry, computer code and other human-like content--has thrust artificial intelligence into the public spotlight, with decidedly mixed reactions. While there's been plenty of positive hype, many people view the launch of ChatGPT--and AI overall --with a good amount of hand-wringing.

Today's digital world is an expanding frontier of emerging technologies. There are endless innovations, inspired by data, informed by data, enabled by data, and that create value from data. One thing we've seen more and more enterprises do to keep up with this digital revolution is the adoption of cloud services for a variety of IT functions, to an extent that modern approaches to building and running programs are often described as "cloud-native." According to Gartner, while only about 10 percent of enterprise-generated data is created and processed outside a traditional data center or cloud, this figure is expected to soar to 75 percent by 2025. The cloud alone simply isn't efficient enough to keep up with the volume and velocity of data that enterprises will be faced with as time goes on. So what is the missing piece to keeping up?

Model monitoring for predictive analytics at the edge begins with input, i.e. the data and how it is collected. I like to say, the Internet used to be a thing, but now, things are the Internet. In the Internet of Things, "things" are embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems. The data that is collected at the edge often needs to be processed in real-time in order to fuel predictive modeling or to reveal novel patterns in the data that may inspire questions we didn't think to ask about the things that we are monitoring. Some examples of edge applications are technologies like drones or self-driving cars, which operate autonomously through software controlled plans and onboard edge sensors, including GPS.

Qualcomm, one of the world's leading 5G inventors, has introduced its newest modem technology, codenamed Snapdragon X70 at Mobile World Congress in Barcelona, Spain. According to Qualcomm, the Snapdragon X70 5G Modem-RF (Radio Front End) system also uses AI and machine learning to provide "breakthrough 5G speeds, coverage, low latency, and battery economy," according to Qualcomm. Qualcomm also unveiled an extended range of 5G Open RAN (Radio Access Networks) solutions. Client devices will soon benefit from a variety of AI-optimized performance advancements that are important for unleashing the full potential of 5G cellular connection in the years ahead. Qualcomm is presenting the Snapdragon X70 today, claiming it to be the world's first 5G AI processor in a modem-RF combination.

About 10 minutes into your ride, you hear a chatbot in your Bluetooth-enabled helmet telling you that your blood pressure is 40% above normal and your front brakes are significantly above normal temperature. Both conditions increase the risk of an accident by 45%, says the disembodied voice in your ear. You're also told that via permissions you set, your doctor will be notified of the issues with your blood pressure, including being sent a history of readings. An appointment has been made with your motorcycle dealer to check the overheating front brakes. Perhaps the artificial intelligence (AI) doesn't save your life directly, but it significantly does reduce the risk of being injured or killed during your ride.

The recent ransomware attack on Colonial Pipeline is another painful reminder of how vulnerable we are to such attacks and difficult it is to defend our infrastructures against them. RaaS (Ransomware as a service) is a thriving industry in many dark corners of the world, and protecting against it at the intelligent edge is particularly difficult. Challenges include day zero detection with no previous example or known signature, low latency response time, and high detection throughput rate needed to handle the ever-increasing online transactions at the intelligent edge. Additional challenges included limited compute and power resources and hardware architecture flexible enough to change when threat conditions change. Center for Advanced Electronics through Machine Learning (CAEML) researchers have been investigating machine learning hardware solutions that can accelerate ransomware detection at the intelligent edge.

Though challenges and headwinds exist, we believe that the intelligent edge is poised to transform the computing landscape, propelling the world's largest technology companies toward the next generation of connectivity and operational efficiency. By bringing powerful computing capabilities closer to where data originates and needs to be consumed, the intelligent edge unlocks the potential for faster, less expensive, and more secure operations in everything from autonomous vehicles to virtual reality to the Internet of Things (IoT)--helping to accelerate the Fourth Industrial Revolution.5 The intelligent edge is the combination of advanced connectivity, compact processing power, and artificial intelligence (AI) located near devices that use and generate data.6 It represents an evolution and convergence of trends in industrial monitoring, automated manufacturing, utility management, and telecommunications, amplified by cloud computing, data analytics, and AI. The intelligent edge puts these latter capabilities physically near where data needs rapid analysis and response, enabling that data to be acted on directly or filtered to push only the most important bits to the core. In particular, the intelligent edge's ability to bring cloud capabilities to remote operations could greatly amplify their performance.

This drastic reduction in latency alone makes a number of futuristic technologies – such as autonomous vehicles – possible. The advent of cloud computing set off a colossal centralization fever that has caught almost every business that understands the importance of a digital-first business strategy. Even the world's governments and public sector organizations are leveraging the advantages offered by cloud computing. Easy access to data, powerful analytical tools, and improved business agility have enabled organizations to make more "intelligent" and informed decisions than ever before. However, over the next few years, a rival computing architecture approach – decentralization – will witness a sharp uptick in popularity, fueled by edge computing.

Deloitte has released a slew of predictions for 2021, including in the enterprise tech, data and tech, media, telecom spaces. Deloitte picked resilience as the theme for its 12th annual tech trends report; a word that became a mantra in nearly every organization after their 2020 plans were upended by the coronavirus pandemic. In a webinar Monday, the firm identified nine trends separated into three groups that focus on how organizations can use technology to digitize, modernize, and enhance their businesses. Some have been spurred by COVID-19 and some by changes that have been ongoing for years, said Scott Buchholz, a managing director with Deloitte Consulting and emerging tech research director. The first group is dubbed "Strategy, engineered," and addresses the notion that the corporate and tech strategies "have really become intertwined as we move forward and increasingly become one and the same," Buchholz said.

Welcome to my first blog on topics in artificial intelligence! Here I will introduce the topic of edge computing, with context in deep learning applications. This blog is largely adapted from a survey paper written by Xiaofei Wang et al.: Convergence of Edge Computing and Deep Learning: A Comprehensive Survey. If you're interested in learning more about any topic covered here, there are plenty of examples, figures, and explanations in the full 35 page survery: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp & arnumber 8976180 Now, before we begin, I'd like to take a moment and motivate why edge computing and deep learning can be very powerful when combined: Deep learning is becoming an increasingly-capable practice in machine learning that allows computers to detect objects, recognize speech, translate languages, and make decisions. More problems in machine learning are solved with the advanced techniques that researchers discover by the day.