Edge AI Applications Broaden Horizons for Innovations and Opportunities Across Industries

Edge AI Applications Broaden Horizons for Innovations and Opportunities Across Industries

The high data-driven nature of IoT (Internet of Things) systems calls for the need for technologies such as edge AI (Edge computing and Artificial Intelligence).

Today, businesses realize the fact that edge AI is crucial not only because it is revolutionizing industries but also because it is our ultimate future. The technology gains much attention at present as it enables processing of data at the edge, i.e. directly on the device or on the server near the device instead of the cloud, thereby reducing latency in making critical decisions, increasing the speed of processing tasks and mitigating any delayed communication with the cloud. In addition, it reduces bandwidth requirement and cost by processing it on site. It also offers high data security as it operates in a closed network, making it difficult to steal information. These, along with many others, are important reasons for businesses to incorporate edge in their applications.

Edge AI finds applications in many areas. Current examples include Amazon’s Alexa or Apple’s Siri, smartphones with face recognition, mapping and cartography in drones, autonomous vehicles, smart speakers, drones and robots. Implementation of edge AI is also seen in various industries such as healthcare, manufacturing, transportation, retail, and more for upgrading their operations to ensure higher productivity, accuracy, efficiency, and safety. Here’s a look:

Edge AI in Healthcare

Use of edge computing and AI in medicine helps promote patient care and operational efficiency. It also facilitates enhanced data security which is important for smart hospitals to carry out their tasks efficiently. Healthcare firms are able to perform medical tasks such as remote monitoring of patients, diagnostics, precise thermal screening, inventory management, and prediction of ailments.

Edge AI in Manufacturing

The manufacturing industry implements edge AI to enhance and protect its processes and resources. It also seeks solutions that enhance productivity, quality, and reduce risks. For example, advanced machine vision or video analytics, an example of industrial edge AI, allows to gauge product quality with great precision. It is capable of detecting even the smallest quality deviations that almost go unnoticed with the human eye and predict machine failure to prevent bottlenecks. Thus, it helps avoid downtime and addresses problems that may lead to machine repairs and requirements.

Edge AI in Transportation

With an aim to create smart cities where roads, vehicles and buildings communicate with one another, many technology companies adopt edge AI to provide smart cameras that assess traffic in real-time to identify obstructions in the road, reckless drivers and other situations.

Edge AI in Retail

For long, many retail chains have been implementing customer analytics, which is based on an analysis of completed purchases, i.e. receipt data. Even if this technique helps in getting accurate results, the receipt data does not give information about how people move around the store, what they stop to watch, and the like. With the help of video analytics, retail companies can analyze anonymized data extracted from a video image and get informed about people’s purchasing behavior that can improve customer service and the overall shopping experience.

AI application at the edge is seeing a tremendous growth and companies that are investing and embracing this technology are also growing subsequently. This infers that the market for edge AI is growing by leaps and bounds and has a promising future. A report by Allied Market Research predicts that the edge AI processor market is projected to amass $9.6 billion by 2030, registering a CAGR of 16% during the forecast period 2022-2030. The prime factors propelling the market growth are nothing but the benefits offered by Edge AI and rise in adoption of electronic items globally.

In a nutshell, AI on the edge is sure to increase opportunities in future. It is primed to enhance standards across various sectors, be those standards about safety, speed or accessibility.

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AI Poised to Transform Outcomes in Cardiovascular Health Care

Employing artificial intelligence (AI) to help improve outcomes for people with cardiovascular disease is the focus of a three-year, USD 15 million collaboration between world-leading experts in machine learning and AI, and outstanding cardiologists and clinicians. The Cardiovascular AI Initiative aims to improve heart failure treatment, as well as predict and prevent heart failure.

Researchers from Cornell University (Ithaca, NY, USA) along with physicians from NewYork-Presbyterian (New York, NY, USA) will use AI and machine learning to examine data from NewYork-Presbyterian in an effort to detect patterns that will help physicians predict who will develop heart failure, inform care decisions and tailor treatments for their patients. The Cardiovascular AI Initiative will develop advanced machine-learning techniques to learn and discover interactions across a broad range of cardiac signals, with the goal of providing improved recognition accuracy of heart failure and extend the state of care beyond current, codified and clinical decision-making rules. It will also use AI techniques to analyze raw data from time series (EKG) and imaging data.

Researchers and clinicians anticipate the data will help answer questions around heart failure prediction, diagnosis, prognosis, risk and treatment, and guide physicians as they make decisions related to heart transplants and left ventricular assist devices (pumps for patients who have reached end-stage heart failure). Future research will tackle the important task of heart failure and disease prediction, to facilitate earlier intervention for those most likely to experience heart failure, and preempt progression and damaging events. Ultimately this would also include informing the specific therapeutic decisions most likely to work for individuals.

“AI is poised to fundamentally transform outcomes in cardiovascular health care by providing doctors with better models for diagnosis and risk prediction in heart disease,” said Kavita Bala, professor of computer science and dean of Cornell Bowers CIS. “This unique collaboration between Cornell’s world-leading experts in machine learning and AI and outstanding cardiologists and clinicians from NewYork-Presbyterian, Weill Cornell Medicine and Columbia will drive this next wave of innovation for long-lasting impact on cardiovascular health care.”

“Artificial intelligence and technology are changing our society and the way we practice medicine,” said Dr. Nir Uriel, director of advanced heart failure and cardiac transplantation at NewYork-Presbyterian, an adjunct professor of medicine in the Greenberg Division of Cardiology at Weill Cornell Medicine and a professor of medicine in the Division of Cardiology at Columbia University Vagelos College of Physicians and Surgeons. “We look forward to building a bridge into the future of medicine, and using advanced technology to provide tools to enhance care for our heart failure patients.”

“Major algorithmic advances are needed to derive precise and reliable clinical insights from complex medical data,” said Deborah Estrin, the Robert V. Tishman ’37 Professor of Computer Science, associate dean for impact at Cornell Tech and a professor of population health science at Weill Cornell Medicine. “We are excited about the opportunity to partner with leading cardiologists to advance the state-of-the-art in caring for heart failure and other challenging cardiovascular conditions.”

In a post-pandemic world, the security risk landscape has become incredibly complex. We are more digitally connected than ever in both our private and professional lives. At the same time, businesses are accelerating their digital transformation programs to find new business opportunities.

But the risks are growing at the same pace, and companies must be eyes-open to the threats in this new economy — a thriving criminal ecosystem built upon digital attack, state-sponsored espionage and cyber warfare.

The true challenge in securing tomorrow’s digital world lies in a dangerous intersection:

? the explosion of digitalization and cloud computing power

? the scale of the cybersecurity threat

? the shortage of skilled cyber professionals.

That’s where intelligent technology comes in, says Mike Beck, Global CISO of Darktrace. Self-learning AI adapts to an organization’s digital ecosystem and leverages the massive amounts of data traversing the post-pandemic, decentralized workforce to make informed decisions. It can augment human teams, giving them a way to fight back against threats that have grown far more sophisticated and increased to machine speed.

“In the next few years, self-learning AI will become ubiquitous to meet the pure scale factor of digitalization inside businesses,” Beck says. “Nobody can scale this problem with people alone, even the top-tier banks and tech firms. Using self-learning AI alongside human security teams will give the industry a genuine scalable partnership to meet today’s cybersecurity challenge.”

Why IT teams need self-learning AI now

Cybersecurity staff working in the trenches today need support. More than anything, they need to be able to take holidays, take weekends, and look up from playing endless whack-a-mole. But companies must also stop wasting their cybersecurity on single tickets driven from a single alert. Instead, companies should be leveraging their depth of knowledge and ensuring they’re spending time on what they’re good at doing.

With an AI-powered cybersecurity platform, IT professionals can overlay their understanding of cybersecurity with their domain knowledge of the business to make decisions that can have a more wide-reaching effect. Using self-learning AI to augment the human security team improves the overall security operation and stops attackers autonomously.

Machine learning algorithms learn to harden defenses proactively by feasting on a continuous cycle of mapping your organization’s attack surface and modeling potential attacks. That means alerting teams to add controls or patches to the most likely attack paths — or the paths of least resistance. If that’s not possible, they can preemptively apply detection and autonomous response lenses over those vulnerable areas.

If processes or workloads experience significant changes that amount to a security issue, AI can step in to remediate the system, healing to a known good state. All the outputs from each phase can improve the state of security, creating a continuous feedback loop.

“The future of intelligent security is a technology that can proactively harden and mature the controls environment to make it more challenging for an attacker to succeed if they do get some foothold on your digital estate. We are on track to realize this future,” Beck says.

Techniques like attack surface management (looking at the business from the outside to spot digital weaknesses) and attack path modeling (looking at the company from the inside to see how an attack might traverse) repeatably serve up intelligence-led hardening opportunities to the IT team to keep them one step ahead of what lurks around the corner.

“CISOs need to be scaling security operations in a repeatable, consistent way that is agnostic to digital changes in the business and agnostic of attacker techniques,” Beck says. “Cybersecurity today is about AI that can work in partnership with human security teams.”