BYND Cannasoft Subsidiary Zigi Carmel Initiatives & Investments LTD. filed U.S. Provisional Patent Application 63461609 on April 25, 2023 covering the mechanical structure, operation, and controlling aspects of a treatment device monitored by sensors and capable of stimulating the male sexual organs based on user preferences

ASHKELON, Israel and VANCOUVER, British Columbia, April 27, 2023 (GLOBE NEWSWIRE) -- BYND Cannasoft Enterprises Inc. (Nasdaq: BCAN) (CSE: BYND) ("BYND Cannasoft" or the "Company") announced today that its Zigi Carmel Initiatives & Investments LTD. subsidiary filed U.S. Provisional Patent Application 63461609 on April 25, 2023, covering the mechanical structure, operation, and controlling aspects of a male treatment device for external use capable of gathering information and creating custom programs according to the collected data from the sensors and uploading the data to the cloud. This U.S. Provisional Patent Application marks BYND Cannasoft's third potential candidate that could introduce new advanced haptic experiences to the fast-growing sexual wellness and sextech market.

The male treatment device utilizes artificial intelligence and machine learning algorithms to control its operational parameters based on the user's physiological parameters. The user, or a partner, can control the device with a smartphone app. Data collected by the device's sensors can be uploaded to the cloud where it will be stored to remember user preferences to create a custom experience for the user.

BYND Cannasoftannounced on March 8, 2023that its Zigi Carmel Initiatives & Investments LTD. subsidiary filed U.S. Provisional Patent Application number 63450503 covering the mechanical structure, operation, and controlling aspects of its smart female treatment device. OnApril 25, 2023, the company announcedit received a positive opinion from the Patent Cooperation Treaty (PCT) for its A.I.-based Female Treatment Device. The Patent Cooperation Treaty (PCT) assists applicants in seeking patent protection internationally for their inventions and currently has 157 contracting states. BYND Cannasoft intends to file a similar application with the PCT for its male treatment device.

AnApril 2023 industry reportby Market Research Future projects the Sexual Wellness Market size could grow to $115.92 billion by 2030 from $84.89 billion in 2022. The report cites the growing prevalence of Sexually Transmitted Diseases (STDs), HIV infection, increasing government initiatives, and NGOs promoting contraceptives as the key market drivers dominating the market growth.According to Forbes, the Sextech Market is expected to grow to $52.7 billion by 2026 from its current $30 billion as online sales continue to grow. BYND Cannasoft plans to develop this A.I.-based smart treatment device for men, its A.I.-based smart treatment device for women, and its EZ-G device.

Yftah Ben Yaackov, CEO and Director of BYND Cannasoft, said, "As the multi-billion-dollar sexual wellness and sextech market continues to grow, the industry is undergoing tremendous changes in consumer preferences as devices are increasingly connected online and enabled with interactive content. In this market, A.I., machine learning, and haptic technology have the potential to personalize the operational parameters of sexual wellness devices based on the physiological parameters of the user." Mr. Ben Yaackov continued, "As a corporate lawyer, I recognize the value of licensing our potential A.I. and machine learning patent portfolio to customers in the sexual wellness market and producing innovative new products. The Board of BYND Cannasoft is committed to protecting the company's I.P. covering this potentially lucrative market and bringing this innovative technology to market."

About BYND Cannasoft Enterprises Inc.

BYND Cannasoft Enterprises is an Israeli-based integrated software and cannabis company. BYND Cannasoft owns and markets "Benefit CRM," a proprietary customer relationship management (CRM) software product enabling small and mediumsized businesses to optimize their daytoday business activities such as sales management, personnel management, marketing, call center activities, and asset management. Building on our 20 years of experience in CRM software, BYND Cannasoft is developing an innovative new CRM platform to serve the needs of the medical cannabis industry by making it a more organized, accessible, and price-transparent market. The Cannabis CRM System will include a Job Management (BENEFIT) and a module system (CANNASOFT) for managing farms and greenhouses with varied crops. BYND Cannasoft owns the patent-pending intellectual property for the EZ-G device. This therapeutic device uses proprietary software to regulate the flow of low concentrations of CBD oil, hemp seed oil, and other natural oils into the soft tissues of the female reproductive system to potentially treat a wide variety of women's health issues. The EZ-G device includes technological advancements as a sex toy with a more realistic experience and the prototype utilizes sensors to determine what enhances the users' pleasure. The user can control the device through a Bluetooth app installed on a smartphone or other portable device. The data will be transmitted and received from the device to and from the secure cloud using artificial intelligence (AI). The data is combined with other antonymic user preferences to improve its operation by increasing sexual satisfaction.

For Further Information please refer to information available on the Companys website: http://www.cannasoft-crm.com, the CSEs website: http://www.thecse.com/en/listings/life-sciences/bynd-cannasoft-enterprises-inc and on SEDAR: http://www.sedar.com.

Gabi KabazoChief Financial OfficerTel: (604) 833-6820email: ir@cannasoft-crm.com

For Media and Investor Relations, please contact:

David L. Kugelman(866) 692-6847 Toll Free - U.S. & Canada(404) 281-8556 Mobile and WhatsAppdk@atlcp.comSkype: kugsusa

Cautionary Note Regarding Forward-Looking Statements

This press release contains forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995 involving risks and uncertainties, which may cause results to differ materially from the statements made. We intend such forward-looking statements to be covered by the safe harbor provisions for forward-looking statements contained in Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended. When used in this document, the words "may," "would," "could," "will," "intend," "plan," "anticipate," "believe," "estimate," "expect," "potential," "continue," "strategy," "future," "project," "target," and similar expressions are intended to identify forward-looking statements, though not all forward looking statements use these words or expressions. All statements contained in this press release other than statements of historical fact, including, without limitation, statements regarding our male treatment device, our Cannabis CRM platform, our expanded EZ-G patent application, our market growth, and our objectives for future operations, are forward looking statements. Additional regulatory standards may be required, including FDA approval or any other approval for the purpose of manufacturing, marketing, and selling the devices under therapeutic indications. There is no certainty that the aforementioned approvals will be received, and all the information in this release is forward-looking. Such statements reflect the company's current views with respect to future events and are subject to such risks and uncertainties. Many factors could cause actual results to differ materially from the statements made, including unanticipated regulatory requests and delays, final patents approval, and those factors discussed in filings made by the company with the Canadian securities regulatory authorities, including (without limitation) in the company's management's discussion and analysis for the year ended December 31, 2022 and annual information form dated March 31, 2023, which are available under the company's profile atwww.sedar.com, and in filings made with the U.S. Securities and Exchange Commission. Should one or more of these factors occur, or should assumptions underlying the forward-looking statements prove incorrect, actual results may vary materially from those described herein as intended, planned, anticipated, or expected. We do not intend and do not assume any obligation to update these forwardlooking statements, except as required by law. Any such forward-looking statements represent management's estimates as of the date of this press release. While we may elect to update such forward-looking statements at some point in the future, we disclaim any obligation to do so, even if subsequent events cause our views to change. Shareholders are cautioned not to put undue reliance on such forwardlooking statements.

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The evolution of artificial intelligence (AI) is reshaping the field of oncology by providing new devices to detect cancer, individualize treatments, manage patients, and more.

Given the large number of patients diagnosed with cancer and amount of data produced during cancer treatment, interest in the application of AI to improve oncologic care is expanding and holds potential.

An aspect of care delivery where AI is exciting and holds so much promise is democratizing knowledge and access to knowledge. Generating more data, bringing together the patient data with our knowledge and research, and developing these advanced clinical decision support systems that use AI are going to be ways in which we can make sure clinicians can provide the best care for each individual patient, Tufia C. Haddad, MD, told Targeted OncologyTM.

While cancer treatment options have only improved over past decades, there is an unmet medical need to make these cancer treatments more affordable and personalized for each patient with cancer.1

As we continue to learn about and better understand the use of AI in oncology, experts can improve outcomes, develop approaches to solve problems in the space, and advance the development of treatments that are made available to patients.

AI is a branch of computer science which works with the simulation of intelligent behavior in computers. These computers follow algorithms which are established by humans or learned by the computer to support decisions and complete certain tasks. Under the AI umbrella lay important subfields.

Machine learning is the process in which a computer can improve its own performance by consistently utilizing newly-generated data into an already existing iterative model. According to the FDA, 1 of the potential benefits of machine learning is its ability to create new insights from the vast amount of data generated during the delivery of health care every day.2

Sometimes, we can use machine learning techniques in a way where we are training the computer to, for example, discern benign pathology, benign pathology from malignant pathology, and so we train the computer with annotated datasets, where we are showing the different images of benign vs malignancy. Ultimately, the computer will bring forward an algorithm that we then take separate data sets that are no longer labeled as benign or malignant. Then we continue to train that algorithm and fine tune the algorithm, said Haddad, medical oncologist, associate professor of oncology at the Rochester Minnesota Campus of the Mayo Clinic.

Deep learning is a smaller part of machine learning where mathematical algorithms are installed using multi-layered computational units which resemble human cognition. These include neural networks with different architeture types including recurrent neural networks, convolutional neural network, and long short-term memory.

Danielle S. Bitterman, MD

Many of the applications integrated into commercial systems are proprietary, so it is hard to know what specific AI methods underlie their system. For some applications, even simple rules-based systems still hold value. However, the recent surge in AI advances is primarily driven by more advanced machine learning methods, especially neural network-based deep learning, in which the AI teaches itself to learn patterns from complex data, Danielle S. Bitterman, MD, told Targeted OncologyTM. For many applications, deep learning methods have better performance, but come at a trade-off of being black boxes, meaning it is difficult for humans to understand how they arrive at their decision. This creates new challenges for safety, trust, and reliability.

Utilizing AI is important as the capacity the human brain must process information is limited, causing an urgent need for the implementation of alternative strategies to process big data. With machine learning and AI, clinicians can obtain increased availability of data, and boost the augmentation of storage and computing power.

As of October 5, 2022, the FDA had approved 521 medical devices which utilize AI and/or machine learning, with the majority of devices in the radiology space.2

Primarily, where it is being more robustly developed and, in some cases, now, at the point of receiving FDA approval and starting to be applied and utilized in the hospitals and clinics, is in the cancer diagnostic space. This includes algorithms to help improve the efficiency and accuracy of, for example, interpreting mammograms. Radiology services, and to some extent, pathology, are where some of these machine learning and deep learning algorithms and AI models are being used, said Haddad.

In radiology, there are many applications of AI, including deep learning algorithms to analyze imaging data that is obtained during routine cancer care. According to Haddad, some of this can include evaluating disease classification, detection, segmentation, characterization, and monitoring a patient with cancer.

According to radiation oncologist Matthew A. Manning, MD, AI is already a backbone of some clinical decision support tools.

The use of AI in oncology is rapidly increasing, and it has the potential to revolutionize cancer diagnosis, treatment, and research. It helps with driving automation In radiation oncology, there are different medical record platforms necessary for the practice that are often separate from the hospital medical record. Creating these interfaces that allow reductions in the redundancy of work for both clinicians and administrative staff is important. Tools using AI and business intelligence are accelerating our efforts in radiation oncology, Manning, former chief of Oncology at Cone Health, told Targeted OncologyTM, in an interview.

Through combining AI human power, mamography screening has been improved for patients with breast cancer. Additionally, deep learning models were trained to classify and detect disease subtypes based on images and genetic data.

To find lung nodules or brain metastases on MRI readouts, AI uses bounding boxes to locate a lesion or object of interest and classify them. Detection using AI supports physicians when they read medical images.

Segmentation involves recognizing these lesions and accessing its volume and size to classify individual pixels based on organ or lesions. Examples of this are brain gliomas as they require quantitative metrics for their management, risk stratification and prognostication.

Deep learning methods have been applied to medical images to determine a large number of features that are undetectable by humans.3 An example of using AI to characteroze tumor come from the study of radiomics, which studies combines disease features with clinicogenomic information. This methods can inform models that successfully predict treatment response and/or adverse effects from cancer treatments.

Radiomics can be applied to a variety of cancer types, including liver, brain, and lung tumors. According to research in Future Science OA1, deep learning using radiomic features from brain MRI also can help differentiate brain gliomas from brain metastasis with similar performance to trained neuroradiologists.

Utilizing AI can dramatically change the ways patients with cancer are monitored. It can detect a multitude of discriminative features in imaging that by humans, are unreadable. One process that is normally performed by radiologists and that plays a major role in determining patient outcomes is measuring how tumors react to cancer treatment.4 However, the process is known to be labor-intensive, subjective, and prone to inconsistency.

To try and alleviate this frequent problem, researchers developed a deep learning-based method that is able to automatically annotate tumors in patients with cancer. Using a small study, researchers from Johns Hopkins Kimmel Comprehensive Cancer Center and its Bloomberg~Kimmel Institute for Cancer Immunotherapy successfully trained a machine learning algorithm to predict which patients with melanoma would respond to treatment and which would not respond. This open-source program, DeepTCR, was valuable as a predictive tool and helped researchers understand the biological mechanisms and responses to immunotherapy.

This program can also help clinicians monitor patients by stratifying patient outcomes, identifying predictive features, and helping them manage patients with the best treatments.

Proper screening for early diagnosis and treatment is a big factor when combating cancer. In the current space, AI makes obtaining results easier and more convenient.

One of the important things to think about AI or the capabilities of AI in oncology is the ability to see what the human eye and the human mind cannot see or interpret today. It is gathering all these different data points and developing or recognizing patterns in the data to help with interpretation. This can augment some of the accuracy for cancer diagnostics. added Haddad.

AI may also provide faster, more accurate results, especially in breast cancer screening. While the incorporation of AI into screening methods is a relatively new and emerging field, it is promising in the early detection of breast cancer, thus resulting in a better prognosis of the condition. For patients with breast cancer, a mammography is the most popular method of breast cancer screening.

Another example of AI in the current treatment landscape for patients with colon cancer is the colonoscopy. Colon cancer screening utilizes a camera to give the gastroenterologist the ability to see inside the colon and bowel. By taking those images, and applying machine learning, deep learning neural network techniques, there is an ability to develop algorithms to not only help to better detect polyps or print precancerous lesions, but also to discern from early-stage or advanced cancers.

In addition, deep learning models can also help clinicians predict the future development of cancer and some AI applications are already being implemented in clinical practice. With further development, as well as refinement of the already created devices, AI will be further applied.

In terms of improving cancer screening, AI has been applied in radiology to analyze and identify tumors on scans. In the current state, AI is making its way into computer-assisted detection on diagnostic films. Looking at a chest CT, trying to find a small nodule, we see that AI is very powerful at finding spots that maybe the human eye may miss. In terms of radiation oncology, we anticipate AI will be very useful ultimately in the setting of clinical decision support, said Manning.

For oncologists, the emergence of the COVID-19 pandemic and time spent working on clinical documentation has only heightened the feeling of burnout. However, Haddad notes that a potential solution to help mitigate feelings of burnout is the development and integration of precision technologies, including AI, as they can help reduce the large amount of workload and increase productivity.

There are challenges with workforce shortages as a consequence of the COVID-19 pandemic with a lot of burnout at unprecedented rates. Thinking about how artificial intelligence can help make [clinicians] jobs easier and make them more efficient. There are smart hospitals, smart clinic rooms, where just from the ingestion of voice, conversations can be translated to the physician and patient into clinical documentation to help reduce the time that clinicians need to be spending doing the tedious work that we know contributes to burnout, including doing the clinical documentation, prior authorizations, order sets, etc, said Haddad.

Numerous studies have been published regarding the potential of machine learning and AI for the prognostication of cancer. Results from these trials have suggested that the performance and productivity of oncologists can be improved with the use of AI.5

An example is with the prediction of recurrences and overall survival. Deep learning can enhance precision medicine and improve clinical decisions, and with this, oncologists may feel emotional satisfaction, reduced depersonalization, and increased professional efficacy. This leaves clinicians with the potential of increased job satisfaction and a reduced feeling of burnout.

Research also has highlighted that the intense workload contributes to occupational stress. This in turn has a negative effect on the quality of care that is offered to patients.

Additionally, it has been reported that administrative tasks, such as collecting clinical, billing, or insurance information, contribute to the workload faced by clinicians, and this leads to a significantly limited time for direct face-to-face interaction between patients and their physicians. Thus, AI has helped significantly reduce this administrative burden.

Overall, if clinicians can do less of the tedious clerical work and spend more time doing the things they were trained to do, like having time with the patient, their overall outlook on their job will be more positive.

AI will help to see that joy restored and to have a better experience for our patient. I believe that AI is going to transform most aspects of medicine over the coming years. Cancer care is extremely complex and generates huge amounts of varied digital data which can be tapped into by computational methods. Lower-level tasks, such as scheduling and triaging patient messages will become increasingly automated. I think we will increasingly see clinical decision-support applications providing diagnostic and treatment recommendations to physicians. AI may also be able to generate novel insights that change our overall approach to managing cancers, said Haddad.

While there have been increasing amounts of updates and developments for AI in the oncology space, according to Bitterman, a large gap remains between AI research and what is already being used.

To bridge this gap, Bitterman notes that there must be further understanding by both clinicians and patients regarding how to properly interact with AI applications, and best optimize interactions for safety, reliability, and trust.

Digital data is still very siloed within institutions, and so regulatory changes are going to be needed before we can realize the full value of AI. We also need better standards and methods to assess bias and generalizability of AI systems to make sure that advances in AI dont leave minority populations behind and worsen health inequities.

Additionally, there is a concern that patients voices are being left out of the AI conversation. According to Bitterman, AI applications are developed by using patients data, and as a result, this will likely transform their care journey. To further improve the use of AI for patients with cancer, it is key to get the opinions from patients.

With further research, it should be possible to overcome the current challenges being faced with AI to continue to improve its use, make AI more popular, and improve the overall quality-of-life for patients with cancer.

We need to engage patients at every step of the AI development/implementation lifecycle, and make sure that we are developing applications that are patient-centered and prioritize trust, safety, and patients lived experiences, concluded Bitterman.

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Current Applications of Artificial Intelligence in Oncology - Targeted Oncology

Lityx makes its leading alternative AI and MLOps platform easierto deliver value for organizations focused on digital transformation

WILMINGTON, Del., April 25, 2023 /PRNewswire/ -- Lityx, LLC today announced the release of LityxIQ 6.0, the first AutoML platform to combine machine learning with mathematical optimization in a single, cloud-hosted, no-code platform. A fully integrated enterprise decision engine, LityxIQ 6.0 extends a proven track record of success delivering rapid predictive and prescriptive insights, and simplifies model development, management, deployment, and monitoring to genuinely democratize advanced analytics for organizations of any size.

"Lityx combines a guided Customer Success strategy with our best-in-class LityxIQ platform to get analytics capabilities in the hands of anyone who uses data insights to make critical business decisions," said Paul Maiste, Ph.D., Lityx CEO and president. "LityxIQ is built by data scientists for analysts and statisticians, alike, to accelerate advanced analytics success to days or weeks versus months or years. Plus, LityxIQ provides immediate value to business leaders by making insights easy to understand for arriving at the best decisions faster, at a price to meet any organization's budget."

Lityx next-gen machine learning powers predictive business decisions, making digital transformation easier, affordable.

LityxIQ 6.0 users get enhanced MLOps functionality that streamlines machine learning development and production, ensuring that models remain robust, reliable and scalable. Additionally, through available solution accelerators, LityxIQ 6.0 makes the path from data to insights even faster.

"The platform has included essential tools for managing the end-to-end data lifecycle since our launch, and LityxIQ 6.0 makes decision intelligence even easier through additional data automation and a refreshed interface for a world-class user experience," said Dr. Maiste.

Industries achieving success through LityxIQ include global manufacturers, healthcare, financial services, media and advertising agencies, and more.

Notable enhancements in LityxIQ 6.0 include automated model monitoring, enhanced model performance analysis and comparisons, and additional model exploration tools such as customer engagement profitability optimization and threshold and cost optimization.

About Lityx: Wilmington, Del.-based Lityx, LLC is a software and services company focused on building and deploying advanced analytics and decision intelligence solutions. Founded in 2006, Lityx develops LityxIQ, a cloud-based software-as-a-service, to help business and technical users easily leverage the power of advanced analytics and mathematical optimization to achieve deeper insights and increased ROI rapidly. Lityx delivers LityxIQ 6.0 directly or through a global network of services partners. For more information, visit http://www.lityx.com.

SOURCE Lityx LLC

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News

The security-focused RSA Conference 2023 prominently featured AI, the topic of many sessions and announcements and the specialty of multiple award winners.

The latter includes HiddenLayer, a company that provides security for machine learning, which was named the "Most Innovative Startup" and won the annual RSAC Innovation Sandbox. That contest, in its 18th year, involved 10 finalists who each had three minutes to woo a panel of judges on their respective offerings. As noted, the winner's offering is centered around AI -- specifically machine learning -- like most things these days.

"HiddenLayer was selected by a panel of esteemed judges for helping enterprises safeguard the machine learning models behind their critical products with a comprehensive security platform," RSA Conference said in an April 24 news release. "An AI application security company based out of Austin, Texas, its patent-pending solutions monitor machine learning algorithms for adversarial ML attack techniques."

The company's web site says, "HiddenLayer's patent-pending solution provides a noninvasive, software-based platform that monitors the inputs and outputs of your machine learning algorithms for anomalous activity consistent with adversarial ML attack techniques. Response actions are immediate with a flexible response framework to protect your ML."

Another AI specialist, Concentric AI, a vendor of intelligent AI-based solutions for autonomous data security posture management, won the Publisher's Choice Award for Data Security Posture Management for its Semantic Intelligence solution, bestowed by Cyber Defense Magazine. Concentric AI's site says, "Concentric Semantic Intelligence solution uses sophisticated machine learning technologies to autonomously scan and categorize data. Our solution discovers and categorizes all your data, from financial data to PII/PHI/PCI to intellectual property to business confidential information."

Cyber Defense Magazine has named winners in various categories at the event for years. Several of those categories relate to AI or machine learning, with pertinent winners, their specific award and their category, being:

As far as announcements, there were plenty of AI-themed ones made during the conference from vendors both small and big. For the latter, for example, Google Cloud announced Security AI Workbench, described as an industry-first platform that enables security partners to extend generative AI to their products. You can read more about that in the Virtualization & Cloud Review article, "Google Matches Microsoft with AI-Powered Security Offering."

IBM, which famously switched its focus to hybrid cloud and AI a few years ago, launched the new QRadar Security Suite to speed threat detection and response.

The new offering includes EDR/XDR, SIEM, SOAR and a new cloud-native log management capability, all of which are built around a common user interface, shared insights and connected workflows, said IBM, which listed the following core design elements:

There were also many AI-related sessions, such as "Quick Look: ChatGPT: A New Generation of Dynamic Machine Based Attacks?"

"In the last year there has been much furor around ChatGPT, a chatbot that is evolving both through trained and reinforced learning techniques," RSA said about that session. "As with every scientific advancement that has noble intent, there will inevitably be a scope for misuse. This session will explore the art of the possible and consider whether ML can outsmart the human in the cyber attack domain."

There were plenty more sessions, announcements and awards related to AI at RSA Security 2023, which can be investigated further at the conference site. The big increase in AI-related content compared to a year ago (the pre-ChatGPT era) shows just how much AI is changing the security space, along with most other things these days. The conference is ending today (April 27).

About the Author

David Ramel is an editor and writer for Converge360.

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RSA Conference's 'Most Innovative Startup' Does ... You Guessed It: AI - Virtualization Review

PRESS RELEASE

Published April 27, 2023

The recent analysis by Quadintel on the India Machine Learning Market Report 2023 revolves around various aspects of the market, including characteristics, size and growth, segmentation, regional and country breakdowns, competitive landscape, market shares, trends, strategies, etc. It also includes COVID-19 Outbreak Impact, accompanied by traces of the historic events. The study highlights the list of projected opportunities, sales and revenue on the basis of region and segments. Apart from that, it also documents other topics such as manufacturing cost analysis, Industrial Chain, etc. For better demonstration, it throws light on the precisely obtained data with the thoroughly crafted graphs, tables, Bar & Pie Charts, etc.

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The AI market in India was valued at INR 472.73 Billion in 2020 and is expected to reach INR 2113.60 Billion by 2027, while the global machine learning market was valued at INR 839.55 Billion in 2020 and is anticipated to reach INR 7632.45 Billion by 2027, expanding at a CAGR of ~37.16% during the 2021-2027 period. AI adoption has become significant in various corporations, with employees from non-technological backgrounds incorporating AI processes into their functional roles.

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Key Market Features:Major subjects like revenue, capacity, price, rate, production rate, gross production, capacity utilization, consumption, cost, CAGR, import/export, supply/demand, market share, and gross margin are all assessed in the research and mentioned in the study. It also documents a thorough analysis of the most important market factors and their most recent developments, combined with the pertinent market segments and sub-segments.

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List of Highlights & ApproachThe report is made using a variety of efficient analytical methodologies that offers readers an in-depth research and evaluation on the leading market players and comprehensive insight on what place they are holding within the industry. Analytical techniques, such as Porters five forces analysis, feasibility studies, SWOT analyses, and ROI analyses, are put to use to examine the development of the major market players.

Points Covered in India Machine Learning Market Report:

India Machine Learning Market Research Report

Section 1: India Machine Learning Market Industry Overview

Section 2: Economic Impact on India Machine Learning

Section 3: Market Competition by Industry Producers

Section 4: Productions, Revenue (Value), according to regions

Section 5: Supplies (Production), Consumption, Export, Import, geographically

Section 6: Productions, Revenue (Value), Price Trend, Product Type

Section 7: Market Analysis, on the basis of Application

Section 8: India Machine Learning Market Pricing Analysis

Section 9: Market Chain, Sourcing Strategy, and Downstream Buyers

Section 10: Strategies and key policies by Distributors/Suppliers/Traders

Section 11: Key Marketing Strategy Analysis, by Market Vendors

Section 12: Market Effect Factors Analysis

Section 13: India Machine Learning Market Forecast

..and view more in complete table of Contents

Thank you for reading; we also provide a chapter-by-chapter report or a report based on region, such as North America, Europe, or Asia.

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Available today, customers can automatically generate tailored COIs in under a minute, furthering NEXT's commitment to provide a simple and efficient insurance experience

PALO ALTO, Calif., April 26, 2023 /PRNewswire/ --NEXT Insurance, a leading digital insurtech company transforming small business insurance, today announced the launch and availability of the Certificate of Insurance (COI) Analyzer an innovative, new offering for small business owners to generate free, instant, custom-made COIsto show valid insurance coverage to potential employers in under a minute. This new offering is the latest iteration of NEXT's commitment to advancing innovation in the small business insurance space, fulfilling its promise to provide a simple and streamlined insurance experience.

A COI is often required and may make the difference between being hired or not for a job. NEXT's COI Analyzer enables customers to upload a sample certificate and receive an automatically generated COI within seconds,via the 24/7 self-service portal on desktop or mobile app. Advanced machine learning models read the sample document using Optical Character Recognition (OCR) and an Object Detector Network, to accurately extract and understand the certificate holder details, as well as any special requirements that may be included in the sample certificate.

"Insurance shouldn't stall a small business owner from thriving, it should empower them to build, launch, grow and expand. This new innovation will only speed up the owners' mission to meet the next job opportunity, challenge and goal, and we're excited to be part of that success story," said Effi Fuks-Leichtag CPO at NEXT. "Leveraging the latest machine learning models, we're able to remove the guesswork, likelihood of human error and ensure that the COI is right the first time so that the individual can get back to their passion of running a business."

For businesses including those in construction, retail, cleaning professionals, sports and fitness and more, a new and personalized COI is often required for each and every job. In fact, NEXT has confirmed that some construction business owners may need to share a COI nearly 200 times a year.In 2022, NEXT's customers on average created 16,314 COIs per month, with 9,215 coming from construction businesses 1,204 from retail and 984 from cleaning professionals. With that much documentation from differing customers and businesses, comes countless potential inputs and needs for completing a COI. This also benefits insurance agents who regularly receive COI examples from customers reviewing new job opportunities. They are required to both verify that their clients have the correct coverage, and also create their COI for them. This new feature can now save agents time on both fronts. Now,in less than a minute from start to finish, the COI Analyzer speeds up the process, eliminates errors and ensures a modern experience.

"As a fitness, nutrition and wellness coach, COIs are critical for me to quickly secure jobs and maintain my work with clients," said Laura Jean, founder and CEO of Fit by LJ, Inc. "Every six months I may need to create up to four different COIs, so efficiency and accuracy for each request are crucial. NEXT's COI Analyzer eliminated several tedious steps from the process, saving me an average of 10 minutes. Just recently, I used the COI Analyzer to complete the process and after NEXT automatically sent the proof to my potential employer, I was hired within 20 minutes."

Visit us to learn more about the advantage of NEXT's free digital Certificates of Insurance and how to generate free, instant, custom-made Certificates of Insurance with the COI Analyzer.

About NEXT InsuranceNEXT Insurance is transforming small business insurance with simple, digital and affordable coverage tailored to the self-employed. Trusted by over 450,000 business owners, NEXT offers policies that are easy to buy and provides 24/7 access to Live Certificates of Insurance, Additional Insured, and more, with no extra fees. Revolutionizing a historically complicated insurance industry, NEXT utilizes AI and machine learning to simplify the purchasing process and provide more affordable coverage. Founded in 2016, the company is headquartered in Palo Alto, has received a total of $881 million in venture capital funding, is rated "A- Excellent" by AM Best and has been recognized by CNBC Disruptor 50, Forbes Fintech 50,Inc.'s Best-Led Companies, and Forbes Best Startup Employers. For more information visit NEXTInsurance.com. To learn more about partnering with NEXT and the value of embedded insurance please visit NEXT's partner page. Stay up to date on the latest with NEXT on Twitter, LinkedIn, Facebook and our blog.

SOURCE NEXT Insurance

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The most crucial aspect of every business is its cybersecurity. It aids in ensuring the security and safety of their data. Artificial intelligence and machine learning are in high demand, changing the cybersecurity industry as a whole. Cybersecurity may benefit greatly from machine learning, which can be used to better available antivirus software, identify cyber dangers, and battle online crime. With the increasing sophistication of cyber threats, companies are constantly looking for innovative ways to protect their systems and data. Machine learning is one emerging technology that is making waves in cybersecurity. Cybersecurity professionals can now detect and mitigate cyber threats more effectively by leveraging artificial intelligence and machine learning algorithms. This article will delve into key areas where machine learning is transforming the security landscape.

One of the biggest challenges in cybersecurity is accurately identifying legitimate connection requests and suspicious activities within a companys systems. With thousands of requests pouring in constantly, human analysis can fall short. This is where machine learning can play a crucial role. AI-powered cyber threat identification systems can monitor incoming and outgoing calls and requests to the system to detect suspicious activity. For instance, there are many companies that offer cybersecurity software that utilizes AI to analyze and flag potentially harmful activities, helping security professionals stay ahead of cyber threats.

Traditional antivirus software relies on known virus and malware signatures to detect threats, requiring frequent updates to keep up with new strains. However, machine learning can revolutionize this approach. ML-integrated antivirus software can identify viruses and malware based on their abnormal behavior rather than relying solely on signatures. This enables the software to detect not only known threats but also newly created ones. For example, companies like Cylance have developed smart antivirus software that uses ML to learn how to detect viruses and malware from scratch, reducing the dependence on signature-based detection.

Cyber threats can often infiltrate a companys network by stealing user credentials and logging in with legitimate credentials. It can be challenging to detect with traditional methods. However, machine learning algorithms can analyze user behavior patterns to identify anomalies. By training the algorithm to recognize each users standard login and logout patterns, any deviation from these patterns can trigger an alert for further investigation. For instance, Darktrace offers cybersecurity software that uses ML to analyze network traffic information and identify abnormal user behavior patterns.

Machine learning offers several advantages in the field of cyber security. First and foremost, it enhances accuracy by analyzing vast amounts of data in real time, helping to identify potential threats promptly. ML-powered systems can also adapt and evolve as new threats emerge, making them more resilient against rapidly growing cyber-attacks. Moreover, ML can provide valuable insights and recommendations to cybersecurity professionals, helping them make informed decisions and take proactive measures to prevent cyber threats.

As cyber threats continue to evolve, companies must embrace innovative technologies like machine learning to strengthen their cybersecurity defenses. Machine learning is transforming the cybersecurity landscape with its ability to analyze large volumes of data, adapt to new threats, and detect anomalies in user behavior. By leveraging the power of AI and ML, companies can stay ahead of cyber threats and safeguard their systems and data. Embrace the future of cybersecurity with machine learning and ensure the protection of your companys digital assets.

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Application od Machine Learning in Cybersecurity - Read IT Quik

AI paraphrasing has become so popular nowadays that writers use them every day for improving their own write-ups.

It is no secret that content writing is immensely popular and that content writers have tight deadlines due to huge workloads.

They use content optimization tools to improve their own productivity and spend less time on editing and post-processing. One of the most used types of content optimization tools is paraphrasing tools.

Today, we are going to explore a little bit of what happens behind the scenes in an AI paraphrasing tool.

More specifically, we are going to see what machine learning technologies are applied and how they drive these paraphrasing tools.

We are going to check out what is the process that happens when an AI paraphraser receives some input.

We are also going to look at which kind of ML technology is used during each step of the process. So, lets start.

This is the first step in the paraphrasing process. The software/online tool has to detect the text provided to it and analyze it.

During this analysis, the individual words are recognized, and the meaning of phrases and sentences is extracted.

Depending on the tool being used, tone detection also occurs during this phase.

So, how does all of this happen? Well, in this phase a subfield of machine learning called Natural Language Processing (NLP) is used.

NLP basically combines linguistics, computer science, and artificial intelligence.

With NLP, computer systems are able to understand and interact with natural language in a way similar to humans.

Understanding text with NLP involves the following steps:

This is where it ends if the purpose is just understanding. There are more steps involved if the task requires paraphrasing. So, lets move on.

Now, paraphrasing text can be done in a fair few ways. But lets see what are the two basic ways in which paraphrasing is done with AI tools. There are two steps involved in that.

After understanding the text is over, an AI paraphraser will use machine learning to find out whether the important words and phrases have synonyms or not.

For that purpose, it will run those words/phrases through its own catalog of known words and pick out the ones that have the same meaning.

This is done via machine learning and more specifically it is a machine learning classification task. The tool classifies words according to their meaning. In machine learning, the system learns to find patterns among the given data.

Once it has learned to find these patterns, it can identify them in new and unknown datasets as well.

This is basically what happens during paraphrase identificationpatterns where words having similar meanings are identified.

Then these synonyms are used for paraphrasing the input sentences and changing them syntactically, but not semantically.

Example of a Paraphrasing Tool Using This Technique

You can find a lot of paraphrase tools online that utilize this technique. We will show you an example in which we utilize an AI paraphrasing tool. You can see it below.

In this example, we can see that the different words have been replaced with synonyms that have the same meaning.

Another thing that we can see is that the new words are bold. Clicking on the bold words opens a drop-down list that contains even more synonyms.

This is possible because this paraphrase tool utilizes a machine-learning classification technique.

In paraphrase generation, the classification approach is ditched in favor of the generation approach.

Basically, instead of finding words and phrases that have the same meaning and using them, it generates new sentences and phrases themselves.

There are multiple ways in which this can be done. A popular technique is to use a large language model (LLM) like GPT-4.

This is a pre-trained transformer that can create human-like text from prompts.

Naturally, it is very good at paraphrasing texts too. It is available as an API and many AI paraphrasers use it.

Other approaches that work are using syntactic trees, reinforcement learning, deep learning, and even the combination of several of these techniques.

These approaches are generally more time-consuming compared to using LLMs and pre-trained models.

Example of a Paraphrasing Tool Using This Technique

Nowadays you dont have to find and use GPT-4 raw, instead, you can simply utilize some tools that have GTP-4.

Fortunately, the tool we discussed in our previous example also utilizes GPT-4 in some of its modes. To see an example of this, check out the image below.

You can see that entire phrases are completely changed. Thats possible because of the generation of semantically identical text with the help of large language models.

So, these are some of the machine learning technologies and techniques that are used in AI paraphrasing. Since there are different technologies and not all tools use the same technologies, differences in paraphrasing arise.

Hopefully, this article helped you to understand a little bit more about machine learning technologies used in AI paraphrasing. If you want to learn more about AI, then you can head to our blog and find more information.

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Data augmentation is the process of applying various transformations to the training data. It helps increase the diversity of the dataset and prevent overfitting. Overfitting mostly occurs when you have limited data to train your model.

Here, you will learn how to use TensorFlow's data augmentation module to diversify your dataset. This will prevent overfitting by generating new data points that are slightly different from the original data.

You will use the cats and dogs dataset from Kaggle. This dataset contains approximately 3,000 images of cats and dogs. These images are split into training, testing, and validation sets.

The label 1.0 represents a dog while the label 0.0 represents a cat.

The full source code implementing data augmentation techniques and the one that does not are available in a GitHub repository.

To follow through, you should have a basic understanding of Python. You should also have basic knowledge of machine learning. If you require a refresher, you may want to consider following some tutorials on machine learning.

Open Google Colab. Change the runtime type to GPU. Then, execute the following magic command on the first code cell to install TensorFlow into your environment.

Import TensorFlow and its relevant modules and classes.

The tensorflow.keras.preprocessing.image will enable you to perform data augmentation on your dataset.

Create an instance of the ImageDataGenerator class for the train data. You will use this object for preprocessing the training data. It will generate batches of augmented image data in real time during model training.

In the task of classifying whether an image is a cat or a dog, you can use the flipping, random width, random height, random brightness, and zooming data augmentation techniques. These techniques will generate new data which contains variations of the original data representing real-world scenarios.

Create another instance of the ImageDataGenerator class for the test data. You will need the rescale parameter. It will normalize the pixel values of the test images to match the format used during training.

Create a final instance of the ImageDataGenerator class for the validation data. Rescale the validation data the same way as the test data.

You do not need to apply the other augmentation techniques to the test and validation data. This is because the model uses the test and validation data for evaluation purposes only. They should reflect the original data distribution.

Create a DirectoryIterator object from the training directory. It will generate batches of augmented images. Then specify the directory that stores the training data. Resize the images to a fixed size of 64x64 pixels. Specify the number of images that each batch will use. Lastly, specify the type of label to be binary (i.e., cat or dog).

Create another DirectoryIterator object from the testing directory. Set the parameters to the same values as those of the training data.

Create a final DirectoryIterator object from the validation directory. The parameters remain the same as those of the training and testing data.

The directory iterators do not augment the validation and test datasets.

Define the architecture of your neural network. Use a Convolutional Neural Network (CNN). CNNs are designed to recognize patterns and features in images.

model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3)))

model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, (3, 3), activation='relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())

model.add(Dense(128, activation='relu'))

model.add(Dropout(0.5))

model.add(Dense(1, activation='sigmoid'))

Compile the model by using the binary cross-entropy loss function. Binary classification problems commonly use It. For the optimizer, use the Adam optimizer. It is an adaptive learning rate optimization algorithm. Finally, evaluate the model in terms of accuracy.

Print a summary of the model's architecture to the console.

The following screenshot shows the visualization of the model architecture.

This gives you an overview of how your model design looks.

Train the model using the fit() method. Set the number of steps per epoch to be the number of training samples divided by the batch_size. Also, set the validation data and the number of validation steps.

The ImageDataGenerator class applies data augmentation to the training data in real time. This makes the training process of the model slower.

Evaluate the performance of your model on the test data using the evaluate() method. Also, print the test loss and accuracy to the console.

The following screenshot shows the model's performance.

The model performs reasonably well on never seen data.

When you run code that does not implement the data augmentation techniques, the model training accuracy is 1. Which means it overfits. It also performs poorly on data it has never seen before. This is because it learns the peculiarities of the dataset.

TensorFlow is a diverse and powerful library. It is capable of training complex deep learning models and can run on a range of devices from smartphones to clusters of servers. It has helped power edge computing devices that utilize machine learning.

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Artificial intelligence has transitioned from being justinterestingto deliveringimpact for businesses and consumers

This is an exclusive article series conducted by the Editor Team of CIO News with Abhishek Dwivedi,Vice President of Technology atVista

Introduction

Machine learning and artificial intelligence are rapidly growing fields that have had a significant impact on various industries. Predictions show that the AI market will reach $500 billion by 2023 and an estimated $1,597.1 billion by 2030, highlighting the continued demand for machine-learning technologies in the coming years.

In 2023, we can expect to see increased adoption of ML in several technical segments, including creative AI, autonomous systems, enterprise management, and cybersecurity. ML will continue to play a crucial role in improving efficiency and enhancing work security across a broader range of business fields.

Generative AI

Generative AI allows enterprises to generate a range of content, such as images, videos, and written material, thereby reducing turnaround time. These artificial intelligence networks utilise transfer-style learning or general adversarial networks to create content from various sources. Not only does this technology have obvious applications in marketing, but it could also have a major impact on the media industry. The filmmaking process could be transformed with the ability to restore old films in high definition and enhance special effects. Additionally, building avatars in the metaverse is just one of many limitless possibilities.

Large language models, such as GPT-3, will also play a key role in creating compelling content across various genres, including fiction, non-fiction, and academic articles. However, its important to be aware of potentially malicious applications, including the creation of deep fakes and the spread of fake news and propaganda. To address these emerging threats, GPTZero is already being developed to distinguish between AI-generated content and text written by humans.

Adaptive AI

Artificial intelligence holds the potential for organisations to make rapid progress by continually learning and generating new data insights. Adaptive AI, which can modify its own code to accommodate unforeseen changes, enables design adaptability and resilience. This allows the artificial intelligence system to continuously learn and react to changes in real time, bypassing the traditional learning phase. The operationalization of AI is crucial, as it facilitates the rapid development, deployment, adaptation, and maintenance of artificial intelligence across various enterprise environments. Self-adaptive artificial intelligence models are capable of faster and more accurate development, leading to improved user experiences that adapt to changing real-world situations. The future will belong to a continuous learning approach, adapting to incoming signals and making personalised experiences ubiquitous in any shopping format.

Edge AI

The rise of mobile computing and IoT has led to a massive increase in the number of connected devices, generating a large amount of data at the network edge. This has caused high latency and network bandwidth usage when collecting data in cloud data centres. To address this issue, edge artificial intelligence (Edge AI) has emerged as a solution that balances the use of centralised data centres (the cloud) and devices closer to humans and physical objects (the edge). With advancements in technology such as 5G, low-power, high-performance hardware, and faster networks, edge AI has become more accessible.

Lower computing costs due to reduced data requirements are creating a market for smart and responsive devices, especially in industries such as healthcare and finance, where data management is regulated. With edge AI, models are tailored to the specific edge environment, and critical data is kept within the edge network. Edge AI will see widespread adoption in industries such as smart warehouses, manufacturing, and utilities as organisations aim to reduce the carbon footprint of artificial intelligence and meet sustainability goals.

Explainable AI

Explainable Artificial Intelligence (XAI) is a crucial aspect of artificial intelligence development that enables human users to understand and trust the results generated by machine learning algorithms. XAI helps to describe the workings of an artificial intelligence model, its expected impact, and any potential biases that may be present. This helps to increase the transparency, fairness, and accuracy of artificial intelligence-powered decision-making, building trust and confidence among stakeholders.

There are various techniques that can be used to increase the interpretability of AI models, such as LIME and SHAP. LIME perturbs the inputs and assesses the impact on the output, while SHAP uses a game theory-based approach to analyse the combined effects of various features on the resulting delta. This creates explainability scores that highlight which aspects of the input had the greatest impact on the output. For example, in image-based predictions, the dominant area or pixels contributing to the output can be identified.

As the impact of artificial intelligence continues to increase in business and society, it is crucial to consider the potential ethical issues that may arise from these complex use cases. This includes implementing proper data governance frameworks, tools to detect bias, and factors for transparency to ensure compliance with legal and social structures. Models will need to be thoroughly tested for drifts, humility, and bias, and proper model validation and audit mechanisms with built-in explainability and reproducibility checks will become standard practise to prevent ethical lapses.

Conclusion

In 2023, machine learning will continue to be a promising and rapidly growing field that will present many interesting innovations. Artificial intelligence has transitioned from being justinterestingto deliveringimpact for businesses and consumers. Many core AI technologies like large language models, multimodal machine learning, transformers, and TinyML will gain considerable importance in the near and mid-term future, leading to standardised software and devices that organisations use daily that will become smarter with the infusion of AI.

Also read:AI and ML, two rapidly growing fields in the realm of computer science, Aravind Raghunathan

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CFA Institute Launches Data Science for Investment Professionals Certificate

Certificate will allow participants to learn about the use of AI and machine learning in the investment process and develop in-demand skills for jobs at the intersection of data science and investment management

CFA Institute, the global association of investment professionals, has launched its Data Science for Investment Professionals Certificate designed to provide current or aspiring investment professionals with practical knowledge of the fundamentals of artificial intelligence and machine learning techniques and how they are used in the investment process.

The Data Science for Investment Professionals Certificate is suitable for individuals from a variety of backgrounds and requires no prior data science knowledge. Among those most likely to benefit are current or aspiring investment professionals in roles including, but not limited to, investment analyst, portfolio manager, relationship manager, and trader.

What does studying for the Certificate involve?

The Certificate comprises five interactive courses totalling approximately 100 hours, which participants can study in their own time, followed by a final 90-minute assessment. The content is hands-on application-oriented and includes instructional videos, coding labs, and case studies from industry practitioners.

Participants will learn how to:

The five courses are:

Richard Fernand, Head of Certificate Management at CFA Institute comments:

Data science is sweeping the investment industry, but currently only about one in four investment professionals interested in acquiring the necessary knowledge is actively doing so. As asset managers continue to adapt to the fast-changing dynamics of the AI, big data, and machine learning environment, everyone in an investment role will need to understand how they can utilize data science techniques.

The Data Science for Investment Professionals Certificate seeks to address this skills gap by providing a strong foundational learning and practical content for anyone working in any investment-related job. It equips learners with the knowledge to understand the application of data science in the investment process, as well as the language to be able to explain and translate machine learning concepts and their application to real-world investment problems. These skills will be key for professionals wishing to position themselves for the growing number of jobs found at the intersection of data science and investment management.

According to a CFA Institute report The Future of Work in Investment Management: The Future of Skills and Learning, almost two thirds (64 percent) of surveyed investment professionals report an interest in learning more about AI and machine learning. In the same survey, just three percent of investment professionals say they are already proficient in AI and machine learning concepts.

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New short course launched to upskill the finance sector in Data ... - FE News