Daily Archives: March 5, 2020

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Artificial Intelligence (AI) - a systems ability to collect and interpret data, to learn from data, and to use those learnings to achieve specific goals and complete tasks is at the forefront of the Fourth Industrial Revolution and is transforming every aspect of our global society. From healthcare to business, climate change to entertainment, defense and education, AI is changing our world and how we interact with it and each other.

While AI is simplifying and enhancing many aspects of our lives, the technology does not come without its issues. Many have concerns about privacy, data collection, and the impact on workers who could potentially be displaced by the technology. Regulation and oversight will be imperative to ensure that the technology isnt being abused, and it is important to consider the ethical implications of this emerging technology.

WPI alumni and researchers are at the center of it all.

Join us for this special event, hosted by Michael Wallent 91, corporate vice president of Enterprise Mobility Management Products for Microsoft Corporation, where a panel of WPI alumni and faculty will discuss the possibilities and ethical implications of AI and how we can harness its potential to be a force for good.

Date: Wednesday, March 25, 2020

Time: 6:00 PM

Location: The Kalaloch Room in Building 35 of the Microsoft Corporation Main Campus-3940 159th Ave NE,Redmond, WA 98052

Due to security measures at the Microsoft campus, advanced registration is required.

Michael Wallent works at Microsoft in Redmond, Washington and is the Corporate Vice President of the Enterprise Mobility Management group. His team develops services to help companies manage employee use of PCs and mobile devices. Michael joined Microsoft in 1996 and worked on Internet Explorer, Windows and Windows Server.

Michael lives in Seattle with his wife Anh, and kids Peri, John and Daniel. Even though hes been in Seattle for more than 20 years now, hes a die-hard Pats and Sox fan (root for where you are from!). Michael grew up mostly in Rhode Island, where his parents taught him about east coast perseverance and how to quickly clean lobsters. Hes often found in the mountains, spending time with skis and friends.

Michael graduated from Worcester Polytechnic Institute in 1991 with a B.S. in Computer Science.

The rest is here:

Critical Conversations: Artificial Intelligence, Our World, and You - WPI News

Results from Farnells second IoT Survey also show increasing confidence in companies to lead IoT development.

AIoT is the major emerging trend from the survey, demonstrating the beginning of the process to build a true IoT ecosystem. Research showed that almost half (49%) of respondents already use AI in their IoT applications, with Machine Learning (ML) the most used technology (28%) followed by cloud-based AI (19%).

This adoption of AI within IoT design is coupled with a growing confidence to take the lead on IoT development and an increasing number of respondents seeing themselves as innovators. However, it is still evident that some engineers (51%) are hesitant to adopt AI due to being new to the technology or because they require specialized expertise in how to implement AI in IoT applications.

Other results from Farnells second Global IoT Survey show that security continues to be the biggest concern designers consider in IoT implementation. Although 40% cited security as their biggest concern in 2018 and this has reduced to 35% in 2019, it is still ranked significantly higher than connectivity and interoperability due to the type of data collected from things (machines) and humans, which can be very sensitive and personal. Businesses initiating new IoT projects treat IoT security as a top priority by implementing hardware and software security to protect for any kind of potential threat. Ownership of collected data is another important aspect of security, with 70% of respondents preferring to own the data collected by an edge device as opposed to it being owned by the IoT solution provider.

The survey also shows that although many engineers (46%) still prefer to design a complete edge-to-cloud and security solution themselves, openness to integrate production ready solutions, such as SmartEdge Agile, SmartEdge IIoT Gateway, which offer a complete end-to-end IoT Solution, has increased. 12% more respondents confirmed that they would consider third party devices in 2019 than 2018, particularly if in-house expertise is limited or time to market is critical.

A key trend from last years survey results has continued in 2019 and survey results suggest that the growing range of hardware available to support IoT development continues to present new opportunities. More respondents than ever are seeing innovation coming from start-ups (33%, up from 26%), who benefit from the wide availability of modular solutions and single board computers available on the market. The number of respondents adopting off-the-shelf hardware has also increased to 54% from 50% in 2018.

Cliff Ortmeyer, Global Head of Technical Marketing for Farnell says:

Opportunities within the Internet of Things and AI continue to grow, fueled by access to an increasing number of hardware and software solutions which enable developers to bring products to market more quickly than ever before, and without the need for specialized expertise.

This is opening up IoT to new entrants, and giving more developers the opportunity to innovate to improve lives. Farnell provides access to an extensive range of development tools for IoT and AI which provide off-the-shelf solutions to common challenges.

Despite the swift integration of smart devices such as Amazons Alexa and Google Home into daily life, evidencing a widespread adoption of IoT in the consumer space, in 2019 we saw a slight shift in focus away from home automation with the number of respondents who considered it to be the most impactful application in IoT in the next 5 years reducing from 27% to 22%. Industrial automation and smart cities both gained, at 22% and 16% respectively, underpinned by a growing understanding of the value that IoT data can bring to operations (rising from 44% in 2018 to 50% in 2019). This trend is witnessed in industry where more manufacturing facilities are converting to full or semi-automation in robotic manufacturing and increasing investment in predictive maintenance to reduce production down times.

The survey was conducted between September and December 2019 with 2,015 respondents participating from 67 countries in Europe, North America and APAC. Responses were predominantly from engineers working on IoT solutions (59%), as well as buyers of components related to IoT solutions, Hobbyists and Makers.

Originally posted here:

New Research from Farnell Reveals Strong Adoption of Artificial Intelligence within the Internet of Things Ecosystem - IoT Business News

CARY The ongoing spread of the coronavirus is sparking urgent searches for a vaccine to counteract it. Several Triangle firms such as Lenovo and Heat Biologics have joined the fight. Also bringing powerful and emerging technology to the battle is software and data analytics giantSAS, a heavy investor in artificial intelligence and a world leader of tools to extract information from so-called big data information gathered from many sources.

In an exclusive Q&A with WRAL TechWire,Theresa Do, Support Manager for Federal Healthcare at SAS, talks about the potential uses of AI, machine learning and analytics to combat the corornavirus and future health threats. Do also is a Professor ofEpidemiology & Biostatistics at George Washington Univ. in Washington, DC.

Developing new treatments and creating vaccines and antiviral medications for newly discovered viruses is a difficult and time-consuming process, traditionally involving lots of trial and error. AI and advanced analytics can help improve the application of current treatments and speed up the development of new ones.

Theresa Do

For example, AI specifically deep learning is currently being used to help radiologists make better treatment decisions based on medical imaging. Chest x-rays of patients infected with the new coronavirus may serve as input into AI models that can help physicians make faster diagnoses as the outbreak continues. AI can also help here by examining data from similar viral diseases and using that data to predict what types of vaccines and medicines might be more effective.

Data and analytics are the lifeblood for decision-making during infectious-disease outbreaks.Analytics can provide insights about the spread of a disease and the effectiveness of public health action, which can improve the response.The more information people have about case counts, mortality rates, how a disease spreads and how contagious it is, the better decisions they can make to limit, prevent and treat the disease. Public health and scientific data must be shared freely and rapidly with stakeholders and key decision makers so they can take action.

For decades, SAS has provided analytics software to public health and government agencies in the United States and around the world, helping them improve the health and well-being of their citizens. Governments hold much of the critical data needed to understand current conditions during an outbreak, but analytics companies like SAS offer an ability to synthesize this data with other non-government data and specialized tools to get the most insights from this unified data. These data-driven insights support better, faster government and public health decision-making. Events like the ongoing COVID-19 outbreak require public and private sectors to work closely together to limit disease spread and save lives.

Collaboration, integration and rapid information sharing are essentialto improve response and recovery for infectious disease outbreaks.Gone are the days when only governments and public health organizations had valuable data to fight epidemics. Disparate, non-traditional data setscan serve as sentinel sources everything from travel and census data, to demographic information and animal migration patternscan be applied to the public health threats. But the key is how to take advantage of all this data and emerging new data like genetic sequences.

Advanced analytics and AI (particularly machine learning) are essential tools to put data to work and save lives. With more and diverse data sets, the challenge is to synthesize everything to derive the insights needed to make decisions. Asolid data management ecosystem and platform where the data can be stored, cleaned, scaled and shared among key stakeholders and decision makers is essential. So, its not just about the data, but also how that data can be used effectively in global collaboration to fight the emergence and spread of disease.

Finally, having enough good data is a challenge when a new, or novel virus is causing a disease outbreak. Advanced analytics are only as good as the data they can explore, analyze and sift through. For COVID-19, collaboration will continue to improve as more data is shared.

Analytics has an important and growing role to play in the detection and monitoring of all viral-disease outbreaks. Critical insights about disease spread and the effectiveness of public health action can be derived from analytical approaches, which helps decision-makers adjust and adapt their strategies and responses.

AI and machine learning in particular are valuable tools for healthcare professionals and policymakers to reduce or better manage the impact of emerging infectious diseases like COVID-19. Machine learning is designed to consider large amounts of data, find patterns in that data and detect anomalies, and in many cases offer predictions.

AI can help health authorities better detect infectious disease outbreaks by analyzing sentinel data sources for early warning of potential threats.AI can be applied to models on common themes or topics to help identify common symptoms among new and evolving public health threats. Moreover, AI can help to automate data analysis, identify patterns and build models of risk factors to help in scenario analysis of transmission. And when it comes to identifying paths of transmission, AI can aid in the search for a host and/or index case, as well as tracking possible contacts.

When SARS emerged, there were fewer data sources that could be leveraged, such as social media, Internet of Things (IoT) devices and technologies to help with diagnostics. Phone apps for tracking of health data and diagnostics were not yet present. (The iPhone came out in 2007, four years after SARS broke out). With the advent of the iPhone and new types of apps and technologies, scientists can leverage a lot more data for analysis in addition to the available sentinel sources.

Today there are more ways that people can communicate to quickly distribute public health prevention efforts and quell misinformation regarding public health threats such as the some of the misinformation around COVID-19. Providers have more information at their fingertips via technology versus when SARS first broke out during the early stages, as well as the availability of the internet. However, with COVID-19, there is still a lot more to be uncovered and learned.

For any infectious disease outbreak, good responses need good data that can be shared readily and acted upon quickly. That was true then for SARS, and its true today for COVID-19.

Lenovo, Intel team up to accelerate analysis of coronavirus genome in vaccine search

Morrisville-based Heat Biologics joins global effort to discover coronavirus vaccine

Read more from the original source:

High tech vs. coronavirus: Using artificial intelligence, analytics are weapons SAS brings to the fight - WRAL Tech Wire

element14, the Development Distributor, has published new research on the Internet of Things (IoT) which confirms strong adoption of Artificial Intelligence (AI) within IoT devices, alongside new insights on key markets, enablers and concerns for design engineers working in IoT.

AIoT is the major emerging trend from the survey, demonstrating the beginning of the process to build a true IoT ecosystem. Research showed that almost half (49%) of respondents already use AI in their IoT applications, with Machine Learning (ML) the most used technology (28%) followed by cloud-based AI (19%). This adoption of AI within IoT design is coupled with a growing confidence to take the lead on IoT development and an increasing number of respondents seeing themselves as innovators. However, it is still evident that some engineers (51%) are hesitant to adopt AI due to being new to the technology or because they require specialized expertise in how to implement AI in IoT applications.

Other results from element14s second Global IoT Survey show that security continues to be the biggest concern designers consider in IoT implementation. Although 40% cited security as their biggest concern in 2018 and this has reduced to 35% in 2019, it is still ranked significantly higher than connectivity and interoperability due to the type of data collected from things (machines) and humans, which can be very sensitive and personal. Businesses initiating new IoT projects treat IoT security as a top priority by implementing hardware and software security to protect for any kind of potential threat. Ownership of collected data is another important aspect of security, with 70% of respondents preferring to own the data collected by an edge device as opposed to it being owned by the IoT solution provider.

The survey also shows that although many engineers (46%) still prefer to design a complete edge-to-cloud and security solution themselves, openness to integrate production ready solutions, such as SmartEdge Agile, SmartEdge IIoT Gateway, which offer a complete end-to-end IoT Solution, has increased. 12% more respondents confirmed that they would consider third party devices in 2019 than 2018, particularly if in-house expertise is limited or time to market is critical.

A key trend from last years survey results has continued in 2019 and survey results suggest that the growing range of hardware available to support IoT development continues to present new opportunities. More respondents than ever are seeing innovation coming from start-ups (33%, up from 26%), who benefit from the wide availability of modular solutions and single board

computers available on the market. The number of respondents adopting off-the-shelf hardware has also increased to 54% from 50% in 2018.

Cliff Ortmeyer, Global Head of Technical Marketing for Farnell and element14 says: Opportunities within the Internet of Things and AI continue to grow, fueled by access to an increasing number of hardware and software solutions which enable developers to bring products to market more quickly than ever before, and without the need for specialized expertise. This is opening up IoT to new entrants, and giving more developers the opportunity to innovate to improve lives. element14 provides access to an extensive range of development tools for IoT and AI which provide off-the shelf solutions to common challenges.

Despite the swift integration of smart devices such as Amazons Alexa and Google Home into daily life, evidencing a widespread adoption of IoT in the consumer space, in 2019 we saw a slight shift in focus away from home automation with the number of respondents who considered it to be the most impactful application in IoT in the next 5 years reducing from 27% to 22%. Industrial automation and smart cities both gained, at 22% and 16% respectively, underpinned by a growing understanding of the value that IoT data can bring to operations (rising from 44% in 2018 to 50% in 2019). This trend is witnessed in industry where more manufacturing facilities are converting to full or semi-automation in robotic manufacturing and increasing investment in predictive maintenance to reduce production down times.

The survey was conducted between September and December 2019 with 2,015 respondents participating from 67 countries in Europe, North America and APAC. Responses were predominantly from engineers working on IoT solutions (59%), as well as buyers of components related to IoT solutions, Hobbyists and Makers.

element14 provides a broad range of products and support materials to assist developers designing IoT solutions and integrating Artificial Intelligence. Products are available from leading manufacturers such as Raspberry Pi, Arduino and Beagleboard. element14s IoT hub and AI pages also provide access to the latest products for development and insights and white papers to support the design journey. Readers can view an infographic covering the full results of the element14 Global IoT Survey at Farnell in EMEA, Newark in North America and element14 in APAC.

For more information, visit http://www.element14.com

Continue reading here:

New research on adoption of Artificial intelligence within IoT ecosystem - ELE Times

Last year, the HKMA commissioned a study into the application of Artificial Intelligence technology (AI) in the Hong Kong banking industry. The report, published on 23 December 2019, summarises insights from academics and industry experts on AI. One key finding was that almost 90% of the surveyed retail banks had adopted or planned to adopt AI applications. 95% of banks which had adopted AI expressed their intention to use AI to shape their corporate strategy, mainly prompted by the need to improve customer experience, stay cost effective and better manage risk.

To help the banking industry understand the risk and potential of AI, the report covered the latest development trends, potential use cases, status of AI development in banking, challenges and considerations in designing and deploying the technology, as well as the market outlook.

This report is the first in a series of AI-related publications produced by the HKMA. The full report can be accessed here.

Continue reading here:

HKMA's paper on Artificial Intelligence in the banking industry - Lexology

When applying for a new job, candidates may well find that the use of artificial intelligence (AI) tools is involved at some point in the recruitment process. New recruitment businesses and technology are entering the market, setting up entirely automated initial conversations with candidates to help them find the right vacancy for their skill set, saving time for applicant and recruiter alike.

CV screening is also becoming more prevalent, with AI screening and tracking tools being used to quickly analyse CVs to ascertain whether the individual has the qualifications and experience necessary for the role for example, burger chain Five Guys is said to be utilising such technology.

Unilever recently hit the headlines when it announced that, instead of human recruiters, it uses an AI system to analyse video interviews. Candidates record interviews on their phone or laptop, and the system scans candidates language, tone and facial expressions from the videos, assessing their performance against traits that are considered to indicate job success at Unilever.

But it is not just the recruitment stage where AI and people analytics are being used by businesses performance management is another targeted area. Amazon is leading this charge the company was issued with two patents in the US for a wristband for tracking the performance of workers in their warehouse, which would mean that staff receive a little buzz if they place a product near or in the wrong inventory location.

It is also alleged that Amazon uses a computer system to automatically generate warnings or terminations to employees, when their productivity (or lack of) warrants it.

The benefits of such technology for employers are countless and clear, including costs savings, efficiency, and the purported removal of human unconscious bias and prejudice. However, the use of AI in the workplace has come under scrutiny and has posed serious ethical and legal questions, including whether AI itself could in fact be biased.

Another important aspect when implementing AI in the workplace is its relationship with data protection laws such as the EUs General Data Protection Regulation (GDPR). So, what data protection considerations should an employer make when considering the introduction of AI technology?

The use of AI for processing personal data will usually meet the legal requirement for completing a DPIA.

A DPIA enables the business to analyse how the AI plans will affect individuals privacy, and ensures the company can assess the necessity and proportionality of its technology.

As the UK Information Commissioners guidance confirms, the deployment of an AI system to process personal data needs to be driven by the proven ability of that system to fulfil a specific and legitimate purpose, not just by the availability of the technology.

The DPIA should demonstrate that the applicable purposes the AI is being used for could not be accomplished in another reasonable way. In doing so, organisations need to think about and document any detriment to data subjects that could follow from bias or inaccuracy in the algorithms and data sets being used.

A business cannot simply process personal data because it wishes to do so data can only be processed where one of the legitimate grounds or conditions of processing has been met. There are various bases, including performance of a contract, compliance with a legal obligation, consent and legitimate business interests. For the processing of sensitive personal data (such as health data), the bases are even more limited.

Before using AI or people analytics in the workplace, employers will first need to consider what data is being processed by such activity and second what legal basis can be relied upon in processing the data in that way. If they do not have a legal basis, the data cannot be processed.

One of the key principles of GDPR is transparency, requiring businesses to provide individuals with mandatory information about the processing of their personal data, including the reason why it is being processed, the legal basis, who it will be shared with and how long it will be retained. Employers will need to update their privacy notices to ensure anyone subject to the AI technology is made aware of its use.

The privacy notice needs to be concise and intelligible, using clear and plain language this will be particularly difficult when including a complex AI system, as businesses will need to provide a meaningful explanation of the technology to meet the transparency principle of GDPR. Opaque or complex descriptions of the tech may result in contention or pushback from the employees and candidates affected.

GDPR prohibits instances of computer says no and contains the right for data subjects not to be subjected to a decision based solely on automated processing, which has a legal or similarly significant impact on them. Its aim is to protect individuals against the risk that a potentially damaging decision is taken without human intervention, and will therefore likely capture a recruitment result made without any human input.

There are specific exceptions when automated decision-making is permitted, including where explicit consent was given, contractual necessity, or where authorised by law. Where such an exception is being relied upon, such as with the consent of a candidate, the business must still implement further safeguarding measures, including permitting the individual to request human intervention or to contest the decision.

Employers will need to ensure that their automated technology is being lawfully used, before relying on its output.

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Dont forget to consider GDPR when using artificial intelligence in the workplace - ComputerWeekly.com