Machine learning is set to change the manner in which we work together. Machine learning joins mathematics, statistics, and artificial intelligence into another discipline of study. Big data and faster computing power are opening up new capacities for this innovation that appeared to be outlandish just 10 years back. It is being utilized to drive vehicles, recognize faces, trade stocks, and invent lifesaving medicines.

Data is the driver of artificial intelligence and machine learning. Consider it its food the more it eats up the greater, more complex and natural it becomes. A significant number of the worlds driving cloud suppliers currently offer machine learning tools, including Microsoft, Amazon, Google and IBM. The primary benefit these organizations have over their rivals is their admittance to and ability to produce their own big data, which places them in a totally extraordinary class compared to other smaller businesses or startups who cant rival the amount of information these cloud suppliers create consistently.

This has driven these big tech companies to give machine learning as a service to organizations over the globe, permitting customers to choose from a range of the microservices machine learning has made possible.

To truly benefit from AI, organizations should do one of two things: Invest a ton of resources (cash) in data scientists or developers with a foundation in machine learning, or use machine learning as a service (MLaaS) offerings.

Machine learning as a service (MLaaS) is a range of services that offer ML tools as a feature of cloud computing services, as the name proposes. MLaaS suppliers offer tools including data visualization, APIs, natural language processing, deep learning, face recognition, predictive analytics, etc. The suppliers data centers handle the actual computation.

Machine learning as a service alludes to various services cloud suppliers are providing. The fundamental attraction of these services is that users can begin immediately with machine learning without installing software or setting up their own servers, much like any other cloud service.

Aside from the various advantages MLaaS gives, organizations dont have to bear the relentless and repetitive software installation processes.

Four vital participants in the MLaaS market:

Buying a machine learning service from a cloud provider is only the initial phase of using AI. Whenever you have chosen to deploy a natural language processing (NLP) or computer vision solution, you actually need to train the service or algorithm to give appropriate yields. With an absence of data scientists in the workforce, as well as an absence of assets to enlist those that are accessible, usage and consulting partners will flourish because of their understanding of AI and MLaaS.

Machine learning as a service has various conspicuous advantages, for example, quick and low-cost compute options, independence from the weight of building in-house infrastructure from scratch, no compelling reason to put intensely in storage facilities and computing power, and no compelling reason to recruit costly ML architects and data scientists.

The MLaaS platforms can be the most ideal decision for freelance data scientists, new businesses, or organizations where machine learning isnt a fundamental part of their operations. Large organizations, particularly in the tech business and with a heavy spotlight on machine learning, will in general form in-house ML infrastructure that will fulfill their particular necessities and prerequisites.

Link:

Everything to Know About Machine Learning as a Service (MLaaS) - Analytics Insight

In general, when thinking about the food industry, we are likely to think about customer service and takeaway gig-economy services. More recently, the COVID-19 pandemic and how it ties into making or breaking food businesses are at the forefront. Perhaps one of the last things to come to mind when discussing the food industry is modern technology, especially artificial intelligence, and machine learning. However, these technologies have a massive impact on the food and drink industry, and today were going to explore how.

Whether youre looking at the food or the industrys beverage side, every aspect of the process is impacted by machine learning or AI. Hygiene is a massive and important part of the food industry process, specifically when minimizing cross-contamination and maintaining high standards during a pandemic.

In the past, these tasks would be tedious, time and resource-intensive, and potentially expensive if a mistake was made or overlooked. In large manufacturing plants, complex machines would actually need to be disassembled and then put back together for them to be cleaned properly and pumping a large volume of substances through them.

However, with modern technology, this is no longer the case.

Using a technology known as SOCIP, or Self-Cleaning-in-Place, machines can use powerful ultrasonic sensors and fluorescence optical imaging to track food remains on machinery, as well as microbial debris of the equipment, meaning machines only need to be cleaned when they need to, and only in the parts that need cleaning. While this is a new technology and the current problem of overcleaning, it will still save the UK food industry alone around 100 million pounds a year.

Of course, the food and drink industrys waste aspect is a highly debated and criticized part of the industry. The foodservice industry in the UK alone loses around 2.4 billion in wasted food alone, so its only natural that technology is being used to save this money.

Throughout the worlds supply chains, AI is being used to track every single stage of the manufacturing and supply chain process, such as tracking prices, managing inventory stock levels, and even countries of origin.

Solutions that already exist, such as Symphony Retail AI, uses this information to track transportation costs accurately, all pricing mentioned above, and inventory levels to estimate how much food is needed and where to minimize the waste produced.

No matter where you go in the world, food safety standards are always important to follow, and regulations seem to be becoming stricter all the time. In the US, the Food Safety Modernization Act ensures this happens, especially with COVID-19, and countries become more aware of how contaminated food can be.

Fortunately, robots that use AI and machine learning can handle and process food, basically eliminating the chances that contamination can take place through touch. Robots and machinery cannot transmit diseases and such in a way that humans can, thus minimizing the risk of it becoming a problem.

Even in food testing facilities, robot solutions, such as Next Generation Sequencing, a DNA testing solution for food data capturing, and Electric Noses, a machine solution that tests and records the odors of food, are being used in place for humans for more accurate results. At the time of writing, its estimated that around 30% of the food industry currently works with AI and Machine Learning in this way, although this number is set to grow over the coming years.

Theres no doubt that food production uses a ton of water and resources, especially in the meat and livestock industries. This is extremely unsustainable for the planet and very expensive for the producers. To help curb costs and become more sustainable, AI is being used to manage the power and water consumption needed, thus making it as accurate as possible.

This creates instant benefits to the costs of production and profit margins in all areas of the food and drink sector. When you start adding the ability to manage light sources, food for plants and ingredients, and basically introducing a smart way to grow food at its core, then you really start to see better food, more sustainable production practices, and more profits and savings at each stage of the food chain.

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How the Food and Beverage Industry is Affected by Machine Learning and AI - IoT For All

Amazon announces new machine learning tools to help customers monitor machines and worker safety

Amazon Web Services (AWS) on Tuesday launched five new industrial machine learning services aimed at helping industrial plants and factories to improve safety, operational efficiency, and quality control at their workplace.

The company said that companies can use these services to embed artificial intelligence (AI) in their production processes to identify productivity bottlenecks, potential equipment faults, and worker safety and compliance violations.

The five tools, named Amazon Monitron, AWS Panorama Software Development Kit (SDK), AWS Panorama Appliance, Amazon Lookout for Vision and Amazon Lookout for Equipment, combine computer vision, sensor analysis and machine learning capabilities to address technical challenges faced by industrial customers.

The launching of these new services also indicates Amazon's growing ambitions to strengthen its position as a leading player in the industrial cloud sector.

According to Amazon, its Monitron tool is comprised of a gateway, sensors, and machine learning software. The small sensor in Monitron can be attached to equipment to detect abnormal conditions, such as high or low temperatures or vibrations, and predict potential failures.

AWS says it is already using 1,000 Monitron sensors at its fulfilment centres near Mnchengladbach in Germany to monitor conveyor belts handling packages.

AWS Panorama Appliance, meanwhile, enables industrial facilities to use their existing cameras to improve safety and quality control. The tool uses computer vision to analyse video footage and detect safety and compliance issues.

According to the Financial Times, AWS Panorama can be used to detect vehicles bring driven in places where they are not supposed to be. Some big companies, including Deloitte and Siemens, are already testing the system, it said. AWS Panorama SDK allows industrial camera makers to embed computer vision capabilities in their new cameras.

Amazon Lookout for Vision is designed to find flaws and anomalies in processes or products by utilising AWS-trained computer vision models on videos and images.

Amazon Lookout for Equipment gives customers with existing equipment sensors the ability to use machine learning models to detect unusual equipment behaviour to predict future faults.

While AWS claims that industrial plants can use these new tools to improve productivity and safety at their workplaces, privacy campaigners have also raised concerns about these tools.

Earlier this week, the Trades Union Congress (TUC) in the UK released its report into the impact of AI-powered tools on well-being of workers. The report warned that some intrusive technologies being used in companies can have potentially negative effects on "workers' well-being, right to privacy, data protection rights and the right not be discriminated against".

Silkie Carlo, director of privacy group Big Brother Watch, told the BBC that automated workplace monitoring "rarely results in benefits for employees".

"It's a great shame that social distancing has been leapt on by Amazon as yet another excuse for data collection and surveillance," she added.

With concerns about workplace surveillance rising, this week Microsoft apologised for a new productivity score featured introduced in Microsoft 365, which could be used to track individuals' detailed usage of the cloud based productivity suite by administrators. Microsoft says it will remove individual usernames from the productivity score feature.

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Amazon announces new machine learning tools to help customers monitor machines and worker safety - http://www.computing.co.uk

COVID-19 Impact on Global Machine Learning and Location Data ApplicationsMarket Professional Survey Research Report 2020-2027

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The latestMachine Learning and Location Data Applicationsmarket report published by Reports and Markets offers a competency-based analysis and global market estimate, developed using evaluable methods, to provide a clear view of current and expected growth patterns. The report also contains market analysis by geographic location across the globe as well as major markets.

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The key manufacturers covered in this report are @Lockheed Martin, Raytheon, Northrop Grumman, Thales Group, Boeing, Unisys, IBM, FLIR Systems, BAE Systems, General Dynamics, Honeywell International, Elbit Systems, SAIC, Booz Allen Hamilton, Harris, Leidos, and MotoRoLA Solutio

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The given report has been segmented on the basis of various aspects and critical factors that affect the Machine Learning and Location Data Applicationsmarket. The segmentation helps to understand the research as per the various geographies, purpose, applications and other parameters that help provide an in-depth analysis of the market with foresight into the future predictions up to the period of 20xx. The report is also segmented as per regional analysis and factors that play a significant role in key regions while the overall report focuses on the global factors.

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For understanding the impact of the Machine Learning and Location Data Applicationsmarket on global and particular regions, the report analyzes key players and trends to understand the market potential. The report breaks global impacts with the aim to access potential growth and overall market size while the regional report covers impacts in regions such as North America, Latin America, Asia Pacific, Europe, Middle East, Africa, and Asia. The study also analyses the trends in these regions with particular focus on upcoming companies, outlook and prospects for the period 2027.

customized specific regional and country-wise analysis of the key geographical regions as follows:

North America

Europe

Asia Pacific Counter

Middle East & Africa

Latin America

America Country (United States, Canada)

South America

Asia Country (China, Japan, India, Korea)

Europe Country (Germany, UK, France, Italy)

Other Country (Middle East, Africa, GCC)

Crucial points encompassed in the report:

In the end, Machine Learning and Location Data ApplicationsMarket Report delivers a conclusion that includes Breakdown and Data Triangulation, Consumer Needs/Customer Preference Change, Research Findings, Market Size Estimation, Data Source. These factors will increase the business overall. Major queries related Global Machine Learning and Location Data ApplicationsMarket with covid-19 effect resolves in the report: 1. How market players are performing in this covid-19 event? 2. How the pricing of essential raw material and related market affects Machine Learning and Location Data Applicationsmarket. 3. Is covid-19 pandemic already affected on projected region or what will be the maximum impact of covid-19 in region? 4. What will be the CAGR growth of the Machine Learning and Location Data Applicationsmarket during the forecast period? 5. In 2026 what will be the estimated value of Machine Learning and Location Data Applicationsmarket?

TABLE OF CONTENT

1 Report Overview

2 Global Growth Trends

3 Market Share by Key Players

4 Breakdown Data by Type and Application

5 United States

6 Europe

7 China

8 Japan

9 Southeast Asia

10 India

11 Central & South America

12 International Players Profiles

13 Market Forecast 2020-2027

14 Analysts Viewpoints/Conclusions

15 Appendix

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Machine Learning and Location Data Applications Market 2020 Top Companies report covers, Industry Outlook, Top Countries Analysis & Top...

The views expressed here are solely those of the author and do not necessarily represent the views of FreightWaves or its affiliates.

In this installment of the AI in Supply Chain series (#AIinSupplyChain), we explore how Chain of Demand, an early-stage startup based in Hong Kong, is helping companies in the retail industry apply AI and machine learning to increase their profitability and sustainability.

I spoke with AJ Mak, founder and CEO of Chain of Demand. As is customary with these #AIinSupplyChain articles, my first question for him was, What is the problem that Chain of Demand solves for its customers? Who is the typical customer?

He said: Our goal is to improve profitability and sustainability for the retail and supply chain industries. By using our AI analytics, we help retailers to optimize their inventory, which improves margins by minimizing their inventory risk, markdowns and excess inventory. Reducing excess inventory is a huge factor in reducing carbon emissions and water wastage, and this is now more important than ever.

He added, Our typical customers would be omnichannel retailers and brands in the apparel, footwear and beauty and cosmetics categories.

Next I asked, What is the secret sauce that makes Chain of Demand successful? What is unique about your approach? Deep learning seems to be all the rage these days. Does Pathmind use a form of deep learning? Reinforcement learning? Supervised learning? Unsupervised learning? Federated learning?

Our secret sauce includes our veteran experience and domain expertise in retail, and predictive models tailored for the industry, Mak said. We use deep learning for our image recognition and modeling, which includes supervised learning, unsupervised learning and reinforcement learning.

Data is consistently an issue. I asked, How do you handle the lack of high-quality data for AI and machine learning applied to legacy industries?

Part of our AI is used to extract, transform and load dirty data from legacy systems, Mak said. We have done a lot of data cleaning from many different legacy systems, and we have been able to streamline the ETL (extract, transform and load) process for the retail industry.

In a case study published on its website, Chain of Demand describes how it helps its customers.

Bluebell Group helps luxury brands establish a presence in Asia through a platform consisting of 600 online and brick-and-mortar stores spread over more than 10 countries in the region.

Due to changes in the behavior of shoppers, Bluebell needed to help Jimmy Choo Taiwan reconcile how much revenue would be generated by in-store sales in comparison to online purchases. Using Chain of Demand to test and incorporate AI during the merchandise planning process, Bluebell achieved a 90% improvement in the accuracy of its predictions of best- and worst-selling items. Bluebell also increased its accuracy predicting the number of units sold by 81%.

In my conversation with Mak, he pointed out that one reason he believes Chain of Demand fares well against the alternatives is that his family has operated in the apparel and fashion retail supply chain management business since 1981. He spent nearly a decade in the business, gaining an understanding of the problems in global apparel and fashion retail supply chains. That experience and those insights inform how Chain of Demand goes about building its product.

When I asked him about competitors, he mentioned Blue Yonder and Celect.

Coincidentally, Jos P. Chan, who was then the vice president of business development for Celect, was a speaker at #TNYSCM04 Artificial Intelligence & Supply Chains, organized by The New York Supply Chain Meetup in March 2018.

Celect was purchased by Nike in August 2019 for a reported price of $110 million.

Companies like Chain of Demand want to get large companies away from using spreadsheets for sales forecasting and demand planning. As it becomes necessary to take an increasing number of sources and types of data into account, the case for shifting away from simple spreadsheets and onto more robust and sophisticated platforms will only gain strength.

That must sound like music to Maks ears.

Conclusion

If you are a team working on innovations that you believe have the potential to significantly refashion global supply chains, wed love to tell your story in FreightWaves. I am easy to reach on LinkedIn and Twitter. Alternatively, you can reach out to any member of the editorial team at FreightWaves at media@freightwaves.com.

Dig deeper into the #AIinSupplyChain Series with FreightWaves.

Commentary: Optimal Dynamics the decision layer of logistics? (July 7)

Commentary: Combine optimization, machine learning and simulation to move freight (July 17)

Commentary: SmartHop brings AI to owner-operators and brokers (July 22)

Commentary: Optimizing a truck fleet using artificial intelligence (July 28)

Commentary: FleetOps tries to solve data fragmentation issues in trucking (Aug. 5)

Commentary: Bulgarias Transmetrics uses augmented intelligence to help customers (Aug. 11)

Commentary: Applying AI to decision-making in shipping and commodities markets (Aug. 27)

Commentary: The enabling technologies for the factories of the future (Sept. 3)

Commentary: The enabling technologies for the networks of the future (Sept. 10)

Commentary: Understanding the data issues that slow adoption of industrial AI (Sept. 16)

Commentary: How AI and machine learning improve supply chain visibility, shipping insurance (Sept. 24)

Commentary: How AI, machine learning are streamlining workflows in freight forwarding, customs brokerage (Oct. 1)

Commentary: Can AI and machine learning improve the economy? (Oct. 8)

Commentary: Savitude and StyleSage leverage AI, machine learning in fashion retail (Oct. 15)

Commentary: How Japans ABEJA helps large companies operationalize AI, machine learning (Oct. 26)

Commentary: Pathmind applies AI, machine learning to industrial operations (Nov. 20)

Authors disclosure: I am not an investor in any early stage startups mentioned in this article, either personally or through REFASHIOND Ventures. I have no other financial relationship with any entities mentioned in this article.

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Commentary: Chain of Demand applies AI, machine learning to retail supply chain profitability - FreightWaves

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

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

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

Artificial intelligence

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Hydraulic system

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

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

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

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

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

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

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

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

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

New challenges

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

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

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

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

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

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

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

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

TORONTO (PRWEB) November 30, 2020

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

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

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

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

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

ABOUT XTALKS

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

About Veritone

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

Safe Harbor Statement

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

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

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

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

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

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

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

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

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

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

About Exactech

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

About KenSci

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

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

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