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Stanfords Center for Artificial Intelligence in Medicine and Imaging (AIMI) is increasing engagement around the use of artificial intelligence (AI) and machine learning to build a better understanding of data on womens and childrens health, according to AIMI Director and radiology professor Curt Langlotz.

Langlotz explained that, while AIMI initially focused on applying AI to medical imaging, it has since expanded its focus to applications of AI for other types of data, such as electronic health records.

Specifically, the center conducts interdisciplinary machine learning research that optimizes how data of all forms are used to promote health, Langlotz said during a Monday event hosted by the Maternal and Child Health Research Institute (MCHRI). And that interdisciplinary flavor is in our DNA.

The center now has over 140 affiliated faculty across 20 departments, primarily housed in the engineering department and the school of medicine at Stanford, according to Langlotz.

AIMI has four main pillars: building an infrastructure for data science research, facilitating interdisciplinary collaborations, engaging the community and providing funding.

The center provides funding predominantly through a series of grant programs. Langlotz noted that the center awarded seven $75,000 grants in 2019 to fund mostly imaging projects, but it has since diversified funding to go toward projects investigating other forms of data, such as electronic health records. AIMI also collaborated with the Human-Centered Institute for Artificial Intelligence (HAI) in 2021 to give out six $200,000 grants, he added.

Outside of funding, AIMI hosts a virtual symposium on technology and health annually and has a health-policy committee that informs policymakers on the intersection between AI and healthcare. Furthermore, the center pairs industry partners with laboratories to work on larger research projects of mutual interest as part of the only industry affiliate program for the school of medicine, Langlotz added.

Industry often has expertise that we dont, so they may have expertise on bringing products to markets as they may know what customers are looking for, Langlotz said. And if were building these kinds of algorithms, we really would like them to ultimately reach patients.

Heike Daldrup-Link, a professor of radiology and pediatrics, and Alison Callahan, a research scientist at the Center for Biomedical Informatics, shared their research funded by the AIMI Center that rests at the intersection of computer science and medicine.

Daldrup-Links research involves analyzing childrens responses to lymphoma cancer therapy with a model that examines tumor sites using positron emission tomography (PET) scans. These scans reveal the metabolic processes occurring within tissues and organs, according toDaldrup-Link. The scans also serve as a good source to build algorithms because there are at least 270,000 scans per year from lymphoma patients, resulting in a large amount of available data.

Callahan is building AI models to extract information from electronic health records to learn more about pregnancy and postnatal health outcomes. She explained that much of the health data available from records is currently unstructured, meaning it does not conform to a database or simple model. Still, AI methods can really shine in extracting valuable information from unstructured content like clinical texts or notes, she said.

Callahan and Daldrup-Link are just two examples of researchers who use AI and machine learning methods to produce novel research on womens and childrens health. Developing new methods such as these are important in solving complex problems related to the field of healthcare, according to Langlotz.

If youre working on difficult and interesting applied problems that are clinically important, youre likely to encounter the need to develop new and interesting methods, Langlotz said. And thats proven true for us.

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Stanford center uses AI and machine learning to expand data on women's and children's health, director says - The Stanford Daily

Artificial intelligence and machine learning are changing the way retail works as it creates knowledge out of data that retailers can turn into action.

Sixty-five percent of decision makers at retail companies and organisations said AI and ML are mission-critical technologies, according to a survey sponsored by Rackspace Technology.

The technologies provide an opportunity to enhance customer experiences, improve revenue growth potential, undertake rapid innovation and create smart operations all of which can help businesses to stand out from the competition.

Fifty-eight percent of respondents in the retail space said AI and ML technologies are a high priority for their industry.

Sixty-nine percent reported AI and ML had a positive impact on brand awareness and on brand reputation (67 percent), as well as on revenue generation (72 percent) and on expense reduction (72 percent).

Meanwhile, 75 percent of respondents in retail say they are employing AI and ML as part of their business strategy, IT strategy or both.

Some 68 percent of retail respondents are allocating between 6 percent and 10 percent of their budget to AI and ML projects.

The technology is being used by retailers in an increasingly wide variety of contexts, including improving the speed and efficiency of processes (47 percent), personalising content and understanding customers (43 percent), increasing revenue (41 percent), gaining competitive edge (42 percent) and predicting performance (32 percent), and understanding marketing effectiveness (42 percent).

In an indication of the increasing maturity of the technologies, 66 percent of retail respondents said their AI/ML projects have gone past the experimentation stage and are now either in the optimising/innovating or formalising states of implementation.

There are however challenges when it comes to AI and ML adoption. Thirty-four percent of retail respondents cite difficulties aligning AI and ML strategies to the business.

From a talent perspective, more than half 61 percent of retail respondents said they have necessary AI and ML skills within their organisation.

At the same time, more than half of all respondents say that bolstering internal skills, hiring talent and improving both internal and external training are on their agenda.

Comparing departments, 69 percent of retail respondents say IT staff grasp AI and ML benefits while 46 percent in sales, 45 percent in R&D, 44 percent in senior management and boards, 41 percent in customer service and operations and only 34 percent in marketing departments understand the benefits of these technologies.

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AI and machine learning are the future of retail: Survey - ITP.net

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Cyberattacks. Data breaches. Regulatory investigations. Emergingtechnology. Privacy rights. Data rights. Compliance challenges. Therapidly evolving privacy and cybersecurity landscape has created aplethora of new considerations and risks for almost everytransaction. Companies that engage in corporate transactions andM&A counsel alike should ensure that they are aware of andappropriately manage the impact of privacy and cybersecurity riskson their transactions. To that point, in this article we provide anoverview of privacy and cybersecurity diligence, discuss the globalspread of privacy and cybersecurity requirements, provide insightsrelated to the emerging issues of artificial intelligence andmachine learning and discuss the impact of cybersecurity incidentson transactions before, during and after a transaction.

There is a common misunderstanding that privacy matters only forcompanies that are steeped in personal information and thatcybersecurity matters only for companies with a business modelgrounded in tech or data. While privacy issues may not be the mostcritical issues facing a company, all companies must addressprivacy issues because all companies have, at the very least,personal information about employees. And as recent publicizedcybersecurity incidents have demonstrated, no company, regardlessof industry, is immune from cybersecurity risks.

Privacy and cybersecurity are a Venn diagram of legal concepts:each has its own considerations, and for certain topics theyoverlap. This construct translates into how privacy andcybersecurity need to be addressed in M&A: each stands alone,and they often intermingle. Accordingly, they must both beaddressed and considered together.

Privacy requirements in the U.S. are a patchwork of federal andstate laws, with several comprehensive privacy laws now in effector soon to be in effect at the state level. Notably, while itdoesn't presently apply in full to personnel andbusiness-to-business personal data, the California Consumer PrivacyAct covers all residents of the state of California, not justconsumers (despite confusingly calling residents"consumers" in the law). Further, there are specificlaws, such as the Illinois Biometric Information Privacy Act andthe Telephone Consumer Protection Act, that add further, morespecific privacy considerations for certain business activities.And while there is an assortment of laws with a wide variety ofenforcement mechanisms from private rights of action to regulatorycivil penalties or even disgorgement of IP, one consistent trend isthe increasing potential for financial liability that can befall anon-compliant entity.

Laws in the U.S. related to cybersecurity compliance are not ascommon as laws related to responding to and notifying of a databreach. In recent years, specific laws and regulations have largelyfocused on the healthcare and financial services industries.However, legislative and regulatory activity is expanding in thisspace, requiring increasingly specific technological,administrative and governance safeguards for cybersecurity programswell beyond these two industries. Additionally, while breachresponse and notification where sensitive personal data is impactedhas been a well-established legal requirement for several yearsnow, increasingly complex cyber-attacks on private and publicentities has expanded the focus of cybersecurity incident reportingrequirements and enterprise cybersecurity risk considerations.

What Does This All Mean for Diligence?

For the buy side, identifying the specifics of what data, datauses and applicable laws are relevant to the target company ispivotal to appropriately understanding the array of risks that maybe present in the transaction. Equally, at least basictechnological cybersecurity diligence is important to understandthe risks of the transaction and potential future integration. Forthe sell side, entities should be prepared to address their data,data uses and privacy and cybersecurity obligations in diligencerequests.

Separately, privacy and cybersecurity diligence should not focussolely on the risks created by past business activity but alsoconsider future intentions for the data, systems and company'sbusiness model. If an entity is looking to make an acquisitionbecause it will be able to capitalize on the data that the acquiredentity has, then diligence should ensure that those intended useswon't be legally or contractually problematic. This issue isbest known earlier than later in the transaction, as it may impactthe value of the target or even the desire to move ahead.

In the event that diligence uncovers concerns, some privacy andcybersecurity risks will warrant closing conditions and/or specialindemnities to meet the risk tolerance of the acquiring entity. Inintense situations, such as where a data breach happens or isidentified during a transaction, there may even be a pricerenegotiation. Understanding the depth and presence of these risksshould be front of mind for any entity considering a sale to allowfor timely identification and remediation and in some instances tounderstand how persistent risks may impact the transaction if itmoves ahead. For all of these situations, privacy and cybersecurityspecialists are critical to the process.

The prevalence of global business, even for small entities thatmay have overseas vendors or IT support, creates additional layersof considerations for privacy and cybersecurity diligence.

Privacy and cybersecurity laws have existed in certainjurisdictions for years or even decades. In others, the expandedcreation of, access to and use of digital data, along withexemplars like the European Union (EU) General Data ProtectionRegulation, have caused a profound uptick in comprehensive privacyand cybersecurity laws. Depending on how you count, there are closeto or over 100 countries with such laws currently or soon to be inplace. This proliferation and dispersion of legal requirementsmeans a compounding of risk considerations for diligence.

Common themes in recently enacted and proposed global privacyand cybersecurity laws include data localization, appointed companyrepresentatives, restrictions on use and retention, enumeratedrights for individuals and significant penalties. Moreover, asidefrom comprehensive laws that address privacy and cybersecurity,other laws are emerging that are topic-specific. For example, theEU has a rather complex proposed law related to the use ofartificial intelligence. It is critical to ensure that theappropriate team is in place to diligence privacy and cybersecurityfor global entities and to help companies take appropriaterisk-based approaches to understanding the global complianceposture. It can be difficult to strike a balance in diligencepriorities due to both the growing number of new global laws andthe lack of many (or any) historical examples of enforcement forthese jurisdictions. But robust fact-finding paired with continueddiscussions on risk tolerance and business objectives, and carefulconsideration of commercial terms, will help.

As mentioned, artificial intelligence is a hot topic for privacyand cybersecurity laws. One of the biggest diligence risks relatedto artificial intelligence and machine learning (AI/ML) is notidentifying that it's being used. AI/ML is a technicallyadvanced concept, but its use is far more prevalent than may beimmediately understood when looking at the nature of an entity.Anything from assessing weather impacts on crop production todetermining who is approved for certain medical benefits caninvolve AI/ML. The unlimited potential for AI/ML applicationcreates a variety of diligence considerations.

Where AI/ML is trained or used on personal data, there can besignificant legal risks. The origin of training data needs to beunderstood, and diligence should ensure that the legal support forusing that data is sound. In fact, the legal ability to use allinvolved data should be assessed. Companies commonly treat all dataas traditional proprietary information. But privacy laws complicatethe traditional property-law concepts, and even if laws permit theuse of data, contracts may prohibit it. Recent legal actions haveshown the magnitude of penalties a company can face for wronglyusing data when developing AI/ML. Notably, in 2021 the FTCdetermined that a company had wrongly used photos and videos fortraining facial recognition AI. As part of the settlement, the U.S.Federal Trade Commission ordered that all models and algorithmsdeveloped with the use of the photos and videos be deleted. If acompany's primary offering is an AI/ML tool, such an ordercould have a material impact on the company.

Additionally, the use of AI/ML may not result in the intendedoutput. Despite efforts to use properly sourced data and avoidnegative outcomes, studies have shown that bias or other integrityissues can arise from AI/ML. This is not to say the technologycannot be accurate, but it does demonstrate that when performingdiligence it is crucial to understand the risks that may be presentfor the purposes and uses of AI/ML.

Security incidents have been the topic of many a headline overthe past few years. Some of these incidents are the result of thegrowing trend of ransomware or other cyber extortions, includingdata theft extortions or even denial-of-service extortion. Theidentification of a data security may well have a serious impact ona transaction. Moreover, transactions can be impacted by datasecurity incidents occurring before, during and after atransaction. Below we outline some key considerations for each.

An Incident Happened BEFORE a Transaction Started

An Incident Happens DURING a Transaction

An Incident Happens AFTER a Transaction

While far from the totality of privacy and cybersecurityconsiderations for transactions, these topics should help establisha baseline understanding of what to look for and how to approachprivacy and cybersecurity in the current legal environment.

The content of this article is intended to provide a generalguide to the subject matter. Specialist advice should be soughtabout your specific circumstances.

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Privacy And Cybersecurity Risks In Transactions Impacts From Artificial Intelligence And Machine Learning, Addressing Security Incidents And Other...

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

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