When it comes to the construction industry machine learning means many things. However, at its core, it all comes back to one thing: data.

The more data that is produced through telematics, the more advanced artificial intelligence (AI) becomes, due to it having more data to learn from. The more complex the data the better for AI, and as AI becomes more advanced its decision-making improves. This means that construction is becoming more efficient thanks to a loop where data and AI are feeding into each other.

Machine learning is an application of AI that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. As Jim Coleman, director of global IP at Trimble says succinctly, Data is the fuel for AI.

Artificial intelligence

Coleman expands on that statement and the notion that AI and data are in a loop, helping each other to develop.

The more data we can get, the more problems we can solve and the more processing we can throw on top of that, the broader set of problems well be able to solve, he comments.

Theres a lot of work out there to be done at AI and it all centres around this notion of collecting data, organising the data and then mining and evaluating that data.

Karthik Venkatasubramanian, vice president of data and analytics at Oracle Construction and Engineering agrees that data is key, saying: Data is the lifeblood for any AI and machine learning strategy to work. Many construction businesses already have data available to them without realising it.

This data, arising from previous projects and activities, and collected over a number of years, can become the source of data that machine learning models require for training. Models can use this existing data repository to train on and then compare against a validation test before it is used for real world prediction scenarios.

There are countless examples of machine learning at work in construction with a large number of OEMs having their own programmes in place, not to mention whats being worked on by specialist technology companies.

One of these OEMs is USA-based John Deere. Andrew Kahler, a product marketing manager for the company says that machine learning has expanded rapidly over the past few years and has multiple applications.

Machine learning will allow key decision makers within the construction industry to manage all aspects of their jobs more easily, whether in a quarry, on a site development job, building a road, or in an underground application. Bigger picture, it will allow construction companies to function more efficiently and optimise resources, says Kahler.

He also makes the point that a key step in this process is the ability for smart construction machines to connect to a centralised, cloud-based system John Deere has its JDLink Dashboard, and most of the major OEMs have their own equivalent system.

The potential for machine learning to unlock new levels of intelligence and automation in the construction industry is somewhat limitless. However, it all depends on the quality and quantity of data were able to capture, and how well were able to put it to use though smart machines.

USA-based Built Robotics was founded in 2016 to address what they saw as gap in the market the lack of technology being used across construction sites, especially compared to other industries. The company upgrade construction equipment with AI guidance systems, enabling them to operate fully autonomously.

The company typically works with equipment comprising excavators, bulldozers, and skid steer loaders. The equipment can only work autonomously on certain repetitive tasks; for more complex tasks an operator is required.

Erol Ahmed, director of communications at Built Robotics says that founder and CEO Noah Ready-Campbell wanted to apply robotics to where it would be really helpful and have a lot of change and impact, and thus settled on the construction industry.

Ahmed says that the company are the only commercial autonomous heavy equipment and construction company available. He adds that the business which operates in the US and has recently launched operations in Australia is focused on automating specific workflows.

We want to automate specific tasks on the job site, get them working really well. Its not about developing some sort of all-encompassing robot that thinks and acts like a human and can do anything you tell it to. It is focusing on specific things, doing them well, helping them work in existing workflows. Construction sites are very complicated, so just automating one piece is very helpful and provides a lot of productivity savings.

Hydraulic system

Ahmed confirms that as long as the equipment has an electronically controlled hydraulic system converting a, for example, Caterpillar, Komatsu or a Volvo excavator isnt too different. There is obviously interest in the company as in September 2019 the company announced it had received US$33 million in investment, bringing its total funding up to US$48 million.

Of course, a large excavator or a mining truck at work without an operator is always going to catch the eye, and our attention and imagination. They are perhaps the most visual aspect of machine learning on a construction site, but there are a host of other examples that are working away in the background.

As Trimbles Coleman notes, I think one of the interesting things about good AI is you might not know whats even there, right? You just appreciate the fact that, all of a sudden, theres an increase in productivity.

AI is used in construction for specific tasks, such as informing an operator when a machine might fail or isnt being used productively to a broader and more macro sense. For instance, for contractors planning on how best to construct a project there is software with AI that can map out the most efficient processes.

The AI can make predictions about schedule delays and cost overruns. As there is often existing data on schedule and budget performance this can used to make predictions and these predictions will get better over time. As we said before; the more data that AI has, the smarter it becomes.

Venkatasubramanian from Oracle adds that smartification is happening in construction, saying that: Schedules and budgets are becoming smart by incorporating machine learning-driven recommendations.

Supply chain selection is becoming smart by using data across disparate systems and comparing performance. Risk planning is also getting smart by using machine learning to identify and quantify risks from the past that might have a bearing on the present.

There is no doubt that construction has been slower than other industries to adopt new technology, but this isnt just because of some deep-seated reluctance to new ideas.

For example, agriculture has a greater application of machine learning but it is easier for that sector to implement it every year the task for getting in the crops on a farm will be broadly similar.

New challenges

As John Downey, director of sales EMEA, Topcon Positioning Group, explains: With construction theres a slower adoption process because no two projects or indeed construction sites are the same, so the technology is always confronted with new challenges.

Downey adds that as machine learning develops it will work best with repetitive tasks like excavation, paving or milling but thinks that the potential goes beyond this.

As we move forward and AI continues to advance, well begin to apply it across all aspects of construction projects.

The potential applications are countless, and the enhanced efficiency, improved workflows and accelerated rate of industry it will bring are all within reach.

Automated construction equipment needs operators to oversee them as this sector develops it could be one person for every three or five machines, or more, it is currently unclear. With construction facing a skills shortage this is an exciting avenue. There is also AI which helps contractors to better plan, execute and monitor projects you dont need to have machine learning type intelligence to see the potential transformational benefits of this when multi-billion dollar projects are being planned and implemented

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Machine learning - it's all about the data - KHL Group

The Machine Learning in Medical Imaging Market research report recently presentedby Prophecy Market Insights which provides reliable and sincere insights related to the various segments and sub-segments of the market. The market study throws light on the various factors that are projected to impact the overall dynamics of the Machine Learning in Medical Imaging market over the forecast period (2019-2029).

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Prophecy Market Insights is specialized market research, analytics, marketing/business strategy, and solutions that offers strategic and tactical support to clients for making well-informed business decisions and to identify and achieve high-value opportunities in the target business area. We also help our clients to address business challenges and provide the best possible solutions to overcome them and transform their business.

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Product Portfolio Analysis and Technological Development of Machine Learning in Medical Imaging Market during the forecasted period - Murphy's Hockey...

Learn what is available today in the current landscape, its applications for building efficiency and what is coming in the near future to help life science companies transform their clinical trial imaging.

TORONTO (PRWEB) November 30, 2020

For the first 125 years of medical imaging, technological advances focused primarily on new modes of imaging as technology progressed from the discovery of the X-ray in 1895 to ultrasounds, MRIs, PET and CT scans in the late 20th century. Now, arguably, the most notable advances are being made in how images from those technologies are securely shared, managed, stored and assessed. These advancements are largely due to the application of artificial intelligence (AI) and machine learning (ML) to imaging systems and data platforms.

Automation is improving virtually every stage of the imaging workflow, but there is a lot of hype concerning AI and ML in the marketplace. Companies have underestimated the challenge that complexity presents, and predictions of the end of radiologists have proven false multiple times.

Join experts from ICON Medical Imaging and Medidata for this webinar on the practical applications for AI and ML in clinical trial imaging and what is possible today. Learn what is available today in the current landscape, its applications for building efficiency and what is coming in the near future to help life science companies transform their clinical trial imaging.

Join Paul McCracken, Vice President, Head of Medical Imaging, ICON Medical Imaging; and Dan Braga, VP, Product Management, Acorn AI Product & Ecosystem, Medidata, in a live webinar on Wednesday, December 16, 2020 at 11am EST (8am PST).

For more information, or to register for this event, visit Imaging AI and Machine Learning Beyond the Hype.

ABOUT XTALKS

Xtalks, powered by Honeycomb Worldwide Inc., is a leading provider of educational webinars to the global life science, food and medical device community. Every year, thousands of industry practitioners (from life science, food and medical device companies, private & academic research institutions, healthcare centers, etc.) turn to Xtalks for access to quality content. Xtalks helps Life Science professionals stay current with industry developments, trends and regulations. Xtalks webinars also provide perspectives on key issues from top industry thought leaders and service providers.

To learn more about Xtalks visit http://xtalks.com For information about hosting a webinar visit http://xtalks.com/why-host-a-webinar/

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Imaging AI and Machine Learning Beyond the Hype, Upcoming Webinar Hosted by Xtalks - PR Web

COSTA MESA, Calif.--(BUSINESS WIRE)--Veritone, Inc., (Nasdaq: VERI), the creator of the worlds first operating system for artificial intelligence (AI), aiWARE, today announced it now supports the NVIDIA CUDA platform, enabling organizations across the public and private sectors to run intensive AI and machine learning (ML) tasks on NVIDIA GPUs, whether on-premises or in the Microsoft Azure and Amazon Web Services (AWS) clouds.

This Veritone innovation unlocks new performance levels for organizations using aiWARE, Veritones proprietary OS for AI, as they can now process massive amounts of video, audio and text dramatically faster and more accurately by using the parallel-processing computational power of the newest generation of NVIDIA GPUs.

The NVIDIA CUDA parallel computing platform and programming model enables dramatic increases in computing performance by harnessing the power of NVIDIA GPUs, which can process substantially more concurrent tasks than a central processing unit (CPU).

By taking advantage of the latest CUDA-compatible version of aiWARE running in the Azure and AWS clouds, organizations can leverage GPU auto-scaling to handle more demanding workloads than ever before, seamlessly scaling GPUs in the cloud, whenever faster results are needed.

The marriage of aiWARE and NVIDIA CUDA helps organizations realize artificial intelligence and machine learning solutions that can process vast amounts of data at unparalleled speeds, said Veritone Founder and CEO Chad Steelberg. We built aiWARE to uncover insights from video, audio and text data, at scale, in near real-time. Supporting the CUDA platform advances that mission.

NVIDIA AI technology enables dramatic increases in computing performance and provides the needed foundation for creating GPU-accelerated applications for a variety of business challenges, said Keith Strier, Vice President of Worldwide AI Initiatives at NVIDIA. NVIDIA CUDA offers Veritone aiWARE the power and ease of use required for todays complex GPU-based AI and machine learning workloads across a broad range of industries.

The combination of aiWARE and NVIDIA CUDA opens doors in time-critical AI applications such as:

Energy to optimize energy dispatch in real time and dynamically synchronize and control distributed energy resources such as solar, wind and battery power, down to the device level.

Security to securely and quickly authenticate into applications with multifactor SSO using face and voice biometrics.

Smart Cities to extract valuable insights from large quantities of smart city sensors, including street and municipal vehicle cameras, traffic and roadway sensors, green building and environmental sensors and more.

Media and Entertainment to automatically produce new, synthetic content from massive volumes of existing back catalog and other previously produced content.

Contact Centers to instantly transcribe, translate and voice-recognize customer calls, classify requests, gauge sentiment and intent, and route appropriately.

Industrial and Manufacturing to perform high-volume industrial inspection to efficiently manage the flow of products through fulfillment, distribution and receiving areas.

This new Veritone aiWARE capability is available on any on-prem or cloud GPU that supports NVIDIA CUDA, including AWS and Azure. aiWARE supports the latest GPUs offered by NVIDIA, including for network-isolated deployments of aiWARE. For cloud-based aiWARE deployments, Azure N-series VMs and AWS EC2 P2 and P3 instances are supported.

For more information about aiWARE and Veritones artificial intelligence solutions, visit veritone.com.

About Veritone

Veritone (Nasdaq: VERI) is a leading provider of artificial intelligence (AI) technology and solutions. The companys proprietary operating system, aiWARE powers a diverse set of AI applications and intelligent process automation solutions that are transforming both commercial and government organizations. aiWARE orchestrates an expanding ecosystem of machine learning models to transform audio, video, and other data sources into actionable intelligence. The company's AI developer tools enable its customers and partners to easily develop and deploy custom applications that leverage the power of AI to dramatically improve operational efficiency and unlock untapped opportunities. Veritone is headquartered in Costa Mesa, California, and has offices in Denver, London, New York and San Diego. To learn more, visit Veritone.com.

Safe Harbor Statement

This news release contains forward-looking statements, including without limitation statements regarding aiWAREs support of the NVIDIA CUDA platform, and the expected processing speed, use cases and other benefits to customers of the use of such chipsets with aiWARE. Without limiting the generality of the foregoing, words such as may, will, expect, believe, anticipate, intend, could, estimate or continue or the negative or other variations thereof or comparable terminology are intended to identify forward-looking statements. In addition, any statements that refer to expectations, projections or other characterizations of future events or circumstances are forward-looking statements. Assumptions relating to the foregoing involve judgments and risks with respect to various matters which are difficult or impossible to predict accurately and many of which are beyond the control of Veritone. Certain of such judgments and risks are discussed in Veritones SEC filings. Although Veritone believes that the assumptions underlying the forward-looking statements are reasonable, any of the assumptions could prove inaccurate and, therefore, there can be no assurance that the results contemplated in forward-looking statements will be realized. In light of the significant uncertainties inherent in the forward-looking information included herein, the inclusion of such information should not be regarded as a representation by Veritone or any other person that their objectives or plans will be achieved. Veritone undertakes no obligation to revise the forward-looking statements contained herein to reflect events or circumstances after the date hereof or to reflect the occurrence of unanticipated events.

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Veritone aiWARE Now Supports NVIDIA CUDA for GPU-based AI and Machine Learning - Business Wire

GAINESVILLE, Fla.--(BUSINESS WIRE)--Exactech, a developer and producer of innovative implants, instrumentation and the Active Intelligence platform of technologies for joint replacement surgery, announced today the launch of Predict+, a data-driven, clinical decision support tool that uses machine learning to predict individual patient outcomes after shoulder replacement surgery to assist surgeon decision making.

The software is designed to better inform surgeons regarding the expected outcomes that can be achieved after shoulder arthroplasty, based on the clinical experience documented within the worlds largest single-shoulder prosthesis outcomes database, consisting of more than 10,000 patients.

Predict+ is a new application of clinical research that represents a significant advancement in the patient consultation process, said Chris Roche, Exactechs Vice President of Extremities.Using machine learning analyses, Predict+ delivers personalized, evidence-based predictions that objectively quantify the risk and benefit that an individual patient may experience after anatomic and reverse shoulder replacement and aligns patient and surgeon expectations in order to improve patient satisfaction.

With Predict+, the surgeon inputs as few as 19 data points about a patient and within minutes, the software predicts the patients potential outcomes, including pain and range of motion, based on the results reported by patients with similar age, gender and prosthesis type. In addition, it compares predictive results for anatomic and reverse shoulder arthroplasty at multiple post-operative timepoints. This guidance can help the surgeon better personalize patient treatment by identifying factors that drive the outcome predictions, including modifiable factors such as the patient losing weight, quitting smoking, and completing pre-habilitation. Finally, Predict+ aggregates the outcomes and complications within the database so that surgeons and patients can compare their personalized predictions with the clinical experience of patients of similar age and gender after anatomic and reverse shoulder replacement.

Developed in partnership with KenSci, Predict+ is a first-of-its-kind work that showcases the predictive power of machine learning to transform healthcare. The resultant software builds on previously published, peer-reviewed research in the field.

Machine learning models used within Predict+ have been applied and accelerated by KenScis AI Platform for Digital Health, said Vikas Kumar, Ph.D., Principal Data Science Lead for Innovation and Devices at KenSci. We are witnessing an unprecedented development in computer science to assist hundreds of surgeons globally in improving post-surgical outcomes. This is just the beginning.

Predict+ is the latest in a fast-growing line-up of technologies that power Exactechs Active Intelligence platform. The company continues to aggressively expand its portfolio of uniquely accessible innovations to help surgeons engage with patients and peers, solve challenges with predictive tools and optimize the way they perform surgery.

Predict+ supports Exactechs Equinoxe shoulder, the industrys fastest growing and most studied shoulder system and the ExactechGPS guided personalized surgery system. Predict+ is available to surgeons globally on a limited basis at ExactechGPS Web. Please contact your Exactech representative for additional information. Surgeons may also register to learn more about Predict+ during an educational webinar on Dec. 3 by visiting http://www.exac.com/ActiveIntelligenceWebinar.

About Exactech

Exactech is a global medical device company that develops and markets orthopaedic implant devices, related surgical instruments and the Active Intelligence platform of smart technologies to hospitals and physicians. Headquartered in Gainesville, Fla., Exactech markets its products in the United States, in addition to more than 30 markets in Europe, Latin America, Asia and the Pacific. Visit http://www.exac.com for more information and connect with us on LinkedIn, YouTube and Instagram.

About KenSci

Based in Seattle, WA, KenSci is a healthcare AI platform, built to enable development and production of machine learning for healthcare across the continuum of care. By making AI use within healthcare systems more explainable, interpretable, and assistive, KenSci is helping healthcare become more efficient and accountable.

KenSci was incubated at University of Washington Tacoma's Center for Data Science and designed on the cloud with help from Microsoft Research Azure4Research grant program. KenSci is headquartered in Seattle, with offices in Singapore and Hyderabad. For more information, visit http://www.kensci.com

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Exactech Launches Predict+, First Machine Learning-Based Software that Informs Surgeons with Patient-Specific Outcomes Predictions After Shoulder...

How do you know what machine learning algorithm to choose for your problem? Why dont we try all the machine learning algorithms or some of the algorithms which we consider will give good accuracy. If we apply each and every algorithm it will take a lot of time. So, it is better to apply a technique to identify the algorithm that can be used.

Choosing the right algorithm is linked up with the problem statement. It can save both money and time. So, it is important to know what type of problem we are dealing with.

In this article, we will be discussing the key techniques that can be used to choose the right machine algorithm in a particular work. Through this article, we will discuss how we can decide to use which machine learning model using the plotting of dataset properties. We will also discuss how the size of the dataset can be a considerable measure in choosing a machine learning algorithm.

The dataset is taken from Kaggle, you can find it here. It has information about the diabetic patient and whether or not each patient will have an onset of diabetes. It has 9 columns and 767 rows. Rows and columns represent patient numbers and details.

Practical Implication:

First of all, we will import the required libraries.

After it we will proceed by reading the csv file.

By applying the pair plot we will be able to understand which algorithm to choose.

From the plot, we can see that there is a lot of overlap between the data points.KNN should be preferred as it works on the principle of Euclidean distance. In case KNN is not performing as per the expectation then we can use the Decision Tree or Random Forest algorithm.

A decision tree or Random Forest works on the principle of non-linear classification. We can use it if some of the data points are overlapping with each other.

Many algorithms work on the assumption that classes can be separated by a straight line. In such cases, Logistic regression or Support Vector Machine should be preferred. It easily separates the data points by drawing a line that divides the target class. Linear regression algorithms assume that data trends follow a straight line. These algorithms perform well for the present case.

Import the various algorithm classifiers to check the training time of small and large dataset.

Split the data into train and test. Now we can proceed by applying Decision Tree, Logistic Regression, Random Forest and Support Vector Machine algorithms to check the training time for a classification problem.

Now, we will fit several machine learning models on this dataset and check the training time taken by these models.

From the above results, we can conclude that Decision Trees will take much less time than all algorithms for small dataset. Hence, it is recommended to use a low bias/high variance classifier like a decision tree.

The dataset is taken from Kaggle, you can find it here. It has information about credit card fraud that occurred in two days. Feature Class is a target variable and it takes 1 in case of fraud and 0 otherwise. It has 284807 rows and 31columns.

#Train-Test Split

Now again, on this second dataset, we will fit the above machine learning models on this dataset and check the training time taken by these models.

With the huge dataset size depth of Decision Tree grows, it implements multiple if-else statements which increase complexity and time. Both Random Forest and Xgboost use the Decision Tree algorithm which takes more time. The result shows Logistic regression outperforms others.

I have concluded my analysis in selecting the correct machine learning algorithm. Furthermore, it is always advisable to use two algorithms for addressing the problem statement. This could provide a good reference point for the audience.

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How To Choose The Best Machine Learning Algorithm For A Particular Problem? - Analytics India Magazine

October 15, 2020 07:00 ET | Source: Lantronix, Inc.

IRVINE, Calif., Oct. 15, 2020 (GLOBE NEWSWIRE) -- Lantronix Inc. (NASDAQ: LTRX), a global provider of Software as a Service (SaaS), engineering services and hardware for Edge Computing, the Internet of Things (IoT) and Remote Environment Management (REM), today announced the availability of its new Lantronix Open-Q 610 SOM based on the powerful Qualcomm QCS610System on Chip (SOC). This micro System on Module (SOM) is designed for connected visual intelligence applications with high-resolution camera capabilities, on-device artificial intelligence (AI) processing and native Ethernet interface.

Our long and successful relationship with Qualcomm Technologies enables us to deliver powerful micro SOM solutions that can accelerate IoT design and implementation, empowering innovators to create IoT applications that go beyond hardware and enabletheir wildest dreams, said Paul Pickle, CEO of Lantronix.

The new Lantronix ultra-compact (50mm x 25mm), production-ready Open-Q 610 SOM is based on the powerful Qualcomm QCS610SOC, the latest in the Qualcomm Vision Intelligence Platform lineup targeting smart cameras with edge computing. Delivering up to 50 percent improved AI performance than the previous generation as well as image signal processing and sensor processing capabilities, it is designed to bring smart camera technology, including powerful artificial intelligence and machine learning features formerly only available to high-end devices, into mid-tier camera segments, including smart cities, commercial and enterprise, homes and vehicles.

Bringing Advanced AI and Machine Learning to Smart Camera Application

Created to bring advanced artificial intelligence and machine learning capabilities to smart cameras in multiple vertical markets, the Open-Q 610 SOM is designed for developers seeking to innovate new products utilizing the latest vision and AI edge capabilities, such as smart connected cameras, video conference systems, machine vision and robotics. With the Open-Q 610 SOM, developers gain a pre-tested, pre-certified, production-ready computing module that reduces risk and expedites innovative product development.

The Open-Q 610 SOM provides the core computing capabilities for:

Connectivity solutions include Wi-Fi/BT, Gigabit Ethernet, multiple USB ports and three-camera interfaces.

The Lantronix Open-Q 610 SOM provides advanced artificial intelligence and machine learning capabilities that enable developers to innovate new product designs, including smart connected cameras, video conference systems, machine vision and robotics, said Jonathan Shipman, VP of Strategy at Lantronix Inc. Lantronix micro SOMs and solutions enable IoT device makers to jumpstart new product development and accelerate time-to-market by shortening the design cycle, reducing development risk and simplifying the manufacturing process.

Open-Q 610 Development Kit

The companion Open-Q 610 Development Kit is a full-featured platform with available software tools, documentation and optional accessories. It delivers everything required to immediately begin evaluation and initial product development.

The development kit integrates the production-ready OpenQ 610 SOM with a carrier board, providing numerous expansion and connectivity options to support development and testing of peripherals and applications. The development kit, along with the available documentation, also provides a proven reference design for custom carrier boards, providing a low-risk fast track to market for new products.

In addition to production-ready SOMs, development platforms and tools, Lantronix offers turnkey product development services, driver and application software development and technical support.

For more information, visit Open-Q 610 SOM and Open Q 610 SOM Development kit.

About Lantronix

Lantronix Inc. is a global provider of software as a service (SaaS), engineering services and hardware for Edge Computing, the Internet of Things (IoT) and Remote Environment Management (REM). Lantronix enables its customers to provide reliable and secure solutions while accelerating their time to market. Lantronixs products and services dramatically simplify operations through the creation, development, deployment and management of customer projects at scale while providing quality, reliability and security.

Lantronixs portfolio of services and products address each layer of the IoT Stack, including Collect, Connect, Compute, Control and Comprehend, enabling its customers to deploy successful IoT and REM solutions. Lantronixs services and products deliver a holistic approach, addressing its customers needs by integrating a SaaS management platform with custom application development layered on top of external and embedded hardware, enabling intelligent edge computing, secure communications (wired, Wi-Fi and cellular), location and positional tracking and environmental sensing and reporting.

With three decades of proven experience in creating robust industry and customer-specific solutions, Lantronix is an innovator in enabling its customers to build new business models, leverage greater efficiencies and realize the possibilities of IoT and REM.Lantronixs solutions are deployed inside millions of machines at data centers, offices and remote sites serving a wide range of industries, including energy, agriculture, medical, security, manufacturing, distribution, transportation, retail, financial, environmental, infrastructure and government.

For more information, visit http://www.lantronix.com. Learn more at the Lantronix blog, http://www.lantronix.com/blog, featuring industry discussion and updates. To follow Lantronix on Twitter, please visit http://www.twitter.com/Lantronix. View our video library on YouTube at http://www.youtube.com/user/LantronixInc or connect with us on LinkedIn at http://www.linkedin.com/company/lantronix

Safe Harbor Statement under the Private Securities Litigation Reform Act of 1995: Any statements set forth in this news release that are not entirely historical and factual in nature, including without limitation statements related to our solutions, technologies and products as well as the advanced Lantronix Open-Q 610 SOM, are forward-looking statements. These forward-looking statements are based on our current expectations and are subject to substantial risks and uncertainties that could cause our actual results, future business, financial condition, or performance to differ materially from our historical results or those expressed or implied in any forward-looking statement contained in this news release. The potential risks and uncertainties include, but are not limited to, such factors as the effects of negative or worsening regional and worldwide economic conditions or market instability on our business, including effects on purchasing decisions by our customers; the impact of the COVID-19 outbreak on our employees, supply and distribution chains, and the global economy; cybersecurity risks; changes in applicable U.S. and foreign government laws, regulations, and tariffs; our ability to successfully implement our acquisitions strategy or integrate acquired companies; difficulties and costs of protecting patents and other proprietary rights; the level of our indebtedness, our ability to service our indebtedness and the restrictions in our debt agreements; and any additional factors included in our Annual Report on Form 10-K for the fiscal year ended June 30, 2019, filed with the Securities and Exchange Commission (the SEC) on September 11, 2019, including in the section entitled Risk Factors in Item 1A of Part I of such report, as well as in our other public filings with the SEC. Additional risk factors may be identified from time to time in our future filings. The forward-looking statements included in this release speak only as of the date hereof, and we do not undertake any obligation to update these forward-looking statements to reflect subsequent events or circumstances.

Lantronix Media Contact: Gail Kathryn Miller Corporate Marketing & Communications Manager media@lantronix.com 949-453-7158

Lantronix Analyst and Investor Contact: Jeremy Whitaker Chief Financial Officer investors@lantronix.com 949-450-7241

Lantronix Sales: sales@lantronix.com Americas +1 (800) 422-7055 (US and Canada) or +1 949-453-3990 Europe, Middle East and Africa +31 (0)76 52 36 744 Asia Pacific + 852 3428-2338 China + 86 21-6237-8868 Japan +81 (0) 50-1354-6201 India +91 994-551-2488

2020 Lantronix, Inc. All rights reserved. Lantronix is a registered trademark, and EMG, and SLC are trademarks of Lantronix Inc. Other trademarks and trade names are those of their respective owners.

Qualcomm is a trademark or registered trademark of Qualcomm Incorporated.

Qualcomm Vision Intelligence Platform and Qualcomm QCS610 are products of Qualcomm Technologies, Inc. and/or its subsidiaries.

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Lantronix Brings Advanced AI and Machine Learning to Smart Cameras With New Open-Q 610 SOM Based on the Powerful Qualcomm QCS610 System on Chip (SOC)...

The global digital banking platform market size is expected to reach $10.87 billion by 2027, which means that its expanding at about a 13.6% CAGR (compounded annual growth rate), according to estimates from Allied Market Research (AMR).

A release that summarizes that findings of the report notes:

Growing adoption of online banking over traditional banking drives the growth of the global digital banking platforms market. North America contributed the highest share in 2019, and will maintain its dominance throughout the forecast period. During the Covid-19 pandemic, users are preferring digital banking platforms such as internet banking to avoid physical contact with individuals and prevent transmission of coronavirus.

But the report also mentioned that compliance and online data security issues may begin to limit the growth of the virtual banking market. Despite cybersecurity issues, the digital banking sector is on track to grow steadily in the coming years due to advancements in related technologies such as artificial intelligence (AI) and machine learning (ML).

AI and ML are used to make intelligent decisions about key business and banking processes. They may also be used to analyze large amounts of data in order to determine the creditworthiness of an application. Additionally, AI can help detect suspicious or potentially fraudulent transactions by using context clues or by looking for certain patterns in the way payments or transactions are made.

The report further noted that the Reserve Bank of India (RBI) has confirmed that around twice as many transactions were made via digital banking platforms in April 2020 (when compared to March 2020 which was the time the Coronavirus pandemic began).

Virtual banks across the globe appear to be doing quite well and continue to launch new products and promotional offers. Digital banking group Varo Bank has launched Varo Advice, which is a new product that instantly advances up to $100 to qualifying customers. As noted in a release, the new offer is designed to help customers proactively manage their finances, Varo Advance offers instant access to up to $100 cash right in the Varo Bank app.

US digital banking platform Greenwood recently revealed that there have been over 100,000 sign-ups just days after its debut.

As covered, emerging digital technology breakthroughs in AI and IoT are fundamentally changing consumers banking experience, according to a new report. Meanwhile, another report found that consumers in European countries like Germany are not downloading new digital banking apps as much as expected (in a post COVID world). However, theyre still using the virtual banking apps theyve already installed a lot more than before, the report revealed.

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AI and Machine Learning Technologies Expected to Play a Key Role in Expanding Multi Billion Dollar Digital Banking Sector: Report - Crowdfund Insider

Machine Learning (ML)is constantly being adopted by diverse organizations in an enthusiasm to acquire answers and analysis. As the embracing highly increases, it is often forgotten that machine learning has its flaws that need to be addressed for acquiring a perfect solution.

Applications of artificial intelligence andmachine learning are using new toolsto find practical answers to difficult problems. Companies move forward with the emerging technologies to get a competitive edge on their working style and system. Through the process, organizations are learning a very important lesson that one strategy doesnt fit for all.Business organizations want machine learningto do analysis on large data, which is complex and difficult. They neglect the fact that machine learning cant perform on diverse data storage and even if it does, it will conclude with a wrong prediction.

Analysing unstructured and overwhelming large datasets on machine learning is dangerous. Machine learning might conclude with a wrong solution while performing predictive analysis on such data. The implementation of the misconception in a companys working system might drag down its improvement. Many products that incorporatemachine learning capabilitiesuse predetermined algorithms and many diverse ways to handle data. However, each organizations data has different technical characteristics that might not go well with the existing machine learning configuration.

To address the problems where machine learning falls short, AutoML takes head-on in the companys data analysis perspective. AutoML takes over labour intensive job of choosing and tuning machine learning models. The new technology takes on many repetitive tasks where skilful problem definition and data preparation are needed. It reduces the need to understand algorithm parameters and shortening the compute time needed to produce better models.

Machine learning is an application of artificial intelligence that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. The technology focuses on the development of computer programs that can access data and use it for themselves. It is a model created and trained on a set of previously gathered data, often known as outcomes. The model can be used tomake predictions using that data.

However, machine learning cant get accurate results all the time. It depends on the data scientist handling the machine learning configurations and data inputs. A data scientist studies the input data and understands the desired output to solve business problems. They choose the apt mathematical algorithm from a dozen and tune those parameters called hyperparameters and evaluate the resulting models. The data scientist has the responsibility to adjust the algorithms tuning parameters again and again until the machine learning model produces the desired result. If the results are not tactic, then the data scientist might even start from the very beginning.

Machine learning system struggles to function when the data is too large or unorganised. Some of the other machine learning issues are,

Classification- The process of labeling data can be thought to as a discrimination problem, modeling the similarities between groups.

Regression- Machine learning staggers to predict the value of a new unpredicted data.

Clustering- Data can be divided into groups based on similarity and other measures of natural structure in data. But, human hands are needed to assign names to the groups.

As mentioned earlier, machine learning alone cant address the datasets of an organisation to find predictions. Here are some reasons why tuning a machine learning algorithm is challenging to choose and how AutoML can prove to be useful at such instances.

Choosing the right algorithm: It is not always obvious to choose a perfect algorithm that might work well for building real-value predictions, anomaly detection and classification models for a particular data set. Data scientists have to go through many well-known algorithms of machine learning that could suit the real-world situation. It could take weeks or even months to come up with the right algorithm.

Selecting relevant information: Data storage has diverse data variables or predictors. Henceforth, it is hard to tell which of those data points are significant for making a decision. This process of selecting relevant information to include in data models is called feature selection.

Training machine learning models: The most difficult process in machine learning is to choose a subset of data that can be used for training a machine learning model. In some cases, training against some data variables or predictors can increase training time while actually reducing the accuracy of the ML model.

Automated machine learning (AutoML)basically involves automating the end-to-end process of applying machine learning to real-world problems that are actually relevant in the industry.AutoML makes well-educated guessesto select a suitable ML algorithm and effective initial hyperparameters. The technology tests the accuracy of training the chosen algorithms with those parameters and makes tiny adjustments, and tests the results again. AutoML also automates the creation of small, accurate subsets of data to use for those iterative refinements, yielding excellent results in a fraction of the time.

In a nutshell, AutoML acts as a right tool that quickly chooses, builds and deploys machine learning models that deliver accurate results.

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AutoML Alleviates the Process of Machine Learning Analysis - Analytics Insight

New AI capabilities pave way for an ultra-personalized customer experience

PISCATAWAY, N.J., Oct. 14, 2020 /PRNewswire/ --Futurism Technologies, a leading provider of digital transformation solutions, is bringing to life its Futurism Dimensions business commerce suite with additional artificial intelligence and machine learning capabilities. New AI capabilities will help online companies provide an exceptional personalized online customer experience and user journeys. Futurism Dimensions will not only help companies put their businesses online, but would also help to completely digitize their commerce lifecycle. The commerce life cycle includes digital product catalog creation and placement, AI-driven digital marketing, order generation to fulfillment, tracking, shipments, taxes and financial reporting, all from a unified platform.

With the "new norm," companies are racing to provide a better online experience for their customers. It's not just about putting up a website today, it's about creating personalized and smarter customer experiences. Using customer behavioral analysis, AI, machine learning and bots, Futurism's Dimensions creates that personalized experience. In addition, with Futurism Dimensions, companies become more efficient by transforming the entire commerce value chain and back office to digital.

"Companies such as Amazon have redefined online customer experience and set the bar very high. Every company will be expected to offer personalized, easy-to-use, online experience available from anywhere at any time and on any device," said Sheetal Pansare, CEO of Futurism Technologies. "We've armed Dimensions with advanced AI and ML to help companies provide exceptional personalized experiences to their customers. At the same time, with Dimensions, they can digitize their entire commerce value chain and become more efficient with business automation. Our ecommerce platform is affordable and suited for companies of all sizes," added Mr. Pansare.

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Futurism Dimensions highlights:

Secure and stable platform with 24/7 support and migration

As cybercrimes continue to evolve, e-commerce companies ought to keep up with advanced cybersecurity developments. Futurism Dimensions prides itself on its security for customers allowing them to receive the latest in technological advancements in cybersecurity. Dimensions leverages highly secure two-factor authentication and encryption to safeguard your customers' data and business from potential hackers.

To ensure seamless migration from existing implementations, Dimensions integrates with most legacy systems.

Dimensions offers 24/7 customer support, something you won't find with some of the dead-end platforms of the past. Others will simply have a help page or community forum, but that doesn't necessarily solve the problem. It can also be costly if you need to reach someone for support on other platforms, whereas Dimensions support is included in your plan.

Migrating to Dimensions is a seamless transition with little to no downtime. Protecting online businesses from cyber threats is a top priority while transitioning their websites from another platform or service. You get a dedicated team at your disposal throughout the transition to ensure timely completion and implementation.

Heat Map, Customer Session Playback, Live Chat and Analytics

Dimensions offers intelligent customer insights with Heat Map tracking, Full customer session playback, and live chat allowing you to understand customers' needs. Heat Map will help you identify the most used areas of your website and what your customers are clicking on. Further, customer session playback will help you identify how customers arrived at certain products or pages. Dimensions also has a live customer session that helps you provide prompt support.

Customer insights and analytics are lifeblood for any e-business in today's digital era. Dimensions offers intelligent insights into demographics to help you market to your target audiences.

Highly personalized user experience using Artificial Intelligence

Dimensions lets you deploy smart AI-powered bots that use machine learning algorithms to come up with smarter replies to customer questions thus, reducing response time significantly. Chatbots can help address customer queries that usually drop in after business hours with automated and pre-defined responses. Eureka! Never lose a sale.

Business Efficiency and Automation using AI and Machine Learning

AI and machine learning can help predict inventory and automate processes such as support, payments, and procurement. It can also expand business intelligence to help create targeted marketing plans. Lastly, it can give you live GPS logistics tracking.

Mobile Application

Dimensions team will design your mobile site application to look and function as if a consumer were viewing it on their computer. Fully optimized and designed for ease of use while not limiting anything from your main site.

About Futurism Technologies

Advancements in digital information technology continue to offer companies with the opportunities to drive efficiency, revenue, better understand and engage customers, and redefine their business models. At Futurism, we partner with our clients to leverage the power of digital technology. Digital evolution or a digital revolution, Futurism helps to guide companies on their DX journey.

Whether it is taking a business to the cloud to improve efficiency and business continuity, building a next-generation ecommerce marketplace and mobile app for a retailer, helping to define and implement a new business model for a smart factory, or providing end-to-end cybersecurity services, Futurism brings in the global consulting and implementation expertise it takes to monetize the digital journey.

Futurism provides DX services across the entire value chain including e-commerce, digital infrastructure, business processes, digital customer engagement, and cybersecurity.

Learn more about Futurism Technologies, Inc. at http://www.futurismtechnologies.com

Contact:

Leo J Cole Chief Marketing Officer Mobile: +1-512-300-9744 Email: communication@futurismtechnologies.com

Website: http://www.futurismtechnologies.com

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Futurism Reinforces Its Next-Gen Business Commerce Platform With Advanced Machine Learning and Artificial Intelligence Capabilities - Yahoo Finance