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Artificial Intelligence developed to monitor social distancing on construction sites – The Architect’s Newspaper

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With most Americans complying with nationwide stay-at-home orders enacted to reduce the spread of the novel coronavirus, a handful of states have nonetheless permitted construction sites to continue operations on essential projects. Site safety inspectors have therefore been left with the difficult task of ensuring that the workers they oversee are practicing all safety protocols as advised by the Center for Disease Control (CDC) and the Occupational Safety and Health Administration (OSHA), that include maintaining a distance of six feet apart from one another, wearing face coverings over their noses and mouths during work hours, and minimizing interactions when picking up or delivering equipment or materials.

On April 6, the artificial intelligence (AI) company Smartvid.io unveiled Vinnie, a new feature for its interface that will be able to monitor construction workers level of compliance with the advised social distancing protocols as a virtual safety inspector. The big thing with construction continuing to go on, Josh Kanner, CEO and founder of Smartvid.io, told Engineering News Record, is weve got some projects where the client is paying for extra labor on site to monitor people [for social distancing] and separate them.While Smartvid.io has provided AI technology for construction sites for over three years, the pandemic presented an unexpected set of challenges that required quick advancements.According to the companys website, Vinnie has been trained to findand counta number of indicators of project risk in the areas of safety, productivity and quality that include worker proximity and their use of personal protective equipment. Safety inspectors can either watch the footage in real-time or from recorded photos and videos, allowing their surveillance to be carried out beyond typical working hours.

For construction workers who may be concerned about any potential breaches of privacy afforded by the updated surveillance technology, Smartvid.io has made clear that there is no facial recognition and never will be, and that Vinnie has been certified to be compliant with the strict privacy requirements specified by the European GDPR standard.

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Artificial Intelligence developed to monitor social distancing on construction sites - The Architect's Newspaper

Digital transformation: Artificial intelligence in the public sector – Open Access Government

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Former Prime Minister of the UK, David Cameron, once remarked that: I believe the creation of the Government Digital Service is one of the great unsung triumphs of the last Parliament. Today, the role of the Government Digital Service (GDS) as part of the Cabinet Office concerns the digital transformation of government. On the GDSs blog, they sum up what they are all about in their own words. Were a centre of excellence in digital, technology and data, collaborating with departments to help them with their own transformation. We work with them to build platforms, standards, and digital services. (1)Artificial intelligence in the public sector

In A guide to using artificial intelligence in the public sector, we learn that AI could change the way we live and work, for example, several public sector organisations use AI for tasks ranging from fraud detection to answering customer queries successfully today. While it is estimated that AI could contribute 5% of the UKs GDP by 2030, ethical, fairness and safety considerations must be taken into account

The paper also gives a very useful definition of AI, that includes the following: At its core, AI is a research field spanning philosophy, logic, statistics, computer science, mathematics, neuroscience, linguistics, cognitive psychology and economics

AI can be defined as the use of digital technology to create systems capable of performing tasks commonly thought to require intelligence

Machine learning is the most widely-used form of AI, and has contributed to innovations like self-driving cars, speech recognition and machine translation.

One of the many case studies highlighted in the paper concerns the Department for International Development who partnered with Columbia University, the University of Southampton, and the United Nations Population Fund to apply a random forest machine learning algorithm to satellite image and micro-census data. Another is about how the Driver and Vehicle Standards Agency (DVSA) uses AI to improve MOT testing.

The paper also draws our attention to the fact that AI can benefit the public sector, such as giving more accurate information, predictions and forecasts that result in better outcomes, more accurate medical diagnoses or automating repetitive and time-consuming tasks to free up the valuable time of frontline staff.

However, with an AI project, you need to consider several factors, including AI ethics and safety the paper urges, such as data quality, fairness, accountability, privacy, explainability and transparency, plus costs. The paper also notes that you need to ensure that your AI system is compliant with GDPR and the Data Protection Act 2018 (DPA 2018), including the points that concern automated decision making.

Automated decisions in this context are decisions made without human intervention, which have legal or similarly significant effects on data subjects. For example, an online decision to award a business grant.

If you want to use automated processes to make decisions with legal or similarly significant effects on individuals you must follow the safeguards laid out in the GDPR and DPA 2018. (3)

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Digital transformation: Artificial intelligence in the public sector - Open Access Government

The global artificial intelligence in healthcare market is set to register growth, projecting a CAGR of 38.05% during the forecast period, 2020-2028 -…

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NEW YORK, April 15, 2020 /PRNewswire/ --

KEY FINDINGSThe global artificial intelligence in healthcare market is set to register growth, projecting a CAGR of 38.05% during the forecast period, 2020-2028. The prominent drivers of market growth are estimated to be the rising big data in the healthcare industry, the growing use of AI in genetics, the emergence of personalized medicine in tests for clinical decision making, along with the creation of a real-time monitoring system due to AI.

Read the full report: https://www.reportlinker.com/p05242360/?utm_source=PRN

MARKET INSIGHTSThe utilization of AI in healthcare entails the use of software and algorithms for estimating the human perception for analyzing complex medical data, along with the relationship between treatments or prevention techniques and patient outcomes.The growing demand for real-time monitoring system is one of the key aspects propelling the growth of the global artificial intelligence in healthcare market.

The real-time monitoring devices like health monitoring devices or indicators track real-time health data of patients, which is increasing the demand for AI in healthcare.The devices also drive the relevancy of data interpretation and aid in reducing the time the patients spend in piecing data output.

In healthcare, the devices help in detecting and preventing undesirable patient outputs. The growing number of mobile devices integrated with artificial intelligence assists in the prediction of future outcomes with regard to health, which further benefits market growth.Medical practitioners are reluctant to adopt AI-based technologies, and this is restraining the growth of the market.The reluctance is because of the lack of data that identifies healthcare decisions.

Also, from a diagnostics point of view, AI systems fare less in terms of efficiency in comparison to conventional methods.The companies in the market are competing against each other by providing the same characteristics and similar prices.

The competitive rivalry is projected to be high during the forecast period.

REGIONAL INSIGHTSThe geographical segmentation of the global artificial intelligence in healthcare market includes the analysis of Europe, North America, Asia Pacific, and the rest of the world.Inkwood Research estimates the Asia Pacific region to be the fastest-growing region by the end of the forecast period.

The invention of new technologies, the presence of countries like China, Japan, Australia, and India, and the thriving artificial intelligence market, are the factors propelling the growth of the market.

COMPETITIVE INSIGHTSSome of the prominent companies operating in the market are Enlitic Inc, Next IT Corporation, Recursion, Welltok, GE Healthcare, Microsoft Corporation, etc.

Our report offerings include: Explore key findings of the overall market Strategic breakdown of market dynamics (Drivers, Restraints, Opportunities, Challenges) Market forecasts for a minimum of 9 years, along with 3 years of historical data for all segments, sub-segments, and regions Market Segmentation cater to a thorough assessment of key segments with their market estimations Geographical Analysis: Assessments of the mentioned regions and country-level segments with their market share Key analytics: Porter's Five Forces Analysis, Vendor Landscape, Opportunity Matrix, Key Buying Criteria, etc. Competitive landscape is the theoretical explanation of the key companies based on factors, market share, etc. Company profiling: A detailed company overview, product/services offered, SCOT analysis, and recent strategic developments

Companies mentioned1. DEEP GENOMICS INC2. ENLITIC INC3. GE HEALTHCARE4. GENERAL VISION INC5. GOOGLE6. IBM CORPORATION7. ICARBONX8. INTEL CORPORATION9. MICROSOFT CORPORATION10. NEXT IT CORPORATION11. NVIDIA CORPORATION12. ONCORA MEDICAL13. RECURSION PHARMACEUTICALS INC14. STRYKER CORPORATION15. WELLTOK INC

Read the full report: https://www.reportlinker.com/p05242360/?utm_source=PRN

About Reportlinker ReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need - instantly, in one place.

__________________________ Contact Clare: clare@reportlinker.com US: (339)-368-6001 Intl: +1 339-368-6001

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The global artificial intelligence in healthcare market is set to register growth, projecting a CAGR of 38.05% during the forecast period, 2020-2028 -...

Eyenuk Successfully Fulfills Contract Awarded by Public Health England for Artificial Intelligence Grading of Retinal Images – Yahoo Finance

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60,000 Patient Image Sets from 6 Different Diabetic Eye Screening Programmes Analyzed Using EyeArt AI Eye Screening System

Eyenuk, Inc., a global artificial intelligence (AI) medical technology and services company and the leader in real-world applications for AI Eye Screening, announced that it has successfully fulfilled the contract awarded by Public Health England (PHE) to use Eyenuks EyeArt AI Eye Screening System to grade 60,000 patient image sets from 6 different National Health Service (NHS) Diabetic Eye Screening Programmes in England.

Diabetic retinopathy (DR) is a vision-threatening complication of diabetes and a leading cause of preventable vision loss globally.1 In England, an estimated 4.6 million are living with diabetes, one-third of whom are at risk of developing DR. Diabetes has become a growing health concern as the number of people diagnosed with diabetes in the U.K. has more than doubled in the last 20 years.2

The U.K. has been leading the world in diabetic retinopathy screening, achieving patient uptake rates of over 80% (screening nearly 2.5 million diabetes patients annually),3 as compared with most parts of the world where typically less than half of diabetes patients receive annual eye screening.4 As a result, diabetic retinopathy is no longer the leading cause of blindness in the working age group in England.5 However, the growing diabetes population poses significant challenges ahead.

Public Health England (PHE) is an executive agency of the Department of Health and Social Care (DH) that oversees the NHS national health screening programmes. An independent Health Technology Assessment from the Moorfields Eye Hospital to determine the screening performance and cost-effectiveness of multiple DR detection AI solutions was conducted and published in 2016.6 Subsequently, PHE initiated a tender process seeking to commission an automated retinal image grading software to grade 60,000 patient image sets from multiple diabetic eye screening programmes.

At the end of the competitive tender process, the contract was awarded to Eyenuk.7 The National Diabetic Eye Screening Programme (NDESP) identified 6 local diabetic eye screening (DES) programmes to participate in the project with Eyenuk. The project aim was to compare the number of image sets categorised as having no disease, as determined by human graders (manual programme grading), with the number as determined by the EyeArt AI eye screening system. Results from this latest real-world analysis, together with results from previous assessments have shown that the EyeArt system has excellent agreement and sensitivity and specificity for detecting diabetic retinopathy.

"Eyenuk was honored to have been awarded the PHE contract for diabetic retinopathy grading, and we are gratified that our EyeArt AI system delivered excellent results when compared with six DES programmes in England," said Kaushal Solanki, Ph.D., founder and CEO of Eyenuk. "We look forward to expanding our work in the U.K. with hope to support all diabetic eye screening programmes in the future."

The independent Health Technology Assessment (HTA) from Moorfields Eye Hospital involving more than 20,000 patients was conducted to determine the screening performance and cost-effectiveness of multiple automated retinal image analysis systems. This study demonstrated that the EyeArt AI System delivered much higher sensitivity (i.e., patient safety) for DR screening than other automated DR screening technologies investigated and that its use is cost-effective alternative to the current, purely manual grading approach. The HTA demonstrated that the EyeArt performance was not affected by ethnicity, gender, or camera type.

About the EyeArt AI Eye Screening System

The EyeArt AI Eye Screening System provides fully automated DR screening, including retinal imaging, DR grading on international standards and the option of immediate reporting, during a diabetic patients regular office visit. Once the patients fundus images have been captured and submitted to the EyeArt AI System, the DR screening results are available in a PDF report in less than 60 seconds.

The EyeArt AI System was developed with funding from the U.S. National Institutes of Health (NIH) and is validated by the U.K. National Health Service (NHS). The EyeArt AI System has CE marking as a class IIa medical device in the European Union and a Health Canada license. In the U.S., the EyeArt AI System is limited by federal law to investigational use. It is designed to be General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act of 1996 (HIPAA) compliant.

Story continues

VIDEO: Learn more about the EyeArt AI Eye Screening System for Diabetic Retinopathy

About Eyenuk, Inc.

Eyenuk, Inc. is a global artificial intelligence (AI) medical technology and services company and the leader in real-world AI Eye Screening for autonomous disease detection and AI Predictive Biomarkers for risk assessment and disease surveillance. Eyenuks first product, the EyeArt AI Eye Screening System, is the most extensively validated AI technology for autonomous detection of DR. Eyenuk is on a mission to screen every eye in the world to ensure timely diagnosis of life- and vision-threatening diseases, including diabetic retinopathy, glaucoma, age-related macular degeneration, stroke risk, cardiovascular risk and Alzheimers disease. Find Eyenuk online on its website, Twitter, Facebook, and LinkedIn.

http://www.eyenuk.com

1 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4657234/ 2 https://www.diabetes.org.uk/about_us/news/diabetes-prevalence-statistics 3 https://www.gov.uk/government/publications/diabetic-eye-screening-2016-to-2017-data 4 K. Fitch, T. Weisman, T. Engel, A. Turpcu, H. Blumen, Y. Rajput, and P. Dave. Longitudinal commercial claims-based cost analysis of diabetic retinopathy screening patterns. Am Health Drug Benefits. 2015;8(6):300308.5 G. Liew, M. Michaelides, C. Bunce. A comparison of the causes of blindness certifications in England and Wales in working age adults (1664 years), 19992000 with 20092010. BMJ Open Bd. 4 (2014), Nr. 26 Adnan Tufail, Venediktos V Kapetanakis, Sebastian Salas-Vega, Catherine Egan, Caroline Rudisill, Christopher G Owen, Aaron Lee, et al. "An Observational Study to Assess If Automated Diabetic Retinopathy Image Assessment Software Can Replace One or More Steps of Manual Imaging Grading and to Determine Their Cost-Effectiveness." Health Technology Assessment 20, no. 92 (December 2016). https://doi.org/10.3310/hta20920 7 https://www.contractsfinder.service.gov.uk/Notice/13b069bd-97b4-40b6-ac66-337d1526d1e6

View source version on businesswire.com: https://www.businesswire.com/news/home/20200415005222/en/

Contacts

Frank Cheng, Chief Commercial Officerfrank.cheng@eyenuk.com +1 818 835 3585

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Eyenuk Successfully Fulfills Contract Awarded by Public Health England for Artificial Intelligence Grading of Retinal Images - Yahoo Finance

Automated Machine Learning is the Future of Data Science – Analytics Insight

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As the fuel that powers their progressing digital transformation endeavors, organizations wherever are searching for approaches to determine as much insight as could reasonably be expected from their data. The accompanying increased demand for advanced predictive and prescriptive analytics has, thus, prompted a call for more data scientists capable with the most recent artificial intelligence (AI) and machine learning (ML) tools.

However, such highly-skilled data scientists are costly and hard to find. Truth be told, theyre such a valuable asset, that the phenomenon of the citizen data scientist has of late emerged to help close the skills gap. A corresponding role, as opposed to an immediate substitution, citizen data scientists need explicit advanced data science expertise. However, they are fit for producing models utilizing best in class diagnostic and predictive analytics. Furthermore, this ability is incomplete because of the appearance of accessible new technologies, for example, automated machine learning (AutoML) that currently automate a significant number of the tasks once performed by data scientists.

The objective of autoML is to abbreviate the pattern of trial and error and experimentation. It burns through an enormous number of models and the hyperparameters used to design those models to decide the best model available for the data introduced. This is a dull and tedious activity for any human data scientist, regardless of whether the individual in question is exceptionally talented. AutoML platforms can play out this dreary task all the more rapidly and thoroughly to arrive at a solution faster and effectively.

A definitive estimation of the autoML tools isnt to supplant data scientists however to offload their routine work and streamline their procedure to free them and their teams to concentrate their energy and consideration on different parts of the procedure that require a more significant level of reasoning and creativity. As their needs change, it is significant for data scientists to comprehend the full life cycle so they can move their energy to higher-value tasks and sharpen their abilities to additionally hoist their value to their companies.

At Airbnb, they continually scan for approaches to improve their data science workflow. A decent amount of their data science ventures include machine learning and numerous pieces of this workflow are tedious. At Airbnb, they use machine learning to build customer lifetime value models (LTV) for guests and hosts. These models permit the company to improve its decision making and interactions with the community.

Likewise, they have seen AML tools as generally valuable for regression and classification problems involving tabular datasets, anyway, the condition of this area is rapidly progressing. In outline, it is accepted that in specific cases AML can immensely increase a data scientists productivity, often by an order of magnitude. They have used AML in many ways.

Unbiased presentation of challenger models: AML can rapidly introduce a plethora of challenger models utilizing a similar training set as your incumbent model. This can help the data scientist in picking the best model family. Identifying Target Leakage: In light of the fact that AML builds candidate models amazingly fast in an automated way, we can distinguish data leakage earlier in the modeling lifecycle. Diagnostics: As referenced prior, canonical diagnostics can be automatically created, for example, learning curves, partial dependence plots, feature importances, etc. Tasks like exploratory data analysis, pre-processing of data, hyper-parameter tuning, model selection and putting models into creation can be automated to some degree with an Automated Machine Learning system.

Companies have moved towards enhancing predictive power by coupling huge data with complex automated machine learning. AutoML, which uses machine learning to create better AI, is publicized as affording opportunities to democratise machine learning by permitting firms with constrained data science expertise to create analytical pipelines equipped for taking care of refined business issues.

Including a lot of algorithms that automate that writing of other ML algorithms, AutoML automates the end-to-end process of applying ML to real-world problems. By method for representation, a standard ML pipeline consists of the following: data pre-processing, feature extraction, feature selection, feature engineering, algorithm selection, and hyper-parameter tuning. In any case, the significant ability and time it takes to execute these strides imply theres a high barrier to entry.

In an article distributed on Forbes, Ryohei Fujimaki, the organizer and CEO of dotData contends that the discussion is lost if the emphasis on AutoML systems is on supplanting or decreasing the role of the data scientist. All things considered, the longest and most challenging part of a typical data science workflow revolves around feature engineering. This involves interfacing data sources against a rundown of wanted features that are assessed against different Machine Learning algorithms.

Success with feature engineering requires an elevated level of domain aptitude to recognize the ideal highlights through a tedious iterative procedure. Automation on this front permits even citizen data scientists to make streamlined use cases by utilizing their domain expertise. More or less, this democratization of the data science process makes the way for new classes of developers, offering organizations a competitive advantage with minimum investments.

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Automated Machine Learning is the Future of Data Science - Analytics Insight

Nothing to hide? Then add these to your ML repo, Papers with Code says DEVCLASS – DevClass

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In a bid to make advancements in machine learning more reproducible, ML resource and Facebook AI Research (FAIR) appendage Papers With Code has introduced a code completeness checklist for machine learning papers.

It is based on the best practices the Papers with Code team has seen in popular research repositories and the Machine Learning Reproducibility Checklist which Joelle Pineau, FAIR Managing Director, introduced in 2019, as well as some additional work Pineau and other researchers did since then.

Papers with Code was started in 2018 as a hub for newly published machine learning papers that come with source code, offering researchers an easy to monitor platform to keep up with the current state of the art. In late 2019 it became part of FAIR to further accelerate our growth, as founders Robert Stojnic and Ross Taylor put it back then.

As part of FAIR, the project will get a bit of a visibility push since the new checklist will also be used in the submission process for the 2020 edition of the popular NeurIPS conference on neural information processing systems.

The ML code completeness checklist is used to assess code repositories based on the scripts and artefacts that have been provided within it to enhance reproducibility and enable others to more easily build upon published work. It includes checks for dependencies, so that those looking to replicate a papers results have some idea what is needed in order to succeed, training and evaluation scripts, pre-trained models, and results.

While all of these seem like useful things to have, Papers with Code also tried using a somewhat scientific approach to make sure they really are indicators for a useful repository. To verify that, they looked for correlations between the number of fulfilled checklist items and the star-rating of a repository.

Their analysis showed that repositories that hit all the marks got higher ratings implying that the checklist score is indicative of higher quality submissions and should therefore encourage researchers to comply in order to produce useful resources. However, they simultaneously admitted that marketing and the state of documentation might also play into a repos popularity.

They nevertheless went on recommending to lay out the five elements mentioned and link to external resources, which always is a good idea. Additional tips for publishing research code can be found in the projects GitHub repository or the report on NeurIPS reproducibility program.

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Nothing to hide? Then add these to your ML repo, Papers with Code says DEVCLASS - DevClass

OpenAPI Initiative Welcomes Bloomberg as Newest Member – PRNewswire

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SAN FRANCISCO, April 14, 2020 /PRNewswire/ --The OpenAPI Initiative, the consortium of forward-looking industry experts focused on creating, evolving and promoting the OpenAPI Specification (OAS), a vendor-neutral, open description format for RESTful APIs, is announcing today that Bloomberg has joined as a new member.

As a global leader in business and financial information, data, news and analytics, Bloomberg believes that standardizing Web APIs throughout the financial industry will provide consistency and value across the global capital markets ecosystem. Bloomberg sees the advantages of implementing the OpenAPI Specification to improve time to market, shorten development lifecycles, and reduce implementation costs.

"Bloomberg is excited to join the OpenAPI Initiative, where we'll have the opportunity to help shape the OpenAPI Specification and its role in the global financial industry," said Richard Norton, Head, Data License Engineering Group at Bloomberg. "As our enterprise customers are increasingly looking to access our data feeds to power their in-house analytics and trading applications, we are confident that the OpenAPI Specification will enable them to seamlessly manage their Bloomberg data. Plus, the entire industry will benefit from our involvement in the standard's governance process as we'll be able to take their learnings and contribute back to future iterations of the de facto standard for describing Web APIs."

"We are excited to welcome Bloomberg to the OpenAPI Initiative. Major corporations are taking advantage of the OpenAPI Spec for a simple reason: developer productivity. Instead of producing SDKs, organizations can produce OpenAPI specs, and then generate their SDKs in any language they'd like to use, immediately benefitting their customers," said Marsh Gardiner, Product Manager, Google, and Technical Steering Committee, OpenAPI Initiative. "We see firsthand business and technical productivity wins when organizations use the OpenAPI Spec. Bloomberg has embraced open source, and the benefits for their enterprise customers managing Bloomberg data is immense."

Hundreds of software engineers across Bloomberg's global engineering workforce have provided code, documentation, tests, or other improvements to open source projects. In areas relevant to Bloomberg's infrastructure needs, Bloomberg engineers have become project leaders and committers. To find out more about Bloomberg's open source activities:https://www.TechAtBloomberg.com

OpenAPI ResourcesTo learn more about participate in the evolution of the OpenAPI Specification:https://www.openapis.org/participate/how-to-contribute

About the OpenAPI InitiativeThe OpenAPI Initiative (OAI) was created by a consortium of forward-looking industry experts who recognize the immense value of standardizing on how APIs are described. As an open governance structure under the Linux Foundation, the OAI is focused on creating, evolving and promoting a vendor neutral description format. The OpenAPI Specification was originally based on the Swagger Specification, donated by SmartBear Software. To get involved with the OpenAPI Initiative, please visithttps://www.openapis.org

About Linux FoundationFounded in 2000, the Linux Foundation is supported by more than 1,000 members and is the world's leading home for collaboration on open source software, open standards, open data, and open hardware. Linux Foundation projects like Linux, Kubernetes, Node.js and more are considered critical to the development of the world's most important infrastructure. Its development methodology leverages established best practices and addresses the needs of contributors, users and solution providers to create sustainable models for open collaboration. For more information, please visit us at linuxfoundation.org.

Media ContactJesse CasmanreTHINKit Media[emailprotected] 415-730-2793

SOURCE OpenAPI Initiative

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Here’s What’s In the Latest IBM i Technology Refreshes – IT Jungle

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April 15, 2020Alex Woodie

As expected, IBM today officially unveiled its spring Technology Refreshes for IBM i. Among the goodies that will become available for IBM i 7.4 TR2 or IBM i 7.3 TR8 (or both) are enhancements to Db2 Mirror, a new approach to tape library virtualization, improvements to open source, and new features and functionality around Db2, RPG, RDi, and security, too.

Months ago, IBM execs circled April 14 on their calendars as the day for the big IBM 7.4 TR2 and IBM i 7.3 TR8 reveal, with the idea that the following weeks POWERUp conference in Atlanta, Georgia (now canceled) would provide an excellent venue for the IBM i experts from the Rochester and Toronto labs to educate the IBM i community on the content of the announcements.

Obviously, the novel coronavirus had other plans. Despite COVID-19 locking up most Americans (and much of the rest of the globe) in their homes on an indefinite staycation, IBM is doing its best to stick to its regular schedule for IBM i announcements, which is a commendable thing to do, if only for the temporary sense of normalcy it provides.

IBM i Chief Architect Steve Will and IBMs Product offering Manager Alison Butterill briefed IT Jungle last week on the news. Will, by the way, is hosting a webcast on the COMMON website about the new TRs, with IBM executive Steve Sibley today at 10 a.m. Central Time.

Support for internal disk in Db2 Mirror leads the pack of enhancements for IBM 7.4 TR2 (Db2 Mirror is not available for IBM i 7.3). When Db2 Mirror was unveiled in 2019, it was restricted to IBM i shops that used storage area networks (SANs) to store their data. The interest in Db2 Mirror was greater than IBM had anticipated among small and medium-sized shops, which typically do not have SANs. So IBM did the work to broaden the storage support with Db2 Mirror for 7.4 TR2. Well have more in-depth coverage of this feature in a future issue of The Four Hundred.

Another interesting piece of storage news is a new virtualized method for connecting multiple partitions to a single tape library. IBM i shops could get this by adopting the Virtual Input Output Server (VIOS). But VIOS isnt the most popular technology among smaller IBM i shops (to put it kindly), so IBM once again took the initiative to remedy that situation with a more native approach that leverages whats effectively a new tape library driver. Stay tuned for more on that.

The new TRs bring multiple enhancements on the security front, particularly for IBM i 7.3 TR8. Specifically, IBM i 7.3 TR8 gets support for TLS version 1.3, which previously was only supported on IBM i 7.4. The new digital certificate manager interface that had previously been available only on IBM i 7.4 also is now available on IBM i 7.3 TR8.

Usually were talking about things we put in 7.4 and 7.3 at the same time, but this is a pretty big deal for the security administrator on the platform, Will said. As soon as our clients started seeing the new 7.4 interface, they said This is so much easier. Please target back to 7.3 as well. We found that that wasnt going to be that much effort. And in fact, that made life so much easier for the folks who are doing that management.

Rational Developer for i (RDi), while not part of the IBM i operating system, traditionally gets updated around the same time as the TRs come out. In this case, RDi version 9.6.0.7 will deliver a new capability to create stored procedures by simply highlighting a segment of RPG code and clicking a few buttons. IBM is also adding new real-time SQL validation and formatting assistance, as well as new parameters for setting breakpoints during debugging.

RPG gains several new functions, according to Butterill, including a built-in function for obtaining a unique value for a timestamp. Programmers can also gain a new way to access the number of keys in a keyed data structure, she says. Finally, enhancements have also been made to qualified names in the Like DS keyword.

On the open source front, IBM is delivering a new technique for IBM i shops to tap into open source updates on RPM without exposing the IBM i server to the Internet. RPM, of course, is the new software distribution method that replaces the old 5733-OPS and has become the only way to get tools like Python, Git, Node.js, and other open source software that runs on IBM i. According to Will, the method leverages tunneling technology that allows users to pass open source updates from RPM to the IBM i server by way of another client that is connected to the network.

Db2 also gets several new enhancements, services, and built-in functions with IBM 7.4 TR2 and IBM i 7.3 TR8. This includes the INTERPRET built-in function that transforms data from IBM i internal data types, as well as new COMPARE_FILE tool that compares files object attributes and data (or both) for files on a single IBM i server or against a remote database (works with IBM i 7.4 only).

For several years now, IBM has been adding SQL services to the Db2 for i database that function as alternatives to traditional APIs and CL commands. IBM continues that tradition with the addition of several new SQL services, including the IFS_OBJECT_PRIVILEGES function, which functions similar to the OBJECT_PROVILEGES function for the database. In fact, Will counted them up and determined there are, in fact, 28 new SQL services in this release, he says.

Last but not least are enhancements to Db2 Web Query, the business intelligence and analytics tool offered by IBM. IBM has updated the EZ-Install Package, which is designed to simplify the products installation for first-time users and also function as a demonstration unit for business partners. This release also includes a new report auto-generation feature.

Most of the enhancements in with IBM 7.4 TR2 and IBM i 7.3 TR8 are set to become available on May 15, although some will not be available until June. Stay tuned to future issues of The Four Hundred for details on the latest TRs.

Latest TRs For IBM i Now Available

IBM i TRs Bring Database Enhancements, Too

How The Latest TRs Bolster HA/DR And Security

Digging Into the Latest IBM i TRs

IBM i 7.3 And 7.4 Get Their Autumn Tech Refreshes

IBM i 7.4 Rolled Out, And IBM i 7.3 Tech Refresh Rolled Up

New IBM i Technology Refreshes Announced; Available Mid-March

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Here's What's In the Latest IBM i Technology Refreshes - IT Jungle

Assange fathered two children while holed up in embassy, lawyer says – The Globe and Mail

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Wikileaks founder Julian Assange speaks on the balcony of the Embassy of Ecuador in London on May 19, 2017.

JUSTIN TALLIS/AFP/Getty Images

WikiLeaks founder Julian Assange fathered two children with a lawyer representing him while he was sequestered in the Ecuadorean embassy in London, according to a British newspaper on Sunday.

The mother of his children, 37-year-old South African lawyer Stella Morris, told The Mail on Sunday the couple have two sons, aged 1 and 2, both conceived while Mr. Assange was in the embassy and kept secret from media and intelligence agencies monitoring his activity.

The paper said Ms. Morris has been engaged to Mr. Assange since 2017.

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Both of their children are British citizens. Mr. Assange watched the births on a video link, the paper said.

Australian-born Mr. Assange was dragged out of the embassy last year after a seven-year standoff and is now jailed in Britain fighting extradition to the United States on computer hacking and espionage charges.

His supporters say the U.S. case against him is political and he cannot receive a fair trial.

Ms. Morris said she had chosen to speak out now because she was worried about his susceptibility to the coronavirus in jail.

The paper quoted her as saying, I love Julian deeply and I am looking forward to marrying him.

She added, I am now terrified I will not see him alive again.

WikiLeaks founder Julian Assange fathered two children with a lawyer who was representing him while he was holed up in the Ecuadorian embassy in London fighting extradition, the lawyer told a British newspaper on Sunday. Lisa Bernhard has more. Reuters

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Assange fathered two children while holed up in embassy, lawyer says - The Globe and Mail

Artificial intelligence: The thinking machine – Urgent Communications

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Artificial intelligence is a bit of a buzz term these days but what do people really mean when they say AI? And why should local governments care?

First of all, AI is extremely misunderstood. We arent talking about HAL from 2001: A Space Odyssey, necessarily; were talking about what Alan Turing speculated about thinking machines back in the 1950s. According to the Brookings Institute, AI is generally thought to refer to machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment and intention. More simply put, AI uses algorithms to make decisions using real-time data. But unlike more traditional machines that can only respond in predetermined ways, AI can act on data it can analyze it and respond to it.

The concept has been evolving and the technology has become more sophisticated, but its still a little nebulous particularly for folks working in local government. It seems everyone kind of knows what AI is, but no one is exactly sure how they can apply it in their communities.

I spoke with Eyal Feder-Levy, the CEO and Founder of ZenCity, an AI-based tool that helps local government leaders listen to and synthesize conversations going on in their communities on social media, about the implications of AI for local governments, and how they can utilize these new tools in meaningful, beneficial ways. The following is a gently edited transcription of that discussion.

Derek Prall:So, when you say you use AI tools to analyze social media conversations crunch this data and make it meaningful can you tell me what that means? How does this work?

Eyal Feder-Levy:Lets use the current situation that local governments are facing as an example. First, I have to say I have nothing but admiration for local government leaders right now. Cities and counties are on the front lines of this global crisis that were facing they have to create the policies that will respond to this. They have to shape the information thats going out there. So in this current crisis, cities have a really important job to play. This means they have to constantly know whats working and what isnt working. They need to know if the messaging theyre putting out is resonating with people. They need to know if people are worried about child care or tax breaks for their businesses or are they worried about where to buy groceries. What are the things that they are prioritizing that we as local governments need to respond to in order for our communities to survive this crisis?

One of the only channels where we can still hear the population in this social distancing reality is online. People are talking more than ever on platforms like Facebook, Twitter and Instagram. Were talking about a massive amount of data. If we take a city like Dayton, Ohio, were seeing somewhere along the lines of tens of thousands of these online conversations in a week. Where AI comes into play is that no one in city hall has the time to go over 80,000 conversations a week and try to make sense out of them. We cant.So its amazing we have this information, its amazing we have this data, but we have to find a way to make sense out of it fast. This is where we as a city use AI. These are basically algorithms that break down the data in meaningful ways so it can be acted on.

Prall:Okay that definitely makes sense, but I want to take a bit of a step back. I think a lot of elected and appointed officials arent necessarily the most tech-savvy people. When you say artificial intelligence to someone who doesnt consider themselves to be good with technology how do you talk about what this is this as a concept. What is AI?

Feder-Levy:The first thing I want to say about AI is that its not robots coming to take our jobs its not something scary that only mathematicians can understand. Its actually part of our daily lives already. Its embedded in the technological and software tools we use every day. Its something that if we understand the basic concepts of it can be a very strong tool to help us automate a lot of things we dont have enough staff to do on a manageable level.

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Artificial intelligence: The thinking machine - Urgent Communications


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