Monthly Archives: February 2021

Computer science professor Tina Eliassi-Rad says shes proud to be named on an industry list of 100 Brilliant Women in AI Ethics, which identifies her as one of the top thinkers in the male-dominated field of artificial intelligence. But shes even prouder of what the carefully-curated list represents.

Part of the issue in a field such as computer science is that women and other under-represented minorities arent always seen. Initiatives like this one show that there are a lot of women who are qualified to do this work, says Eliassi-Rad.

Mia Shah-Dand, the CEO of the Oakland, California-based research firm Lighthouse3, created the annual list in 2018. Shah-Dand says she wanted to provide a rebuttal to technology leaders who complained that they couldnt find accomplished, diverse women to hire.

I was a little frustrated with all the times I would hear, There just arent enough qualified women, says Shah-Dand. Its the same old excuse. Well, we have an entire directory of qualified women now. There is no excuse. At this point in 2021, if you have only men on your staff, its intentional.

According to recent research by the World Economic Forum, women hold only 26% of data and artificial intelligence jobs across the globe, and even fewer have senior roles.

Shah-Dand says she included Eliassi-Rad on her 2021 list because of the professors extensive research on racial, gender and other baked-in biases in artificial intelligence algorithms.

Her emphasis on algorithmic accountability and fairness was particularly interesting, says Shah-Dand.

Algorithms, which scan large amounts of data and find whatever information its creators want, are increasingly part of our everyday lives. For example, credit card fraud departments use algorithms to detect abnormal spending, while social media algorithms use viewer interests to determine which ads to run.

Eliassi-Rads research at Northeastern focuses on the unseen but overwhelming influence that artificial intelligence algorithms can make in peoples lives, especially in social media.

Part of the problem with algorithms is that they can impact life-altering decisions if theyre used in criminal justice or even your credit score, says Eliassi-Rad. Microlenders, or individuals who issue small loans, will often check a candidates Facebook and Twitter feeds when deciding whether to grant a loan. A chance connection with someone who has defaulted on a loan could trigger a denial, says Eliassi-Rad.

Sometimes if you dont get the right loan in life, you cant better yourself, she says.

Eliassi-Rads career in computer science was sparked by her fathers early work with autonomous vehicles. She avidly read the many magazines he brought home and decided computer science was the perfect balance between math and electrical engineering. Her focus recently sharpened as she learned about the different class, race, and gender biases in machine learning.

She likens the data used in algorithms to an iconic photo of a police officers German shepherd attacking a Black high school student during a 1963 civil rights event in Birmingham, Alabama.

The German shepherd isnt racist, its the people teaching the dog, Eliassi-Rad says. Even if the data used in an algorithm isnt biased, the algorithm may still produce biased findings.

As you are developing an algorithm you are making choices, and those choices have consequences, Eliassi-Rad says.

Eliassi-Rad and Shah-Dand say the list of top women in AI ethics does more than provide a roster of qualified computer science professionals who also happen to be female, LGTBQ, or women of color. It creates a community to foster networking and support while providing role models for future generations.

Its sort of like a sisterhood, says Eliassi-Rad, who received an Outstanding Mentor Award from the Office of Science at the US Department of Energy in 2010. I hope young women see this and think, I can be somebody like this person.

For media inquiries, please contact media@northeastern.edu.

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She was named one of the 100 most brilliant women in AI ethics - News@Northeastern

The U.S. Food and Drug Administration has authorized the use of CLEW Medical's artificial intelligence tool to predict hemodynamic instability in adult patients inintensive care units, the company announced on Wednesday.

The tool, CLEWICU, uses AI-based algorithms and machine learning models to identify the likelihood of occurrence of significant clinical events for ICU patients.

CLEW says the clearance is the FDA's first for such a device.

"AI can be a powerful force for change in healthcare, enabling assessment of time-critical patient information and predictive warning of deterioration that could enable better informed clinical decisions and improved outcomes in the ICU," said Dr. David Bates, medical director of clinical and quality analysis in information systems at Mass General Brigham and CLEW Advisory Board member, in a statement.

WHY IT MATTERS

Hemodynamic instability is a common COVID-19 complication, so CLEWICU's predictive capabilities could prove especially useful during the ongoing pandemic particularly given ICUs' strained resources around the country.

By analyzing patient data from various sources, including electronic health records and medical devices, CLEWICU provides a picture of overall unit status and helps identify individuals whose conditions are likely to deteriorate.

According to the company, the system notifies users of clinical deterioration up to eight hours in advance, enabling early intervention. The system also identifies low-risk patients who are unlikely to deteriorate, thus potentially enabling better ICU resource management and optimization.

"CLEW's AI-based solution is a huge leap forward in ICU patient care, providing preemptive and potentially lifesaving information that enables early intervention, reduces alarm fatigue and can potentially significantly improve clinical outcomes," said Dr. Craig Lilly of University of Massachusetts Medical School in a statement.

THE LARGER TREND

The FDA granted emergency use authorization to CLEWICU back this past June. The tool was among several AI-powered technology innovations developed, or modified, in response to the ongoing pandemic.

Mayo Clinic Chief Information OfficerCris Ross said in December that AI has been crucial in understanding the pandemic. He noted the variety of COVID-19-specific use cases, while he also flaggedthe risk of algorithmic bias.

"We know that Black and Hispanic patients are infected and die at higher rates than other populations. So we need to be vigilant for the possibility that that fact about the genetic or other predisposition that might be present in those populations could cause us to develop triage algorithms that might cause us to reduce resources available to Black or Hispanic patients because of one of the biases introduced by algorithm development," said Ross.

ON THE RECORD

"We are proud to have received this landmark FDA clearance and deliver a first-of-its-kind product for the industry, giving healthcare providers the critical data that they need to prevent life-threatening situations," said Gal Salomon, CLEW CEO, in a statement.

Kat Jercich is senior editor of Healthcare IT News.Twitter: @kjercichEmail: kjercich@himss.orgHealthcare IT News is a HIMSS Media publication.

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FDA issues landmark clearance to AI-driven ICU predictive tool - Healthcare IT News

HealthTensor, an artificial intelligence company creating software to help augment medical decision-making, has raised a $5 million in a seed round of financing led by Calibrate Ventures, TenOneTen Ventures and Susa Ventures.

WHY IT MATTERS

The round also includes hospitals and physicians, including a medical officer at Amazon Health. Funds will be used to scale the company's software engineering and implementation team to keep up with demand from major health systems, the vendor said.

HealthTensor's software functions between physicians and the troves of raw medical data from any given patient, which often is more than any individual doctor can handle. The company uses advanced algorithms to do AI-enabled diagnosiswith the aim of ensuring no medical condition is overlooked. The software was designed with the physician workflow in mind, enabling frictionless adoption of the product by users, the company contended.

"HealthTensor makes me a better doctor because it allows me to spend less time in front of the computer and more time in front of the patient," said Dr. Tasneem Bholat, an early user of HealthTensor's software. "HealthTensor synthesizes all the data from the patient's chart, saving me from doing chart biopsy and surfacing diagnoses I might have otherwise missed."

The company's software currently is integrated within several hospitals and will expand to more in the coming months, the vendor reported.

THE LARGER TREND

The use of AI in healthcare has been on the rise throughout 2020. According to some experts, 2021 could be a big year for AI and machine learning.

"AI had become mythical, but 2021 looks set to be the year where it may come into its own in the health sector, along with the use of automation," said Dr. Sam Shah, chief medical strategy officer at Numan and former director of digital development at NHSX. "During the next year, we are likely to see more solutions that support, not only imaging, but also the quality of reporting,as well as the greater use of natural language processing.

"The combination of these technologies will help improve efficiency in health systems as they begin to recover from the pandemic," he said.

ON THE RECORD

"We think of HealthTensor as an AI-powered medical resident that is focused specifically on the tedious, data-driven aspects of medicine, which is what computers do best," said Eli Ben-Joseph, cofounder and CEO of HealthTensor.

"Many doctors are forced to spend a majority of their day focused on data aggregation from medical records, which leads to missed diagnoses, patient dissatisfaction and physician burnout. HealthTensor frees up the physician to focus on the conceptual and emotional aspects of medicine, which is what humans do best."

"HealthTensor makes doctors' lives easier and helps provide better patient care, ultimately generating revenue for hospitals, making it one of the rare startups that has massive global potential for both patients and healthcare providers," said Jason Schoettler, general partner at Calibrate Ventures.

Twitter:@SiwickiHealthITEmail the writer:bsiwicki@himss.orgHealthcare IT News is a HIMSS Media publication.

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HealthTensor raises $5M for its AI-based medical diagnosis tools - Healthcare IT News

For all its comparisons to the human brain, AI still isnt much like us. Maybe thats alright. In the animal kingdom, brains come in all shapes and sizes. So, in a new machine learning approach, engineers did away with the human brain and all its beautiful complexityturning instead to the brain of a lowly worm for inspiration.

Turns out, simplicity has its benefits. The resulting neural network is efficient, transparent, and heres the kicker: Its a lifelong learner.

Whereas most machine learning algorithms cant hone their skills beyond an initial training period, the researchers say the new approach, called a liquid neural network, has a kind of built-in neuroplasticity. That is, as it goes about its worksay, in the future, maybe driving a car or directing a robotit can learn from experience and adjust its connections on the fly.

In a world thats noisy and chaotic, such adaptability is essential.

The algorithms architecture was inspired by the mere 302 neurons making up the nervous system of C. elegans, a tiny nematode (or worm).

In work published last year, the group, which includes researchers from MIT and Austrias Institute of Science and Technology, said that despite its simplicity, C. elegans is capable of surprisingly interesting and varied behavior. So, they developed equations to mathematically model the worms neurons and then built them into a neural network.

Their worm-brain algorithm was much simpler than other cutting-edge machine learning algorithms, and yet it was still able to accomplish similar tasks, like keeping a car in its lane.

Today, deep learning models with many millions of parameters are often used for learning complex tasks such as autonomous driving, Mathias Lechner, a PhD student at Austrias Institute of Science and Technology and study author, said. However, our new approach enables us to reduce the size of the networks by two orders of magnitude. Our systems only use 75,000 trainable parameters.

Now, in a new paper, the group takes their worm-inspired system further by adding a wholly new capability.

The output of a neural networkturn the steering wheel to the right, for instancedepends on a set of weighted connections between the networks neurons.

In our brains, its the same. Each brain cell is connected to many other cells. Whether or not a particular cell fires depends on the sum of the signals its receiving. Beyond some thresholdor weightthe cell fires a signal to its own network of downstream connections.

In a neural network, these weights are called parameters. As the system feeds data through the network, its parameters converge on the configuration yielding the best results.

Usually, a neural networks parameters are locked into place after training, and the algorithms put to work. But in the real world, this can mean its a bit brittleshow an algorithm something that deviates too much from its training, and itll break. Not an ideal result.

In contrast, in a liquid neural network, the parameters are allowed to continue changing over time and with experience. The AI learns on the job.

This adaptibility means the algorithm is less likely to break as the world throws new or noisy information its waylike, for example, when rain obscures an autonomous cars camera. Also, in contrast to bigger algorithms, whose inner workings are largely inscrutable, the algorithms simple architecture allows researchers to peer inside and audit its decision-making.

Neither its new ability nor its still-diminutive stature seemed to hold the AI back. The algorithm performed as well or better than other state-of-the art time-sequence algorithms in predicting next steps in a series of events.

Everyone talks about scaling up their network, said Ramin Hasani, the studys lead author. We want to scale down, to have fewer but richer nodes.

An adaptable algorithm that consumes relatively little computing power would make an ideal robot brain. Hasani believes the approach may be useful in other applications that involve real-time analysis of new data like video processing or financial analysis.

He plans to continue dialing in the approach to make it practical.

We have a provably more expressive neural network that is inspired by nature. But this is just the beginning of the process, Hasani said. The obvious question is how do you extend this? We think this kind of network could be a key element of future intelligence systems.

At a time when big players like OpenAI and Google are regularly making headlines with gargantuan machine learning algorithms, its a fascinating example of an alternative approach headed in the opposite direction.

OpenAIs GPT-3 algorithm collectively dropped jaws last year, both for its sizeat the time, a record-setting 175 billion parametersand its abilities. A recent Google algorithm topped the charts at over a trillion parameters.

Yet critics worry the drive toward ever-bigger AI is wasteful, expensive, and consolidates research in the hands of a few companies with cash to fund large-scale models. Further, these huge models are black boxes, their actions largely impenetrable. This can be especially problematic when unsupervised models are trained on the unfiltered internet. Theres no telling (or perhaps, controlling) what bad habits theyll pick up.

Increasingly, academic researchers are aiming to address some of these issues. As companies like OpenAI, Google, and Microsoft push to prove the bigger-is-better hypothesis, its possible serious AI innovations in efficiency will emerge elsewherenot despite a lack of resources but because of it. As they say, necessity is the mother of invention.

Image Credit: benjamin henon / Unsplash

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New 'Liquid' AI Learns Continuously From Its Experience of the World - Singularity Hub

Data science, machine learning, and AI experts highlight the top AI and ML trends they expect to shape the data science industry in 2021

The ongoing impact of Covid-19 is still affecting organizations nearly a year since the pandemic began, with business leaders continuing to leverage technology in order to navigate the crisis. According to Oxylabs dedicated AI and ML advisory board, some of the most important trends in 2021 will include the increased use of ethical AI for diversity, accountability, and model explainability, alongside increased instances of AI solving challenging real-world problems.

Oxylabs advisory board comprises the leading figures in the machine learning, AI, and data science industries and its members outline what they believe are the most important data science predictions for the year ahead:

Firstly, Pujaa Rajan, Machine Learning Engineer at Stripe, USA Ambassador at Women in AI andGoogleDeveloper MLExpert, believes COVID-19 will instigate a renewed enthusiasm for the application of edge AI in the healthcare industry and the use of ethical AI:

Covid-19 defined 2020 and although development in healthcare has historically been slower than other industries due to regulation this year will see a focus on edge AI in the healthcare industry and other industries. This will lead to the ability to run ML models locally, and tiny ML, resulting in smaller sized ML models that fit on smaller devices like phones. Businesses will focus on these specific, technical areas because they are related to data privacy and security, which the general public and government increasingly care about.Model explainability and interpretability is a space that the government, healthcare companies and finance companies are all actively exploring because of technical curiosity and business motivations. Many leaders will also finally prioritise AI ethics, diversity, inclusion, model explainability, and model interpretability after public outrage at many bad, biased, and unethical applications of AI. On the other hand, the biggest AI news last year was OpenAIs GPT-3, so I expect continued innovation in large NLP models. Software and hardware are like yin and yang. Since the larger models will need more efficient hardware, neural network accelerators will be a hot space.

Ali Chaudhry, PhD researcher, Artificial Intelligence atUCL, sees AI as having have more of a contribution in solving challenging real-world problems in 2021:

I think there will be more focus on fairness, transparency, accountability and explainability in AI systems this year, hence, we can expect more regulations from governments around the globe. We will also see AIs contribution in solving more challenging real-world problems, similar to the protein folding problem that was recently solved by AI. In terms of AI techniques that are set to emerge, there will be more real-world applications of Reinforcement Learning (RL) algorithms and RL will also retain its top position in academia.

Another prediction comes from Gautam Kedia, Machine Learning Engineering Manager at Stripe, ex-Applied Scientist Lead at Microsoft, previously Head of Applied ML at Lyft. He considers how AI-generated content could finally become mainstream across multiple sectors:

AI-generated content will become mainstream and in the next few years, I expect truly generative models to be producing logos, short stories, stock images, voiceovers and workouts, DALL-E is just a start and I believe this content will gradually start to pass the Turing Test. Self-driving cars will also take another step forward and I expect Waymo to start a taxi service directly competing with Uber & Lyft. Tesla will also release the much-awaited Full Self Driving computer.

Finally, Jonas Kubilius, AI researcher, Marie Skodowska-Curie Alumnus, and Co-Founder of Three Thirds is optimistic about the implementation of AI in healthcare but also has fears that AI investment may suffer:

Im certainly optimistic about AI-driven solutions making a greater impact in the healthcare sector and drug discovery, however, my only concern is the economic impact of the global COVID-19 pandemic. It may well be that there is a slowdown of investments in AI-driven solutions and research labs, forcing companies to justify any investments they make and focus very clearly on problems where AI brings a clear added value. With ever increasing pressure on governments and organisations to take action in regard to climate change, I expect to see more AI-driven solutions being leveraged in this field. Particularly in the areas that could benefit from the optimisation of manufacturing and logistics processes to reduce the impact they have on the environment.

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AI Solving Real-world Problems and AI Ethics Among Top Trends for 2021, According to Oxylabs' AI and ML Advisory Board - insideBIGDATA

CHICAGO & DALLAS--(BUSINESS WIRE)--Reveal, the global provider of the leading AI-powered eDiscovery platform, announced today that Thompson Knight, the Dallas-based, full-service law firm, has engaged the AI technology powerhouse to provide superior, end-to-end solutions driving todays forward-thinking legal organizations.

Our platform has completely redefined AI innovation in the practice of law. Fueled by the worlds most powerful AI technology and backed by the largest team of data scientists in the industry, Reveal is uniquely positioned to fulfill Thompson Knights eDiscovery needs, said Wendell Jisa, founder & CEO of Reveal. Were looking forward to working with the Thompson Knight team and helping the entire organization gain a massive competitive advantage in a rapidly evolving space.

Thompson Knight will take full advantage of Reveals platform, which includes industry-leading processing, early case assessment, AI, review, production functionality and customizable API-enabled back-end, along with superior visual analytics. For the first time, all of these superior AI-powered technologies are now available on one comprehensive hub creating a customer experience unrivaled in the industry. The firm will be specifically leveraging the platforms advanced AI and complete end-to-end eDiscovery capabilities.

Thompson Knight selected Reveals eDiscovery solution because it allows for the frictionless automation of key eDiscovery processing, data analytics, utilization of sophisticated artificial intelligence models for review, compliance and more all of which can be used across their operations.

Reveals unique set of artificial intelligence and analytics tools allow our attorneys to navigate and identify key documents very early in the eDiscovery process, thus providing great value to our clients, says Kevin Clark, Litigation Support Manager at Thompson Knight. With the addition of NexLP and Brainspace to complement their already robust review technology, Reveal is at the top of the list when it comes to innovation in the legal technology industry.

The new partnership announcement comes on the heels of other significant moves made by Reveal shaping the legal industry. In less than six months, Reveal has acquired the two leading AI solutions, Brainspace and NexLP catapulting the company to its position as the largest legal-focused AI provider globally. Reveals customers include the leading legal service providers, law firms, corporations and government agencies around the globe.

For more information about Reveal and its AI platform for legal, enterprise and government organizations, visit http://www.revealdata.com.

About Reveal Data Corporation

Reveal is the industrys only eDiscovery platform powered by artificial intelligence. As a cloud-based software provider, Reveal offers the full range of processing, early case assessment, review, infrastructure and artificial intelligence capabilities. Reveal clients include law firms, Fortune 500 corporations, legal service providers, government agencies and financial institutions in more than 40 countries across five continents. Featuring deployment options in the cloud or on-premise, an intuitive user design, multilingual user interfaces and the automatic detection of more than 160 languages, Reveal accelerates legal review, saving users time and money. For more information, visit http://www.revealdata.com.

About Thompson Knight LLP

Established in 1887, Thompson & Knight is a full-service law firm with approximately 300 attorneys. The Firm provides legal solutions to clients and communities around the world. For 10 consecutive years, Thompson & Knight has been recognized in The Best Lawyers in America among the most highly recognized law firms in the United States. For more information, visit http://www.tklaw.com.

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Thompson Knight Joins Forces With Reveal to Bolster Its AI-Powered eDiscovery Capabilities - Business Wire

How many of us love animals and think of our pets as part of the family? Well, what if I told you that among the 6-8 million animals that enter the rescue shelters every year, nearly 3-4 million (i.e., 50% of the incoming animals) are euthanized. Even more heartbreaking is that 10% 25% of them are put to death specifically because of shelter overcrowding each year.

The problem of overpopulation of domestic animals continues to rise, leaving shelters faced with the challenge of how to increase adoption rates. Though animal shelters provide incentives such as reduced adoption fees and sterilizing animals before adoption, only a quarter of total animals living in the shelter are adopted.

Among the 6-8 million animals that enter rescue shelters every year, nearly 3-4 million are euthanized

These staggering statistics led us to investigate the length of stay of animals at shelters and the factors influencing the rate of animal adoption. The overarching goal of this study was to use these factors to predict and then minimize how long an animal will stay in a shelter, thereby decreasing the number of animals euthanized due to overcrowding. Several steps must be conducted to accomplish this goal, such as a literature search for the factors, collection of data from databases and animal shelters, and utilizing machine learning algorithms on this data to make predictions on length of stay in the shelters.

To answer the question of what factors influence the length of stay, a thorough literature review was conducted. Several factors were found to influence the length of stay including color, gender, breed, animal type, and age. To make the predictions about the length of stay using these factors, we evaluated using machine learning algorithms and predictive analytics.

Machine learning is just the ability to program computers to learn and improve by itself using training experience. The developed system needs to analyze big data, quickly deliver accurate and repeatable results, and adapt to new data. A system can be trained to make accurate predictions by learning from examples of desired input-output data. In other words, we wanted to utilize a labeled data set with the output (length of stay) already known, so that the computer could learn from it. The next step was to obtain this data from databases and animal shelters across the country.

The data that was collected from the databases and animal shelters included information such as animal type, intake and outcome date, gender, color, breed, and intake and outcome status (behavior of animal entering the shelter and behavior of animal at outcome type). These data sets included information from mostly southern and southwestern states. For the length of stay, the categories included low, medium, high, and very high (euthanized). Once the data was collected and cleaned, it was time to input it into the machine learning algorithms.

There are so many different types of algorithms that can be used on a data set to make predictions. The hard part is determining which algorithm will perform the best on the given data set, as the performance of the models depends on the application. Simple classification algorithms such as logistic regression, artificial neural network, gradient boosting, and random forest were used in this study.

Examining the results, the most proficient predictive model was developed by the gradient boosting algorithm for this dataset, followed by the random forest algorithm. The logistic regression algorithm appeared to have the worst performance metrics for all categories of length of stay. What was interesting was that the gradient boosting and random forest algorithms performed well when predicting the very high length of stay or when the outcome was euthanization at around 70-80%.

Age vs. Days in Shelter for Cats and Dogs

Looking at the results from the exploratory data above, it was observed that the number of days a dog stays in the shelter decreases as the age increases. This was not expected, as it is predicted that the number of days in a shelter would be lower for younger dogs and puppies. This observation could be due to having more data points for younger dogs.

Results showed that age, size, and color have a significant impact or influence on the length of stay.

Another interesting result from the study was the top features or factors from each machine learning algorithm. Results showed that age (senior, super senior, and puppy), size (large and small), and color (multicolor) have a significant impact or influence on the length of stay.

For future studies, a prescriptive analytics approach will be utilized. Not only is our goal to increase adoption rates of pets in animal shelters, but to also determine the optimal animal shelter location where the animal will have the least amount stay in a shelter and most likely be adopted.

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A 'furever' home: AI to the rescue! - BMC Series blog - BMC Blogs Network

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Weve got a full month of 2021 behind us now, and a few trends are coming clearer. The coronavirus crisis may still be with us, but as vaccination programs expand, the end is in sight. With President Trump out of the picture, and the Democrats holding both Houses of Congress and the White House, politics is looking more predictable. And both of those developments bode well for an economic recovery this year. Looking back, at the year that was, we can also see some trends that stayed firm despite the pandemic, the shutdowns, and the supercharged election season. One of the most important is the ongoing rollout of 5G networking technology. These new networks bring with them a fuller realization of the promises inherent in the digital world. Faster connections, lower latency, higher online capacity, clearer signals all will strongly enhance the capabilities of the networked world. And it wont just be mundane things like telecommuting or remote offices that will benefit 5G will allow Internet of Things and autonomous vehicles to further develop their potential. There is even talk of medical applications, of remotely located doctors performing surgery via digitally controlled microsurgical tools. And these are just the possibilities that we can see from now. Who know what the future will really bring? To this end, we pulled up TipRanks database to learn more about three exciting plays in the 5G space. According to the Street, we are likely to see further interesting developments in the next few years as this technology takes over. Skyworks Solutions (SWKS) The first 5G name were looking at, Skyworks, is a semiconductor chip manufacturer that brought in $3.4 billion in total revenues for FY2020. Skyworks, which is a prime supplier of chips for Apples iPhone series, saw a massive 68% year-over-year increase in 1QFY21 revenues the top line reached $1.51 billion, a company record, and also much higher than analysts had forecast. Much of Skyworks fiscal Q1 sales success came after Apple launched the 5G-capable iPhone 12 line. Strong sales in the popular handset device meant that profits trickled down the supply line and Skyworks channels a disproportionate share of its business to Apple. In fact, Apple orders accounted for 70% of Skyworks revenue in the recent quarter. iPhone wasnt the only 5G handset on the receiving end of Skyworks chips, however the company is also an important supplier to Koreas Samsung and Chinas Xiaomi, and has seen demand rise as these companies also launch 5G-capable smartphones. Finally, Skyworks supplies semiconductor chip components to the wireless infrastructure sector, specifically to the small cell transmission units which are important in the propagation network of wireless signals. As the wireless providers switch to 5G transmission, Skyworks has seen orders for its products increase. In his note on Skyworks for Benchmark, 5-star analyst Ruben Roy writes: SWKS significantly beat consensus estimates and provided March quarter guidance that is also well ahead of consensus estimates as 5G related mobile revenue and broad-based segment revenue continued to accelerate In addition to continued strength of design win momentum and customer activity, we are encouraged with SWKS confident tone relative to the overall demand environment and content increase opportunities. In line with his comments, Roy rates SWKS a Buy along with a $215 price target. At current levels, this implies an upside of 20% for the coming year. (To watch Roys track record, click here) Roy is broadly in line with the rest of Wall Street, which has assigned SWKS 13 Buy ratings and 7 Holds over the past three month -- and sees the stock growing about 15% over the next 12 months, to a target price of $205.69.(See SWKS stock analysis on TipRanks) Qorvo, Inc. (QRVO) Qorvos chief products are chipsets used in the construction of radio frequency transmission systems that power wifi and broadband communication networks. The connection of this niche to 5G is clear as network providers upgrade their RF hardware to 5G, they also upgrade the semiconductor chips that control the systems. This chip maker has a solid niche, but it is not resting on its laurels. Qorvo is actively developing a range of new products specifically for 5G systems and deployment. This 5G radio frequency product portfolio includes phase shifters, switches, and integrated modules, and contains both infrastructure and mobile products. Qorvo posted $3.24 billion in total revenues for fiscal 2020. That revenue represents a 4.8% year-over-year increase and the companys sales have been accelerating in fiscal 2021. The most recent quarterly report, for the second fiscal quarter, showed $1.06 billion in revenues, a 31% yoy increase. Rajvindra Gill, 5-star analyst with Needham, is bullish on Qorvos prospects, noting: Qorvo reported strong sales and gross margins as 5G momentum rolls into CY21 on atypical seasonality... The company is planning for 500M 5G handsets to be manufactured in 2021, with an incremental $5-7 of content/unit from 4G to 5G. Management believes that ultra-wideband adoption will be a key growth driver in for smartphones going forward..." To this end, Gill puts a $220 price target on QRVO shares, suggesting room for 31% upside in 2021. Accordingly, he rates the stock a Buy. (To watch Gills track record, click here) What do other analysts have to say? 13 Buys and and 6 Holds add up to a Moderate Buy analyst consensus. Given the $192.28 average price target, shares could climb ~15% from current levels. (See QRVO stock analysis on TipRanks) Telefonakiebolaget LM Ericsson (ERIC) From chipsets, well move on to handsets. Ericsson, the Swedish telecom giant has long been a leader in mobile tech, and is well known for its infrastructure and software that make possible IP networking, broadband, cable TV, and other telecom services. Ericsson is the largest European telecom company, and the largest 2G/3G/4G infrastructure provider outside of China. But that is all in the background. Ericsson is also a leader in the rollout of Europes growing 5G networks. Ericsson is involved in 5G rollout in 17 countries in Europe, the Americas, and Asia, and its product line includes infrastructure base units and handsets, giving the company an interest in all aspects of the new 5G networks. Ericssons revenue performance in 2020 was not notably distressed by the corona crisis. Yes, the top line dipped in Q1, but that was in line with the companys historical pattern of rising revenue from Q1 through Q4. While the companys 1H20 revenues showed small yoy declines, the 2H20 gains were higher. In Q3, the $6.48 billion top line was up 8.7% yoy, and Q4s $8.08 billion revenue was up 17% from the prior year. The companys shares have also performed well during the corona year, and show a 12 month gain of 64%. Raymond James 5-star analyst Simon Leopold bluntly assigns Ericssons recent gains to its participation in 5G rollouts. Japan's awaited 5G roll-out has started. Share gains continue as Ericsson benefits from challenges facing its biggest competitors and more operators embrace 5G it seems obvious that Ericsson should be gaining market share... Competitor Nokia shunned the Chinese 5G projects, citing profitability challenges, yet Ericsson appears to be profiting in the challenging region. Leopold rates this stock an Outperform (i.e. Buy), and his $15 price target implies an upside potential of ~14% for the year ahead. (To watch Leopolds track record, click here) The Raymond James analyst, while bullish on ERIC, is actually less so than the Wall Street consensus. The stock has a Strong Buy consensus rating, based on a unanimous 5 reviews, and the $16.50 average price target indicates 25% growth potential from the share price of $13.19. (See ERIC stock analysis on TipRanks) To find good ideas for 5G stocks trading at attractive valuations, visit TipRanks Best Stocks to Buy, a newly launched tool that unites all of TipRanks equity insights. Disclaimer: The opinions expressed in this article are solely those of the featured analysts. The content is intended to be used for informational purposes only. It is very important to do your own analysis before making any investment.

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SALT LAKE CITY, Feb. 05, 2021 (GLOBE NEWSWIRE) -- OMNIQ Corp.(OTCQB: OMQS) (OMNIQ or the Company), a provider of Supply Chain and Artificial Intelligence (AI)-based solutions, today announced that the Company has been selected by a city in the state of Georgia to deploy its Q Shield vehicle recognition systems (VRS) technology to identify any vehicle driving through city which is uninsured or in violation of its registration requirements. Q Shield addresses a problem in Georgia that is endemic across the United States, that approximately 36 million uninsured vehicles are traversing our nation's roads every day and states are losing millions of dollars from unregistered vehicles on the road.

Q Shield, OMNIQs AI-based machine vision VRS solution uses patented Neural Network algorithms that imitate human brains for pattern recognition and decision-making. More than 17,000 OMNIQ AI-based machine vision sensors are installed worldwide, including approximately 7,000 in the U.S. Based on superior accuracy and patented features like identification of make and color combined with superior accuracy based on the sophisticated algorithm and machine learning that largely depends on accumulated provided by thousands of sensors already deployed.

OMNIQs Battle Proven AI-based Machine Vision systems are installed in over 30 airports in the US, including JFK, La Guardia, LAX, Miami and many others, as well as in sensitive areas worldwide, for Safe City/Security purposes.

When a vehicle that does not have the required liability coverage or is in violation of the vehicle registration requirements passes Q Shields sensors, deployed throughout the city, in real-time OMNIQs Q Shield system triggers a 'notice of violation' which will be mailed to the vehicles registered owner, said Sandy Mayer VP Sales and Marketing OMNIQ Vision.

For this phase of the program, Q Shield, OMNIQs VRS solution will be installed in several key intersections throughout the city to efficiently and accurately capture vehicle data, including license plate number, color, make, and model. Q Shields technology will also be used to provide local law enforcement with timely alerts for any vehicle on a federal, state, and local law enforcement wanted list in addition to enforcing the traffic violations above,said Sandy Mayer.

We are excited to provide our machine vision VRS technology to benefit the citizens of the city and assist the citys local Police Department, said Shai Lustgarten, CEO of OMNIQ.

Despite their usefulness in helping police solve crimes, automatic license plate and vehicle recognition (VRS) solutions are often beyond the reach of many smaller municipalities. The cost of a such needed, especially in todays environment, efficient solution, can often exceed budgetary limits. We are proud with our Q Shield product, deployed and a major player in terror prevention for governments, around the world, now available and affordable to protect all citizens anywhere,said Shai Lustgarten.

Municipalities now are able to join our program which thanks to the revenues generated through Q Shields offering, and a unique pricing model introduced by OMNIQ, we are delighted to be able to overcome those hurdles that prevent municipalities today, from getting their citizens the security, safety and services they deserve, said Mr. Lustgarten.

About OMNIQ Corp.OMNIQ Corp. (OTCQB: OMQS) provides computerized and machine vision image processing solutions that use patented and proprietary AI technology to deliver data collection, real-time surveillance and monitoring for supply chain management, homeland security, public safety, traffic & parking management, and access control applications. The technology and services provided by the Company help clients move people, assets, and data safely and securely through airports, warehouses, schools, national borders, and many other applications and environments.

OMNIQs customers include government agencies and leading Fortune 500 companies from several sectors, including manufacturing, retail, distribution, food and beverage, transportation and logistics, healthcare, and oil, gas, and chemicals. Since 2014, annual revenues have grown to more than $50 million from clients in the USA and abroad.

The Company currently addresses several billion-dollar markets, including the Global Safe City market, forecast to grow to $29 billion by 2022, and the Ticketless Safe Parking market, forecast to grow to $5.2 billion by 2023. For more information, visit http://www.omniq.com.

Information about Forward-Looking Statements

Safe Harbor Statement under the Private Securities Litigation Reform Act of 1995. Statements in this press release relating to plans, strategies, economic performance and trends, projections of results of specific activities or investments, and other statements that are not descriptions of historical facts may be forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995, Section 27A of the Securities Act of 1933 and Section 21E of the Securities Exchange Act of 1934.

This release contains forward-looking statements that include information relating to future events and future financial and operating performance. The words anticipate, may, would, will, expect, estimate, can, believe, potential and similar expressions and variations thereof are intended to identify forward-looking statements. Forward-looking statements should not be read as a guarantee of future performance or results, and will not necessarily be accurate indications of the times at, or by, which that performance or those results will be achieved. Forward-looking statements are based on information available at the time they are made and/or managements good faith belief as of that time with respect to future events, and are subject to risks and uncertainties that could cause actual performance or results to differ materially from those expressed in or suggested by the forward-looking statements. Important factors that could cause these differences include, but are not limited to: fluctuations in demand for the Companys products particularly during the current health crisis, the introduction of new products, the Companys ability to maintain customer and strategic business relationships, the impact of competitive products and pricing, growth in targeted markets, the adequacy of the Companys liquidity and financial strength to support its growth, the Companys ability to manage credit and debt structures from vendors, debt holders and secured lenders, the Companys ability to successfully integrate its acquisitions, and other information that may be detailed from time-to-time in OMNIQ Corp.s filings with the United States Securities and Exchange Commission. Examples of such forward-looking statements in this release include, among others, statements regarding revenue growth, driving sales, operational and financial initiatives, cost reduction and profitability, and simplification of operations. For a more detailed description of the risk factors and uncertainties affecting OMNIQ Corp., please refer to the Companys recent Securities and Exchange Commission filings, which are available at https://www.sec.gov. OMNIQ Corp. undertakes no obligation to publicly update or revise any forward-looking statements, whether as a result of new information, future events or otherwise, unless otherwise required by law.

Investor Contact:888-309-9994IR@omniq.com

James CarbonaraHayden IR(646)-755-7412james@haydenir.com

Brett MaasHayden IR(646) 536-7331brett@haydenir.com

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OMNIQ's Q Shield AI-Based Vehicle Recognition Technology Selected in Georgia to Crack Down on Crime and Enforce Uninsured and Registration Violations...