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Acumen Research and Consulting, a global provider of market research studies, in a recently published report titledArtificial Intelligence (AI) in Retail Market Global Industry Analysis, Market Size, Opportunities and Forecast, 2020-2027

LOS ANGELES, March 10, 2021 (GLOBE NEWSWIRE) -- The Global Artificial Intelligence (AI) In Retail Market is expected to grow at a CAGR of around 34.9% from 2020 to 2027 and reach the market value of over US$ 6,762.3 Mn by 2027.

Based on regional landscape, North America is dominating the AI in retail market growth owing to its surging investments in retail development activities and presence of large pool of tech- startups. Within North America, US is considered as one of the early adopters for innovative technologies involving AI, augmented reality, virtual reality, and robotics. The presence of prominent players is one of the key factors responsible for the growth of North America regional market. On the other hand, Europe holds the second largest market share for artificial intelligence (AI) in retail market. The factors that contribute for the growth of the regional market involves large pool of retail supply chains involved in the development of the apparel industry utilizing artificial intelligence research. This factor acts positively witnessing healthy regional growth resulting in global market growth of AI in retail.

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Furthermore, APAC is projected to record all time high CAGR in the forthcoming years. As retail companies in the emerging Asia-Pacific are taking up the AI-oriented initiatives it is stimulating the growth of AI in retail in APAC regional market. In addition, Latin America and the Middle East & Africa are projected to demonstrate significant growth in the global market in the forecast period. Development of customer engagement and advanced transition platform are witnessing healthy adoption for AI-driven retail solutions across Latin America.

Market Drivers

Advancements in virtual fitting rooms augment the overall market growth of artificial intelligence (AI) in retail market

Through integration of artificial intelligence with digital mirrors it enhances the buyers to try a variety of dresses, goggles, accessories, and other products without actually wearing them. Artificial intelligence along with advanced technologies such as AI, AR, VR, provides real-time simulation for virtual dressings solutions. Such technology enhances the customer experiences as well as engagement in both online and offline medium.

Restraints

Well established retailers are trying every possible way to improve the engagement of their customers; however, certain factors are limiting the AI in retail market growth rate. The prominent suppliers and global level retailers like Wal-Mart have already deployed artificial based systems to their shops as well as to online portals. However, small and medium sized retailers (SMEs) are still far away from the technology owing to a lack of infrastructure and absence of skilled expertise. The high implementation cost associated retail solutions acts as a major barrier for small retailers that is involved in limiting the adoption. These factors refrain the market of artificial intelligence (AI) in retail market to grow worldwide.

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Market Trend

Product optimization

Product optimization and planning will be growing at a faster pace for AI in retail market globally. The advancements in big data analytics drive the growing adoption of artificial enabled devices and services across different industrial domains and verticals. According to the Consumer Technology Association, AI in retail markets have benefits involving cost saving, increasing productivity, faster resolution of business problems, faster delivery of new products and services, rising innovation, and many more. These factors have a positive influence and help to improve customer analytics and behavior experiences raising the significance of product optimization.

Segmental Outlook

Artificial intelligence (AI) in retail markets is segmented based on technology, deployment model, and application. By technology, the AI retail market is segmented as machine learning, natural language processing, and among others. By deployment model, the AI in retail market is bifurcated into cloud and on-premise. Furthermore, by application, the market is segmented as predictive merchandising, programmatic advertising, in-store visual monitoring and surveillance, location-based marketing, and others.

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Competitive Landscape

The prominent players of the data center servers industry involve International Business Machines Corporation(IBM), Microsoft Corporation, Amazon Web Services, Oracle Corporation, SAP SE, Intel Corporation, NVIDIA Corporation, Google LLC, Sentient Technologies, and among others.

Some of the key observations regarding artificial intelligence (AI) in retail industry include:

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Artificial Intelligence (AI) In Retail Market Value Expected To Reach US$ 6,762.3 Million By 2027: Acumen Research And Consulting - GlobeNewswire

Artificial intelligence can predict with up to 90% accuracy if someone is going to die from the novel coronavirus before they are even infected, a group of scientists from the University of Copenhagen Faculty of Science have found in a study published in the science magazine Nature.Machine learning or artificial intelligence-based computer algorithms that improve automatically through experience by using the collected data was developed during the study and was found to be able to predict the risks at the different stages of illness.The researchers studied 3,944 positive cases in Denmark and used positive cases taken by UK Biobank for "external validation" and took common risk factors such as age, BMI and hypertension into account to formulate the algorithm.The AI model predicted risk of death at different stages: at diagnosis, at hospital admission, and at Intensive Care Unit (ICU) admission.Out of the 3,944 patients who were tracked for the study, 324 died of COVID-19. The men who died were all between 73 and 87 years old with clear signs of high blood pressure and BMI impacting the results. This group of men proved to be the one with the highest risk of mortality as a result, and so the AI program would predict that men in that age range with high blood pressure and BMI are at higher risk.Surprisingly, some of the top risk features "shifted towards markers of shock and organ dysfunction in ICU patients" rather than the aforementioned common risk factors.

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The study developed an algorithm which managed to predict the risk of death and the findings were further backed by the results in the external validation cohort.

Such technology could help hospitals and medical care facilities throughout the world take extra preventative measures and may help prioritizing some patients over others and therein preventing unnecessarily high mortality rates.

This is not the first study to present the potential use of machine learning in taking preventative measures amid the coronavirus pandemic. The Copenhagen study, however, points out that these studies focused on patients already admitted to the hospital while it is unclear "whether the classification ability transfers to other healthcare systems." Another concern was that they were not entirely accurate machine learning algorithms because they did not take milder cases into account.

In addition, the previous studies, according to the researchers, were based on Chinese models which are vulnerable to bias.

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Can artificial intelligence predict whether someone will die from COVID? - The Jerusalem Post

RESEARCH TRIANGLE PARK IBM scientists have utilized artificial intelligence to help speed up development of molecules for potential use in new novel antibiotics that are needed as the spread of antibiotic resistance grows and the need for new drugs increases.

In a blog post and a paper published in Nature Biomedical Engineering, the IBM team said the system would help pace the way to accelerated discovery.

[O]ur IBM Research team has developed an AI system that can help speed up the design of molecules for novel antibiotics. And it works, wroteAleksandra MojsilovicandPayel Das in the blog.

Noting the rise of resistance to antibiotics, the two said the threat is no joke. Its a huge threat to human health even more so during the raging pandemic. We need new antibiotics, and we need them fast.

AI could help provide part of a better solution.

The paper is titled Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics.

[W]e outline how we used it to create two new non-toxic antimicrobial peptides (AMPs) with strong broad-spectrum potency. Peptides are small molecules they are short strings of amino acids, the building blocks of proteins. Our approach outperforms other leading de novo AMP design methods by nearly 10 percent, the two scientists wrote.

The IBM scientists warned that very few newantibiotics are being developed to replace those that no longer work.Thats because drug design is an extremely difficult and lengthy process there are more possible chemical combinations of a new molecule than there are atoms in the Universe.

We want to help, they wrote.

In the papers abstract, the research team notes progress was madein less than seven weeks:

The de novo [from the beginning] design of antimicrobial therapeutics involves the exploration of a vast chemical repertoire to find compounds with broad-spectrum potency and low toxicity. Here, we report an efficient computational method for the generation of antimicrobials with desired attributes. The method leverages guidance from classifiers trained on an informative latent space of molecules modelled using a deep generative autoencoder, and screens the generated molecules using deep-learning classifiers as well as physicochemical features derived from high-throughput molecular dynamics simulations. Within 48days, we identified, synthesized and experimentally tested 20 candidate antimicrobial peptides, of which two displayed high potency

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IBM uses artificial intelligence to develop potential break-throughs in antibiotics - WRAL Tech Wire

Verdict lists the top five terms tweeted on cloud computing in February 2021, based on data from GlobalDatas Influencer Platform. The top tweeted terms are the trending industry discussions happening on Twitter by key individuals (influencers) as tracked by the platform.

The top strategic technology trends for 2021, and the role of AI-powered cloud-based solutions in supporting Covid-19 vaccination drive and businesses were the most popular topics discussed on Twitter in February 2021. Antonio Grasso, the founder and CEO of Digital Business Innovation Srl, an information technology company, tweeted an article on nine interdependent strategic technology trends that will support businesses coherently to overcome the existing socioeconomic challenges of the world.

AI engineering is one such technology listed in the article, which will facilitate execution, scalability, interpretability and trust-worthiness of AI models with the full AI investments value. The distributed cloud that allows organisations to benefit from the public cloud without managing their own expensive and complex private cloud is another technology trend. Privacy-enhancing computation, hyper-automation, cybersecurity mesh, Internet of Behaviour (IoB), total experience strategy, were some of the technology trends discussed in the article.

Ronald Van Loon, CEO of the Intelligent World, an analyst and influencer network, discussed the term to note how AI can remove the IT challenges being faced by healthcare organisations during the Covid-19 vaccination programme. The management of the patient and consumer data through the ordering, shipping and tracking of medicines and vaccines is very complicated due to the lack of coordination among the local, state, and federal government entities.

The potential threat to data security and regulatory compliances are also a major cause of concern. Therefore, various healthcare providers in the US are utilising data analytics and AI to determine who should receive the initial Covid-19 vaccines, according to the specified protocols. Cloud-based programming optimisation solutions are also being utilised to assist in improving the rate of vaccination, simulating the demand and supply disparities and facilitating decision-making on locations for vaccine administration.

AI was also discussed by Craig Milroy, a data capitalist, in relation to the low-code or no-code platforms for rapid design and launch of applications. The platforms are built upon extended cloud-based platform-as-a-service environments to provide rapid and comprehensive solutions to business problems. Salesforce, an all-business platform, OutSystems, an AI-enhanced development, and Appian Low-code Automation Platform are a few leading vendors with low- and no-code offerings.

A new technique of cyberattack called dependency confusion and the ways of destroying the cybercrime economy were most popularly discussed in February 2021. Ratan Jyoti, the chief information security officer at the Ujjivan Small Finance Bank, shared an article published by Microsoft about a new type of cyberattack technique called dependency confusion that can compromise the process of application development in a corporate setting.

A team of security researchers demonstrated that the attackers discover the names of private libraries used in a companys app-building process and upload public libraries containing malicious code. During the app development process, developers confuse their internal library with the malicious library hosted on the public repository and their package manager prioritises the latter. Microsoft has recommended referring to one private feed, protecting the private packages using controlled scopes on public package repositories, and utilising client-side verification features to prevent such cyberattacks.

Malware was also discussed by Marc Wilczek, COO at the IT security provider Link11, through an article on the ways to destroy the cybercrime economy. The distributed denial-of-service (DDoS) attacks have become a more common and effective method for online extortion. The companies need to protect their IT infrastructure with cloud-based services, which can ward off large-scale attacks by spotting anomalies using AI and machine learning before the attack.

Data security management amid rising Big Data and connected devices, Amazon Web Services (AWS) partnership with space companies for streamlining data and the availability of Databricks data platform across all major clouds were the most discussed topics in February. Robin Kiera, the founder of Digitalscouting, a fintech services company, shared an article on how the growth of Big Data and connected devices poses a security risk to collected data.

Cloud-based services, however, can protect this data without the need for organisations to invest in IT infrastructure. The organisations can set extensive cloud-based control security solutions, hosted by a cloud provider to secure the data collected from multiple Internet of Things (IoT)-enabled devices for analysis, the article noted.

Kirk Borne, a principal data scientist at Booz Allen Hamilton, a technology and management consulting services provider, discussed the term with respect to the partnership between Amazons AWS and space companies to streamline the collection, distribution and analysis of data for businesses. The companies such as Capella Space, a satellites operating company, are utilising AWS cloud computing services to reduce the time required to provide data to its customers and to serve them better.

Big Data was also discussed by Andrew Brust, the founder of Blue Badge Insights, a computer software company, in relation to Databricks, the Apache Spark-based cloud data platform, which is now available across three major cloud platforms including AWS, Microsoft Azure and Google Cloud. Databricks can intake a big set of data from various sources and execute a scalable transformation of data. It makes Big Data simple by preparing data, performing analytics, and building data products.

The utility of IoT in safer policing, the requirement for a new type of IoT service provider, and IoT-driven cloud decentralisation were the popularly discussed topics during the month. Giuliano Liguori, the founder of Digital Leaders, digital transformation services company, tweeted on how the police department can be digitally transformed using IoT devices. A graphical representation by BCX, an information and communications company, shared by Liguori highlights the smarter digital solutions for safer policing.

The solutions including mobile applications, biometrics, cloud technology, e-courts, smart corrections, and connected officers can empower the police department and improve the safety of citizens in the long-term. Cloud technology, for example, can offer a secure online portal that connects law enforcement officials, optimise processes and offer sustainable data storage, while reducing paperwork and costs.

Shane Brighton, a customer solutions architect at KCOM, an integration services provider, shared an article that mentioned how the rise in IoT deployments has changed the enterprise connectivity requirements for a more secure and resilient network designed for specific IoT applications. The article highlighted a survey conducted by Pod Group, an Enterprise Network Operator, among technology decision-makers working in organisations in the UK. The survey revealed that the technology enterprises need a new type of IoT connectivity service provider for addressing issues related to technical support and troubleshooting, sensitive data security and new technologies implementation.

IoT was also discussed by Shelly Kramer, principal analyst at Future Research, a digital technology advisory firm, in the context of how IoT and 5G are accelerating the cloud decentralisation process and contributing to the growth of distributed edge computing. IoT and Big Data are the major contributors to the high-performance computing requirements of organisations.

These requirements are increasing cloud computing exponentially and posing unprecedented challenges to technical infrastructure of data centres apart from power, cooling and connectivity. Regionalised hybrid edge clouds are increasingly becoming popular due to these challenges. They bridge the centralised and decentralised IT by combining private cloud with the public cloud services to create an integrated and well-managed computing environment.

The digital transformation of healthcare using cloud computing, data storage advancements helping digital transformation of businesses, and how cloud computing can help overcome the digital transformation challenges were the most talked about topics in February 2021. Yves Mulkers, a data strategist, shared an article on how the digital transformation of the healthcare sector has been influenced by cloud computing. Cloud computing has helped healthcare providers to facilitate high quality and customised patient care at lower operational expenses.

The on-demand availability of computer resources via cloud computing reduces the need for healthcare facilities to spend on hardware and servers. Cloud computing makes the patients data available to the physicians and patients instantaneously from sources, irrespective of locations. It also applies high-powered analytics to improve medical research, while promoting interoperability among sectors such as pharmaceuticals and insurance.

Dana Gardner, the principal analyst at Interarbor Solutions, a digital market research company, discussed the term by sharing an article on the introduction of new flash storage, cloud storage and hybrid-cloud storage systems by IBM. The FlashSystem storage platform of IBM supports hybrid cloud and deployment of container and provides high-end features to the customers. It also includes IBM Storage Insights to make the complex storage environments more visible, and IBM Spectrum Virtualize to virtualise and bring together multi-vendor storage technologies.

Another discussion surrounding digital transformation was shared by Craig Milroy about overcoming the challenges associated with digital transformation using the cloud. The implementation of the work from anywhere (WFA) model during the pandemic across organisations accelerated the use of cloud technologies and improved collaboration and sharing of information across data platforms and digital ecosystems. Cloud technologies will be helpful in integrated business applications and innovation, data modernisation, and social impact driven by AI technologies, the article highlighted.

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Cloud Computing trends: Artificial Intelligence leads Twitter mentions in February 2021 - Verdict

Baidu Inc., is one of the largest providers of Chinese language Internet services. Today, it is also one of the leading artificial intelligence innovators in the world.The company has helped China position itself on the global tech map while also boosting its economy along with Alibaba and Tencent.

Sources reveal that in 2020 alone, Baidus core R&D expenditure accounted for 21.4% of its revenue, becoming one of the top Internet companies with the highest R&D spending.Further, Baidu also claims to have most artificial intelligence-related patent applications in China. This is a testament to Baidus long-term commitment to driving technological advancement. Today, Baidu isactively and often successfully integrating artificial intelligence technologies into all of its major businesses. This ranges from search engine, to drug discovery and even autonomous driving.In 2018, Baidu became the first Chinese company to join an artificial intelligence ethics group (Partnership on AI (PAI)) led by top U.S. tech firms, Alphabet IncsGoogle,AppleInc and Facebook Inc.

Here are some notable innovations in artificial intelligence applications from Baidu:

During the COVID-19 outbreak too, the company had leveraged its expertise in artificial intelligence, and associated technologies and products, to support frontline efforts to prevent and control the pandemic. It created an artificial intelligence system that uses infrared technology to predict passengers temperatures at Beijings Qinghe Railway Station. Its Smart Consulting Assistant has also proved resourceful in helping doctors make rapid diagnoses and initiate treatment online.

Last year, Baidu had also open-sourced its Ribonucleic acid (RNA) prediction algorithm LinearFold. This artificial intelligence algorithm aims to accelerate the prediction time of a viruss RNA secondary structure, which is crucial to understand it and developing vaccines. Researchers found that LinearFold is capable of predicting the secondary structure of the SARS-CoV-2 RNA sequence in only 27 seconds, 120 times faster than other methods. Apart from LinearFold, Baidu has also launched PaddleHelix, a machine learning-based bio-computing framework aimed at facilitating the development of vaccine design, drug discovery, and precision medicine.

Apollois an ambitious, open-source platform from Baidu that is designed to support self-driving vehicles.Apollos deep-learning inference support is designed to handle complex driving environments, including sensor fusion and AI processing. Baidus Automated Valet Parking (AVP) which runs on ACU-Advanced and Xilinxs hardware is also built on Apollo. Last year, Baidu made headlines for its demonstration of Fully Automated Driving without a safety driver via live streaming. Using Apollos new Fully Automated Driving capability, the artificial intelligence system can independently drive without a safety driver inside the vehicle, a breakthrough that will accelerate the large-scale deployment of autonomous driving technology across China.

Baidus PaddlePaddle offers software developers of all skill levels the tools, services, and resources they need to rapidly adopt and implement deep learning at scale. It also hosts toolkits for cutting-edge research purposes, like Paddle Quantum for quantum-computing models and Paddle Graph Learning for graph-learning models. Companies like LinkingMed have used PaddlePaddle to develop an AI-powered pneumonia screening and the lesion-detection system being used in the hospital affiliated with Xiangnan University in Hunan Province. By using Paddle Detection, a PaddlePaddle toolkit for image processing, Jinlu Technology trains an instance-segmentation model for sorting waste plastic bottles.

One of the most sought after yet trickiest challenges of artificial intelligence algorithms is to enhance its NLP abilities. Baidus ERNIE (short for Enhanced Representation through kNowledge Integration), is presently the best in the world by GLUE (General Language Understanding Evaluation) score. ERNIE can understand blocks of language in context and therefore comprehend commands and interactions of all kinds efficiently.Some of the ERNIEs iterations like ERNIE-GEN enable language generation tasks, like dialogue engagement, question generation, and abstractive summarization. In contrast, ERNIE-ViL helps with visual understanding.

In 2015, Baidu launched its intelligent personal assistant, Duer. Also dubbed as the Chinese Apple Siri, Duer includes multi-modal interaction, natural language processing, and other such technologies for a natural interaction and smarter understanding.

Baidus Deep Speechis a state-of-the-art speech recognition system developed using end-to-end deep learning by Baidu Research. It has also developed a production-quality text-to-speech (TTS) system using deep neural network Deep Voice. Baidu mentions that its Deep Voice is faster and more efficient than Googles WaveNet. Baidu also has SwiftScribe, an AI-Powered Transcription Software among its wide array of artificial intelligence innovations. Based on Deep Speech 2, themain function of SwiftScribeis to transcribe audio material into the text in order to solve the problem of consuming a large amount of time-by-word dictation.

Baidu Brain, is another core artificial intelligence innovation from the Beijing based company that features advanced technology for recognizing and processing speech, images and words as well as building user profiles based on big data analysis.

Moreover, last year, Baidu launched its own artificial intelligence-based accelerator called Kunlun K200 SoC. This 256-TOPS accelerator was designed to handle its internal deep-learning workloads. The K200 accelerates common neural-network and SQL operations. On artificial intelligence inference benchmarks, it matches the power efficiency of Nvidias T4 card. This year, Baidu will start mass production of Kunlun 2.

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Baidu : Top Artificial Intelligence Innovations From the Chinese 'Google' - Analytics Insight