Daily Archives: December 3, 2019

Companies planning on using artificial intelligence (AI) in their work should ensure it is transparent and accountable, the Information Commissioners Office (ICO) has said.

The UKs data watchdog has published its first draft regulatory guidance into the use of AI in collaboration with the Alan Turing Institute.

It warned that the public are still uneasy over the use of computer software to make decisions previously made by humans, so any systems must be transparent and provide clear explanations of decisions made.

The guidance identified four key principles for AI: transparency, accountability, consideration of context and reflection on impacts.

The ICO said it had found that more than half of people remain concerned about machines making complex, automated decisions about them.

The potential for AI is huge, but its implementation is often complex, which makes it difficult for people to understand how it works, said Simon McDougall, the ICOs executive director of technology and innovation.

And when people dont understand a technology, it can lead to doubt, uncertainty and mistrust.

Last year, ministers published the AI Sector Deal, a joint venture between the Government and industry to try to push the UK to the forefront of emerging technology such as AI.

The ICO and the Alan Turing Institutes draft guidance comes after an independent review by Professor Dame Wendy Hall and also the Government urged both parties to provide input on the subject.

The guidance said the four main principles are rooted in the General Data Protection Regulations (GDPR), EU-wide laws introduced last year to hand greater control over personal data to individuals.

The principles say organisations should ensure decisions made by AI are obvious and appropriately explained to people in a meaningful way.

On accountability, it says firms should ensure appropriate oversight of AI decision systems, and be answerable to others.

It also called for companies to reflect on the impact their AI use would have by ensuring they ask and answer questions about the ethical purposes and objectives of your AI project at the initial stages of formulating the problem and defining the outcome.

The ICO said it will consult on its guidance until January 24, and Mr McDougall encouraged industry experts to respond to its draft before then.

The decisions made using AI need to be properly understood by the people they impact, he said.

This is no easy feat and involves navigating the ethical and legal pitfalls around the decision-making process built in to AI systems.

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Artificial intelligence use 'must be transparent and accountable' - The Irish News

02 December 2019

A quarter of UK jobs in artificial intelligence and other specialist technology roles were filled by women this year, the lowest proportion in two decades, according to research from Datatech Analytics for professional network Women in Data.

The research showed that the percentage of women taking on positions related to data science, out of the overall number of people entering the industry, fell from 41% of the total in 2005, to 34% in 2009 and then to 27% this year.

According to the research, the drop in the proportion of women working in these roles is due to a surge in men choosing career fields such as AI over the past 19 years, a 400% increase, compared to a 68% uptick for women during the same period.

Payal Jain, who chairs the Women in Data campaign, said this could appear "fairly bleak reading for us", but said, because of that, "women are some of the most sought after talent in the UK right now" and that there is no better time to be a woman in data."

Kelly Metcalf, Head of Diversity, Inclusion and Wellbeing at Fujitsu UK & Ireland, also commented, Diverse teams allow organisations to provide environments where different styles of thinking come together, allowing for more innovation and productivity, so its a concern to see such a small proportion of artificial intelligence roles being filled by women. With no signs of digital transformation slowing down, it is becoming increasingly important that all businesses not just technology organisations do more to tackle this gap and attract a diverse range of talent.

Ultimately, there are many steps organisations can implement to encourage a diverse and inclusive work environment to ensure that the UK sustains its technology top spot, Metcalf said. And for women in the UK, if organisations are to deliver genuine change they must commit to a big vision that diversity and inclusion produces much better results.

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UK Proportion of women in AI and data jobs hits 20-year low, research finds - Staffing Industry Analysts

Advanced technologies in robotic process automation (RPA) such as AI, machine learning, cognitive computing and pattern matching have been transforming how we manage projects. Whether you are a program manager responsible for the portfolio of initiatives in healthcare or a technical project manager overseeing the redeployment of an enterprise resource planning (ERP) solution, you need to understand key elements of advanced technologies such as machine learning and AI so you can spend less time on planning and more time on execution.

As a project manager who has led project management offices for Fortune 500 organizations for over 12 years, I've experienced firsthand the various iterations of this trade transformation. Having credentials such as PMP, ACP and Six Sigma is a proven way to demonstrate your commitment to project management as a profession, but I am also a strong believer that adapting and staying ahead of the latest trends in technology will help you continue to deliver value as a project manager.

Here, I'll provide a series of recommendations on best practices and skills required to effectively apply and use advanced technologies for better project delivery. Automation or other advanced technologies such as AI will not replace project management as a practice, but project managers need to evolve and continuously develop their technical skills in order to use these technologies in conjunction with the interpersonal elements of managing projects.

Indeed, there are plenty of areas of the project management or software development life cycle (SDLC) that could be automated, but as we revisit the first proponent of the Agile Manifesto, "Individuals and interactions over processes and tools."

Critical skills for project managers in advanced technologies

Project managers should continuously enhance their technical expertise and skill set. Even if you're a nontechnical PM who is working with the business side of the project, you would still benefit greatly from being able to have a meaningful conversation with the development team. RPA technologies such as Automation Anywhere, Blue Prism and UiPath can provide solid frameworks to get introduced to key principles.

Understanding the key principles in the application or system design methodologies can help you manage your projects more efficiently. For instance, when analyzing the proposed solutions for a given design architecture, you can evaluate the overall durations and resources required for a given set of tasks and provide recommendations on estimated timelines and resource capacity.

Some high-level examples of system design methodologies that can provide a framework for further research include object-oriented analysis and design (OOAD) a method or a framework for designing a business process or a system through visual modeling and one of the popular techniques that the project managers should be familiar with when advancing their skills in RPA domain-driven design (DDD) and layered application development with layers such as application/presentation, data, services and business.

There are various courses and certifications that may provide for additional knowledge as well as credibility in RPA.

Resource management with AI and ML

Resource planning and resource capacity modeling are the most critical phases in the project management life cycle. Many companies have well-defined databases of specific roles and preestablished templates with MS Project for project managers, which provide for a solid framework to analyze the data for potential automation. Whether you need to crash the schedule or fast-track it, AI can be a powerful tool in creating predictive models based on the historical performance of similar tasks. However, this would require a diligent and consistent initiative to create a historical database of all past successful and failed projects.

RPA relies on historical data, and project managers need to drive the closing phases of each project with due diligence. Consequently, when certain repetitive tasks are automated, your staff is left with more time to make project-centric decisions that positively influence its delivery.

There are various recommendations for project managers who are taking on projects in advanced technologies or looking to transition into leading the companywide transformation initiatives. Whether you are leading a retrospective session with your Agile team or a lessons-learned committee upon completion of a project, it is imperative to create a repository of historical data used in each project.

RPA and AI should both go through the five lean principles: identify value, map the value stream, create the flow, establish pull and seek perfection. Each PM who is looking to adopt RPA should aim to adapt to some of these principles of lean in order to create a more robust framework for managing RPA projects.

In summary

Whether you are leading a small upgrade project for the latest software update or spearheading a companywide ERP redeployment initiative, identifying opportunities for automation and process improvement is critical for future success. We continuously identify tools and methods for project managers such as templates for resource capacity planning and modeling or a predefined set of automated tasks on project plans as well as define critical-to-quality (CTQ) skills for PMs when sourcing for the latest candidates for an assigned project.

Project managers play an integral part in driving these initiatives and making sure that each phase of the project life cycle is consistently adhered to. Without the historical data and lessons learned, RPA and other advanced technologies will be challenging to implement and adapt.

Originally posted here:

How To Use RPA And AI For Project Management - Forbes

Amazon Web Services Inc.s big re:Invent conference officially kicked off today in Las Vegas and the cloud giant has wasted no time today, announcingan array of services and features headlined by new artificial intelligence offerings.

First up is Amazon Transcribe Medical, an AI transcription service that enables medical professionals to dictate notes and record patient conversations. Its AWS attempt to free up some of the several hours per day that the average primary care physical spends on administrative tasks such as data entry.

Amazon Transcribe Medical is provided as an application programming interface that can be plugged into healthcare services. Its compliant with the HIPAA medical data protection regulation, automatically adds pronunciation marks into transcribed text where appropriate and, according to AWS, supports virtually any device that has a microphone. That means developers couldtheoreticallybring it tosmart speakers such as Amazons Echo devices, which are already finding usein some medical settings.

SageMaker is an AWS service that allows developers to build and train AI models withoutmanaging theinfrastructure below. Now, software teams can carry outSageMaker projects via Kubernetes thanks to a set of new operators that the cloud giant is rolling out.

Each Amazon SageMaker Operator for Kubernetes provides you with a native Kubernetes experience for creating and interacting with your [SageMaker] jobs, either with the Kubernetes API or with Kubernetes command-line utilities such as kubectl, AWS senior product manager Aditya Bindal explained in a blog post.

SiteWise, AWS service for analyzing data from industrial equipment, is getting a new visualization tool that displaysoperational information in graphs. Its available as a browser-based applicationunder the name SiteWise Monitor. A plants maintenance team can use it to build dashboards that shows how often each piece of equipment is down due to technical issues, while a business analyst at the same facility could visualize factory output.

SiteWise Monitorhas arrived as part of abroader update to the service that brings other analytics features, too. Chief among them is a new digital twins capability. Companies can now take sensor measurements from physical assets such as a production line and create a digital copy, or twin, of the hardware that lets analysts virtually experiment with new ways to optimize operations.

Copies of Windows Server and Linux running on AWS instances need to be updated when theres a new version available just like the operating system of a physical server. Administrators typically either perform the task manually or create custom scripts to automate the process, but AWS claims thatEC2 Image Builderoffers a better way.

The newly revealed service provides a graphical interface for handling operating system updates. Administrators can use EC2 Image Builder to customize Windows Server and Linux distributionupdatesas needed, testthe new version to see if works and then automatically deploy the update to their companies cloud environments.

Topping off the list of new offerings unveiled during AWS midnight announcement bonanza is DeepCompose, a AI-powered musical keyboard. The device allows developers to familiarize themselves with machine learning by building and training models that generate music. AWS now offers no fewer than three differentAI learning devices: DeepComposer joins the recently upgraded DeepRacerremote-controlled model car and the DeepLens camera.

Lastly, AWS also rolled out several smaller enhancements, including new features for managing software licenses companies use on its cloud platform.

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Musical keyboards and AI on Kubernetes: AWS fires off first salvo of re:Invent updates - SiliconANGLE

"Now you can build your AI app for free on Arterys and in minutes, instead of years, distribute them with the speed of the internet," said CEO and co-founder Fabien Beckers. The developer tools available on Arterys include the first online deployment environment with a radiologist viewer, a scalable backend, seamless integration capabilities, and support for both regulatory approved and experimental AI models. Now creators can focus on creating the best AI models and publishing full clinical applications online without having to spend years building them. "Because every radiologist's workflow is different, we're putting the power in their hands to give feedback and iterate with a community of AI developers."

Arterys has invested more than $50M and over seven years of development into building its proprietary internet platform and clinical-grade web viewer to take diagnostic imaging online. After building its own clinical AI applications, Arterys is making its platform available to a growing global community of AI innovators. Unlike others, the Arterys Marketplace is available to all meaning anyone can share their AI models via a simple URL, and anyone on the Arterys Marketplace can try it on their own medical images.

Arterys invites all developers to share their content on the Arterys Marketplace, regardless of where in the world or what stage of development they're in (research or regulated AI apps). The company doesn't exert editorial control over the content and provides a set of guidelines for best practices. Arterys encourages developers to use content and editorial curation to drive audience development and engagement with their AI.

"We're making the process of uploading, sharing, and testing your medical image models on external data as easy as uploading, sharing, and watching a YouTube video," said Arterys Marketplace Product Manager Christian Ulstrup. "We firmly believe innovation can come from anyone, anywhere in the world. That's why we're working hard to make the Marketplace the only open, frictionless, and user-driven medical image AI platform available."

The Arterys Marketplace will be on display at the company's booth at RSNA. Check it out at the RSNA 2019 AI Showcase, Booth #10918 or on our website: https://www.arterys.com/marketplace

Are you an AI model developer? Interested in taking part in the Arterys Marketplace closed beta? Arterys is taking applications through the end of the yearjoin the growing global community of AI developers working to bring deliver on the promise of AI in clinical practice by signing up at https://www.arterys.com/developers

SOURCE Arterys Inc.

http://www.arterys.com

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Arterys launches the first viewer-based AI Marketplace for medical imaging, fueling open innovation - PRNewswire

MRP Prelytix is an ABM Platform designed to power global enterprise programs. The technology and services aim to help identify the needs and buyer's journey stage of each target account and apply real-time AI to trigger orchestrations across seven channels, in 20 languages.

Prelytix offers a variety of tools to power lead intelligence, marketing campaigns and execution. Key features include:

Lead Intelligence:

Marketing Campaigns:

Campaign Execution:

MRP Prelytix's target audience includes mid-range to large enterprises that serve multiple geographies, lines of business or industries. These enterprises require a flexible, mature and scalable ABM solution that can support global companies and coordinate execution across multiple marketing channels.

While integrated custom engagement data includes hardened connections directly into SFDC, Siebel, Eloqua, REST API, Marketo, HubSpot and Pardot, MRP Prelytix enables client systems to behave like ABM tools, making ABM a consistent strategy across client teams and technology.

MRP Prelytixis delivered via software-as-a-service (SaaS) and licensed on an annual subscription basis and is customized to meet the needs and objectives of clients.

MRP works with more than 450 companies, including Oracle, HPE, SAP and Thomson Reuters, managing over 1,000 engagements across the globe.

For enterprise organizations that serve multiple geographies, lines of business or industries, MRP Prelytix is an ABM platform designed to give you control of your data, visibility into your target market and scale in the delivery of the highest impact engagement strategy.

MRP1818 Market Street37th FloorPhiladelphia, PA 19103Tel: 215-587-8800Email: marketing@mrpfd.com

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MRP Prelytix Is Designed To Power ABM Campaigns With Real-Time AI - Demand Gen Report

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The process repeats until the AI model reaches its desired accuracy.

This week at the annual conference of the Radiological Society of North America, machine learning and AI developers Nvidia unveiled its federated learning platform Clara. A system designed to protect patient privacy while still enabling medical centres to collaboratively train models and process patient data.

Due to the sensitive nature of medical data hospitals and medical centers, for both legal and privacy concerns, dont share images or data. Unfortunately, keeping all of this data in a silo means that no AI or ML models can be trained on it. Federated learning involves creating a central global server that sends a training algorithm to each medical centre taking part in the model training. Each institution trains the model on their private dataset, before sending it back to be aggregated by the central server. At no point does the sensitive data leave the medical centre.

Hospitals or medical centres using the Clara Federated Learning (Clara FL) system will label all of their data using an AI-assisted annotation SDK that is currently integrated in medical viewers such as 3D slicer, MITK and Philips Intellispace Discovery. This data is then trained on in-house servers before it is sent to the global server.

Nvidia commented in a release that: Clara FL is a reference application for distributed, collaborative AI model training that preserves patient privacy. Running on NVIDIA NGC-Ready for Edge servers from global system manufacturers, these distributed client systems can perform deep learning training locally and collaborate to train a more accurate global model.

Currently the University of California, Los Angeles is using Clara FL to introduce AI technology into its radiology department. Radiology departments produce a wealth of medical images captured from patients with a host of medical conditions, these include X-Rays, Computed tomography (CT scans) and magnetic resonance imaging (MRI) images.

Training an AI model on these images in order for it to spot patterns and help identify potential illness earlier in the diagnostic process has in the past been difficult due to patient privacy concerns, as such these medical images rarely leave the radiology department. With Clara AI and ML models can be trained without compromising patient privacy.

Clara FL operates using Nvidias EGX Edge platform, a high-performance platform that has been created to tackle the massive amounts of data created by modern technology. The EGX stack includes a driver, Kubernetes plug-in, container runtime plug-in and GPU monitoring software. Telcos can install all required Nvida software as containers that run on Kubernetes, giving flexibility. (The stack architecture is supported by Canonical, Cisco, Nutanix, Red Hat and VMware.)

Nvidia have also teased the release of Clara AGX an AI developer kit that aims to process high-data rate video and images that are flooding in from sensors embedded in medical devices.

Nvidia states that the: Clara AGX is powered by NVIDIA Xavier SoCs, the same processors that controls self-driving cars. They consume as little as 10W, making them suitable for embedding inside a medical instrument or running in a small adjacent system.

Originally posted here:

Privacy Concerns About Training AI On Medical Data, Nvidia Thinks Clara is the Answer - Computer Business Review

December 02, 2019 -Healthcare produces more data than any other industry, but mastering how to use data to be actionable while safeguarding sensitive data from unauthorized access can be a Sisophean effort. The double-digit year-over-year growth of the medical internet of things (IoT) coupled with a more data driven approach to patient care is accelerating the volume of sensitive healthcare data that could becoming vulnerable to misuse or thief. So, it should be of little surprise that traditional forms of identity management lack the agility to keep pace with health IT infrastructure changes. Fortunately, new forms of identity management leveraging artificial intelligence (AI) and machine learning (ML) can enable health systems, hospitals, and physician practices to remain productive and secure.

As Healthcare data collection accelerates, the need to safeguard who needs access and when is going to require new levels of sophistication and foresight that some healthcare entities do not currently possess. In general, less than 3% of a health system budget is spent on IT, says SailPoint Vice President of Healthcare Matthew Radcliffe. Outside of their core clinical, the investment in IT has not kept pace with the speed of growth and expansion, but if we make identity management processes more efficient through the lens of healthcare; healthcare organizations can reprioritize IT resources and invest more in patient care.

For many healthcare organizations, there is a process gap onboarding new clinicians. A lag in enabling clinicians to have data access creates operational inefficiencies that can impact patient care. And while data security is a top of mind issue for healthcare organizations, most organizations do not have the security expertise, bandwidth, or know-how to rapidly onboard clinicians seamless. Over the last decade or so, healthcare organizations have come to realize that they need more effect processes to manage access to patient data within enterprise systems by developing an identity governance program, Radcliffe continues.

To enable a clinician with first-day access to those systems they need what we call birthright access, the access they need on day one to treat a patient the sooner they can begin treating patients, the sooner the hospital can realize the operating benefits of a physician/clinician doing their job and the revenue associated with treating patients, adds Radcliffe.

Turning AI into ROI

New forms of identity management enabled by the cloud and powered by AI and ML have the potential to eliminate inefficiencies such as, access gaps that impact clinical and operational workflows without introducing risks to health data security and privacy.

Process gaps and administrative error can impact clinical and operational productivity and workflows. However, a predictive, AI-driven approach that automates the identity management processes can improve operational efficiencies. The more systems are automated, the level of error and inefficiencies are removed from the process. By removing redundancies, organizations can securely and efficiently give staff access all while improving identity governance.

It's very easy to demonstrate an ROI around productivity and how these solutions can help staff gain access sooner and more securely explains Gianni Aiello, Director of Product Management at SailPoint.

We have been solving these problems for a long time, but in some cases the way tools were used led to over entitled access that users did not use or need. This was the result of a focus on productivity over security. The outcome was higher risk of a breach and the potential for massive fines. AI and ML approaches can help us reduce the potential risk of incorrect access by 1030%. Whilst also improving productivity around governing access by up to 60%. This represent a huge return for healthcare organizations.

The challenge of present-day identity management is one of bandwidth. Healthcare is transforming so fast that humans can't keep up with ever-changing user populations, the rapid need for access, and more importantly the need to govern that access, Radcliffe stresses. Now consider marrying the transformation challenge with the rapid increase and use of of connected devices in healthcare connected IoT devices, infusion pumps, heart monitors, and smart beds as the most common examples. These connected devices are generating enormous amounts of data, and there's no way a human would be able to respond to these dynamics without leverging efficient and automated governance platforms . But the use of machine learning for identity management is able to turn current data into actionable information in two areas: access automation and regulatory compliance.

Healthcare organization need a convenient way to have staff log-on/log-off of shared clinical desktops and historically, to authenticate users, most organization would leverage the tap in tap out single sign-on method and this is where healthcare-based identity management programs would start and stop. Clinicians were historically enabled with these types of access management solutions without first understanding the specific role the clinician would serve within the organization, determinging if the clinician had previous access that could potentially conflict with net-new access or if the user should even be enabled with access due to some level of security policy conflict . As the number of users and systems have expentionally grown, there is greater need for healthcare organizations to establish a full identity management program. Innovative healthcare organizations should broaden their identity management program principles by adapting identity governance, data governance, privileged access management, and enterprise single sign-on as a full identity program.

In reality, the amount of data that needs to be collected, and ultimately looked at and analyzed, is huge, says Aiello. Looking for that needle in a haystack, for a human, is quite frankly nearly impossible. It's just not achievable. And so, machine learning is, in real terms, the only way you can start to better see and understand how people are using their access.

Unlike early machine learning and AI applications that were mainly rule-based approaches, were looking at specific scenarios that can be solved by discovered risk not pre-determined rules notes Aiello. That knowledge can be transferred to how you ultimately model access for the efficiency of staff around what they need to have access to do that job, Aiello states. Providing snapshots of access is a step toward homing in on appropriate access rights and ultimately determining how to grant and manage access moving forward.

Improving regulatory compliance

The Health Insurance Portability and Accountability Act (HIPAA) Security Rule requires that an individual or entity accessing protected health information (PHI) electronically be authenticated before access is granted. Unlike other industries, health systems, hospitals, and physician practices that have a data breach are often faced with consequences that go beyond significant fines, e.g., an erosion of patient and clinician trust.

Federal and state regulations for reporting health data security and privacy are increasing; and the task of maintaining healthcare data compliance will continue to be a daunting, labor intensive administrative process that will require consistent organizational commitment and vigilance. By automating access rights and augmenting decisions processes, organizations can leverage their data to help reduce the burden of user compliance.

At SailPoint we catalog access that is inherently low risk and doesnt expose data to inappropriate functions or allow for a clinician to see data that's inherently risky to the organization, Aiello reveals.

Automating user access creates a building block for more intelligent decision-making around identity management and governance. As healthcare focuses on their mission of driving operational and clinical efficiencies to improving patient outcomes, an AI-enabled identity management solutions can be strategic investment that can evolve with an organizations current and future IT roadmaps.

Minimize costs and complexity

With a drive towards digitalization, many healthcare organizations are leveraging the prodigious amount of sensitive data for decision making. When it comes to leveraging data to drive better patient outcomes, healthcare leaders are vacillating between innovation and compliance.

Effective identity management is critical to data governance. As such, healthcare organizations can lay the groundwork to ensuring that increasing data access doesn't lead to exponential growth in risk.

This is a business opportunity for healthcare organizations, Radcliffe advises. The way they see the opportunity to grow their business is to obtain access to more data and more patients beyond the brick-and-mortar hospital. This means broader access to digital records with the aim of caring for patients across the continuum of care.

We have to infuse integrated identity and data governance platforms into the digitization of healthare while leveraging AI and machine learning to keep up with the pace of healthcare business transformation, Radcliffe concludes.

To improve patient outcomes and driving operational efficiencies, healthcare organizations should invest in an AI-powered identity management solution for future operational success.

________________________________________

About SailPoint

SailPoint enables healthcare provider organizations to cost-effectively protect healthcare data, reduce financial risk from poor audit performance, and avoid disruptions to patient care. Infused with artificial intelligence, its predictive identity governance anticipates how access should change, shows where attention is needed, and recommends actions. Ideally suited for healthcare, the SailPoint platform increases IT and operational efficiencies by automating processes and simplifying the on-boarding and management of complex healthcare user populations (employees, affiliated physicians, contractors and others). SailPoint is consistently recognized by Gartner, Forrester and KuppingerCole as the leading authority on identity governance, and the preferred partner for numerous healthcare organizations.

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Minimize Costs and Complexity With AI-Powered Identity Management - HealthITSecurity.com