For companies operating in the transportation marketplace, speed and accuracy are paramount. Carriers and shippers alike are continually on the lookout for ways to be faster, less expensive and more efficient to the point where standard delivery seems to mirror expedited shipping.

As the industry moves faster and faster, the documents and data generated grow at an exponential rate. Managing these documents often requires manual processes that are time-consuming and can bring any efforts to maximize efficiency to a standstill. The answer isnt to work harder but to work smarter. To avoid wasting time and resources on document processing, it is crucial for every transportation company to update document and data management strategies.

The transportation industry is in the midst of an artificial intelligence (AI) renaissance. Everything from workflow solutions to fleet management and financial decisions can be streamlined using an AI-based automated content management system (CMS).

A CMS is a software application that is used to manage a companys digital content. Transportation companies rely on a CMS to upload, process and distribute the countless documents and data involved in the shipping process. These software applications must be able to interpret data quickly without compromising accuracy.

Transportation organizations need to start applying more technology to manage and react to data, said Larry Kerr, president and CEO of EBE Technologies. In many cases, having the data and not reacting is worse than not having the data at all.

EBE Technologies provides automated workflow solutions for the transportation and logistics industry. The East Moline, Illinois-based companys SHIPS enterprise business process management solution is utilized by more than 600 transportation companies.

It goes without saying that everyone in transportation wants to achieve efficiency, reduce unnecessary costs and improve billing accuracy, but not all believe they have the power to upgrade. The truth, however, is that its never been easier for transportation companies to utilize an AI-powered CMS.

Convincing companies to adopt AI workflow solutions is not difficult, according to Kerr.

Recently, many of our clients have been affected by rising insurance costs, Kerr said. Rates have been softer, which means the additional revenue to cover such costs must come from improvements in operating efficiencies.

However, simply using any CMS isnt sufficient to manage workflows effectively. Some transportation companies are unaware of the additional operating costs associated with the use of their current systems. Many such systems lack the ability to provide interoperability among companies critical systems. In fact, market research firm IDC estimates that companies lose 20-30% in revenue every year due to inefficiencies related to process and content management.

The use of a CMS does save employees a tremendous amount of time from the often tedious process of manually routing documents and entering data. However, these systems may still require each transaction to be manually processed, ensuring its routed safely to the proper department or critical system. According to an EBE Technologies white paper, a CMS with the power of AI workflows allows transportation providers to work by exception. Through this process, only out-of-standard transactions require staff intervention. AI allows providers to staff for exception levels, not 100% of the transaction levels.

The AI-based work-by-exception process is further enhanced by optical character recognition (OCR) technology that possesses the ability to collect data by reading documents. Recognized OCR technology scans and automatically indexes a wide range of documents, including bills of lading, proof-of-delivery documents and invoices. A truly effective CMS, however, is incomplete without unstructured OCR processing, which converts unstructured text and optical marks into data and provides the catalyst for interoperability among critical systems, according to the white paper.

Utilizing both AI and OCR technology allows employees to focus their attention on completing out-of-standard transactions and determining the root cause for the failure. Once determined, the AI engine can be configured to manage such exceptions going forward. As a result, overhead costs and time to completion are reduced while data accuracy is greatly improved.

A CMS powered by AI workflows has a dramatic impact throughout the enterprise. As an example, it is not unheard of for companies relying on a traditional CMS to take upward of a week to gather and process the information needed to submit an invoice. The use of an AI-powered CMS with automated workflows has transportation providers benchmarking their invoice processing time to less than half an hour from the time of delivery, according to EBE.

With AI managing the required documents and how they should be delivered, the possibility of human error is eliminated, which improves your billing functions, Kerr said. When you provide the right documents to your customer quickly, hopefully youll get your payment faster. With many shippers enforcing carrier scorecards regarding document and data availability, AI is now a requirement to meet shippers expectations without additional labor.

According to EBE, utilizing a CMS powered by AI has advantages beyond improved data accuracy and working by exception to lower costs within a transportation organization. In the accounting department, carriers no longer find themselves paying duplicate invoices, net 10 terms are realized and fees for late payments are eliminated. In recruiting, AI allows carriers to respond to and onboard qualified candidates more quickly. In the safety department, data from disparate systems can be analyzed to identify at-risk driver behavior and provide corrective action automatically. In addition, the expiration of Department of Transportation documents and endorsements can be managed through automated processes. These are just a few examples of how an AI-powered CMS eliminates revenue leaks in operations and mitigates potential incidents and fines within the safety department.

Many transportation companies have embraced AI as a functional requirement, but not everyone is on board. As Kerr explained, the use of AI in the back office, as well as interoperability between systems, has only become viable in the last couple of years.

According to Kerr, in the past many systems lacked the ability to integrate with one another, resulting in redundant labor tasks. He noted that those barriers have now been broken, thanks to standard API interfaces among databases allowing for interoperability using AI. The API standardization greatly lowered the cost and risk associated with implementing an AI-based CMS.

EBE possessed the foresight to understand the critical relationship between the data within disparate systems. Through the open architecture of EBE solutions, the company was able to develop a robust AI-powered CMS solution, building upon its prior releases. This solution helps carriers achieve work-by-exception staffing levels, improve data integrity among systems and deliver a superior customer experience.

More here:
Artificial intelligence is critical in todays workflow solutions - FreightWaves

Digital transformation is a multifaceted beast. While the implementation of more bog-standard items like ERP systems are well understood and fairly easily achieved, where does the enterprise stand when implementing emerging technologies such as artificial intelligence (AI) or machine learning?

Such concerns are leading governments to increasingly step in. One of the major perils lies in overreaching; in implementing too much, too fast and being left with solutions for problems that dont exist.

Helpfully, analytics firm EXL released its best practices for orchestrating AI solutions white paper in November 2019, which recommended a number of methods to best implement AI, including a four stage process. The four, from first to last are: envision and define; solution orchestration; operationalization; and shaping and scaling for the future.

Succinctly, the first step involves identifying and limiting the scope of any implementation, with the report reading: Long-term AI strategies are vital, but, the best results come from narrowing that vision so execution can occur in an iterative, agile manner.

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The second involves identifying the real-world factors that could have a potential impact on the implementation: existing infrastructure, the state of data and the talent present at the company.

Third is related to properly rolling it out across the enterprise, what the report terms as ensuring [the solution] solves the business problem or delivers the desired outcomes. That includes determining the method of execution, ensuring change management procedures are in place and identifying areas where the solution can be reused with minimal alteration.

The final stage, meanwhile, is about continually evolving the AI strategy with an eye to the future, to avoid being left behind; as the report reads: Organizations should continually evaluate what they want their operations to look like in the future, and how they can leverage their existing AI investment to shape and scale for that vision.

Whether enterprises will heed such suggestions is yet to be seen. What is certain, however, is that, asas the technology becomes more realistically understood, 2020 represents something of a reckoning for the relationship of AI and business,as a PwC report outlined.

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The struggle to implement AI during digital transformation - Gigabit Magazine - Technology News, Magazine and Website

The smart use of AI requires thoughtful choices.

Our lives are full of trade-offs.

Speed versus accuracy. Efficiency versus predictability. Flexibility versus commitment. Surely Some versus Maybe More.

Artificial Intelligence (AI) presents us with yet another round of trade-offs. Theres no doubt about AIs labor-saving benefits. But at what price? And are the benefits worth the price?

For some thoughtful insights we can turn to Rhonda Scharfs bookAlexa Is Stealing Your Job: The Impact of Artificial Intelligence on Your Future.

In the first part of my conversation with Rhonda (see What Role Will [Does] Artificial Intelligence Play In Your Life?), we explored the evolution of AI in recent years. In this second part of the conversation, Rhonda addresses the all-important issue of privacy and the ways AI is already affecting career choices and opportunities.

Rodger Dean Duncan:If theyre concerned about privacy with their technology devices, what can people do?

Rhonda Scharf

Rhonda Scharf:Turning off the geotracking on your cell phone doesnt mean you cant be tracked. You can be tracked through your phones internal compass, air pressure reading, weather reports, and more. Your location can be accurately identified, even if you are on an airplane!

So, I say, too little, too late. Even if you refuse to use any technology at all, the fact that your cousin posted your photo online means you can be facially identified in the future.

That doesnt mean you have zero privacy, or that big brother is watching. You can limit the privacy invasion by shutting off your phone, passing on wearable technology, removing yourself from social media, and making sure you have no AI gadgets in your home (thermostats, smart speakers, automated plugs, motion sensors, etc.). However, youll remove a lot of conveniences as well as the time- and money-saving features that come with them.

Is it worth it? For some, yes. For me, no. Ill give up my privacy for convenience and support. My theory is that Ive got nothing to hide, so why worry?

Duncan:With rapid advances in AI, the choices for workers seem clearpassively wait for technology to replace their jobs, or be proactive and strategic in discovering how to use technology to create better careers. What are the keys to succeeding with the latter approach?

Scharf:It is essential to ask a lot of questions to determine how quickly youll need to make changes to protect your career.

By asking yourself these key questions, you will open your eyes to your imminent future. By responding rather than reacting, you can create a better career.

Duncan:What contributions do you expect AI to make in the fields of teaching and learning?

If you don't want to be left behind, you'd better get educated on AI.

Scharf:There is undoubtedly potential for AI to impact the fields of teaching and learning through the use of systems, such as the automatic grading of papers (the same way AI can scan resumes and identify ideal applicants today).

Imagine if droids or chatbots taught our children. Each child would have a customized learning environment, with the lessons specific to the needs of the child. Imagine having the ability to ask every single question you needed to ask, and having things explicitly explained for you. AI would know that it took you 10 percent longer than average to answer a math question about fractions. It would instinctively know you were taking a little longer to process this information, indicating you were struggling with it. The chatbot or droid would see that you needed more time or more review with that concept. Classrooms would no longer move at the speed of the slowest learners but instead move at the speed of each learner.

Duncan:What can todays companies learn from the Blockbuster versus Netflix experience?

Scharf:Blockbuster was a giant in the video-rental business. But six years after its peak in the market, it filed for bankruptcy. This wasnt because Blockbuster refused to adapt (the company added video games, video-on-demand, DVDs by mail, etc.). It was because its executives lacked vision; they adapted but didnt forecast.

Netflix did the opposite and forecasted its future based on the changing needs of its clients. Interesting enough, Netflix offered itself for sale to Blockbuster for only $50 million, and Blockbuster turned it down. Netflix is currently worth shy of $135 billion, which makes it the worlds most highly valued media and entertainment company.

When we look to a future with AI, we need to look further than next week. Strategic planning needs to be strategic, not reactive. By taking a long-range view, you can stay ahead of the curve. If you havent employed any AI in your business at this point, you are already reactive. Jump on the bandwagon now; otherwise, youll end up just like Blockbuster: a great company with lousy vision. AI is your prescription for a bright future.

Next: How Will Your Career Be Impacted By Artificial Intelligence?

Read the rest here:
Artificial Intelligence, Privacy, And The Choices You Must Make - Forbes

Radiology is one of the most essential fields in clinical medicine. Experts in this field are specialists in deciphering and diagnosing disease based on various imaging modalities, ranging from ultrasound, magnetic resonance imaging (MRI), computerized tomography (CT), and x-rays. Studies have shown that the use of radiology in clinical practice has exponentially grown over the years: at the Mayo Clinic, between the years 1999 to 2010, use of CT scans increased by 68%, MRI use increased by 85%, and overall use of imaging modalities for diagnostic purposes increased by 75%, all numbers that have likely continued to rise, and indicate the sheer demand and growth of this robust field.

A unique proposal that has become prominent over the last few years to help alleviate this increased demand is the introduction of artificial intelligence (AI) technology into this field. Simply put, the premise of AI as an addition to the practice of radiology is straightforward, and has been envisioned in two main ways: 1) a system that can be programmed with pre-defined criteria and algorithms by expert radiologists, which can then be applied to new, straightforward clinical situations, or 2) deep learning methods, where the AI system relies on complex machine learning and uses neural-type networks to learn patterns via large volumes of data and previous encounters; this can then be used to interpret even the most complicated and abstract images.

Variety of body scans.

However, while much of the theoretical basis for AI in the practice of radiology is extremely exciting, the reality is that the field has not yet fully embraced it. The most significant issue is that the technology simply isnt ready, as many of the existing systems have not yet been matured to compute and manage larger data sets or work in more general practice and patient settings, and thus, are not able to perform as promised.Other issues exist on the ethical aspects of AI. Given the sheer volume of data required to both train and perfect these systems, as well as the immense data collection that these systems will engage in once fully mainstream, key stakeholders are raising fair concerns and the call for strict ethical standards to be put into place, simultaneous to the technological development of these systems.

Furthermore, the legal and regulatory implications of AI in radiology are numerous and complex. There are significant concerns in the data privacy space, as the hosting of large volumes of patient data for deep learning networks will require increased standards for data protection, cybersecurity, and privacy infrastructure. Additionally, given that AI systems will act as an additional diagnostic tool that must be accounted for in the patient encounter, legal frameworks will be required to fully flush out and navigate where liability falls in the case of misdiagnosis or medical negligence. Will this become an issue for the product manufacturer, or will there be a dynamic sharing of the responsibility by multiple parties? This will depend significantly on the amount of autonomy afforded to these systems.

However, radiologists must remain central to the diagnostic process. While AI systems may be able to detect routine medical problems based on pre-defined criteria, there is significant value provided by a trained radiologist that software simply cannot replace. This includes the clinical correlation of images with the physical state of the patient, qualitative assessments of past images with current images to determine progression of disease, and ultimately the most human aspect of medicine, working with other healthcare teams to make collaborative care decisions.

Using a human brain model to interpret MRI scans.

Indeed, there are significant potential benefits to the mass integration of certain AI systems into the practice of radiology, mainly as a means to augment a physicians workflow, especially given increasing radiology demands in clinical medicine. With some reports citing an expected rise in the use of AI in radiology by nearly 16.5% within the next decade, significant complexities remain unaddressed. However, these issues will ultimately need to be resolved in order to achieve a comprehensively capable and ethically mindful AI infrastructure that can become an integral part of clinical radiology.

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Artificial Intelligence Is Not Ready For The Intricacies Of Radiology - Forbes

The White House is pushing to dedicate an additional billion dollars to fund artificial intelligence research, effectively doubling the budget for that purpose outside of Defense Department spending, Reuters reported today, citing people briefed on the plan. Investment in quantum computing would also receive a major boost.

The 2021 budget proposal would reportedly increase AI R&D funding to nearly $2 billion, and quantum to about $860 million, over the next two years.

The U.S. is engaged in what some describe as a race with China in the field of AI, though unlike most races this one has no real finish line. Instead, any serious lead means opportunities in business and military applications that may grow to become the next globe-spanning monopoly, a la Google or Facebook which themselves, as quasi-sovereign powers, invest heavily in the field for their own purposes.

Simply doubling the budget isnt a magic bullet to take the lead, if anyone can be said to have it, but deploying AI to new fields is not without cost and an increase in grants and other direct funding will almost certainly enable the technology to be applied more widely. Machine learning has proven to be useful for a huge variety of purposes and for many researchers and labs is a natural next step but expertise and processing power cost money.

Its not clear how the funds would be disbursed; Its possible existing programs like federal Small Business Innovation Research awards could be expanded with this topic in mind, or direct funding to research centers like the National Labs could be increased.

Research into quantum computing and related fields is likewise costly. Googles milestone last fall of achieving quantum superiority, or so the claim goes, is only the beginning for the science and neither the hardware nor software involved have much in the way of precedents.

Furthermore quantum computers as they exist today and for the foreseeable future have very few valuable applications, meaning pursuing them is only an investment in the most optimistic sense. However, government funding via SBIR and grants like those are intended to de-risk exactly this kind of research.

The proposed budget for NASA is also expected to receive a large increase in order to accelerate and reinforce various efforts within the Artemis Moon landing program. It was not immediately clear how these funds would be raised or from where they would be reallocated.

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White House reportedly aims to double AI research budget to $2B - TechCrunch

For Immediate Release: February 07, 2020

Today, the U.S. Food and Drug Administration authorized marketing of software to assist medical professionals in the acquisition of cardiac ultrasound, or echocardiography, images. The software, called Caption Guidance, is an accessory to compatible diagnostic ultrasound systems and uses artificial intelligence to help the user capture images of a patients heart that are of acceptable diagnostic quality.

The Caption Guidance software is indicated for use in ultrasound examination of the heart, known as two-dimensional transthoracic echocardiography (2D-TTE), for adult patients, specifically in the acquisition of standard views of the heart from different angles. These views are typically used in the diagnosis of various cardiac conditions.

Echocardiograms are one of the most widely-used diagnostic tools in the diagnosis and treatment of heart disease, said Robert Ochs, Ph.D., deputy director of the Office of In Vitro Diagnostics and Radiological Health in the FDAs Center for Devices and Radiological Health. Todays marketing authorization enables medical professionals who may not be experts in ultrasonography, such as a registered nurse in a family care clinic or others, to use this tool. This is especially important because it demonstrates the potential for artificial intelligence and machine learning technologies to increase access to safe and effective cardiac diagnostics that can be life-saving for patients.

According to the Centers for Disease Control and Prevention, heart disease is the leading cause of death in the United States, killing one out of every four people, or approximately 647,000 Americans each year. The term heart disease refers to several types of heart conditions. The most common type is coronary artery disease, which can cause heart attack. Other kinds of heart disease may involve the valves in the heart, or the heart may not pump well and cause heart failure.

Cardiac diagnostic tests are necessary to identify heart conditions. Among them are electrocardiograms (more widely known as an EKG or ECG), Holter monitors and cardiac ultrasound examinations. The software authorized today is the first software authorized to guide users through cardiac ultrasound image acquisition. The Caption Guidance software was developed using machine learning to train the software to differentiate between acceptable and unacceptable image quality. This knowledge formed the basis of an interactive AI user interface that provides prescriptive guidance to users on how to maneuver the ultrasound probe to acquire standard echocardiographic images and video clips of diagnostic quality. The AI interface provides real-time feedback on potential image quality, can auto-capture video clips, and automatically saves the best video clip acquired from a particular view. Importantly, the cardiologist still reviews the images for a final assessment of the images and videos for patient evaluation.

The Caption Guidance software currently can be used with a specific FDA-cleared diagnostic ultrasound system produced by Teratech Corporation, with the potential to be used with other ultrasound imaging systems that have technical specifications consistent with the range of ultrasound systems used as part of the development and testing.

In its review of this device application, the FDA evaluated data from two independent studies. In one study, 50 trained sonographers scanned patients, with and without the assistance of the Caption Guidance software. The sonographers were able to capture comparable diagnostic quality images in both settings. The other study involved training eight registered nurses who are not experts in sonography to use the Caption Guidance software and asking them to capture standard echocardiography images, followed by five cardiologists assessing the quality of the images acquired. The results showed that the Caption Guidance software enabled the registered nurses to acquire echocardiography images and videos of diagnostic quality.

The FDA is dedicated to ensuring medical device regulation keeps pace with technological advancements, such as todays marketing authorization. This February, the FDA is hosting a public workshop titled Evolving Role of Artificial Intelligence (AI) in Radiological Imaging and seeks to discuss emerging applications of AI in radiological imaging, including AI devices intended to automate the diagnostic radiology workflow, as well as guided image acquisition. Discussions will also focus on best practices for the validation of AI-automated radiological imaging software and image acquisition devices, which is critical to assess safety and effectiveness.

The FDA reviewed the device through the De Novo premarket review pathway, a regulatory pathway for low- to moderate-risk devices of a new type. Along with this authorization, the FDA is establishing special controls for devices of this type, including requirements related to labeling and performance testing. When met, the special controls, along with general controls, provide reasonable assurance of safety and effectiveness for devices of this type. This action creates a new regulatory classification, which means that subsequent devices of the same type with the same intended use may go through FDAs 510(k) premarket process, whereby devices can obtain marketing authorization by demonstrating substantial equivalence to a predicate device.

The FDA granted marketing authorization of the Caption Guidance software to Caption Health Inc.

The FDA, an agency within the U.S. Department of Health and Human Services, protects the public health by assuring the safety, effectiveness, and security of human and veterinary drugs, vaccines and other biological products for human use, and medical devices. The agency also is responsible for the safety and security of our nations food supply, cosmetics, dietary supplements, products that give off electronic radiation, and for regulating tobacco products.

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FDA Authorizes Marketing of First Cardiac Ultrasound Software That Uses Artificial Intelligence to Guide User - FDA.gov

The "Artificial Intelligence (AI) in the Global Freight Transportation Industry, Forecast to 2025" report has been added to ResearchAndMarkets.com's offering.

This study analyses the key trends and applications of artificial intelligence in the freight transportation industry by mode of transport i.e. road, rail, air, and ocean freight transportation. This research also analyses the disruptive impact of artificial intelligence on freight transportation business operations and discusses its adoption prospects until 2025.

Market Insights

With increased trade flow, the fleet population in freight transportation has become denser, and expectations of customers have evolved beyond recognition, resulting in complex transport operations, requiring operational flexibility from freight operators. Human errors in operations, underutilized assets, low workforce productivity, inefficient operational planning, inability to match supply with demand, and trimmed profit margins are key prevailing concerns with freight operators.

The emergence of digital technologies and the rapid technological advancements in digitization have transformed the business and operational landscape of the global freight transportation industry. It is essential for freight operators to embrace such operational complexity and evolve by adopting technologies to turn complexity into an advantage.

Today, the world is connected more than ever, and the growth of data generation has been exponential with smart devices and process automation. Data-driven insights help freight operators move forward and gain a competitive advantage over their peers. Artificial intelligence enables freight operators to harness data more effectively for actionable insights.

Artificial intelligence-powered systems in conjunction with other digital technologies such as internet of things and big data analytics utilize data to its full potential to anticipate events for freight operators, aiding them to avoid risks and create innovative solutions. Machine learning algorithms based on neural networks powered by artificial intelligence would unlock multiple benefits for companies operating in the freight transportation industry.

AI brings changes to the supply chain with autonomous vehicles, helping fleet operators reduce operating cost with and fuel consumption and plan optimized routes for service. The freight operators that are enhancing their capabilities with artificial intelligence are reaping its benefits by increasing efficiency with predictive intelligence. Artificial intelligence also enriches the relationship between the shipper and carrier with personalized service offerings.

Advanced sensor fusion with artificial intelligence supports the integration of smart infrastructure and operating assets and the freight operators in the development of connected freight ecosystem, aiding autonomous fleet management. The transformation of the logistics industry due to artificial intelligence is imperative in the near future; however, the readiness and openness of freight operators for an AI-based data-driven environment will determine how well this industry copes with challenges.

Key Topics Covered:

1. Executive Summary

2. Research Scope and Methodology

3. AI in Logistics Industry

4. AI in Freight Forwarding

5. AI in Freight Transportation

6. Stature of AI Adoption in Freight Transportation

7. Growth Opportunities and Companies to Action

8. The Last Word

Companies Mentioned

For more information about this report visit https://www.researchandmarkets.com/r/nusb51

View source version on businesswire.com: https://www.businesswire.com/news/home/20200207005450/en/

Contacts

ResearchAndMarkets.comLaura Wood, Senior Press Managerpress@researchandmarkets.com For E.S.T Office Hours Call 1-917-300-0470For U.S./CAN Toll Free Call 1-800-526-8630For GMT Office Hours Call +353-1-416-8900

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Global Forecast on Artificial Intelligence (AI) in the Freight Transportation Industry (2020 to 2025) - Disruptive Impact of AI on Freight...

The "5G, Artificial Intelligence, Data Analytics, and IoT Convergence: The 5G and AIoT Market for Solutions, Applications and Services 2020 - 2025" report has been added to ResearchAndMarkets.com's offering.

This research evaluates applications and services associated with the convergence of AI and IoT (AIoT) with data analytics and emerging 5G networks. The AIoT market constitutes solutions, applications, and services involving AI in IoT systems and IoT support of various AI facilitated use cases.

This research assesses the major players, strategies, solutions, and services. It also provides forecasts for 5G and AIoT solutions, applications and services from 2020 through 2025.

Report Findings:

The combination of Artificial Intelligence (AI) and the Internet of Things (IoT) has the potential to dramatically accelerate the benefits of digital transformation for consumer, enterprise, industrial, and government market segments. The author sees the Artificial Intelligence of Things (AIoT) as transformational for both technologies as AI adds value to IoT through machine learning and decision making and IoT adds value to AI through connectivity and data exchange.

With AIoT, AI is embedded into infrastructure components, such as programs, chipsets, and edge computing, all interconnected with IoT networks. APIs are then used to extend interoperability between components at the device level, software level, and platform level. These units will focus primarily on optimizing system and network operations as well as extracting value from data.

It is important to recognize that intelligence within IoT technology market is not inherent but rather must be carefully planned. AIoT market elements will be found embedded within software programs, chipsets, and platforms as well as human-facing devices such as appliances, which may rely upon a combination of local and cloud-based intelligence.

Just like the human nervous system, IoT networks will have both autonomic and cognitive functional components that provide intelligent control as well as nerve end-points that act like nerve endings for neural transport (detection and triggering of communications) and nerve channels that connect the overall system. The big difference is that the IoT technology market will benefit from engineering design in terms of AI and cognitive computing placement in both centralized and edge computing locations.

Taking the convergence of AI and IoT one step further, the publisher coined the term AIoT5G to refer to the convergence of AI, IoT, 5G. The convergence of these technologies will attract innovation that will create further advancements in various industry verticals and other technologies such as robotics and virtual reality.

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.

There will be a positive feedback loop created and sustained by leveraging the interdependent capabilities of AIoT5G. AI will work in conjunction with IoT to substantially improve smart city supply chains. Metropolitan area supply chains represent complex systems of organizations, people, activities, information, and resources involved in moving a product or service from supplier to customer.

Research Benefits

Key Topics Covered

1. Executive Summary

2. Introduction

3. AIoT Technology and Market

4. AIoT Applications Analysis

5. Analysis of Important AIoT Companies

6. AIoT Market Analysis and Forecasts 2020-2025

7. Conclusions and Recommendations

Artificial Intelligence in Big Data Analytics and IoT: Market for Data Capture, Information and Decision Support Services 2020-2025

1. Executive Summary

2. Introduction

3. Overview

4. AI Technology in Big Data and IoT

5. AI Technology Application and Use Case

6. AI Technology Impact on Vertical Market

7. AI Predictive Analytics in Vertical Industry

8. Company Analysis

9. AI in Big Data and IoT Market Analysis and Forecasts 2020-2025

Story continues

10. Conclusions and Recommendations

11. Appendix

5G Applications and Services Market by Service Provider Type, Connection Type, Deployment Type, Use Cases, 5G Service Category, Computing as a Service, and Industry Verticals 2020-2025

1. Executive Summary

2. Introduction

3. LTE and 5G Technology and Capabilities Overview

4. LTE and 5G Technology and Business Dynamics

5. Company Analysis

6. LTE and 5G Application Market Analysis and Forecasts

7. Conclusions and Recommendations

Companies Mentioned

For more information about this report visit https://www.researchandmarkets.com/r/rigm8o

View source version on businesswire.com: https://www.businesswire.com/news/home/20200207005390/en/

Contacts

ResearchAndMarkets.comLaura Wood, Senior Press Managerpress@researchandmarkets.com For E.S.T Office Hours Call 1-917-300-0470For U.S./CAN Toll Free Call 1-800-526-8630For GMT Office Hours Call +353-1-416-8900

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2020-2025 Worldwide 5G, Artificial Intelligence, Data Analytics, and IoT Convergence: Embedded AI Software and Systems in Support of IoT Will Surpass...

The role AI plays in your life is a matter of choice (but only to a certain extent).

It doesnt seem too long ago that artificial intelligence (AI) was mostly the stuff of science fiction. Today it seems to be everywhere: in our home appliances, in our cars, in the workplace, even on our wrists.

To some extent, our use of AI is still a matter of personal choice. But because AI is becoming increasing ubiquitous, we need to make a lot of conscious decisions.

Regardless of the choices we make, we need to stay educated on the evolution of this science. A thoughtful primer on this is Rhonda Scharfs bookAlexa Is Stealing Your Job: The Impact of Artificial Intelligence on Your Future.

My conversation with Rhonda provides some good tips what we should know and what we can do.

Rodger Dean Duncan:AI today is similar to the introduction of the desktop computer three decades ago. Many people resisted computers and got left behind. Whats the best argument for AI today?

Rhonda Scharf

Rhonda Scharf:Artificial Intelligence is not going away. When the desktop computer was introduced in the 1980s, many people felt it was a fad, and it would disappear over time.

Hazel, a woman I worked with, was willing to bet her career on it.When the company I worked at insisted we transition to desktops or leave the company, she rolled the dice and called their bluff. She lost. She believed there was no way a company could exist without tried-and-true manual systems and that computers were a big waste of time and money.

We are in precisely that situation again.

If you can write instructions for a task so that someone can follow them, then AI can replicate those actions.

Duncan:So whats the implication?

Scharf:Not only can your company exist without you performing these tasks, it will also (eventually) be more profitable (with fewer errors) because of it.

By refusing to learn about AIand by refusing to adapt and be flexibleyoure rolling the dice that AI will not take over the tasks you currently do. Call yourself Hazel, and youll soon be out of a job.

AI is alive and well in the workplace, only many people dont realize it. Being nave and refusing to acknowledge what is right under your nose is a recipe for disaster. Take a look around at how much AI we already have in our lives. Artificial Intelligence is not going away. Adapt or become unemployed.

Duncan:Most people have grown comfortable with the idea of letting machines replace humans to do monotonous, heavy, repetitive, and dangerous tasks. But the notion of having AI make decisions and predictions about the future often evokes skepticism or even fear. What do you say to people who have such concerns?

Scharf:Movies like2001: A Space Odyessyand its AI character, HAL 9000, have planted the seeds of fear and mass destruction in our minds. We are afraid of what computers can do on their own. AI learns from its experiences and will make decisions on its owncalculated, logical, and statistically accurate decisions.

What AI doesnt do is make emotional decisions. Take AI stock trading as an example. Without any emotions involved, the robo-advisers can determine the optimal price to buy and sell specific stocks. They dont get emotionally tied into one more day and potentially lose profits. AI can evaluate millions of data points and make conclusions instantly that neither humans nor computers can do. As quickly as the market changes, AI changes its course of action based on the data.

Im not about to have AI make life-or-death decisions for me. The same way we now trust machines to handle monotonous, heavy, repetitive, and dangerous tasks, I will rely on AI to do some heavy thinking and bring me logical conclusions, quickly and efficiently.

If you don't want to be left behind, you'd better get educated on AI.

Duncan:What do you tell people who have privacy concerns about AI applications?

Scharf:The privacy concerns are real, but you gave up your privacy when you got your first mobile phone (for some this was as early as 1996). It could track you. Technically, that impacted your privacy 20-plus years ago.

Once the Blackberry was introduced in 1999, followed by the iPhone eight years later, your privacy became severely compromised. Your phone knows where you are, and it knows what youre doing. Even if you keep your Bluetooth off, your device and its apps know a lot about you.

If you wear any technology whatsoever, you are giving up your privacy. According to a 2014 study by GlobalWebIndex, 71% of people ages 16 to 24 want wearable tech. That was over five years ago before we had much wearable technology.

In the same study, 64% of internet users aged 16 to 64 said theyve either already used a piece of wearable tech or were keen to do so in the future.

Fast forward five years, and half of Americans use fitness trackers daily. More than 96% of Americans have a cell phone of some kind.

People may say they have privacy concerns, but when it comes to using technology that improves our lives, we forgo privacy for convenience.

Next: Artificial Intelligence, Privacy, And The Choices You Must Make

See the original post here:
What Role Will (Or Does) Artificial Intelligence Play In Your Life? - Forbes

In this weeks roundup from the Association for Unmanned Vehicle Systems International, which highlights some of the latest news and headlines in unmanned vehicles and robotics, studying autonomous vehicle operation in Canadian winters, the foundation is laid out for ZM Interactive customers to conduct beyond-line-of-sight drone flights, and unmanned surface vehicles conduct seabed surveys on offshore wind farm turbines.

Scale AI open-sources data set to help in the development of autonomous vehicles capable of driving in wintry weather

This week, a startup called Scale AI open-sourced Canadian Adverse Driving Conditions (CADC), which is a data set that contains more than 56,000 images in conditions including snow created with the University of Waterloo and the University of Toronto.

The move is designed to help in the development of autonomous vehicles capable of driving in wintry weather, as Scale AI claims that CADC is the first corpora with snowy sensor samples to focus specifically on real-world driving in snowy weather.

Snow is hard to drive in as many drivers are well aware. But wintry conditions are especially hard for self-driving cars because of the way snow affects the critical hardware and AI algorithms that power them, explains Scale AI CEO Alexandr Wang in a blog post, via VentureBeat.

A skilled human driver can handle the same road in all weathers but todays AV models cant generalize their experience in the same way. To do so, they need much more data.

According to Scale AI, the routes captured in CADC were chosen based on levels of traffic and the variety of objects such as cars, pedestrians, animals, and most importantly, snowfall. Teams of engineers used an autonomous vehicle platform called Autonomoose to drive a Lincoln MKZ Hybrid mounted with a suite of lidar, inertial sensors, GPS, and vision sensors (including eight wide-angle cameras) along 12.4 miles of Waterloo roads.

Combining human work and review with smart tools, statistical confidence checks, and machine learning checks, Scale AIs data annotation platform was used to label each of the resulting camera images, 7,000 lidar sweeps, and 75 scenes of 50-100 frames. The company says that the accuracy is consistently higher than what a human or synthetic labeling technique could achieve independently, as measured against seven different annotation quality areas.

For University of Waterloo professor Krzysztof Czarnecki, his hope is that the data set will put the wider research community on equal footing with companies that testing self-driving cars in winter conditions, including Alphabets Waymo, Argo, and Yandex.

We want to engage the research community to generate new ideas and enable innovation, Czarnecki says. This is how you can solve really hard problems, the problems that are just too big for anyone to solve on their own.

ZM Interactive selects Iris Automation as detect and avoid provider for its UAS

ZM Interactive (ZMI) has selected Iris Automation as the detect and avoid (DAA) provider for its drones, which will allow ZMI customers to conduct beyond visual line of sight (BVLOS) operations.

ZMI manufactures the xFold drone, which is an industrial, military-grade UAS that comes in various sizes and configurations. Its frame can change between a x4 (Quad), x6 (Hexa), X8 (octo) and X12 (Dodeca) configurations in minutes, and it has a heavy payload capability of more than 300 pounds, making the UAS ideal for a wide range of commercial, industrial, military and emergency response applications. Some of its use cases include aerial cinematography, 3-D Mapping and inspections, and cargo delivery.

Having selected Iris Automation as its DAA provider, ZMI will provide the option of equipping its UAS platforms with Iris Automations Casia system. Described as a turnkey solution, Casia detects, tracks and classifies other aircraft and makes informed decisions about the threat they could potentially pose to the UAS. To avoid collisions, Casia triggers automated maneuvers, and alerts the pilot in command of the mission.

This collaboration between Iris Automation and ZMI allows xFold drone customers to use their drones to their full potential, explains Iris Automation CEO Alexander Harmsen.

Having drones pre-equipped with the option for advanced BVLOS capabilities is a basic requirement the industry will soon expect to see on all drones out-of-the-box.

Under its partnership with ZMI, Iris says that it will also offer customers with Casia onboard regulatory support for Part 107 waiver writing and regulatory approval processes to secure the permissions needed to conduct their unique BVLOS operations.

XOCEAN's XO-450 USV conducts seabed surveys for Greater Gabbard Offshore Wind Farm

Considered a first for the offshore wind sector, XOCEANs XO-450 USV recently conducted seabed surveys on seven of the turbines at the Greater Gabbard Offshore Wind Farm, a joint venture between SSE Renewables and innogy.

To validate data collection before the vessel departed the work locations, experts located in the United Kingdom monitored the data collected from shore in real-time throughout the survey.

According to XOCEAN, the survey demonstrates the highly flexible and collaborative nature of this technology, which ultimately allows industry experts to have direct access to real time data, from any location.

We are constantly looking for innovative ways in which we can operate our fleet of renewables assets, says Jeremy Williamson, SSE Renewables Head of Operations.

XOCEANs vessel will allow us to carry out our work in a more efficient, and most importantly for SSE Renewables and our partners innogy, in the safest way possible. Were really interested to see how this sort of work can help improve our industry and look forward to working with XOCEAN in future.

XOCEAN says that its USVs offer a number of benefits, including keeping operators safe as they remain onshore, efficiency with operations 24 hours a day, seven days a week, and environmental benefits with ultra-low emission. These benefits result in significant economic savings, the company adds.

Our USV platform has demonstrated itself to be a safe, reliable and low carbon solution for the collection of ocean data, says James Ives, CEO of XOCEAN.

We are delighted to be working with SSE and innogy on this ground-breaking project.

Share your vision-related news by contactingDennis Scimeca, Associate Editor, Vision Systems Design

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Artificial intelligence assists in the study of autonomous vehicle performance in winter conditions - Vision Systems Design