Edge computing plays a crucial role in supporting the future development of business.

As technology use has grown dramatically during the pandemic, producing increased volumes of significant business data, organizations are going to the edge to assist with speeding up growth and drive business transformation. Indeed, this is the data age, and data is produced at exponential levels. Notwithstanding, if the physical data storage devices for the cloud are far away from where the information is gathered, it is exorbitant to move this data on the grounds that the bandwidth costs are crazy and there is additionally a higher data latency. Enter Edge Computing.

According to a report by IDC, by 2025, 175 zettabytes (or 175 trillion gigabytes) of data will be created throughout the planet. Edge devices will make more than 90 zettabytes of that data.

This data explosion has triggered desperation among organizations hoping to expand their utilization of technologies like artificial intelligence (AI), edge computing, and 5G communications. Edge computing technology can play a crucial role in driving efficiencies and supporting the future development of business. Business leaders are addressing the infrastructure demands of upcoming technologies and the job that edge computing can do is driving competitive advantage through accelerated digital transformation. The edge is where organizations can transform their ideas into reality.

Edge cloud computing is significant on the grounds that it makes better ways for industrial and large-scale organizations to amplify operational effectiveness, enhance performance and safety, automate all core business processes, and guarantee consistent availability. It is a significant technique to undergo the digital transformation of how one can do business.

In a highly connected world, where workforces are dispersed, smart devices are multiplying and the quality of customer experiences is a higher priority than ever. Hence, latency matters. High latency intrudes data flow and reduces application performance, which can affect business processes.

Edge computing technology moves computation and storage resources nearer to where data is produced and consumed, decreasing the distance that significant data needs to travel. This diminishes latency, speeds up the accessibility of data, mitigates bandwidth pressure, and reduces the expense of supporting the movement of massive amounts of data.

Edge computing applications are additionally assisting with providing exceptional patient care and improving the efficiency of medical services in healthcare by empowering incessant patient monitoring and data collection, incorporation of electronic health records, and AI-powered patient data analysis. Deep learning is utilized in image-based diagnostics to speed up the identification of medical problems and save lives. With edge computing, Philips figured out how to speed CT scan imaging by 188 times without the need for hardware acceleration.

Edge cloud computing works inseparably with the cloud to give a flexible solution depending on the data collection and analysis needs of every enterprise. For real-time collection and analysis, the edge is ideal for specific jobs. Simultaneously, the cloud can give a concentrated location for large-scale analytics. Together they give real-time and longer-term insights into performance and other initiatives like machine learning and asset performance management.

Edge computing devices can also accelerate the development of smart cities. Smart cities rely upon gigantic sources of data and the inborn decentralization of Edge Computing is the ideal answer to prevent system collapses while simultaneously working on the effectiveness of all the elements of a smart city. Edge computing examples in smart cities include self-driving vehicles, smart grids, public transport, etc.

Many retail stores are also adopting different technologies. This implies that customers can swipe into the store with their smartphone application or a QR code and begin picking anything they desire to purchase. Then, customers can simply leave the store and the cost of whatever they have purchased will be automatically deducted from their balance or bank account. Retail stores can do this utilizing a blend of motion sensors and in-store cameras to analyze what all customers are purchasing.

In any case, this likewise requires edge computing as too much delay in data analysis can cause the customers to simply get stuff and leave for free! One edge computing example is the Amazon Go store which was launched in January 2018.

Presently, we are stretching out these advantages to the edge to improve operational productivity and further develop performance and safety, while decreasing unplanned downtime and cost. Edge computing can make our lives simpler, proficient, and more useful.

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Mayo Clinic and health imaging company Visage Imaging signed a collaboration agreement to research and develop artificial intelligence in healthcare.

Mayo Clinic signed a multi-year collaboration agreement with Visage Imaging, the US subsidiary of Australian company Pro Medicus Limited, to research and develop artificial intelligence in healthcare, according to a recent announcement.

The partnership will enable both parties to commercialize and make developments in artificial intelligence. Specifically, Mayo Clinic will leverage the Visage AI Accelerator, “an end-to-end AI solution that bridges research and diagnostic imaging on the same, unified platform,” Visage Imaging’s website states.

“Our AI Accelerator program was designed to closely align Visage’s engineering and product development capability with clinical research partners such as Mayo Clinic who have a depth of clinical knowledge and extensive research expertise,” said Malte Westerhoff, PhD, Visage Imaging Global CTO, in the announcement.

“It provides a unique set of tools for data de-identification, collection, curation, analysis and ‘path-to-production’ in research projects bringing the efficiency and speed of Visage technology to research, resulting in a unified link between the two domains.”

The collaboration agreement extends a previous six-year contract between Mayo Clinic and Visage Imaging established in 2016 that enabled the implementation of Visage 7 technology across Mayo Clinic’s radiology departments.

“We see AI playing a significant role in healthcare particularly in our field of imaging IT,” continued Westerhoff in the announcement.

“We have optimized our Visage 7 platform for AI enabling both our own, as well as third-party algorithms to be seamlessly integrated into the clinician’s desktop. We see this research collaboration agreement with Mayo Clinic as another significant piece of our AI strategy, one that has the potential to develop innovative AI solutions that meet well defined clinical goals and ultimately lead to better patient outcomes.”

In other artificial intelligence news, the University of Pittsburgh Schools of the Health Sciences (UPMC) recently launched a new company, Realyze Intelligence. The company will use both artificial intelligence and natural language processing to determine optimal treatments for patients with chronic diseases.

In addition, Ohio State University recently announced that it will be the first academic medical center in the US to use an FDA-approved tool for colonoscopy procedures, driven by artificial intelligence. The tool can detect polyps and lesions better than the human eye, aiding gastroenterologists in detecting colorectal cancer.

Google recently announced a new artificial intelligence-driven dermatology tool that allows people to use their phone’s camera to identify various dermatologic conditions.

These partnerships and innovations signify the growing popularity and trust in artificial intelligence and its applications in healthcare. But developments have a long way to go. Recent research suggests that developers will have to eliminate bias in artificial intelligence tools. Because the bulk of available patient data is from White males, algorithms may be inequitable and neglect to represent minorities. Feeding the programs well-rounded datasets is a way to combat this issue, but most public datasets do not currently reflect diverse populations.

Although these hurdles are not to be overlooked, artificial intelligence has proven to be a helpful assistant to providers in many cases. With the ability to analyze large amounts of data, predict the likelihood of illnesses, and even boost patient satisfaction, artificial intelligence is likely to become an asset in the future of healthcare.

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The new ML technique quickly calculates the binding affinities between drug candidates and their targets.

Artificial intelligence and machine learning techniques are already proving effective in pharmaceutical procedures. Drug discovery is one of the crucial procedures to find new candidate medications in the field of medicine, biotechnology and pharmacology. According to the U.S. FDA, there are five steps for the development of a new drug. These include discovery and development, preclinical research, clinical research, FDA review, and FDA post-market safety monitoring. Since drug discovery requires huge amounts of data and research, many pharmaceutical companies are embracing AI and machine learning to accelerate the pace of drug discovery.

AI and ML techniques can also lower the costs of drug development. Drug discovery is a data-driven process. It involves a voluminous amount of data such as high-resolution medical images, genomic profiles, metabolites, molecular structures, and biological information. Machine learning and deep learning-fuelled artificial intelligence can correlate, integrate, and connect existing data more rapidly to help discover patterns in the data pools.

As drugs can only work based on their stickiness to their target proteins in the body, analyzing that stickiness is a key hurdle in the drug discovery and screening process. New research combining chemistry and machine learning could lower that hurdle. The new technique, called DeepBAR, can quickly calculate the binding affinities between drug candidates and their targets. DeepBAR combines traditional chemistry calculations with recent advances in machine learning. It computes binding free energy exactly, but it requires just a fraction of the calculations demanded by previous methods.

The “BAR” in DeepBAR stands for “Bennett acceptance ratio”. It is a decades-old algorithm used in exact calculations of binding free energy. According to the researchers, DeepBAR could one day quicken the pace of drug discovery and protein engineering.

The research has appeared in the Journal of Physical Chemistry Letters and led by Xinqiang Ding, a postdoc in MIT’s Department of Chemistry.

As per the study, using the Bennet acceptance ratio typically requires knowledge of two “endpoint” states. A drug molecule bound to a protein and a drug molecule completely dissociated from a protein, plus knowledge of many intermediate states, e.g., varying levels of partial binding, all of which bog down calculation speed.

The new machine learning technique slashes those in-between states by implementing the Bennett acceptance ratio in machine learning frameworks called deep generative models. These models create a reference state for each endpoint, the bound state and the unbound state, according to Bin Zhang, the Pfizer-Laubach Career Development Professor in Chemistry at MIT, and a co-author of a new paper describing the technique.

In using deep generative models, the researchers were borrowing from the field of computer vision. Though adapting a computer vision approach to chemistry was DeepBAR’s key innovation, the crossover also raised some challenges. “These models were originally developed for 2D images,” says Xinqiang Ding. “But here we have proteins and molecules—it’s really a 3D structure. So, adapting those methods in our case was the biggest technical challenge we had to overcome.”

In tests using small protein-like molecules, DeepBAR calculated binding free energy nearly 50 times faster than previous methods. The researchers then start thinking about using this to do drug screening, particularly in the context of COVID. “DeepBAR has the exact same accuracy as the gold standard, but it’s much faster,” says Zhang. They also believe that in addition to drug screening, DeepBAR could aid protein design and engineering, since the method could be used to model interactions between multiple proteins. They also plan to improve the ability of the new machine learning technique in the future to run calculations for large proteins, a task made feasible by recent advances in computer science.

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British technology companies XMOS and Plumerai have agreed a new strategic partnership that will support the development of binarised neural network (BNN) capabilities, enabling AI to be embedded in a wide range of everyday devices efficiently at low power and at low cost.

The partnership combines Plumerai’s Larq software library for training BNNs and the xcore.ai crossover processor from XMOS which provides native support for inference of BNNs. The combination is intended to deliver a BNN capability that’s 2 to 4x more efficient than existing edge AI solutions.

This solution will enable a new generation of devices and could include everything from identifying that a shopping package has been delivered to a safe place to managing traffic flows more efficiently, supporting remote healthcare applications or keeping shelves in stores stocked more efficiently. While BNNs are an emerging technology, the future potential is said to be enormous.

A typical application uses deep learning models with tens of millions of parameters — and despite the move to 16-bit and 8-bit encoding there is still an insatiable demand to increase the speed and efficiency of deep learning and AI systems.

BNNs are seen as the most efficient form of deep learning, offering to transform the economics and efficiency of edge intelligence by going all the way down to just a single bit. However, there are significant challenges involved in making BNNs commercially viable — for example, they demand specific attention in chip design for efficient inference and new software algorithms for training.

XMOS and Plumerai have combined their respective expertise in embedded chip design and deep learning algorithms to enable this breakthrough technology and extend the use of AI.

Commenting Mark Lippett, CEO, XMOS said: “BNNs gained prominence in the news recently with Apple’s purchase of Xnor.ai for a reported $200m. It’s little surprise that Apple is exploring AI capabilities at the edge, with advanced machine learning algorithms that can run efficiently in low-power, offline environments.

“Regardless of other moves in the market, our partnership with Plumerai is exciting for AI developers around the world. The combination of Larq and xcore.ai offers the first consolidated path to commercially deploying BNNs, which will be highly disruptive in intelligent embedded systems.”

Roeland Nusselder, CEO, Plumerai added: “We share XMOs’ excitement about the emerging era of intelligent connectivity. Binarized deep learning has tremendous potential for enabling a new generation of energy-efficient, AI-powered applications. Our two companies are perfectly positioned to turn this potential into reality.”

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