Swami Sivasubramanian, vice-president of machine learning at AWS, spoke about the five tenets of innovation that AWS strives towards while announcing new machine learning tools, during AWS re:Invent

AWS vice-president of machine learning, Swami Sivasubramanian, announced new machine learning capabilities during re:Invent

As machine learning disrupts more and more industries, it has demonstrated its potential to reduce time spent by employees on manual tasks. However, training machine learning models can take months to achieve, creating excessive costs.

With this in mind, AWS vice-president of machine learning, Swami Sivasubramanian used his keynote speech at AWS re:Invent to announce new tools that aim to speed up operations and save costs. Sivasubramanian went through five tenets for machine learning that AWS observes, which acted as vessels for further explanations of use cases for the new tools.

Firstly, Sivasubramanian explained the importance of providing firm foundations, vital for freedom of creativity. The technology has provided foundations for autonomous vehicles and robotic communication, among other budding spaces. One drawback of machine learning, however, is that a single framework is yet to be established for all practitioners, with Tensorflow, Pytorch and Mxnet being the main three.

AWS SageMaker, the cloud service providers machine learning service, has been able to speed up training processes. During the keynote, availability of faster distribution training on Amazon SageMaker was announced, which is predicted to complete training up to 40% faster than before and can allow for completion in the space of a few hours.

This article explores the ways in which Kubernetes enhances the use of machine learning (ML) within the enterprise. Read here

From preparing and optimising data and algorithms to training and deployment, machine learning training can be time-consuming and costly. AWS released SageMaker in 2017 to break down barriers for budding data engineers.

Following its predecessor, SageMaker, Data Wrangler was launched during re:Invent to accelerate data preparation, which commonly takes up most of the time spent on training machine learning algorithms. This tool allows for the preparation of data from multiple sources without the need to write code. With more than 300 data transformations, Data Wrangler can cut the time taken to aggregate and prepare data from weeks to minutes.

To then make it even easier for builders to reach their project goals in the quickest time possible, the Sagemaker Feature Store was launched, which allows features to stay in sync with each other and aggregate data faster.

Sagemaker Pipelines is another new tool which allows developers to leverage end-to-end continuous integration and delivery.

There is also a need to understand and eradicate biases, and in response to this, AWS announced Sagemaker Clarify. This tool works in four steps; by detecting bias during analyses with algorithms before delivering a report which allows steps to be taken; models are checked for unbalanced data, and once deployed, a report is given for each input for prediction, which helps to provide information to customers. Bias detection can be carried out over time, with notifications being given if any bias is found.

As artificial intelligence becomes more prevalent throughout business and society, companies need to be mindful of human bias creeping into their machine models. Richard Downs, UK director at Applause discusses how businesses can use the wisdom of crowds to source the diverse set of data and inputs needed to train algorithms. Read here

John Loughlin, chief technologist in data and analytics at Cloudreach, said: The Clarify product really caught my eye, because bias is an important problem that we need to address, so that people maintain their trust in these kinds of technology. We dont want adoption to be impeded because models arent doing what theyre supposed to.

Also announced during the keynote was deep profiling for Sagemaker Debugger, which allows builders to monitor performance in order to move the training process along faster.

With the aim of making machine learning accessible to as many builders as possible, SageMaker Autopilot was introduced last year to provide recommendations on the best models for any project. The tool features added visibility, showing users how models are built, and ranking models using a leaderboard, before one is decided on.

Integration of this kind of technology for databases, data warehouses, data lakes and business intelligence (BI) tools were referred to as future frontiers that customers have been demanding, and machine learning tools were announced for Redshift and Neptune during the keynote. While capabilities for Redshift make it possible to get predictions for data warehouses starting from a SQL query, ML for Neptune can make predictions for connected datasets without the need for prior experience in using the technology.

Brad Campbell, chief technologist in platform development at Cloudreach, said: What stands out when I look at ML for Redshift is that what you have in Redshift, which you dont get in other data sources, is the true composite of your businesss end-to-end value chain in one place.

Typically when Ive worked in Redshift, there was a lot of ETL work to be done, but with ML, this can really unlock value for people who have all this end-to-end value chain data coalesced in a data warehouse.

Another recently launched tool, Amazon Quicksight ML, provides stories of data dashboards in natural language, cutting the time spent on gaining business intelligence information from days or weeks to seconds. The tool takes into consideration the different terms that various departments within an organisation may use, meaning that the tool can be used by any member of staff, regardless of the department they work in.

Kevin Davis, cloud strategist at Cloudreach, said: There is another push in this area to lower the bar of entry for ML consumption in the business space. There is a broadening of scope for people who can implement these services, and a lot of horizontal integration for ML capabilities, along with some deep vertical implementation capabilities.

Yair Green, CTO at GlobalDots, explains how artificial intelligence and machine learning changed the Software-as-a-Service industry. Read here

Without considering problems that the business needs to solve, no project can be truly successful. According to Sivasubramanian, any good machine learning problem to focus on is rich in data, impacts the business, but cant be solved using traditional methods.

AI-powered tools such as Code Guru, DevOps Guru, Connect and Kendra from AWS allow staff to quickly solve business problems that arise within DevOps, call centres and intelligent search services, which can range from performance issues to customer complaints.

During the keynote, the launch of Amazon Lookout for Metrics was announced, which will allow developers to find anomalies within their machine learning models, with the tool ranking them according to severity. This ensures that models are working as they should be.

The caveat I have around Lookout for Metrics is that its clearly directed, and intended to look at the most common business insights, said Davis.

In terms of generally lowering the bar of entry, you can potentially put this in the hands of business analysts that are familiar enough with SQL queries, and allow them to directly pull insights or anomalies from business data stores.

For the healthcare sector, AWS also announced the launch of Amazon Healthlake, which provides an analysis of patient data that would otherwise be difficult to make conclusions on due to its usually unstructured nature.

Commenting on the release of Amazon Healthlake, Samir Luheshi, chief technologist in application modernisation at Cloudreach, said: Healthlake stands out as very interesting. There are a lot of challenges around managing HIPAA and EU GDPR, and its not an easy lift, so Id be interested to see how extra layers can be applied to this to make it suitable for consumption in Europe.

Andrew Pellegrino, director of intelligent automation at DataRobot, analyses RPA and the rise of intelligent automation in healthcare. Read here

Just as algorithms need to be learned so that tasks can be automated effectively, the final tenet of ML discussed by Sivasubramanian calls for companies that deploy machine learning to encourage their engineers to continuously learn new skills and technologies, if they arent doing so already.

AWS has been looking to educate the next generation of builders through its own Machine Learning University, which offers solution-based machine learning training and certification, and where budding builders can learn from AWS practitioners. Learners can also develop skills specific to a particular job role, such as a cloud architect or cloud developer.

Furthermore, AWS DeepRacer, the cloud service providers 3D racing simulator, allows developers of any skill level to learn the essentials of reinforcement learning, and submit models in an aim to win races. The decision making of models can be evaluated with the aid of a 1/18th scale car thats driven by machine learning.

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How AWS's five tenets of innovation lend themselves to machine learning - Information Age

PISCATAWAY, N.J., Nov. 19, 2020 /PRNewswire/ --IEEE, the world's largest technical professional organization dedicated to advancing technology for humanity, today released the results of a survey of Chief Information Officers (CIOs) and Chief Technology Officers (CTOs) in the U.S., U.K., China, India and Brazil regarding the most important technologies for 2021 overall, the impact of the COVID-19 pandemic on the speed of their technology adoption and the industries expected to be most impacted by technology in the year ahead.

2021 Most Important Technologies and ChallengesWhich will be the most important technologies in 2021? Among total respondents, nearly one-third (32%) say AI and machine learning, followed by 5G (20%) and IoT (14%).

Manufacturing (19%), healthcare (18%), financial services (15%) and education (13%) are the industries that most believe will be impacted by technology in 2021, according to CIOs and CTOS surveyed. At the same time, more than half (52%) of CIOs and CTOs see their biggest challenge in 2021 as dealing with aspects of COVID-19 recovery in relation to business operations. These challenges include a permanent hybrid remote and office work structure (22%), office and facilities reopenings and return (17%), and managing permanent remote working (13%). However, 11% said the agility to stop and start IT initiatives as this unpredictable environment continues will be their biggest challenge. Another 11% cited online security threats, including those related to remote workers, as the biggest challenge they see in 2021.

Technology Adoption, Acceleration and Disaster Preparedness due to COVID-19CIOs and CTOs surveyed have sped up adopting some technologies due to the pandemic:

The adoption of IoT (42%), augmented and virtual reality (35%) and video conferencing (35%) technologies have also been accelerated due to the global pandemic.

Compared to a year ago, CIOs and CTOs overwhelmingly (92%) believe their company is better prepared to respond to a potentially catastrophic interruption such as a data breach or natural disaster. What's more, of those who say they are better prepared, 58% strongly agree that COVID-19 accelerated their preparedness.

When asked which technologies will have the greatest impact on global COVID-19 recovery, one in four (25%) of those surveyed said AI and machine learning,

CybersecurityThe top two concerns for CIOs and CTOs when it comes to the cybersecurity of their organization are security issues related to the mobile workforce including employees bringing their own devices to work (37%) and ensuring the Internet of Things (IoT) is secure (35%). This is not surprising, since the number of connected devices such as smartphones, tablets, sensors, robots and drones is increasing dramatically.

Slightly more than one-third (34%) of CIO and CTO respondents said they can track and manage 26-50% of devices connected to their business, while 20% of those surveyed said they could track and manage 51-75% of connected devices.

About the Survey"The IEEE 2020 Global Survey of CIOs and CTOs" surveyed 350 CIOs or CTOs in the U.S., China, U.K., India and Brazil from September 21 - October 9, 2020.

About IEEEIEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Through its highly cited publications, conferences, technology standards, and professional and educational activities, IEEE is the trusted voice in a wide variety of areas ranging from aerospace systems, computers, and telecommunications to biomedical engineering, electric power, and consumer electronics

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Artificial Intelligence and Machine Learning, 5G and IoT will be the Most Important Technologies in 2021, According to new IEEE Study - PRNewswire

Facebook has launched a multilingual machine learning translation model. Previous models tended to rely on English data as an intermediary. However, Facebooks many-to-many software, called M2M-100, can translate directly between any pair of 100 languages. The software is open-source with the model, raw data, training, and evaluation setup available on GitHub.

M2M-100, if it works correctly, provides a functional product with real-world applications, which can be built on by other developers. In a globalized world, accurate translation of a wide variety of languages is vital. It enables accurate communication between different communities, which is essential for multinational businesses. It also allows news articles and social media posts to be accurately portrayed, reducing instances of misinformation.

GlobalDatas recent thematic report on AI suggests that years of bold proclamations by tech companies eager for publicity have resulted in AI becoming overhyped. The reality has often fallen short of the rhetoric. Principal Microsoft researcher Katja Hofmann argues that AI is transitioning to a new phase, in which breakthroughs occur but at a slower rate than previously suggested. The next few years will require practical uses of AI with tangible benefits, addressing AI to specific use cases.

M2M-100 provides 2,200 translation combinations of 100 languages without relying on English data as a mediator. Among its main competitors, Amazon Translate and Microsoft Translator both support significantly fewer languages than Facebook. However, Google Translate supports 108 languages, both dead and alive, having added five new languages in February 2020.

Google and Facebooks products have offer differences. Google uses BookCorpus and English Wikipedia as training data, whereas Facebook analyzes the language of its users. Facebook is, therefore, more suitable for conversational translation, while Google excels at academic style web page translation. Google performs best when English is the target language, which correlates to the training data used. Facebooks multi-directional model claims there is no English bias, with translations functioning between 2,200 language pairs. Accurate conversational translations based on real-time data and multiple language pairs can fulfil global business needs, making Facebook a market leader.

Facebooks strength in this aspect of AI is unsurprising. GlobalData has given the company a thematic score of 5 out of 5 for machine learning, suggesting that this theme will significantly improve Facebooks future performance.

However, natural language processing (NLP) can be problematic, with language semantics making it hard for algorithms to provide accurate translations. In 2017, Facebook translated the phrase good morning in Arabic, posted on its platform by a Palestinian man, as attack them in Hebrew, resulting in the senders arrest by Israeli police. The open-source nature of the software will help developers recognize pain points. It also allows innovation, enabling multilingual models to be advanced in the future by developers.

Language translation is a high-profile use case for AI due to its applications in conversational plaforms like Amazons Alexa, Googles Assistant, and Apples Siri. The tech giants are racking to improve the performance of their virtual assistants. Facebooks M2M-100 announcement will raise the stakes in AI translation software, pushing the companys main competitors to respond.

In an interconnected, globalized world, accurate translation is essential. Facebook has used its global community and access to large datasets to progress machine learning and AI, creating a practical, real-world use case. Allowing access to the training data and models propels future developments, moving linguistic machine learning away from a traditionally Anglo-centric model.Related Report Download the full report from GlobalData's Report StoreGet the Report

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Facebook's machine learning translation software raises the stakes - Verdict

The Adversarial ML Threat Matrix provides guidelines that help detect and prevent attacks on machine learning systems.

This article is part ofDemystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI.

With machine learning becoming increasingly popular, one thing that has been worrying experts is the security threats the technology will entail. We are still exploring the possibilities: The breakdown of autonomous driving systems? Inconspicuous theft of sensitive data from deep neural networks? Failure of deep learningbased biometric authentication? Subtle bypass of content moderation algorithms?

Meanwhile, machine learning algorithms have already found their way into critical fields such as finance, health care, and transportation, where security failures can have severe repercussion.

Parallel to the increased adoption of machine learning algorithms in different domains, there has been growing interest in adversarial machine learning, the field of research that explores ways learning algorithms can be compromised.

And now, we finally have a framework to detect and respond to adversarial attacks against machine learning systems. Called the Adversarial ML Threat Matrix, the framework is the result of a joint effort between AI researchers at 13 organizations, including Microsoft, IBM, Nvidia, and MITRE.

While still in early stages, the ML Threat Matrix provides a consolidated view of how malicious actors can take advantage of weaknesses in machine learning algorithms to target organizations that use them. And its key message is that the threat of adversarial machine learning is real and organizations should act now to secure their AI systems.

The Adversarial ML Threat Matrix is presented in the style of ATT&CK, a tried-and-tested framework developed by MITRE to deal with cyber-threats in enterprise networks. ATT&CK provides a table that summarizes different adversarial tactics and the types of techniques that threat actors perform in each area.

Since its inception, ATT&CK has become a popular guide for cybersecurity experts and threat analysts to find weaknesses and speculate on possible attacks. The ATT&CK format of the Adversarial ML Threat Matrix makes it easier for security analysts to understand the threats of machine learning systems. It is also an accessible document for machine learning engineers who might not be deeply acquainted with cybersecurity operations.

Many industries are undergoing digital transformation and will likely adopt machine learning technology as part of service/product offerings, including making high-stakes decisions, Pin-Yu Chen, AI researcher at IBM, told TechTalks in written comments. The notion of system has evolved and become more complicated with the adoption of machine learning and deep learning.

For instance, Chen says, an automated financial loan application recommendation can change from a transparent rule-based system to a black-box neural network-oriented system, which could have considerable implications on how the system can be attacked and secured.

The adversarial threat matrix analysis (i.e., the study) bridges the gap by offering a holistic view of security in emerging ML-based systems, as well as illustrating their causes from traditional means and new risks induce by ML, Chen says.

The Adversarial ML Threat Matrix combines known and documented tactics and techniques used in attacking digital infrastructure with methods that are unique to machine learning systems. Like the original ATT&CK table, each column represents one tactic (or area of activity) such as reconnaissance or model evasion, and each cell represents a specific technique.

For instance, to attack a machine learning system, a malicious actor must first gather information about the underlying model (reconnaissance column). This can be done through the gathering of open-source information (arXiv papers, GitHub repositories, press releases, etc.) or through experimentation with the application programming interface that exposes the model.

Each new type of technology comes with its unique security and privacy implications. For instance, the advent of web applications with database backends introduced the concept SQL injection. Browser scripting languages such as JavaScript ushered in cross-site scripting attacks. The internet of things (IoT) introduced new ways to create botnets and conduct distributed denial of service (DDoS) attacks. Smartphones and mobile apps create new attack vectors for malicious actors and spying agencies.

The security landscape has evolved and continues to develop to address each of these threats. We have anti-malware software, web application firewalls, intrusion detection and prevention systems, DDoS protection solutions, and many more tools to fend off these threats.

For instance, security tools can scan binary executables for the digital fingerprints of malicious payloads, and static analysis can find vulnerabilities in software code. Many platforms such as GitHub and Google App Store already have integrated many of these tools and do a good job at finding security holes in the software they house.

But in adversarial attacks, malicious behavior and vulnerabilities are deeply embedded in the thousands and millions of parameters of deep neural networks, which is both hard to find and beyond the capabilities of current security tools.

Traditional software security usually does not involve the machine learning component because itsa new piece in the growing system, Chen says, adding thatadopting machine learning into the security landscape gives new insights and risk assessment.

The Adversarial ML Threat Matrix comes with a set of case studies of attacks that involve traditional security vulnerabilities, adversarial machine learning, and combinations of both. Whats important is that contrary to the popular belief that adversarial attacks are limited to lab environments, the case studies show that production machine learning system can and have been compromised with adversarial attacks.

For instance, in one case study, the security team at Microsoft Azure used open-source data to gather information about a target machine learning model. They then used a valid account in the server to obtain the machine learning model and its training data. They used this information to find adversarial vulnerabilities in the model and develop attacks against the API that exposed its functionality to the public.

Other case studies show how attackers can compromise various aspect of the machine learning pipeline and the software stack to conduct data poisoning attacks, bypass spam detectors, or force AI systems to reveal confidential information.

The matrix and these case studies can guide analysts in finding weak spots in their software and can guide security tool vendors in creating new tools to protect machine learning systems.

Inspecting a single dimension (machine learning vs traditional software security) only provides an incomplete security analysis of the system as a whole, Chen says. Like the old saying goes: security is only asstrong as its weakest link.

Unfortunately, developers and adopters of machine learning algorithms are not taking the necessary measures to make their models robust against adversarial attacks.

The current development pipeline is merely ensuring a model trained on a training set can generalize well to a test set, while neglecting the fact that the model isoften overconfident about the unseen (out-of-distribution) data or maliciously embbed Trojan patteninthe training set, which offers unintended avenues to evasion attacks and backdoor attacks that an adversary can leverage to control or misguide the deployed model, Chen says. In my view, similar to car model development and manufacturing, a comprehensive in-house collision test for different adversarial treats on an AI model should be the new norm to practice to better understand and mitigate potential security risks.

In his work at IBM Research, Chen has helped develop various methods to detect and patch adversarial vulnerabilities in machine learning models. With the advent Adversarial ML Threat Matrix, the efforts of Chen and other AI and security researchers will put developers in a better position to create secure and robust machine learning systems.

My hope is that with this study, the model developers and machine learning researchers can pay more attention to the security (robustness) aspect of the modeland looking beyond a single performance metric such as accuracy, Chen says.

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The security threat of adversarial machine learning is real - TechTalks

Duke neurologists and electrical engineers are teaming up in an ambitious effort to develop a better way to monitor brain health for all patients in the ICU. Dubbed Neurologic Injury Monitoring and Real-time Output Display, the method will use machine learning and continuous electroencephalogram (EEG) data along with other clinical information to assist providers with assessment of brain injury and brain health.

Current practices for monitoring brain-injured patients include regular clinical exam assessments made every few hours around the clock. However, many patients are not able to follow commands or participate in the physical exam, so doctors can only examine gross responses to loud noises, pinches and noxious stimulation as well as rudimentary brain stem reflexes.

Not only are these exams often limited in their scope, imaging only provides a snapshot of the brain at the time the images are taken, said Brad Kolls, MD, PhD, MMCI, associate professor of neurology at Duke University School of Medicine and principal investigator on the new research study.

The new approach will leverage continuous brainwave activity along with other clinical information from the medical record and standard bedside monitoring to allow a more comprehensive assessment of the state of the brain. Kolls and Leslie Collins, professor of electrical and computer engineering at Duke, hope to improve the care of brain-injured patients by correlating this data with outcomes. This will allow clinicians to optimize brain function and personalize recovery.

With extensive experience in combining machine learning applications with biological signals, Collins will use unsupervised learning such as topic modeling and automated feature extraction to delve into the novel dataset.

We have promising results from using this approach to analyze data taken from sleeping patients, said Collins. Were excited to be able to change the care, and potentially the outcomes, of patients with brain injury.

The program is sponsored by CortiCare Inc., a leading provider of electroencephalography services to hospitals in the U.S. and internationally. CortiCare has funded this multi-year research agreement supporting the program and intends to commercialize the work once completed. The program is expected to run until the fall of 2022.

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Duke researchers to monitor brain injury with machine learning - Duke Department of Neurology

In the given unprecedented times, digital transformation is vital. One of the significant challenges is modernizing banks and legacy business systems without disrupting the existing system. However, artificial intelligence (AI) and machine learning (ML) have played a pivotal role in conducting hassle-and risk-free digital transformation. An artificial intelligence and machine learning-led approach to system modernization will enable businesses to associate with other fintech services into embracing modern demands and regulations while ensuring safety and enabling security.

In the banking industry, with the growing pressure in managing risk along with increasing governance and regulatory requirements, banks must improve their services towards more unique and better customer service. Fintech brands are increasingly applying AI and ML in a wide range of applications across several channels to leverage all the available client data to predict how customers requirements are evolving. And they are also speculating what services will prove beneficial for them, what type of fraudulent activity has the highest possibility to attack customers systems. Leveraging the power of AI and ML in banking is required along with data science acceleration to enhance customers portfolio offerings.

Here are some significant roles of Artificial intelligence and Machine Learning in banking and finance listed below:

One of the practical examples to showcase the benefits of machine learning could be described in it. While sanctioning loans to customers, banks had to rely on the clients history to comprehend that particular customers creditworthiness. However, the process was not always seamless and accurate, and banks had to face challenges in approving the loans at times. With the digital transformation, the machine learning algorithms analyzes the user better to process the loan further in a much convenient manner.

Banks are undoubtedly one of the most highly regulated institutions and observe strict government regulations in order to protect defaulting or prevent fishing financial crimes within their systems. This is one of the primary reasons for the banking processes to shift to all-digital in such a short span of time. It is essential to be aware of the risk before any suspicious activity has begun to mitigate fraudulent activity. During the traditional process, banks had to violate some pre-set protocols to prevent users from fraudulent activity. Advances in machine learning can sense suspicious activity even before the external threat violates the customers account. The underlying benefit from this is that machines are capable of performing high-level analysis in real-time, which is impossible for humans to perform manually.

Chatbots are one of AI-led software that clones human conversation. The technology imbibed in chatbots makes it convenient for banks to respond to customers questions faster. The chatbots are proven beneficial for financial institutions to serve users issues at a large scale in a matter of a few hours.

The ability to identify the users past behaviour and craft targeted campaigns is a boon for both customers and banks. Such customised campaign creates all the necessary information the client would require while making it and will save both time and energy. Todays customers also enjoy services that are customised as per their preferences and enhance their banking experience.

With the increase of fintech companies and the rapid change in technology use, it was a matter of time that artificial intelligence and machine learning would enter modern banking, redefining the dynamics forever. The application of AI and ML will offer predictive data analysis as banks and financial institutions will try to offer better services with more actionable information like patterned data sets of customers behaviour and spending behaviour. Artificial intelligence adoption for financial institutions will be the key to obtaining a competitive edge as they will offer a fast, secure, and personalised banking experience to its customers.

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AI and Machine Learning are Redefining Banking Industry - Analytics Insight

IRVINE, Calif., Oct. 15, 2020 (GLOBE NEWSWIRE) -- Lantronix Inc. (NASDAQ: LTRX), a global provider of Software as a Service (SaaS), engineering services and hardware for Edge Computing, the Internet of Things (IoT) and Remote Environment Management (REM), today announced the availability of its new Lantronix Open-Q 610 SOM based on the powerful Qualcomm QCS610System on Chip (SOC). This micro System on Module (SOM) is designed for connected visual intelligence applications with high-resolution camera capabilities, on-device artificial intelligence (AI) processing and native Ethernet interface.

Our long and successful relationship with Qualcomm Technologies enables us to deliver powerful micro SOM solutions that can accelerate IoT design and implementation, empowering innovators to create IoT applications that go beyond hardware and enabletheir wildest dreams, said Paul Pickle, CEO of Lantronix.

The new Lantronix ultra-compact (50mm x 25mm), production-ready Open-Q 610 SOM is based on the powerful Qualcomm QCS610SOC, the latest in the Qualcomm Vision Intelligence Platform lineup targeting smart cameras with edge computing. Delivering up to 50 percent improved AI performance than the previous generation as well as image signal processing and sensor processing capabilities, it is designed to bring smart camera technology, including powerful artificial intelligence and machine learning features formerly only available to high-end devices, into mid-tier camera segments, including smart cities, commercial and enterprise, homes and vehicles.

Bringing Advanced AI and Machine Learning to Smart Camera Application

Created to bring advanced artificial intelligence and machine learning capabilities to smart cameras in multiple vertical markets, the Open-Q 610 SOM is designed for developers seeking to innovate new products utilizing the latest vision and AI edge capabilities, such as smart connected cameras, video conference systems, machine vision and robotics. With the Open-Q 610 SOM, developers gain a pre-tested, pre-certified, production-ready computing module that reduces risk and expedites innovative product development.

The Open-Q 610 SOM provides the core computing capabilities for:

Connectivity solutions include Wi-Fi/BT, Gigabit Ethernet, multiple USB ports and three-camera interfaces.

The Lantronix Open-Q 610 SOM provides advanced artificial intelligence and machine learning capabilities that enable developers to innovate new product designs, including smart connected cameras, video conference systems, machine vision and robotics, said Jonathan Shipman, VP of Strategy at Lantronix Inc. Lantronix micro SOMs and solutions enable IoT device makers to jumpstart new product development and accelerate time-to-market by shortening the design cycle, reducing development risk and simplifying the manufacturing process.

Open-Q 610 Development Kit

The companion Open-Q 610 Development Kit is a full-featured platform with available software tools, documentation and optional accessories. It delivers everything required to immediately begin evaluation and initial product development.

The development kit integrates the production-ready OpenQ 610 SOM with a carrier board, providing numerous expansion and connectivity options to support development and testing of peripherals and applications. The development kit, along with the available documentation, also provides a proven reference design for custom carrier boards, providing a low-risk fast track to market for new products.

In addition to production-ready SOMs, development platforms and tools, Lantronix offers turnkey product development services, driver and application software development and technical support.

For more information, visit Open-Q 610 SOM and Open Q 610 SOM Development kit.

About Lantronix

Lantronix Inc. is a global provider of software as a service (SaaS), engineering services and hardware for Edge Computing, the Internet of Things (IoT) and Remote Environment Management (REM). Lantronix enables its customers to provide reliable and secure solutions while accelerating their time to market. Lantronixs products and services dramatically simplify operations through the creation, development, deployment and management of customer projects at scale while providing quality, reliability and security.

Lantronixs portfolio of services and products address each layer of the IoT Stack, including Collect, Connect, Compute, Control and Comprehend, enabling its customers to deploy successful IoT and REM solutions. Lantronixs services and products deliver a holistic approach, addressing its customers needs by integrating a SaaS management platform with custom application development layered on top of external and embedded hardware, enabling intelligent edge computing, secure communications (wired, Wi-Fi and cellular), location and positional tracking and environmental sensing and reporting.

With three decades of proven experience in creating robust industry and customer-specific solutions, Lantronix is an innovator in enabling its customers to build new business models, leverage greater efficiencies and realize the possibilities of IoT and REM.Lantronixs solutions are deployed inside millions of machines at data centers, offices and remote sites serving a wide range of industries, including energy, agriculture, medical, security, manufacturing, distribution, transportation, retail, financial, environmental, infrastructure and government.

For more information, visit http://www.lantronix.com. Learn more at the Lantronix blog, http://www.lantronix.com/blog, featuring industry discussion and updates. To follow Lantronix on Twitter, please visit http://www.twitter.com/Lantronix. View our video library on YouTube at http://www.youtube.com/user/LantronixInc or connect with us on LinkedIn at http://www.linkedin.com/company/lantronix

Safe Harbor Statement under the Private Securities Litigation Reform Act of 1995: Any statements set forth in this news release that are not entirely historical and factual in nature, including without limitation statements related to our solutions, technologies and products as well as the advanced Lantronix Open-Q 610 SOM, are forward-looking statements. These forward-looking statements are based on our current expectations and are subject to substantial risks and uncertainties that could cause our actual results, future business, financial condition, or performance to differ materially from our historical results or those expressed or implied in any forward-looking statement contained in this news release. The potential risks and uncertainties include, but are not limited to, such factors as the effects of negative or worsening regional and worldwide economic conditions or market instability on our business, including effects on purchasing decisions by our customers; the impact of the COVID-19 outbreak on our employees, supply and distribution chains, and the global economy; cybersecurity risks; changes in applicable U.S. and foreign government laws, regulations, and tariffs; our ability to successfully implement our acquisitions strategy or integrate acquired companies; difficulties and costs of protecting patents and other proprietary rights; the level of our indebtedness, our ability to service our indebtedness and the restrictions in our debt agreements; and any additional factors included in our Annual Report on Form 10-K for the fiscal year ended June 30, 2019, filed with the Securities and Exchange Commission (the SEC) on September 11, 2019, including in the section entitled Risk Factors in Item 1A of Part I of such report, as well as in our other public filings with the SEC. Additional risk factors may be identified from time to time in our future filings. The forward-looking statements included in this release speak only as of the date hereof, and we do not undertake any obligation to update these forward-looking statements to reflect subsequent events or circumstances.

Lantronix Media Contact:Gail Kathryn MillerCorporate Marketing &Communications Managermedia@lantronix.com949-453-7158

Lantronix Analyst and Investor Contact:Jeremy WhitakerChief Financial Officerinvestors@lantronix.com 949-450-7241

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Lantronix Brings Advanced AI and Machine Learning to Smart Cameras With New Open-Q 610 SOM Based on the Powerful Qualcomm QCS610 System on Chip (SOC)...

Artificial intelligence can be used to diagnose cancer, predict suicide, and assist in surgery. In all these cases, studies suggest AI outperforms human doctors in set tasks. But when something does go wrong, who is responsible?

Theres no easy answer, says Patrick Lin, director of Ethics and Emerging Sciences Group at California Polytechnic State University. At any point in the process of implementing AI in healthcare, from design to data and delivery, errors are possible. This is a big mess, says Lin. Its not clear who would be responsible because the details of why an error or accident happens matters. That event could happen anywhere along the value chain.

Design includes creation of both hardware and software, plus testing the product. Data encompasses the mass of problems that can occur when machine learning is trained on biased data, while deployment involves how the product is used in practice. AI applications in healthcare often involve robots working with humans, which further blurs the line of responsibility.

Responsibility can be divided according to where and how the AI system failed, says Wendall Wallace, lecturer at Yale Universitys Interdisciplinary Center for Bioethics and the author of several books on robot ethics. If the system fails to perform as designed or does something idiosyncratic, that probably goes back to the corporation that marketed the device, he says. If it hasnt failed, if its being misused in the hospital context, liability would fall on who authorized that usage.

Surgical Inc., the company behind the Da Vinci Surgical system, has settled thousands of lawsuits over the past decade. Da Vinci robots always work in conjunction with a human surgeon, but the company has faced allegations of clear error, including machines burning patients and broken parts of machines falling into patients.

Some cases, though, are less clear-cut. If diagnostic AI trained on data that over-represents white patients then misdiagnoses a Black patient, its unclear whether the culprit is the machine-learning company, those who collected the biased data, or the doctor who chose to listen to the recommendation. If an AI program is a black box, it will make predictions and decisions as humans do, but without being able to communicate its reasons for doing so, writes attorney Yavar Bathaee in a paper outlining why the legal principles that apply to humans dont necessarily work for AI. This also means that little can be inferred about the intent or conduct of the humans that created or deployed the AI, since even they may not be able to foresee what solutions the AI will reach or what decisions it will make.

The difficulty in pinning the blame on machines lies in the impenetrability of the AI decision-making process, according to a paper on tort liability and AI published in the AMA Journal of Ethics last year. For example, if the designers of AI cannot foresee how it will act after it is released in the world, how can they be held tortiously liable?, write the authors. And if the legal system absolves designers from liability because AI actions are unforeseeable, then injured patients may be left with fewer opportunities for redress.

AI, as with all technology, often works very differently in the lab than in a real-world setting. Earlier this year, researchers from Google Health found that a deep-learning system capable of identifying symptoms of diabetic retinopathy with 90% accuracy in the lab caused considerable delays and frustrations when deployed in real life.

Despite the complexities, clear responsibility is essential for artificial intelligence in healthcare, both because individual patients deserve accountability, and because lack of responsibility allows mistakes to flourish. If its unclear whos responsible, that creates a gap, it could be no one is responsible, says Lin. If thats the case, theres no incentive to fix the problem. One potential response, suggested by Georgetown legal scholar David Vladeck, is to hold everyone involved in the use and implementation of the AI system accountable.

AI and healthcare often work well together, with artificial intelligence augmenting the decisions made by human professionals. Even as AI develops, these systems arent expected to replace nurses or automate human doctors entirely. But as AI improves, it gets harder for humans to go against machines decisions. If a robot is right 99% of the time, then a doctor could face serious liability if they make a different choice. Its a lot easier for doctors to go along with what that robot says, says Lin.

Ultimately, this means humans are ceding some authority to robots. There are many instances where AI outperforms humans, and so doctors should defer to machine learning. But patient wariness of AI in healthcare is still justified when theres no clear accountability for mistakes. Medicine is still evolving. Its part art and part science, says Lin. You need both technology and humans to respond effectively.

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When AI in healthcare goes wrong, who is responsible? - Quartz

Samsung is rolling out a new course offering an introduction to machine learning and artificial intelligence in the UAE.

The course, which is part of its global Future Academy initiative, will target UAE residents between the ages of 18 and 35 with a background in science, technology, engineering and mathematics and who are interested in pursuing a career that would benefit from knowledge of AI, the South Korean firm said.

The five-week programme will be held online and cover subjects such as statistics, algorithms and programming.

The launch of the Future Academy in the UAE reaffirms our commitment to drive personal and professional development and ensure this transcends across all areas in which we operate, said Jerric Wong, head of corporate marketing at Samsung Gulf Electronics.

In July, Samsung announced a similar partnership with Misk Academy to launch AI courses in Saudi Arabia.

The UAE, a hub for start-ups and venture capital in the the Arab world, is projected to benefit the most in the region from AI adoption. The technology is expected to contribute up to 14 per cent to the countrys gross domestic product equivalent to Dh352.5 billion by 2030, according to a report by consultancy PwC.

In Saudi Arabia, AI is forecast to add 12.4 per cent to GDP.

Held under the theme be ready for tomorrow by learning about it today, the course will be delivered through a blended learning and self-paced format. Participants can access presentations and pre-recorded videos detailing their course materials.

Through the Future Academys specialised curriculum, participants will learn about the tools and applications that feature prominently in AI and machine learning-related workplaces, Samsung said.

The programme promises to be beneficial, providing the perfect platform for determined beginners and learners to build their knowledge in machine learning and establishing a strong understanding of the fundamentals of AI, it added.

Applicants can apply here by October 29.

Updated: October 6, 2020 07:57 PM

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Samsung launches online programme to train UAE youth in AI and machine learning - The National

Machine Learning Operationalization Software market research report bestows clients with the best results and for the same, it has been produced by using integrated approaches and the latest technology. With this market report, it becomes easier to establish and optimize each stage in the lifecycle of an industrial process that includes engagement, acquisition, retention, and monetization. This market report gives a wide-ranging analysis of the market structure and the evaluations of the various segments and sub-segments of this industry. Not to mention, several charts and graphs have been used effectively in the Machine Learning Operationalization Software Market report to represent the facts and figures in a proper way.

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Major Industry Competitors:Machine Learning Operationalization Software Market

The Major Players Covered In The Machine Learning Operationalization Software Report Are The Mathworks, Inc, Sas Institute Inc, Microsoft, Parallelm, Inc, Algorithmia Inc, Tibco Software Inc, Sap, Ibm Corporation, Seldon Technologies Ltd, Actico Gmbh, Rapidminer, Inc And Knime Ag Among Other Domestic And Global Players. Market Share Data Is Available For Global, North America, Europe, Asia-Pacific, Middle East And Africa And South America Separately. Dbmr Analysts Understand Competitive Strengths And Provide Competitive Analysis For Each Competitor Separately.

Market Analysis:Machine Learning Operationalization Software Market

Machine Learning Operationalization Software Market Is Expected To Gain Market Growth In The Forecast Period Of 2020 To 2027. Data Bridge Market Research Analyses The Market Growing At A Cagr Of 44.2% In The Above-Mentioned Forecast Period.

The 2020 Annual Machine Learning Operationalization Software Market offers:

=> 100+ charts exploring and analysing the Machine Learning Operationalization Software market from critical angles including retail forecasts, consumer demand, production and more=> 10+ profiles of top Machine Learning Operationalization Software producing states, with highlights of market conditions and retail trends=> Regulatory outlook, best practices, and future considerations for manufacturers and industry players seeking to meet consumer demand=> Benchmark wholesale prices, market position, plus prices for raw materials involved in Machine Learning Operationalization Software type

Some extract from Table of Contents

Overview of Global Machine Learning Operationalization Software Market

Machine Learning Operationalization Software Size (Sales Volume) Comparison by Type

Machine Learning Operationalization Software Size (Consumption) and Market Share Comparison by Application

Machine Learning Operationalization Software Size (Value) Comparison by Region

Machine Learning Operationalization Software Sales, Revenue and Growth Rate

Machine Learning Operationalization Software Competitive Situation and Trends

Strategic proposal for estimating availability of core business segments

Players/Suppliers, Sales Area

Analyse competitors, including all important parameters of Machine Learning Operationalization Software

Global Machine Learning Operationalization Software Manufacturing Cost Analysis

The most recent innovative headway and supply chain pattern mapping

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Rapid Business Growth Factors

In addition, the market is growing at a fast pace and the report shows us that there are a couple of key factors behind that. The most important factor thats helping the market grow faster than usual is the tough competition.

Key Points of this Report:

The depth industry chain includes analysis value chain analysis, porter five forces model analysis and cost structure analysis

It describes present situation, historical background and future forecast

Comprehensive data showing Machine Learning Operationalization Software capacities, production, consumption, trade statistics, and prices in the recent years are provided

The report indicates a wealth of information on Machine Learning Operationalization Software manufacturers

Machine Learning Operationalization Software market forecasts for next five years, including market volumes and prices is also provided

Raw Material Supply and Downstream Consumer Information is also included

Any other users requirements which is feasible for us

What Porters Five Forces of Competitive Analysis Provides?

Supplier power: An assessment of how easy it is for suppliers to drive up prices. This is driven by the: number of suppliers of each essential input; uniqueness of their product or service; relative size and strength of the supplier; and cost of switching from one supplier to another.

Buyer power: An assessment of how easy it is for buyers to drive prices down. This is driven by the: number of buyers in the market; importance of each individual buyer to the organisation; and cost to the buyer of switching from one supplier to another. If a business has just a few powerful buyers, they are often able to dictate terms.

Competitive rivalry: The main driver is the number and capability of competitors in the market. Many competitors, offering undifferentiated products and services, will reduce market attractiveness.

Threat of substitution: Where close substitute products exist in a market; it increases the likelihood of customers switching to alternatives in response to price increases. This reduces both the power of suppliers and the attractiveness of the market.

Threat of new entry: Profitable markets attract new entrants, which erodes profitability. Unless incumbents have strong and durable barriers to entry, for example, patents, economies of scale, capital requirements or government policies, then profitability will decline to a competitive rate.

Five forces analysis helps organizations to understand the factors affecting profitability in a specific industry, and can help to inform decisions relating to: whether to enter a specific industry; whether to increase capacity in a specific industry; and developing competitive strategies.

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Why Is Data TriangulationImportantin Qualitative Research?

This involves data mining, analysis of the impact of data variables on the market, and primary (industry expert) validation. Apart from this, other data models include Vendor Positioning Grid, Market Time Line Analysis, Market Overview and Guide, Company Positioning Grid, Company Market Share Analysis, Standards of Measurement, Top to Bottom Analysis and Vendor Share Analysis. Triangulation is one method used while reviewing, synthesizing and interpreting field data. Data triangulation has been advocated as a methodological technique not only to enhance the validity of the research findings but also to achieve completeness and confirmation of data using multiple methods

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Machine Learning Operationalization Software Market Size, Trends, Analysis, Demand, Outlook And Forecast 2027 The Mathworks, Inc, Sas Institute Inc,...

Panalgos new Data Science module seamlessly integrates machine-learning techniques to identify new insights for patient care

Panalgo, a leading healthcare analytics company, announced the launch of its new Data Sciencemodule for Instant Health Data (IHD), which allows data scientists and researchers to leverage machine-learning to uncover novel insights from the growing volume of healthcare data.

Panalgos flagshipIHD Analytics softwarestreamlines the analytics process by removing complex programming from the equation and allows users to focus on what matters mostturning data into insights. IHD Analytics supports the rapid analysis of a wide range of healthcare data sources, including administrative claims, electronic health records, registry data and more. The software, which is purpose-built for healthcare, includes the most extensive library of customizable algorithms and automates documentation and reporting for transparent, easy collaboration.

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Panalgos new IHD Data Science module is fully integrated with IHD Analytics, and allows for analysis of large, complex healthcare datasets using a wide variety of machine-learning techniques. The IHD Data Science module provides an environment to easily train, validate and test models against multiple datasets.

Healthcare organizations are increasingly using machine-learning techniques as part of their everyday workflow. Developing datasets and applying machine-learning methods can be quite time-consuming, said Jordan Menzin, Chief Technology Officer of Panalgo. We created the Data Science module as a way for users to leverage IHD for all of the work necessary to apply the latest machine-learning methods, and to do so using a single system.

Our new IHD Data Science product release is part of our mission to leverage our deep domain knowledge to build flexible, intuitive software for the healthcare industry, said Joseph Menzin, PhD, Chief Executive Officer of Panalgo. We are excited to empower our customers to answer their most pressing questions faster, more conveniently, and with higher quality.

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The IHD Data Science module provides advanced analytics to better predict patient outcomes, uncover reasons for medication non-adherence, identify diseases earlier, and much more. The results from these analyses can be used by healthcare stakeholders to improve patient care.

Research abstracts using Panalgos IHD Data Science module are being presented at this weeks International Conference on Pharmacoepidemiology and Therapeutic Risk Management, including: Identifying Comorbidity-based Subtypes of Type 2 Diabetes: An Unsupervised Machine Learning Approach,andIdentifying Predictors of a Composite Cardiovascular Outcome Among Diabetes Patients UsingMachine Learning.

Recommended AI News: LG Revolutionizes Multi-Screen Experience With Unique LG Wing 5G Smartphone

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Panalgo Brings the Power of Machine-Learning to the Healthcare Industry Via Its IHD Software - AiThority

Strategic growth, latest insights, developmental trends in Global & Regional Machine Learning Courses Market with post-pandemic situations are reflected in this study. End to end Industry analysis from the definition, product specifications, demand till forecast prospects are presented. The complete industry developmental factors, historical performance from 2015-2027 is stated. The market size estimation, Machine Learning Courses maturity analysis, risk analysis, and competitive edge is offered. The segmental market view by types of products, applications, end-users, and top vendors is stated. Market drivers, restraints, opportunities in Machine Learning Courses industry with the innovative and strategic approach is offered. Machine Learning Courses product demand across regions like North America, Europe, Asia-Pacific, South and Central America, Middle East, and Africa is analyzed. The emerging segments, CAGR, revenue accumulation, feasibility check is specified.

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Etihad is testingLumitics Insight Lite technology totrack unconsumed meals from a plane after it lands.

Etihad Airways has partnered with Singapore-based startup Lumitics to trial the use of computer vision and machine learning in order to reduce food wastage on Etihad flights.

The partnership will see Etihad and Lumitics track unconsumed Economy class meals from Etihads flights, with the collated data used to highlight food consumption and wastage patterns across the network. Analysis of the results will help to reduce food waste, improve meal planning and reduce operating costs.

Mohammad Al Bulooki, Chief Operating Officer, Etihad Aviation Group, said: Etihad Airways started the pilot with Lumitics earlier this year before global flying was impacted by COVID-19, and as the airline scales up the flight operations again, it is exciting to restart the project and continue the work that had begun. Etihad remains committed to driving innovation and sustainability through all aspects of the airlines operations, and we believe that this project will have the potential to support the drive to reduce food wastage and, at the same time, improve guest experience by enabling Etihad to plan inflight catering in a more relevant, effective and efficient way.

Lumitics product Insight Lite will track unconsumed meals from a plane after it lands. Using artificial intelligence (AI) and image recognition, Insight Lite is able to differentiate and identify the types and quantity of unconsumed meals based on the design of the meal foils, without requiring manual intervention.

Lumitics Co-founder and Chief Executive Rayner Loi said: Tackling food waste is one of the largest cost saving opportunities for any business producing and serving food. Not only does it make business sense, it is also good for the environment. We are excited to be working with Etihad Airways to help achieve its goals in reducing food waste.

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Etihad trials computer vision and machine learning to reduce food waste - Future Travel Experience

Cartesiam, Edge Impulse and Motion Gestures integrate their machine-learning (ML) offerings into Microchips MPLAB X Integrated Development Environment

CHANDLER, Ariz., September 15, 2020 Microchip Technology(Nasdaq: MCHP)today announced it has partnered with Cartesiam, Edge Impulse and Motion Gestures to simplify ML implementation at the edge using the companys ARM Cortex based 32-bit micro-controllers and microprocessors in its MPLAB X Integrated Development Environment (IDE). Bringing the interface to these partners software and solutions into its design environment uniquely positions Microchip to support customers through all phases of their AI/ML projects including data gathering, training the models and inference implementation.

Adoption of our 32-bit MCUs in AI-at-the-edge applications is growing rapidly and now these designs are easy for any embedded system developer to implement, said Fanie Duvenhage, vice president of Microchips human machine interface and touch function group. It is also easy to test these solutions using our ML evaluation kits such as the EV18H79A or EV45Y33A.

About the Partner Offerings

Cartesiam, founded in 2016,is a software publisher specializing in artificial intelligence development tools for microcontrollers. NanoEdge AI Studio, Cartesiams patented development environment, allows embedded developers, without any prior knowledge of AI, to rapidly develop specialized machine learning libraries for microcontrollers. Devices leveraging Cartesiamstechnology are already in production at hundreds ofsites throughout theWorld

Edge Impulse is the end-to-end developer platform for embedded machine learning, enabling enterprises in industrial, enterprise and wearable markets. The platform is free for developers, providing dataset collection, DSP and ML algorithms, testing and highly efficient inference code generation across a wide range of sensor, audio and vision applications. Get started in just minutes thanks to integrated Microchip MPLAB X and evaluation kit support.

Motion Gestures, founded in 2017, provides powerful embedded AI-based gesture recognition software for different sensors, including touch, motion (i.e. IMU) and vision. Unlike conventional solutions, the companys platform does not require any training data collection or programming and uses advanced machine learning algorithms. As a result, gesture software development time and costs are reduced by 10x while gesture recognition accuracy is increased to nearly 100 percent.

See Demonstrations During Embedded Vision Summit

The MPLAB X IDE ML implementations will be featured during theEmbedded Vision Summit 2020 virtual conference, September 15-17. Attendees can see video demonstrations at the companys virtual exhibit, which will be staffed each day from 10:30 a.m. to 1 p.m. PDT.

Please let us know if you would like to speak to a subject matter expert on Microchips enhanced MPLAB X IDE for ML implementations, or the use of 32-bit microcontrollers in AI-at-the-edge applications. For more information visitmicrochip.com/MLCustomers can get a demo by contacting a Microchip sales representative.

Microchips offering of ML development kits now includes:

EV18H79A: SAMD21 ML Evaluation Kit with TDK 6-axis MEMS

EV45Y33A: SAMD21 ML Evaluation Kit with BOSCH IMU

SAMC21 xPlained Pro evaluation kit (ATSAMC21-XPRO) plus its QT8 xPlained Pro Extension Kit (AC164161): available for evaluating the Motion Gestures solution.

VectorBlox Accelerator Software Development Kit (SDK): enables developers to create low-power, small-form-factor AI/ML applications on Microchips PolarFireFPGAs.

About Microchip Technology

Microchip Technology Inc. is a leading provider of smart, connected and secure embedded control solutions. Its easy-to-use development tools and comprehensive product portfolio enable customers to create optimal designs which reduce risk while lowering total system cost and time to market. The companys solutions serve more than 120,000 customers across the industrial, automotive, consumer, aerospace and defense, communications and computing markets. Headquartered in Chandler, Arizona, Microchip offers outstanding technical support along with dependable delivery and quality. For more information, visit the Microchip website atwww.microchip.com.

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Microchip Partners with Machine-Learning (ML) Software Leaders to Simplify AI-at-the-Edge Design Using its 32-Bit Microcontrollers (MCUs) - EE Journal

Geoffrey Levene is the Director of Global Business Development for Data Science and Space at NVIDIA.

On this episode of the Connected Aircraft Podcast, we learn how airlines and aerospace manufacturers are adopting the use of data science workstations to develop task-specific machine learning models with Geoffrey Levene, Director, Global Business Development for Data Science and Space at NVIDIA.

In a May 7 blog, NVIDIA one of the worlds largest suppliers of graphics processing units and computer chips to the video gaming, automotive and other industries explained how American Airlines is using its data science workstations to integrate machine learning into its air cargo operations planning. During this interview, Levene expands on other airline and aerospace uses of those same workstations and how they are creating new opportunities for efficiency.

Have suggestions or topics we should focus on in the next episode? Email the host, Woodrow Bellamy atwbellamy@accessintel.com, or drop him a line on Twitter@WbellamyIIIAC.

Listen to this episode below, orcheck it out on iTunesorGoogle PlayIf you like the show, subscribe on your favorite podcast app to get new episodes as soon as theyre released.

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PODCAST: NVIDIA's Director of Data Science Talks Machine Learning for Airlines and Aerospace - Aviation Today

We often equate machine learning to fictional scenarios such as those presented in films including the Terminator franchise and 2001: A Space Odyssey. While these are all entertaining stories, the fact of the matter is that this type of artificial intelligence is not nearly as threatening. On the contrary, it has helped to dramatically enhance the overall user experience (UX) and to streamline many online functions (such as common search results) that we take for granted. Machine learning is also making its presence known within the digital gaming community. Without becoming overly technical, what transformations can we expect to witness and how will these impact the experience of the average gaming enthusiast?

Although games such as Pong and Super Mario Bros. were entertaining for their time, they were also quite predictable. This is why so many users have uploaded speed runs onto websites such as YouTube. However, what if a game actually learned from your previous actions? It is obvious that the platform itself would be much more challenging. This concept is now becoming a reality.

Machine learning can also apply to numerous scenarios. It may be used to provide a greater sense of realism with interacting with a role-playing game. It could be employed to offer speech recognition and to recognise voice commands. Machine learning may also be implemented to create more realistic non-playable characters (NPCs).

Whether referring to fast-paced MMORPGs to traditional forms of entertainment including slot games offered by websites such as scandicasino.vip, there is no doubt that machine learning will soon make its presence known.

We can clearly see that the technical benefits associated with machine learning will certainly be leveraged by game developers. However, it is just as important to mention that this very same technology will have a pronounced impact upon the players themselves. This is largely due to how games can be personalised based around the needs of the player.

We are not only referring to common options such as the ability to modify avatars and skins in this case. Instead, games are evolving to the point that they will base their recommendations off of the behaviours of the players themselves. For example, a plot may change as a result of how a player interacts with other characters. The difficulty of a specific level may be automatically adjusted in accordance with the skill of the player. As machine learning and AI both have the ability to model extremely complex systems, the sheer attention to graphical detail within the games (such as character features and backgrounds) will also become vastly enhanced.

We can see that the future of gaming looks extremely bright thanks to the presence of machine learning. While such systems might appear to have little impact upon traditional platforms such as solitaire, there is no doubt that they will still be felt across numerous other genres. So, get ready for a truly amazing experience in the months and years to come!

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How Machine Learning is Set to Transform the Online Gaming Community - Techiexpert.com - TechiExpert.com

ST. PAUL, Minn., Jan. 19, 2021 /PRNewswire/ -- San Diego-based Mission Healthcare, one of the largest home health, hospice, and palliative care providers in California, will adopt Muse Healthcare's machine learning and predictive modeling tool to help deliver a more personalized level of care to their patients.

The Muse technology evaluates and models every clinical assessment, medication, vital sign, and other relevant data to perform a risk stratification of these patients. The tool then highlights the patients with the most critical needs and visually alerts the agency to perform additional care. Muse Healthcare identifies patients as "Critical," which means they have a greater than 90% likelihood of passing in the next 7-10 days. Users are also able to make accurate changes to care plans based on the condition and location of the patient. When agencies use Muse's powerful machine learning tool, they have an advantage and data proven outcomes to demonstrate they are providing more care and better care to patients in transition.

According to Mission Healthcare's Vice President of Clinical and Quality, Gerry Smith, RN, MSN, Muse will serve as an invaluable tool that will assist their clinicians to enhance care for their patients. "Mission Hospice strives to ensure every patient receives the care and comfort they need while on service, and especially in their final days. We are so excited that the Muse technology will provide our clinical team with additional insights to positively optimize care for patients at the end of life. This predictive modeling technology will enable us to intervene earlier; make better decisions for more personalized care; empower staff; and ultimately improve patient outcomes."

Mission Healthcare's CEO, Paul VerHoeve, also believes that the Muse technology will empower their staff to provide better care for patients. "Predictive analytics are a new wave in hospice innovation and Muse's technology will be a valuable asset to augment our clinical efforts at Mission Healthcare. By implementing a revolutionary machine learning tool like Muse, we can ensure our patients are receiving enhanced hands-on care in those critical last 7 10 days of life. Our mission is to take care of people, with Muse we will continue to improve the patient experience and provide better care in the final days and hours of a patient's life."

As the only machine learning tool in the hospice industry, the Muse transitions tool takes advantage of the implemented documentation within the EMR. This allows the agency to quickly implement the tool without disruption. "With guidance from our customers in the hundreds of locations that are now using the tool, we have focused on deploying time saving enhancements to simplify a clinician's role within hospice agencies. These tools allow the user to view a clinical snapshot, complete review of the scheduled frequency, and quickly identify the patients that need immediate attention. Without Muse HC, a full medical review must be conducted to identify these patients," said Tom Maxwell, co-Founder of Muse Healthcare. "We are saving clinicians time in their day, simplifying the identification challenges of hospice, and making it easier to provide better care to our patients. Hospice agencies only get one chance to get this right," said Maxwell.

CEO of Muse Healthcare, Bryan Mosher, is also excited about Mission's adoption of the Muse tool. "We welcome the Mission Healthcare team to the Muse Healthcare family of customers, and are happy to have them adopt our product so quickly. We are sure with the use of our tools,clinicians at Mission Healthcare will provide better care for their hospice patients," said Mosher.

About Mission Healthcare

As one of the largest regional home health, hospice, and palliative care providers in California, San Diego-based Mission Healthcare was founded in 2009 with the creation of its first service line, Mission Home Health. In 2011, Mission added its hospice service line. Today, Mission employs over 600 people and serves both home health and hospice patients through Southern California. In 2018, Mission was selected as a Top Workplace by the San Diego Union-Tribune. For more information visit https://homewithmission.com/.

About Muse Healthcare

Muse Healthcare was founded in 2019 by three leading hospice industry professionals -- Jennifer Maxwell, Tom Maxwell, and Bryan Mosher. Their mission is to equip clinicians with world-class analytics to ensure every hospice patient transitions with unparalleled quality and dignity. Muse's predictive model considers hundreds of thousands of data points from numerous visits to identify which hospice patients are most likely to transition within 7-12 days. The science that powers Muse is considered a true deep learning neural network the only one of its kind in the hospice space. When hospice care providers can more accurately predict when their patients will transition, they can ensure their patients and the patients' families receive the care that matters most in the final days and hours of a patient's life. For more information visit http://www.musehc.com.

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Mission Healthcare of San Diego Adopts Muse Healthcare's Machine Learning Tool - Southernminn.com

Computer vision systems sometimes make inferences about a scene that fly in the face of common sense. For example, if a robot were processing a scene of a dinner table, it might completely ignore a bowl that is visible to any human observer, estimate that a plate is floating above the table, or misperceive a fork to be penetrating a bowl rather than leaning against it.

Move that computer vision system to a self-driving car and the stakes become much higher for example, such systems have failed to detect emergency vehicles and pedestrians crossing the street.

To overcome these errors, MIT researchers have developed a framework that helps machines see the world more like humans do. Their new artificial intelligence system for analyzing scenes learns to perceive real-world objects from just a few images, and perceives scenes in terms of these learned objects.

The researchers built the framework using probabilistic programming, an AI approach that enables the system to cross-check detected objects against input data, to see if the images recorded from a camera are a likely match to any candidate scene. Probabilistic inference allows the system to infer whether mismatches are likely due to noise or to errors in the scene interpretation that need to be corrected by further processing.

This common-sense safeguard allows the system to detect and correct many errors that plague the deep-learning approaches that have also been used for computer vision. Probabilistic programming also makes it possible to infer probable contact relationships between objects in the scene, and use common-sense reasoning about these contacts to infer more accurate positions for objects.

If you dont know about the contact relationships, then you could say that an object is floating above the table that would be a valid explanation. As humans, it is obvious to us that this is physically unrealistic and the object resting on top of the table is a more likely pose of the object. Because our reasoning system is aware of this sort of knowledge, it can infer more accurate poses. That is a key insight of this work, says lead author Nishad Gothoskar, an electrical engineering and computer science (EECS) PhD student with the Probabilistic Computing Project.

In addition to improving the safety of self-driving cars, this work could enhance the performance of computer perception systems that must interpret complicated arrangements of objects, like a robot tasked with cleaning a cluttered kitchen.

Gothoskars co-authors include recent EECS PhD graduate Marco Cusumano-Towner; research engineer Ben Zinberg; visiting student Matin Ghavamizadeh; Falk Pollok, a software engineer in the MIT-IBM Watson AI Lab; recent EECS masters graduate Austin Garrett; Dan Gutfreund, a principal investigator in the MIT-IBM Watson AI Lab; Joshua B. Tenenbaum, the Paul E. Newton Career Development Professor of Cognitive Science and Computation in the Department of Brain and Cognitive Sciences (BCS) and a member of the Computer Science and Artificial Intelligence Laboratory; and senior author Vikash K. Mansinghka, principal research scientist and leader of the Probabilistic Computing Project in BCS. The research is being presented at the Conference on Neural Information Processing Systems in December.

A blast from the past

To develop the system, called 3D Scene Perception via Probabilistic Programming (3DP3), the researchers drew on a concept from the early days of AI research, which is that computer vision can be thought of as the "inverse" of computer graphics.

Computer graphics focuses on generating images based on the representation of a scene; computer vision can be seen as the inverse of this process. Gothoskar and his collaborators made this technique more learnable and scalable by incorporating it into a framework built using probabilistic programming.

Probabilistic programming allows us to write down our knowledge about some aspects of the world in a way a computer can interpret, but at the same time, it allows us to express what we dont know, the uncertainty. So, the system is able to automatically learn from data and also automatically detect when the rules dont hold, Cusumano-Towner explains.

In this case, the model is encoded with prior knowledge about 3D scenes. For instance, 3DP3 knows that scenes are composed of different objects, and that these objects often lay flat on top of each other but they may not always be in such simple relationships. This enables the model to reason about a scene with more common sense.

Learning shapes and scenes

To analyze an image of a scene, 3DP3 first learns about the objects in that scene. After being shown only five images of an object, each taken from a different angle, 3DP3 learns the objects shape and estimates the volume it would occupy in space.

If I show you an object from five different perspectives, you can build a pretty good representation of that object. Youd understand its color, its shape, and youd be able to recognize that object in many different scenes, Gothoskar says.

Mansinghka adds, "This is way less data than deep-learning approaches. For example, the Dense Fusion neural object detection system requires thousands of training examples for each object type. In contrast, 3DP3 only requires a few images per object, and reports uncertainty about the parts of each objects' shape that it doesn't know."

The 3DP3 system generates a graph to represent the scene, where each object is a node and the lines that connect the nodes indicate which objects are in contact with one another. This enables 3DP3 to produce a more accurate estimation of how the objects are arranged. (Deep-learning approaches rely on depth images to estimate object poses, but these methods dont produce a graph structure of contact relationships, so their estimations are less accurate.)

Outperforming baseline models

The researchers compared 3DP3 with several deep-learning systems, all tasked with estimating the poses of 3D objects in a scene.

In nearly all instances, 3DP3 generated more accurate poses than other models and performed far better when some objects were partially obstructing others. And 3DP3 only needed to see five images of each object, while each of the baseline models it outperformed needed thousands of images for training.

When used in conjunction with another model, 3DP3 was able to improve its accuracy. For instance, a deep-learning model might predict that a bowl is floating slightly above a table, but because 3DP3 has knowledge of the contact relationships and can see that this is an unlikely configuration, it is able to make a correction by aligning the bowl with the table.

I found it surprising to see how large the errors from deep learning could sometimes be producing scene representations where objects really didnt match with what people would perceive. I also found it surprising that only a little bit of model-based inference in our causal probabilistic program was enough to detect and fix these errors. Of course, there is still a long way to go to make it fast and robust enough for challenging real-time vision systems but for the first time, we're seeing probabilistic programming and structured causal models improving robustness over deep learning on hard 3D vision benchmarks, Mansinghka says.

In the future, the researchers would like to push the system further so it can learn about an object from a single image, or a single frame in a movie, and then be able to detect that object robustly in different scenes. They would also like to explore the use of 3DP3 to gather training data for a neural network. It is often difficult for humans to manually label images with 3D geometry, so 3DP3 could be used to generate more complex image labels.

The 3DP3 system combines low-fidelity graphics modeling with common-sense reasoning to correct large scene interpretation errors made by deep learning neural nets. This type of approach could have broad applicability as it addresses important failure modes of deep learning. The MIT researchers accomplishment also shows how probabilistic programming technology previously developed under DARPAs Probabilistic Programming for Advancing Machine Learning (PPAML) program can be applied to solve central problems of common-sense AI under DARPAs current Machine Common Sense (MCS) program, says Matt Turek, DARPA Program Manager for the Machine Common Sense Program, who was not involved in this research, though the program partially funded the study.

Additional funders include the Singapore Defense Science and Technology Agency collaboration with the MIT Schwarzman College of Computing, Intels Probabilistic Computing Center, the MIT-IBM Watson AI Lab, the Aphorism Foundation, and the Siegel Family Foundation.

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Machines that see the world more like humans do - MIT News

ATLANTACompared to standard machine learning models, deep learning models are largely superior at discerning patterns and discriminative features in brain imaging, despite being more complex in their architecture, according to a new study in Nature Communications led by Georgia State University.

Advanced biomedical technologies such as structural and functional magnetic resonance imaging (MRI and fMRI) or genomic sequencing have produced an enormous volume of data about the human body. By extracting patterns from this information, scientists can glean new insights into health and disease. This is a challenging task, however, given the complexity of the data and the fact that the relationships among types of data are poorly understood.

Deep learning, built on advanced neural networks, can characterize these relationships by combining and analyzing data from many sources. At the Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State researchers are using deep learning to learn more about how mental illness and other disorders affect the brain.

Although deep learning models have been used to solve problems and answer questions in a number of different fields, some experts remain skeptical. Recent critical commentaries have unfavorably compared deep learning with standard machine learning approaches for analyzing brain imaging data.

However, as demonstrated in the study, these conclusions are often based on pre-processed input that deprive deep learning of its main advantagethe ability to learn from the data with little to no preprocessing. Anees Abrol, research scientist at TReNDS and the lead author on the paper, compared representative models from classical machine learning and deep learning, and found that if trained properly, the deep-learning methods have the potential to offer substantially better results, generating superior representations for characterizing the human brain.

We compared these models side-by-side, observing statistical protocols so everything is apples to apples. And we show that deep learning models perform better, as expected, said co-author Sergey Plis, director of machine learning at TReNDS and associate professor of computer science.

Plis said there are some cases where standard machine learning can outperform deep learning. For example, diagnostic algorithms that plug in single-number measurements such as a patients body temperature or whether the patient smokes cigarettes would work better using classical machine learning approaches.

If your application involves analyzing images or if it involves a large array of data that cant really be distilled into a simple measurement without losing information, deep learning can help, Plis said.. These models are made for really complex problems that require bringing in a lot of experience and intuition.

The downside of deep learning models is they are data hungry at the outset and must be trained on lots of information. But once these models are trained, said co-author Vince Calhoun, director of TReNDS and Distinguished University Professor of Psychology, they are just as effective at analyzing reams of complex data as they are at answering simple questions.

Interestingly, in our study we looked at sample sizes from 100 to 10,000 and in all cases the deep learning approaches were doing better, he said.

Another advantage is that scientists can reverse analyze deep-learning models to understand how they are reaching conclusions about the data. As the published study shows, the trained deep learning models learn to identify meaningful brain biomarkers.

These models are learning on their own, so we can uncover the defining characteristics that theyre looking into that allows them to be accurate, Abrol said. We can check the data points a model is analyzing and then compare it to the literature to see what the model has found outside of where we told it to look.

The researchers envision that deep learning models are capable of extracting explanations and representations not already known to the field and act as an aid in growing our knowledge of how the human brain functions. They conclude that although more research is needed to find and address weaknesses of deep-learning models, from a mathematical point of view, its clear these models outperform standard machine learning models in many settings.

Deep learnings promise perhaps still outweighs its current usefulness to neuroimaging, but we are seeing a lot of real potential for these techniques, Plis said.

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Deep Learning Outperforms Standard Machine Learning in Biomedical Research Applications, Research Shows - Georgia State University News

PALO ALTO, Calif.--(BUSINESS WIRE)--Businesses run on documents as documents help build connections. They cement relationships and enable trust and transparency between stakeholders. Documents bring certainty, continuity, and clarity. When it comes to reviewing documents, most intelligence platforms perceive documents for their language content. A business document is not just written text, its a record of information and data - from simple entities such as names or addresses to more nuanced ones such as notice period or renewal dates - this information is required to optimize workflows and processes. Revv recently added Metalens, an intelligent document analyzer that breaks this barrier and applies artificial intelligence to extract data and intent from business documents to scale up business processes.

Metalens allows users to extract relevant information and identify potential discussion points from any document (pdf or Docx) within Revv. This extracted data can be reused to set up workflows, feed downstream business apps with relevant information, and optimize business processes. Think itinerary processing, financial compliance, auditing, renewal follow-up, invoice processing, and so on, all identified and automated. The feature improves process automation, which is otherwise riddled with copy-pasting errors and other manual data entry bottlenecks.

Rishi Kulkarni, the co-founder, adds, Revvs Metalens feature is fast, efficient, and a powerful element that sifts through the content and turns your documents into datasets. This unlocks new insights that allow our users to empower themselves and align their businesses for growth.

Metalens is another aspect of Revvs intelligence layer used to understand document structure and compare and review contracts with current industry standards. Businesses can identify their risk profile and footprint in half the time, with half the resources. It helps to get a grip on the intent of business documents and ensure your business objectives are met.

With Metalens, users can -

Excited about this new feature, Sameer Goel, co-founder, adds, The impact of this intelligent layer is clear and immediate as it is able to process complex documents with legalese and endless text thats easy to miss. It can process unstructured and structured document data even when datasets formats and locations change over time. This machine learning approach provides users with an alternative solution that allows them to circumvent their dependence on intimately knowing the document to extract information from it.

Revvs new Metalens feature gives its users the speed and flexibility to generate meaningful insights and accelerate business outcomes by putting machine learning front and center. It quickens the review process and makes negotiation smoother. It brings transparency that helps reduce errors and lets users save time and effort.

Metalens is part of Revvs larger offering designed to simplify business paperwork. Revv is an all-in-one document platform that brings together the power of eSignature, an exhaustive template library, a drag-n-drop editor, payments and Gsheet integrations, and API connections. Specially designed for owner-operators, consultants, agencies, and service providers who want a simple no-code tool to manage their business paperwork, Revv gives them the ability to draft, edit, share online, eSign, collect payments, and centrally store documents with one tool.

About Revv:

Backed by Lightspeed, Matrix Partners, and Arka Ventures, Revv was founded by Freshworks alumni Rishi Kulkarni and Sameer Goel in 2018. With operations in Silicon Valley and Bangalore, India, Revv is designed as a document management system for entrepreneurs. As of now, Revv has more than 3000+ businesses trusting the platform and is poised for even greater growth with features like attaching supporting media/doc files, multi-language support, bulk creation of documents, and even user groups.

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Unlock Insights From Business Documents With Revv's Metalens, a Machine Learning Based Document Analyzer - Business Wire