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Dublin, Oct. 22, 2020 (GLOBE NEWSWIRE) -- The "Artificial Intelligence of Things: AIoT Market by Technology and Solutions 2020 - 2025" report has been added to ResearchAndMarkets.com's offering.

This AIoT market report provides an analysis of technologies, leading companies and solutions. The report also provides quantitative analysis including market sizing and forecasts for AIoT infrastructure, services, and specific solutions for the period 2020 through 2025. The report also provides an assessment of the impact of 5G upon AIoT (and vice versa) as well as blockchain and specific solutions such as Data as a Service, Decisions as a Service, and the market for AIoT in smart cities.

Many industry verticals will be transformed through AI integration with enterprise, industrial, and consumer product and service ecosystems. It is destined to become an integral component of business operations including supply chains, sales and marketing processes, product and service delivery, and support models.

We see AIoT evolving to become more commonplace as a standard feature from big analytics companies in terms of digital transformation for the connected enterprise. This will be realized in infrastructure, software, and SaaS managed service offerings. More specifically, we see 2020 as a key year for IoT data-as-a-service offerings to become AI-enabled decisions-as-a-service-solutions, customized on a per industry and company basis. Certain data-driven verticals such as the utility and energy services industries will lead the way.

As IoT networks proliferate throughout every major industry vertical, there will be an increasingly large amount of unstructured machine data. The growing amount of human-oriented and machine-generated data will drive substantial opportunities for AI support of unstructured data analytics solutions. Data generated from IoT supported systems will become extremely valuable, both for internal corporate needs as well as for many customer-facing functions such as product life-cycle management.

The use of AI for decision making in IoT and data analytics will be crucial for efficient and effective decision making, especially in the area of streaming data and real-time analytics associated with edge computing networks. Real-time data will be a key value proposition for all use cases, segments, and solutions. The ability to capture streaming data, determine valuable attributes, and make decisions in real-time will add an entirely new dimension to service logic.

In many cases, the data itself, and actionable information will be the service. AIoT infrastructure and services will, therefore, be leveraged to achieve more efficient IoT operations, improve human-machine interactions, and enhance data management and analytics, creating a foundation for IoT Data as a Service (IoTDaaS) and AI-based Decisions as a Service.

The fastest-growing 5G AIoT applications involve private networks. Accordingly, the 5GNR market for private wireless in industrial automation will reach $4B by 2025. Some of the largest market opportunities will be AIoT market IoTDaaS solutions. We see machine learning in edge computing as the key to realizing the full potential of IoT analytics.

Select Report Findings:

Key Topics Covered:

1.0 Executive Summary

2.0 Introduction2.1 Defining AIoT2.2 AI in IoT vs. AIoT2.3 Artificial General Intelligence2.4 IoT Network and Functional Structure2.5 Ambient Intelligence and Smart Lifestyles2.6 Economic and Social Impact2.7 Enterprise Adoption and Investment2.8 Market Drivers and Opportunities2.9 Market Restraints and Challenges2.10 AIoT Value Chain2.10.1 Device Manufacturers2.10.2 Equipment Manufacturers2.10.3 Platform Providers2.10.4 Software and Service Providers2.10.5 User Communities

3.0 AIoT Technology and Market3.1 AIoT Market3.1.1 Equipment and Component3.1.2 Cloud Equipment and Deployment3.1.3 3D Sensing Technology3.1.4 Software and Data Analytics3.1.5 AIoT Platforms3.1.6 Deployment and Services3.2 AIoT Sub-Markets3.2.1 Supporting Device and Connected Objects3.2.2 IoT Data as a Service3.2.3 AI Decisions as a Service3.2.4 APIs and Interoperability3.2.5 Smart Objects3.2.6 Smart City Considerations3.2.7 Industrial Transformation3.2.8 Cognitive Computing and Computer Vision3.2.9 Consumer Appliances3.2.10 Domain Specific Network Considerations3.2.11 3D Sensing Applications3.2.12 Predictive 3D Design3.3 AIoT Supporting Technologies3.3.1 Cognitive Computing3.3.2 Computer Vision3.3.3 Machine Learning Capabilities and APIs3.3.4 Neural Networks3.3.5 Context-Aware Processing3.4 AIoT Enabling Technologies and Solutions3.4.1 Edge Computing3.4.2 Blockchain Networks3.4.3 Cloud Technologies3.4.4 5G Technologies3.4.5 Digital Twin Technology and Solutions3.4.6 Smart Machines3.4.7 Cloud Robotics3.4.8 Predictive Analytics and Real-Time Processing3.4.9 Post Event Processing3.4.10 Haptic Technology

4.0 AIoT Applications Analysis4.1 Device Accessibility and Security4.2 Gesture Control and Facial Recognition4.3 Home Automation4.4 Wearable Device4.5 Fleet Management4.6 Intelligent Robots4.7 Augmented Reality Market4.8 Drone Traffic Monitoring4.9 Real-time Public Safety4.10 Yield Monitoring and Soil Monitoring Market4.11 HCM Operation

5.0 Analysis of Important AIoT Companies5.1 Sharp5.2 SAS5.3 DT425.4 Chania Tech Giants: Baidu, Alibaba, and Tencent5.4.1 Baidu5.4.2 Alibaba5.4.3 Tencent5.5 Xiaomi Technology5.6 NVidia5.7 Intel Corporation5.8 Qualcomm5.9 Innodisk5.10 Gopher Protocol5.11 Micron Technology5.12 ShiftPixy5.13 Uptake5.14 C3 IoT5.15 Alluvium5.16 Arundo Analytics5.17 Canvass Analytics5.18 Falkonry5.19 Interactor5.20 Google5.21 Cisco5.22 IBM Corp.5.23 Microsoft Corp.5.24 Apple Inc.5.25 Salesforce Inc.5.26 Infineon Technologies AG5.27 Amazon Inc.5.28 AB Electrolux5.29 ABB Ltd.5.30 AIBrian Inc.5.31 Analog Devices5.32 ARM Limited5.33 Atmel Corporation5.34 Ayla Networks Inc.5.35 Brighterion Inc.5.36 Buddy5.37 CloudMinds5.38 Cumulocity GmBH5.39 Cypress Semiconductor Corp5.40 Digital Reasoning Systems Inc.5.41 Echelon Corporation5.42 Enea AB5.43 Express Logic Inc.5.44 Facebook Inc.5.45 Fujitsu Ltd.5.46 Gemalto N.V.5.47 General Electric5.48 General Vision Inc.5.49 Graphcore5.50 H2O.ai5.51 Haier Group Corporation5.52 Helium Systems5.53 Hewlett Packard Enterprise5.54 Huawei Technologies5.55 Siemens AG5.56 SK Telecom5.57 SoftBank Robotics5.58 SpaceX5.59 SparkCognition5.60 STMicroelectronics5.61 Symantec Corporation5.62 Tellmeplus5.63 Tend.ai5.64 Tesla5.65 Texas Instruments5.66 Thethings.io5.67 Veros Systems5.68 Whirlpool Corporation5.69 Wind River Systems5.70 Juniper Networks5.71 Nokia Corporation5.72 Oracle Corporation5.73 PTC Corporation5.74 Losant IoT5.75 Robert Bosch GmbH5.76 Pepper5.77 Terminus5.78 Tuya Smart

6.0 AIoT Market Analysis and Forecasts 2020 - 20256.1 Global AIoT Market Outlook and Forecasts6.1.1 Aggregate AIoT Market 2020 - 20256.1.2 AIoT Market by Infrastructure and Services 2020 - 20256.1.3 AIoT Market by AI Technology 2020 - 20256.1.4 AIoT Market by Application 2020 - 20256.1.5 AIoT in Consumer, Enterprise, Industrial, and Government 2020 - 20256.1.6 AIoT Market in Cities, Suburbs, and Rural Areas 2020 - 20256.1.7 AIoT in Smart Cities 2020 - 20256.1.8 IoT Data as a Service Market 2020 - 20256.1.9 AI Decisions as a Service Market 2020 - 20256.1.10 Blockchain Support of AIoT 2020 - 20256.1.11 AIoT in 5G Networks 2020 - 20256.2 Regional AIoT Markets 2020 - 2025

7.0 Conclusions and Recommendations7.1 Advertisers and Media Companies7.2 Artificial Intelligence Providers7.3 Automotive Companies7.4 Broadband Infrastructure Providers7.5 Communication Service Providers7.6 Computing Companies7.7 Data Analytics Providers7.8 Immersive Technology (AR, VR, and MR) Providers7.9 Networking Equipment Providers7.10 Networking Security Providers7.11 Semiconductor Companies7.12 IoT Suppliers and Service Providers7.13 Software Providers7.14 Smart City System Integrators7.15 Automation System Providers7.16 Social Media Companies7.17 Workplace Solution Providers7.18 Enterprise and Government

For more information about this report visit https://www.researchandmarkets.com/r/aw2mh9

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Global Artificial Intelligence of Things Markets 2020-2025: Focus on Technology & Solutions - AIoT Solutions Improve Operational Effectiveness and...

After spending $2.5 billion over five years, Uber is still far from delivering its self-driving vehicles.

As corporate spending on artificial intelligence systems is set to pass $50 billion this year, the vast majority of companies may not be seeing much return on that record investment.

In a survey of more than 3,000 company managers about their AI spend, only 10% reported significant financial benefits from their investment so far, the new report from MIT Sloan Management Review and Boston Consulting Group found.

Gains from the tech havent kept pace with increased adoption, says Shervin Khodabandeh, who led the study and is co-head of BCGs AI business in North America. We are seeing more activity, which also means more investment in technology and data science, Khodabandeh says. But that impact line hasnt really changed.

The results should prove concerning to corporations that continue to pour money into AI projects at a breakneck clip, looking to use the tools for everything from managing contracts to powering home assistants and self-driving cars. More than $50 billion is expected to be invested in AI systems globally this year, according to IDC, up from $37.5 billion in 2019. By 2024, investment is expected to reach $110 billion, IDC forecasts.

But despite the billions invested, failed AI projects have become an increasing factor. IBM has deprioritized its Watson technology after drawing scorn for ventures like one $62 million oncology project that made inaccurate suggestions on cancer treatments. Amazon canned an AI recruitment tool after it showed misogynistic biases. And smaller businesses have found that building the technology is harder than it looks, as supposedly AI-powered virtual assistants and meetings schedulers end up relying on actual humans behind the scenes.

Companies are struggling to deliver on AI projects, Khodabandeh says, because they overspend on technology and data scientists, without implementing changes in the business processes that could benefit from AI a conclusion that echoes a Harvard Business Review report published in June.

Take Uber. Last month, engineers at the ride-hailing company concluded that its self-driving cars couldnt drive more than half a mile before encountering a problem. The programs artificial intelligence still struggles with simple routines and simple maneuvers, per a report in The Information. Part of the reason for the failure, according to an internal memo: competing internal ideas on how to implement the tech.

But with AIs promise of large-scale business savings and improvements, companies arent likely to stop investing in the technology soon. The BCG and MIT researchers found that 57% of companies said they've deployed or piloted their own AI projects, up from 44% in 2018.

For those projects to pay off, Khodabandeh says more AI adopters will need to rethink how the tech is integrated within their businesses. There's clearly a lot of hype, he says. And some of that hype comes out in the data.

The rest is here:
Companies Will Spend $50 Billion On Artificial Intelligence This Year With Little To Show For It - Forbes

Dr. Ahmed Ghazi, a urologist and director of the simulation innovation lab at the University of Rochester (N.Y.) Medical Center, once thought autonomous robotic surgery wasnt possible. He changed his mind after seeing a research group successfully complete a running suture on one of his labs tissue models with an autonomous robot.

It was surprisingly preciseand impressive, Ghazi said. But whats missing from the autonomous robot is the judgment, he said. Every single patient, when you look inside to do the same surgery, is very different. Ghazi suggested thinking about autonomous surgical procedures like an airplane on autopilot: the pilots still there. The future of autonomous surgery is there, but it has to be guided by the surgeon, he said.

Its also a matter of ensuring AI surgical systems are trained on high-quality and representative data, experts say. Before implementing any AI product, providers need to understand what data the program was trained on and what data it considers to make its decisions, said Dr. Andrew Furman, executive director of clinical excellence at ECRI. What data were input for the software or product to make a particular decision must also be weighed, and are those inputs comparable to other populations? he said.

To create a model capable of making surgical decisions, developers need to train it on thousands of previous surgical cases. That could be a long-term outcome of using AI to analyze video recordings of surgical procedures, said Dr. Tamir Wolf, co-founder and CEO of Theator, another company that does just that.

While the companys current product is designed to help surgeons prepare for a procedure and review their performance, its vision is to use insights from that data to underpin real-time decision support and, eventually, autonomous surgical systems.

UC San Diego Health is using a video-analysis tool developed by Digital Surgery, an AI and analytics company Medtronic acquired earlier this year. The acquisition is part of Medtronics strategy to bolster its AI capabilities, said Megan Rosengarten, vice president and general manager of surgical robotics at Medtronic.

Theres a lot of places where were going to build upon that, Rosengarten said. She described a likely evolution from AI providing recommendations for nonclinical workflows, to offering intra-operative clinical decision support, to automating aspects of nonclinical tasks, and possibly to automating aspects of clinical tasks.

Autonomous surgical robots arent a specific end goal Medtronic is aiming for, she said, though the companys current work could serve as building blocks for automation.

Intuitive Surgical, creator of the da Vinci system, isnt actively looking to develop autonomous robotic systems, according to Brian Miller, the companys senior vice president and general manager for systems, imaging and digital.Its AI products so far use the technology to create 3D visualizations from images and extract insights from how surgeons interact with the companys equipment.

To develop an automated robotic product, it would have to solve a real problem identified by customers, Miller said, which he hasnt seen. Were looking to augment what the surgeon or what the users can do, he said.

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Artificial intelligence gets real in the OR - Modern Healthcare

The coronavirus pandemic hit the world at the beginning of the year 2020, and only two months short of entering 2021, the pandemic is still there and the disease is still inflicting thousands of people around the globe. Scientists and researchers from top universities and healthcare facilities have been spending their days and nights in trying to find a way to stop the spread of the disease and to find a cure for the novel virus, but not much has been achieved as yet despite all our modern technology and scientific advancement. Even a vaccine has not been successfully released by any country despite many tall claims.

Amidst all the chaos, the Silicon Valley giant Facebook has recently published a paper in which the company has announced to use Artificial Intelligence to predict the spread of COVID-19 beforehand. This would help the hospitals and healthcare facilities of that area where a spike is predicted to prepare themselves, and It will also give a chance to the people residing in those areas to take proper measures to limit the spread of the virus.

Facebook is going to use the Artificial Intelligence technique to develop a system that will be workable in counties across entire America. This system will look into the inherent qualities and features of the diseases, as well as the social conditions and limitations of the counties that may play a role in the spread of the virus. This way, this system will be able to predict the disease 14-days before it spikes in a county.

The algorithm of this system will be factored with the traits of the disease and the public and anonymized data collected from the county, its time-based case data, etc. This will help the company to predict and influence measures like social distancing, limited mobility, etc. Facebook has also designed a neural autoregressive model, which is capable of separating regional elements from disease-related content in those data sets. So, if one county gets affected by the COVID-19, this model will look into all the data about the cases, the severity of the diseases, and the number of infected people, and based on all the data from one county, it can predict the spike of disease in nearby counties too. This way, those other counties will get a go-ahead and a chance to prepare themselves and their hospitals, etc. with the right elements to limit the spread of the disease. The researchers think that this complex AI model will help many people in the long haul.

Facebook will publish all its predictions every week on the platform of the Humanitarian Data Exchange. And the company is also teaming up with the Universitat Politcnica de Catalunya in Barcelona to include Europe in this new venture too. All the forecasts from America will also be included in European Commission reports helping the researchers and experts there also to understand this disease and to find a way to limit its spread globally.

So, far, these predictions will be county-based. It is not known if Facebook will be able to achieve much success on the state level or not. Besides, the adoption of the system and real-time accuracy in relation to the entire world are several factors that make the whole proposal a little dubious. Let us see how Facebook achieves what it is looking for through this AI system?

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Facebook announces to use Artificial Intelligence to predict the spike of COVID-19 fourteen days before - Digital Information World

Once the AI Partners are integrated in these classrooms, Whitehill and his team will be able to collect data on how students interact with them, and then iteratively make them more intelligent and effective. Initially, the AI Partner might be controlled by a human teacher in a backroom (Wizard of Oz-style interaction), but over time, it can learn from its human controller what to do when and thereby become more autonomous. Whitehill and his team anticipate that the particular form that the Partner takes is likely also important.

Students might find an embodied robot creepy, but they might like interacting with an animated avatar on a touchscreen, he says.

This project represents a shift in how researchers envision AI in the classroom. While earlier work in this field sought to fully automate the teaching process, which Whitehill considers to be infeasible, this project is about human-AI teaming, and how humans and teachers possess complementary abilities. AI Partners can help to magnify teachers existing strengths by increasing the number of students in the classroom who receive the real-time feedback they need for optimal learning.

Whitehill also says that this research will be greatly informative even during the COVID-19 pandemic, when many school districts across the country are participating in remote learning. In fact, he says testing agent-student interactions over platforms like Zoom have certain advantages over in-person interactions.

With Zoom, each student and teacher in the classroom is cleanly separated from each other, and all their audiovisual inputs are channeled through a common software interface. This makes it much easier to analyze their speech, gestures, language, and interactions with each other, Whitehill says. In contrast, in normal, in-person classrooms, the interactions are much messier, since students often sit in all kinds of different positions, might be touching their faces, and work in a noisy environment, which makes it more challenging for the Partner to observe and analyze.

By the end of this research, Whitehill says he hopes to find practical teaching and coaching strategies that AI Partners can execute that work well with students. Its not clear at all that the way humans teach would work well for a computer, robot, or avatar, he says.

While the computational challenges of the projectsignal processing in extremely noisy and cluttered settings, real-time control in an uncertain environment, and human-computer interaction for a novel settingare formidable, Whitehill says the potential rewards make it worth the effort.

The exciting thing about this project is that we get to completely rethink the role of AI in the classroom, he says. My hope is that, through next-generation educational AI, we will be able to stimulate deeper critical thinking and collaboration among students to help them learn better and achieve more.

Jessica Messier

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Can Artificial Intelligence Help Students Work Better Together? According to Research, the Answer is Yes. - WPI News