Archive for the ‘Machine Learning’ Category

Podcast: Five Reasons to Go to Machine Learning Week, June 19-24, 2022 in Vegas Machine Learning Times – The Machine Learning Times

Welcome to the next episode ofThe Machine Learning TimesExecutive Editor Eric Siegels podcast,The Doctor Data Show. Click here for all episodes and links to listen on your preferred platform. Podcast episode description: In this special episode, rather than the usual conceptual coverage of machine learning, Eric Siegel will pitch you on the machine learning conference series he founded in 2009, the leading cross-vendor, cross-industry event covering the commercial deployment of machine learning and predictive analytics. Join him in Las Vegas June 19-24 for Machine Learning Week 2022, with seven tracks of sessions covering the commercial deployment of

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Podcast: Five Reasons to Go to Machine Learning Week, June 19-24, 2022 in Vegas Machine Learning Times - The Machine Learning Times

Praisidio Uses Machine Learning to Identify At-Risk Employees and Build Tailored Retention Plans with Procaire 3.0 – PR Newswire

New machine learning-driven retention path technology identifies urgently needed actions and enables HR executives to take immediate steps to retain at-risk employees

SAN FRANCISCO, April 5, 2022 /PRNewswire/ -- Praisidio, the leader in talent retention management, today announced the general availability of Procaire 3.0, which includes new patent-pending retention path functionality. Retention paths, auto-generated by machine learning technology, feature curated groups of employees with similar risk factors and include specific retention recommendations. Support for user-defined retention paths is also provided.

Procaire 3.0's retention recommendation engine presents contextually effective recommendations which HR professionals may choose and track. Retention paths enable HR leaders to take immediate actions to significantly reduce voluntary employee attrition.

Additionally, Procaire 3.0 includes retention impact dashboards that reflect in real-time the cumulative business impact of implemented retention actions. Metrics shown include retention improvement, maker time increases, management one-on-one improvement, time in role decreases, etc.

"Procaire provides us early visibility into the causes of attrition, recommends retention activities, and measures the impact of our HR organization's proactive actions. With Procaire retention paths, we were able to identify the main causes of attrition with employees grouped into risk and cause cohorts, allowing us to target retention activities across the company," said Gail Jacobs, Head of Talent and HR Operations, Guardant Health.

"With Procaire retention paths, I was able to identify the main problems in my organization and help our employees. In one example, I helped my organization increase their weekly maker time significantly to reduce the risk of Zoom burnout" said Iga Opanowicz, Sr. People Generalist, Guardant Health.

Customers can use Retention Paths to address groups of employees with similar risk factors such as bias, burnout, stagnation, and disconnection. Moreover, critical employees are surfaced in high-risk cohorts or groups who report to high-attrition managers.

Ben Eubanks, Chief Research Officer of Lighthouse Research & Advisory, remarked: "Our research shows that employers struggle with retention because it's hard to know what specific steps to take. With Procaire retention paths, HR professionals now have the power of machine learning at their fingertips and can easily see the exact retention drivers for their best employees."

After retention actions are taken, Procaire helps ensure follow-up and follow-through via retention workflows and optimizes future recommendations by gauging action efficacy over time.

Procaire 3.0 is immediately available.

About Praisidio

Praisidio is a talent retention management company solving employee attrition. Praisidio's Procaire unifies enterprise and HCM data, applies advanced machine learning, reveals talent risks early in real-time, provides actionable insights, root cause explanations, comparisons, recommendations, and enables employee care at scale to improve employee engagement and retention materially. For more information, visit http://www.praisidio.com.

For media contact, please reach out at[emailprotected]

SOURCE Praisidio, Inc.

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Praisidio Uses Machine Learning to Identify At-Risk Employees and Build Tailored Retention Plans with Procaire 3.0 - PR Newswire

The Federal Executive Forum’s Machine Learning and AI in Government 2022 – Federal News Network

Date:April 12, 2022Time:1 p.m. ETDuration:1 hourCost:No Fee

DescriptionMachine learning and artificial intelligence technology is very important in helping agencies with their people, processes and technology. But how are agencies utilizing this technology and what benefits do they see?

During this webinar, you will learn how federal IT practitioners from the Department of Veterans Affairs and Defense Intelligence Agency are implementing strategies and initiatives around machine learning and artificial intelligence.

The following experts will explore what the future of machine learning and AI in government means to you:

Panelists also will share lessons learned, challenges and solutions and a vision for the future.

Registration is complimentary. Please register using the form on this page or call (202) 895-5023.

By providing your contact information to us, you agree: (i) to receive promotional and/or news alerts via email from Federal News Network and our third party partners, (ii) that we may share your information with our third party partners who provide products and services that may be of interest to you and (iii) that you are not located within the European Economic Area.

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The Federal Executive Forum's Machine Learning and AI in Government 2022 - Federal News Network

Leverage machine learning on your iPhone to translate Braille with this free app – 9to5Mac

If you ever thought about learning Braille or just wanted to quickly translate something written in UEB to your iPhone, theres a new app that can help you with that.

Software engineer Aaron Stephenson started learning Braille a few years ago. To put his knowledge into practice, he built an app using CoreML and Vision to find Braille. Now, he has just released an app that can translate Braille (and more) using just your iPhone.

Braille Scanner allows users to take a photo of a piece of paper with Braille on it using their iPhones and then within seconds, its translated to text.

The developer explains his intention behind the project and also the limitations so far:

Braille Scanner was created to help transcribe from Braille to text. It uses a combination of machine learning and vision to do this. The current transcribing model uses Unified English Braille, grade 1, and Im planning on adding more in the coming app updates.

Here are the top features of Braille Scanner for iPhone users:

Since the app just launched, the developer asks for feedback whether users find incorrectly translated braille, so he can build a more accurate machine learning model.

Braille Scanner requires iOS 14.7 or later. Its free to download and you can find it here on the App Store.

What do you think of this initiative? Share your thoughts in the comment section below.

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Leverage machine learning on your iPhone to translate Braille with this free app - 9to5Mac

California FEHC Proposes Sweeping Regulations Regarding Use of Artificial Intelligence and Machine Learning in Connection With Employment Decision…

The California Fair Employment and Housing Council (FEHC) recently took a major step towards regulating the use of artificial intelligence (AI) and machine learning (ML) in connection with employment decision-making. On March 15, 2022, the FEHC published Draft Modifications to Employment Regulations Regarding Automated-Decision Systems, which specifically incorporate the use of "automated-decision systems" in existing rules regulating employment and hiring practices in California.

The draft regulations seek to make unlawful the use of automated-decision systems that "screen out or tend to screen out" applicants or employees (or classes of applicants or employees) on the basis of a protected characteristic, unless shown to be job-related and consistent with business necessity. The draft regulations also contain significant and burdensome recordkeeping requirements.

Before the proposed regulations take effect, they will be subject to a 45-day public comment period (which has not yet commenced) before FEHC can move toward a final rulemaking.

"Automated-Decision Systems" are defined broadly

The draft regulations define "Automated-Decision Systems" broadly as "[a] computational process, including one derived from machine-learning, statistics, or other data processing or artificial intelligence techniques, that screens, evaluates, categorizes, recommends, or otherwise makes a decision or facilitates human decision making that impacts employees or applicants."

The draft regulations provide the following examples of Automated-Decision Systems:

Similarly, "algorithm" is broadly defined as "[a] process or set of rules or instructions, typically used by a computer, to make a calculation, solve a problem, or render a decision."

Notably, the scope of this definition is quite broad and will likely cover certain applications or systems that may only be tangentially related to employment decisions. For example, the term "or facilitates human decision making" is ambiguous. A broad reading of that term could potentially allow for the regulation of technologies designed to aid human decision-making in small or subtle ways.

The draft regulations would make it unlawful for any covered entity to use Automated-Decision Systems that "screen out or tend to screen out" applicants or employees on the basis of a protected characteristic, unless shown to be job-related and consistent with business necessity

The draft regulations would apply to employer (and covered third-party) decision-making throughout the employment lifecycle, from pre-employment recruitment and screening, through employment decisions including pay, advancement, discipline, and separation of employment. The draft regulations would incorporate the limitations on Automated-Decision Systems to apply to characteristics already protected under California law.

The precise scope and reach of the draft regulations are ambiguous in that key definitions define Automated-Decision Systems as those systems that screen out "or tend to screen out" applicants or employees on the basis of a protected characteristic. No clear explanation of the scope of the phrase "tend to screen out" is offered in the proposed regulations, and the inherent ambiguity of the language itself presents a real risk that these regulations will extend to certain systems or processes that are not involved in screening applicants or employees on the basis of a protected characteristic.

The draft regulations apply not just to employers, but also to "employment agencies," which could include vendors that provide AI/ML technologies to employers in connection with making employment decisions

The draft regulations apply not just to employers, but also to "covered entities," which include any "employment agency, labor organization[,] or apprenticeship training program." Notably, "employment agency" is defined to include, but is not limited to, "any person that provides automated-decision-making systems or services involving the administration or use of those systems on an employer's behalf."

Therefore, any third-party vendors that develop AI/ML technologies and sell those systems to third-parties using the technology for employment decisions are potentially liable if their automated-decision system screens out or tends to screen out an applicant or employee based on a protected characteristic.

The draft regulations require significant recordkeeping

Covered entities are required to maintain certain personnel or other employment records affecting any employment benefit or any applicant or employee. Under FEHC's draft regulations, those recordkeeping requirements would increase from two to four years. And, as relevant here, those records would include "machine-learning data."

Machine-learning data includes "all data used in the process of developing and/or applying machine-learning algorithms that are used as part of an automated-decision system." That definition expressly includes datasets used to train an algorithm. It also includes data provided by individual applicants or employees. And it includes the data produced from the application of an automated-decision system operation (i.e., the output from the algorithm).

Given the nature of algorithms and machine learning, that definition of machine-learning data could require an employer or vendor to preserve data provided to an algorithm not just four years looking backward, but to preserve all data (including training datasets) ever provided to an algorithm and extending for a period of four years after that algorithm's last use.

The regulations add that any person who engages in the advertisement, sale, provision, or use of a selection tool, including but not limited to an automated-decision system to an employer or other covered entity, must maintain records of "the assessment criteria used by the automated-decision system for each such employer or covered entity to whom the automated-decision system is provided."

Additionally, the draft regulations would add causes of action for aiding and abetting when a third party provides unlawful assistance, unlawful solicitation or encouragement, or unlawful advertising when that third party advertises, sells, provides, or uses an automated-decision system that limits, screens out, or otherwise unlawfully discriminates against applicants or employees based on protected characteristics.

Conclusion

The draft rulemaking is still in a public workshop phase, after which it will be subject to a 45-day public comment period, and it may undergo changes prior to its final implementation. Although the formal comment period has not yet opened, interested parties may submit comments now if desired.

Considering what we know about the potential for unintended bias in AI/ML, employers cannot simply assume that an automated-decision system produces objective or bias-free outcomes. Therefore, California employers are advised to:

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California FEHC Proposes Sweeping Regulations Regarding Use of Artificial Intelligence and Machine Learning in Connection With Employment Decision...