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Megan Berry and Alex Gurevich appointed Chief Product Officer and Chief Operating Officer at Octane AI.

As CPO of Octane AI, Megan Berry will lead the product, design, and engineering teams in enhancing Octane AI's platform to enable more users to create relationships with their site visitors based on real data, and turn them into customers. Berry has over a decade of experience working with and managing remote product teams, having served as a Vice President of Product at Octane AI and RebelMouse. Berry previously worked at ad pioneer Mobclix and social influence platform Klout before its acquisition by Lithium.

"I've never been more excited about what we're building here at Octane AI! We want to empower every ecommerce merchant to have more conversations with their customers and to leverage zero-party data to humanize their shopping experience," said Megan Berry, CPO of Octane AI. "Merchants know the importance of collecting and owning customer data, but they need solutions that make it easy to use this data to improve the full customer journey. That's why we are prioritizing AI features that will make collecting zero-party data a no-brainer. We are making it incredibly fast and easy for marketers to get up and running with zero-party data marketing."

After joining Octane AI as the Vice President of Finance and Operations, Alex Gurevich quickly transformed the operations of the company to be in a position to scale both the customer base and the internal team. With a track record of impact in high-growth technology businesses like Google, Zendesk, and Credit Karma, and seeing each of these companies go either through an IPO or acquisition, Gurevich is well-suited to lead the operations of Octane AI. In the new COO role, Gurevich will oversee internal functions, such as finance and people operations, as well as customer facing functions, such as account management.

"Since I joined in March of 2021, it's been really great to see our customer base grow 80%+ and the monthly Gross Merchandise Value these customers generate through our software more than double," said Alex Gurevich, COO of Octane AI. "We've been able to have this kind of growth with AI only in our name. Looking at our roadmap and seeing how powerful our software will become with machine learning, I am excited about driving increasing value for our customers and making their businesses more automated, smarter, and personalized."

To learn more about Octane AI, visit http://www.octaneai.com. Follow @OctaneAI on Twitter to stay up-to-date with the latest news, offerings and marketing tips.

About Octane AI

Octane AI is the zero-party data marketing platform for Shopify and Shopify Plus merchants. Octane AI's patented conversational technology enables thousands of merchants to collect zero-party data and leverage it for personalization at scale across their website, email and SMS. Ultimately, brands using Octane AI see an increase in sales conversions, opt-ins, AOV and LTV.

Elected the Best Storefront App by Shopify in 2021, Octane AI is helping brands build deep relationships with their customers and personalize the shopping experience. The fully-remote martech company employs team members in over 12 countries and has received funding from Javelin Venture Partners, General Catalyst, Bullpen Capital, and Boost VC top Silicon Valley investors behind big brands like Shopify, Masterclass, Snapchat, BigCommerce, Canva and Alibaba. For more information, visit: https://www.octaneai.com/

SOURCE Octane AI

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Octane AI Promotes Two Executives to C-Suite as Company Bets on Artificial Intelligence to Fuel its Zero-Party Data Platform - PR Newswire

While the healthcare industry has faced unprecedented operational constraints in recent years, including limited physical capacity, vulnerable patients, loss of revenue, and shortages of staff, it has also reaped opportunities to adapt and excel.

Health systems and hospitals have been especially primed to rapidly adopt digital transformation and technology initiatives to predict and manage optimal scheduling, staffing, and patient flow.

With the support of AI-based analytics tools, health systems can better use the assets in which they have already invested. By utilizing critical resources like operating rooms, infusion clinics, and inpatient bed units effectively, they can improve financial performance, relieve burden on staff, and treat higher volumes of patients in shorter periods of time. Many healthcare organizations have already deployed analytics to achieve these results, quickly seeing a large return on a relatively small investment in implementation.

With their upcoming Transform Hospital Operations Summit, hosted in partnership with Beckers, healthcare analytics expert LeanTaaS will share these providers journeys, results, and stories. Driven by a focus on deploying AI to achieve better return on investment, the two-day program will connect over 1,000 attendees with health system executives, technology leaders, and industry experts to discuss how hospitals across the U.S. use AI and predictive and prescriptive analytics tools to solve critical challenges arising from case backlogs, provider burnout and staffing shortages, and increased patient wait times.

Summit attendees will learn about success stories from C-suite hospital and health system leaders who have transformed operations and unlocked revenue by using AI and machine learning solutions. These sessions will encompass a wide range of perspectives on this topic, including the strategies of breaking through operational barriers with partnerships, the urgency behind implementing high-powered AI, the best practices for scaling disruptive new technology at scale, the potential for AI to revolutionize the healthcare industry, and more.

Primary speakers include Dr. Patrick McGill, EVP, Chief Transformation Officer at Community Health Network; Dr. Douglas Flora, Executive Medical Director of Oncology Services at St. Elizabeth Healthcare; and Dr Eric Eskiolu, Executive Vice President, Chief Medical and Scientific Officer and Co-Director of the Institute of Innovation and Artificial Intelligence at Novant Health.

Were excited to speak at Transform and share our experiences with AI and analytics, but just as importantly, about how were building a culture that supports transformation through a commitment to clinical excellence, workforce development, and process improvement, shared Aaron Miri, Senior Vice President and Chief Digital and Information Officer and Amy Huveldt, VP of Performance Excellence, both of Baptist Health and who will also be primary speakers.

Further speakers include healthcare leaders and experts from Cone Health, Mount Nittany Medical Center, Multicare, UCHealth, University of Utah Health, Vanderbilt-Ingram Cancer Center, and Yale New Haven Health. These sessions will feature healthcare executives highlighting the results they have achieved by leveraging AI in their operations, including increasing surgical case length accuracy by 4%; reducing infusion patient wait times by 30%; and decreasing inpatient time-to-admit by 16%, despite an 18% increase in COVID-19 census. Attendees can build a hospital operations summit schedule based on interest and specialty, choosing from three Learning Tracks: Perioperative, Infusion Centers, and Inpatient Beds.

As the healthcare industry continues to grapple with lingering effects of the pandemic, its no secret that health systems need to do more with less while also prioritizing the valuable time and wellbeing of staff. Were looking forward to our June Transform event, as it will hone in on critical healthcare issues and how AI can support hardworking hospital leaders, said Mohan Giridharadas, LeanTaaS founder and CEO. This event will provide all attendees with the resources needed to compete and thrive by using smarter capacity management decisions every single day.

Transform registration is free for all attendees. To register and learn more about the sessions and speakers that will be featured at the summit, view the conference agenda here.

Read more:
Virtual hospital operations summit to focus on role of Artificial Intelligence in achieving ROI for system wide impact - Becker's Hospital Review

Malta is turning to Artificial Intelligence to reduce traffic congestion, improve the distribution of energy and avoid scenarios of out-of-stock medicine among others through an investment of 4 million.

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The funds will be injected in six national projects led by the Malta Digital Innovation Authority:

-Management traffic project (2million). Existing traffic lights, CCTV and display panels systems will be incorporated into one system to be able to draw up traffic patterns and understand better what is causing congestion. The project would eventually help reduce congestion and time spent in traffic as the information will be passed on to Transport Malta's control centre, which will in turn distribute the information among motorists through apps such as Google Maps.

-Understand and better manage energy load distribution across the island by collecting data from ARMS, Enemalta and Water Service Corporation (376,000). Analysing the data collected through these three entities would allow the same entities to maximise their resources and provide real-time support. It would also become possible to forecast problems by analysing past data. This investment is a stepping stone towards the implementation of a smart grid network that would see energy clients receiving information about possible energy wastage at home.

-24/7 chatbot for servizz.gov and 153 phone clients (590,000). The chatbot service will be in Maltese and English and will help clients avoid having to physically turn up at one of the branches if they don't find the information they require online.

-Personalised educational programmes depending on children's achievements, skills and their aspirations (150,000).

-Analysing budget and stock levels of the health services' Central Procurement Supply Unit (300,000). The system would help forecast demand and supply to avoid out-of-stock medicine.

-A system that creates what is known as digital tourist 'personas' depending on visitors' interests, lifestyle and demographics (800,000). With the help of these personas, the Malta Tourism Authority would be able to better understand what tourists are actually after and can draft policies and initiatives based on visitors' changing interests.

Nearly all five projects are set to be completed by 2023, with the traffic management project being completed by 2024.

The AI systems are being applied to scenarios that involve a huge amount of data that cannot be processed manually, Minister Silvio Schembri assured at a press conference on Thursday.

The projects, he added, formed part of the national AI strategy, with the ultimate aim being to support citizens in their everyday life, incentivise research and ensure progress in the health and education sectors.

"We aspire to become a place from where companies can set up and develop AI systems. Through these projects we can also show that Malta can adapt to such new technologies."

AI, he noted, could be adapted to everyday processes that most take for granted. The strategy was launched in 2019, but the launch of projects required a lot of research, he said.

MDIA CEO Kenneth Brincat told the same conference that a further 500,000 had been invested in research and scholarships.

Since the strategy's launch in 2019, the authority itself had carried out research and work to ensure that its aims are effectively implemented by 2030.

He added that the authority's most important role was its regulatory responsibilities.

Brincat said that while Malta already had a certified regulatory programme for the AI sector, the EU was still discussing a mandatory regulatory framework.

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Can artificial intelligence solve the traffic problem? - Times of Malta

Dr. Sephus discusses breaking down barriers to machine learning at Ars Frontiers 2022. Click here for transcript.

Welcome to the week after Ars Frontiers! This article is the first in a short series of pieces that will recap each of the day's talks for the benefit of those who weren't able to travel to DC for our first conference. We'll be running one of these every few days for the next couple of weeks, and each one will include an embedded video of the talk (along with a transcript).

For today's recap, we're going over our talk with Amazon Web Services tech evangelist Dr. Nashlie Sephus. Our discussion was titled "Breaking Barriers to Machine Learning."

Dr. Sephus came to AWS via a roundabout path, growing up in Mississippi before eventually joining a tech startup called Partpic. Partpic was an artificial intelligence and machine-learning (AI/ML) company with a neat premise: Users could take photographs of tooling and parts, and the Partpic app would algorithmically analyze the pictures, identify the part, and provide information on what the part was and where to buy more of it. Partpic was acquired by Amazon in 2016, and Dr. Sephus took her machine-learning skills to AWS.

When asked, she identified accessasthe biggest barrier to the greater use of AI/MLin a lot of ways, it's another wrinkle in the old problem of the digital divide. A core component of being able to utilize most common AI/ML tools is having reliable and fast Internet access, and drawing on experience from her background, Dr. Sephus pointed out that a lack of access to technology in primary schools in poorer areas of the country sets kids on a path away from being able to use the kinds of tools we're talking about.

Furthermore, lack of early access leads to resistance to technology later in life. "You're talking about a concept that a lot of people think is pretty intimidating," she explained. "A lot of people are scared. They feel threatened by the technology."

One way of tackling the divide here, in addition to simply increasing access, is changing the way that technologists communicate about complex topics like AI/ML to regular folks. "I understand that, as technologists, a lot of times we just like to build cool stuff, right?" Dr. Sephus said. "We're not thinking about the longer-term impact, but that's why it's so important to have that diversity of thought at the table and those different perspectives."

Dr. Sephus said that AWS has been hiring sociologists and psychologists to join its tech teams to figure out ways to tackle the digital divide by meeting people where they are rather than forcing them to come to the technology.

Simply reframing complex AI/ML topics in terms of everyday actions can remove barriers. Dr. Sephus explained that one way of doing this is to point out that almost everyone has a cell phone, and when you're talking to your phone or using facial recognition to unlock it, or when you're getting recommendations for a movie or for the next song to listen tothese things are all examples of interacting with machine learning. Not everyone groks that, especially technological laypersons, and showing people that these things are driven by AI/ML can be revelatory.

"Meeting them where they are, showing them how these technologies affect them in their everyday lives, and having programming out there in a way that's very approachableI think that's something we should focus on," she said.

Read more from the original source:
How we learned to break down barriers to machine learning - Ars Technica

May 18, 2022 A new app developed by the Mayo Clinic transmits Apple Watch electrocardiograms (ECG) signals recorded in non-clinical environments seamlessly and securely to the medical center to permit artificial intelligence (AI) analysis to identify leftventricular dysfunction, a life-threatening, asymptomatic heart disease. Additionally, the tracings are presented on an interactive dashboard integrated with the electronic health record to facilitate clinician review. The findings were presented today as late-breaking clinical science during Heart Rhythm 2022.

Heart failure is a progressive disorder that impacts more than six million Americans and leads to more than one million hospitalizations annually1. Considered stage B heart failure, left-ventricular dysfunction is often asymptomatic and, if left untreated, can lead to adverse events. Traditional ECGs are unable to identify and diagnose ventricular dysfunction (weak heart pump), even with expert human interpretation. The introduction of AI to 12 lead ECG analysis in a clinical setting enabled the ECG to identify left ventricular dysfunction. The authors of this study sought to adapt the neural network so that instead of reading a 12 lead ECG, it could read an Apple Watch ECG to assess applying the test remotely and, by using patient-owned equipment, inexpensively.

Mayo Clinic patients with the Mayo Clinic iOS app and an Apple Watch were invited to participate in the study. Of the 3,884 Mayo Clinic patients who reported Apple Watch ownership, 2,454 subjects from 46 states and 11 countries participated and downloaded the study app. The average age of participants was 5315 years and 56% were female. The app, developed by Mayo Clinic, sent all previously recorded ECGs for clinician review. ECGs acquired from the wearable devices within one month of a clinically ordered ECG were analyzed by AI for the presence of ejection fraction 40% using a model adapted for single lead use.

We have seen how artificial intelligence has revolutionized the already common ECG into a tool that can be used to identify occult cardiovascular diseases. Our team saw vast potential to expand tracking outside of a physicians office by using popular wearable devices, said Zachi Itzhak Attia, MSEE, PhD, Mayo Clinic. We set out to create a platform that could not only provide accurate readings, but also would yield high patient engagement with an easy to navigate, user-friendly process that can be completed from the comfort of a patients home.

Between August, 2021 and August, 2022, patients shared 125,610 ECGs and 92% of patients used the app more than once. Of the participants, 421 had at least one sinus rhythm (NSR) ECG (avg: 17 ECGs, with NSR determined by watch algorithm) within 30 days of an echocardiogram. Among the participants, 16 of these patients (3.8%) had an EF 40%, and 13 out of these 16patients were identified by the watch AI ECG, which had an Area Under the Curve (AUC) 0.875, sensitivity 81.2% and specificity 81.3%.

These findings show that the application of AI to a wearable device ECG can effectively monitor left ventricular dysfunction. For patients who might unknowingly have this condition, such as those with hypertension, diabetes, advancing age, and people receiving some forms of chemotherapy, the tool could enable early detection and help physicians optimize treatment options, said Paul Friedman, MD, FHRS, Mayo Clinic. This technology has the potential to be scaled and adopted by hospital systems to better serve patients, particularly in remote communities or geographically diverse populations around the world, potentially addressing health care disparities, and enabling physicians to offer more coordinated patient care.

The authors are currently seeking FDA approval for the current algorithm used in this trial, and would like to see further studies test additional AI algorithms developed by their team. They would also like expand this current interface for additional data collection and to screen for other common heart conditions among patients, such as atrial fibrillation.

For more information: http://www.hrsonline.org

Reference:

1 Mozaffarian D, Benjamin EJ, Go AS et al. Heart disease and stroke statistics 2015 update: a report from the American Heart Association. Circulation. 2015;131:e29322. doi: 10.1161/CIR.0000000000000152.

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New App for Apple Watch Uses Artificial Intelligence to Detect Left-ventricular Dysfunction - Diagnostic and Interventional Cardiology