Monthly Archives: September 2021

Investments in artificial intelligence and machine learning are finally on the rise in healthcare.

While the industry has been slow to adopt AI in comparison to other sectors like financial services and manufacturing with 70% of health systems yet to establish a formal program a recent survey found that 68% of health system executives plan to invest more in AI in the next five years to help reach their strategic goals. And the investments are expected to be significant; the global AI in healthcare market size is estimated to reach $120.2 billion by 2028.

The opportunities for AI in healthcare are widespread, spanning both operational and clinical use cases including fraud prevention, voice-assisted charting, registration, remote patient monitoring and more. AI holds particular promise for connected medical devices and telehealth an integral part of the Internet of Medical Things (IoMT) as it enables faster triage, intake, detection and decision making.

In fact, new patient apps and connected medical devices leveraging AI are already being launched regularly. For example, Google recently introduced a new AI-powered dermatology app that uses image recognition algorithms to provide expert, personalized help by suggesting possible skin conditions based on patient-uploaded photos. A Philips device leverages insights from AI to diagnose and treat oncology patients. And Amwells new telehealth platform enables providers to receive alerts on their patients health status via an AI-powered, automated real-time early warning score system.

While there is significant potential for AI in healthcare, there are also limitations. The primary challenge that has not yet been widely discussed, however, is how best to secure AI-powered connected medical devices from increasingly frequent and complex cybersecurity risks.

Securing the IoMT in the age of AI is imperative

While AI can and often has been used for good, it can also be used to discover and exploit vulnerabilities. For example, the same type of algorithm being implemented in a medical device to more accurately and quickly diagnose cancer may also be used by a bad actor to attack that device. To illustrate, a 2019 study from Ben-Gurion University demonstrated how AI-savvy hackers could manipulate CT and MRI results of lung cancer patients gaining complete control over the number, size and location of tumors.

Both radiologists and AI algorithms were unable to differentiate between the altered and correct scans. This kind of tampering has the potential to impact patient lives, and can also result in insurance fraud, ransomware attacks and other issues for both patients and providers.

Bad actors often need little more than an emulator which enables one computer system to behave like another and a piece of code from the system being targeted in order to successfully program AI to hack a device.

Cyber threats are clearly a significant and increasing challenge for the connected industries. In 2019 alone, cyberattacks on IoT devices increased dramatically, accounting for more than 2.9 billion events. And its estimated that 50 billion medical devices will be connected to clinical systems within the next 10 years, making the IoMT (Internet of Medical Things) industry an increasingly opportune target for hackers. Despite the repercussions of a cyberattack, data shows that many manufacturers are challenged to practice Security by Design due to shortage of knowledge and know-how. According to a recent survey we did, only 13% of IoMT leaders believe their business is very prepared to mitigate future risks, while 70% believe that they are only somewhat prepared at best.

However, there are steps manufacturers can take to protect their devices from the start.

How to ensure AI-enabled devices are secure

Although AI and machine learning models are expensive and time intensive to create, once they are built, they are very easy to replicate. Restricting and preventing access to a system is thus a critical first step in protecting systems from adversaries.

In order for bad actors to successfully attack a system built on AI, they need access to the systems data, or a digital twin, for their algorithms to process. In most cases, machine learning lifting, or emulation of data is possible because the automated system answers thousands of questions without being flagged as a potential threat; with answers to these questions, the bad actors can easily use AI to replicate the system or program, even if its a complex medical device software or process.

Limiting access is thus crucial, and includes a few steps:

Beyond access control and anomaly detection, its also important to harden connected devices against reverse engineering. Manufacturers can use many different tactics and solutions to make the code in their devices difficult to reverse engineer and thereby help keep them secure.

All of these protections should be built into devices during the original R&D process, as it is much more of an arduous task to add cybersecurity once a product is already on the market.

Additionally, its important for medtech manufacturers to ensure the regulatory readiness of their medical devices, particularly as the regulatory landscape continues to evolve. While 80% of medtech executives believe that regulatory compliance is the biggest business benefit of implementing a strong cybersecurity strategy, only four in 10 respondents rated themselves very aware or knowledgeable about forthcoming EU and U.S. cybersecurity regulations. Leveraging an assessment tool can help manufacturers examine their regulatory preparedness and identify any weak spots so they can address them before the device goes to market.

Machine learning has the power to be used both for good and unfortunately for nefarious purposes. As more connected medical devices are built on AI, cybersecurity risks will increase as well and its more important than ever before for manufacturers to implement advanced security protections in the design phase to ensure the safety of healthcare organizations, providers and patients.

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The future of healthcare is dependent on securing AI-powered medical devices - MedCity News

Sep 10, 2021 - by Laura Snider

The National Center for Atmospheric Research (NCAR) is a collaborator on a new $25 million initiative that will use artificial intelligence to improve traditional Earth system models with the goal of advancing climate research to better inform decision makers with more actionable information.

The Center for Learning the Earth with Artificial Intelligence and Physics (LEAP) is one of six new Science and Technology Centers announced by the National Science Foundation to work on transformative science that will broadly benefit society. LEAP will be led by Columbia University in collaboration with several other universities as well as NCAR and NASAs Goddard Institute for Space Studies.

The goals of LEAP support NCARs Strategic Plan, which emphasizes the importance of actionable Earth system science.

LEAP is a tremendous opportunity for a multidisciplinary team to explore the potential of using machine learning to improve our complex Earth system models, all for the long-term benefit of society, said NCAR scientist David Lawrence, who is the NCAR lead on the project. NCARs models have always been developed in collaboration with the community, and were excited to work with skilled data scientists to develop new and innovative ways to further advance our models.

LEAP will focus its efforts on the NCAR-based Community Earth System Model. CESM is an incredibly sophisticated collection of component models that when connected can simulate atmosphere, ocean, land, sea ice, and ice sheet processes that interact with and influence each other, which is critical to accurately project how the climate will change in the future. The result is a model that produces a comprehensive and high-quality representation of the Earth system.

Despite this, CESM is still limited by its ability to represent certain complex physical processes in the Earth system that are difficult to simulate. Some of these processes, like the formation and evolution of clouds, happen at such a fine scale that the model cannot resolve them. (Global Earth system models are typically run at relatively low spatial resolution because they need to simulate decades or centuries of time and computing resources are limited.) Other processes, including land ecology, are so complicated that scientists struggle to identify equations that accurately capture what is happening in the real world.

In both cases, scientists have created simplified subcomponents known as parameterizations to approximate these physical processes in the model. A major goal of LEAP is to improve on these parameterizations with the help of machine learning, which can leverage the incredible wealth of Earth system observations and high-resolution model data that has become available.

By training the machine learning model on these data sets, and then collaborating with Earth system modelers to incorporate these subcomponents into CESM, the researchers expect to improve the accuracy and detail of the resulting simulations.

Our goal is to harness data from observations and simulations to better represent the underlying physics, chemistry, and biology of Earths climate system, said Galen McKinley, a professor of earth and environmental sciences at Columbia. More accurate models will help give us a clearer vision of the future.

To learn more, read the NSF announcement and the Columbia news release.

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NCAR will collaborate on new initiative to integrate AI with climate modeling | NCAR & UCAR News - UCAR

AI is everywhere in tech, and you can bet it will be a vital topic at TechCrunch Disrupt 2021 on September 21-23. Its the driving force behind just about everything from rockets, robotics and customer service to deep science and autonomous vehicles. Its even changing the game for dystopian fiction writers.

As always, every Disrupt features peerless experts and boundary-pushing visionaries, and Disrupt 2021 will not disappoint on that or any other score. With more than 80 interviews, panel discussions, events and breakout sessions and counting were shining a spotlight on sessions related to AI and robotics.

Get amongst it: Buy your pass today, join thousands of attendees from around the world and get ready to learn about the latest in AI, robotics, rockets and so much more.

Here are just some of the AI and robotics presentations we have on tap. Youll find the specific days and times listed in the Disrupt 2021 agenda.

A Fictional Future Built with Real AI

A leading mind in AI research and investment and a bold new voice in science fiction collaborate in AI 2041, a remarkable new collection of stories imagining a future shaped by technology being built today. Hear Kai-Fu Lee, Sinovation Ventures chairman and CEO, and author Chen Qiufan (AKA Stanley Chan) discuss the tech that inspired their book and the changes they expect over the next two decades.

Drones, Self-driving Cars and Everything in Between

Pete Buttigieg first came on the scene as a small-town mayor in Indiana. He launched onto the national stage as a presidential candidate for the Democratic party in 2020. He now serves as Secretary of Transportation under the Biden administration and oversees everything from public transport to autonomous vehicle regulation. Hear Secretary Buttigiegs take on micromobility, the future of cities, drone delivery, autonomous vehicles and more in this fireside chat.

Crafting a Lunar Trajectory in Newspace

Rocket Lab has upgraded its ambitions from building a global launch empire to designing its own spacecraft and visiting the moon and beyond. Founder and CEO Peter Beck will speak to the challenges and opportunities lying ahead for his fast-growing space and tech outfit.

Demo Derby: How startups are disrupting the status quo with innovative data analytics, AI and modern app development

Startups need to move quickly and focus limited resources on areas where they can differentiate. In this fast-paced session, learn from startups and Google Cloud experts like Dave Elliott, developer advocacy lead, AI how you can leverage Google technologies to serve customers better and get to market more quickly. In a series of short demos, see how innovative startups and Google experts have used Google compute, storage, networking and AI technologies to disrupt the status quo. Presented by Google Cloud.

Humanizing AI: How Brands Are Revolutionizing Customer Experience in an Increasingly Digital World

Empathy deficit is the largest imminent threat to a businesses growth, but theres hope. Humanized AI is allowing brands to create empathetic customer experiences by offering uniquely personal interactions with digital people. But what is empathy, really? And how can it help brands and storytellers better connect with their audiences in a cookie-less world? Soul Machines co-founder and CBO Greg Cross explains how embracing AI could be just the competitive advantage that your brand needs. Presented by Soul Machines.

The New Human and Planetary Health Pioneers

Mammoth Biosciences, co-founded by Nobel Laureate Jennifer Doudna and Trevor Martin, is the industrys first CRISPR platform company. It has already delivered a breakthrough COVID test and has inked partnerships for novel CRISPR diagnostics, therapeutics and biomanufacturing with leading healthcare companies. NotCo, founded by Matias Muchnick, is combining artificial intelligence and deep science to re-invent the food industry, starting with a milk alternative product, with many more to come. Hear about the founder journey from these breakout companies and tips for scaling your business. Presented by Mayfield.

So You Want to Build a Space Business?

The space industry is changing faster than ever, with new technologies and lower launch costs democratizing access to space and driving a new era of innovation. The opportunities to build the next great business are seemingly endless, but space can be a difficult and unforgiving place, especially for those new to the domain. This session will feature practical insights and advice from experienced space leaders for entrepreneurs looking to get their business off the ground.

Korea Pavilion Pitch Session

We present the 13 pioneering Korean companies that will enrich our lives with their innovative edge. The companies specialize in various technologies, including Green Tech, AR/VR, 3D Display, AI & Big Data, and Cybersecurity. Dont miss your chance to catch a glimpse of ingenuity from the technology powerhouse. Presented by KOTRA.

TechCrunch Disrupt 2021 takes place on September 21-23. Buy your pass today and learn about the latest trends and developments in AI and robotics.

Is your company interested in sponsoring or exhibiting at Disrupt 2021? Contact our sponsorship sales team byfilling out this form.

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Everything AI and robotics at TechCrunch Disrupt 2021 - TechCrunch

Given that artificial intelligence is increasingly overtaking people on a range of expert tasks, will it someday make human managers obsolete? Luckily, theres one cognitive ability where people still have a big edge: reframing. Reframing is not about solving a problem (with either intuition or conscious reasoning) but about defining what exactly is the problem to be solved. It isnt easy, and its usually time-consuming, but it is key to both discovering breakthrough innovations and adapting to a rapidly changing environment. Four tactics can help you cultivate this ability: dedicating time to not thinking about the problem, making hidden assumptions explicit, playful exploration, and leveraging surprising analogies.

Of all the tools managers use to lead their businesses, thinking is the most crucial. It involves two distinct ways of processing information: intuitive and conscious, which the Nobel laureate Daniel Kahneman labeled thinking fast and slow. Today computers increasingly outperform people in both. With their raw calculative power, computers easily beat humans in conscious-reasoning tasks, as long as the rules and parameters of the situation are known. Managers routinely turn to mathematical optimization and simulation to build investment portfolios, make pricing decisions, and understand supply-chain risks. And while humans used to be superior at pattern recognition, which is largely intuitive, computers now can be trained to develop their own intuitions from large masses of data using machine learning. In recent studies they proved better than humans at expert tasks such as detecting cancer in computer tomography scans and choosing investment targets.

Given the way things are going, can managers continue to add value to organizations? Luckily, theres one cognitive ability where people still have the edge over computers: thinking really slow.

Really slow thinking is used in reframing the process by which we reexamine the parameters, objectives, and assumptions we approach decisions with. Reframing is not about solving the problem (with either intuition or conscious reasoning) but about defining what exactly is the problem to be solved.

Reframing isnt easy. The way in which managers frame decisions can be deeply entrenched in industry traditions, organizational history, and executives own education and experience. Reframing can be extremely time-consuming, which is why we see it as thinking really slow.

Reframing is crucial because groundbreaking business model innovations often result when companies break away from established ideas about how value is created and captured. Look at Amazon. In 1999 a CNBC reporter challenged Jeff Bezos because the company, with its large, expensive distributions centers and many employees, was no longer the pure internet play investors were high on. Internet, shminternet, Bezos replied. He rejected the view that a low-cost online business model was essential to competing. Instead of accepting the pure internet versus traditional retail dichotomy, he reframed the conversation in terms of an obsession with delivering a great customer experience and explained how all Amazons strategic choices focused on that goal.

When market dynamics change, reframing can be especially critical. Consider Nokia. In the feature phone business, it had learned to expect that with successful new offerings, sales took off quickly and profits were good. As a result, the company decided against some costly investments and walked back courses of action that didnt produce immediate results. In the early 2000s it pulled the plug on many pioneering innovations that were seen as too risky or didnt initially experience widespread adoption, including touchscreen phones, tablet devices, and mobile gaming. This approach was particularly damaging when competition moved to the ecosystem level. While Nokia continued to flood the market with new hardware, software development kits and third-party ecosystem and apps were a second priority, a former Nokia executive lamented. Moreover, as a former Nokia manager put it in an interview, Large-scale consumer services are not made in a year or two. We have often lacked patience for that. The smartphone era required a new long-game mindset that the quickly moving hardware king lacked.

Humans ability to think really slow also is key to state-of-the-art AI, which doesnt function unless people first reframe a business problem as an AI problem. As Ajay Agrawal, Joshua Gans, and Avi Goldfarb have argued, AI is simply a variety of prediction algorithms. Reframing problems that demand time-consuming human judgment and careful analysis (such as identifying insurance fraud and assessing creditworthiness) as prediction problems is precisely how the likes of Lemonade and Kabbage have shaken up mature businesses such as consumer insurance and small-business lending.

In a world where managers can use computers to enhance their ability to think fast and slow, the ability to reframe will increasingly separate the wheat from the chaff. Here are four strategies to help you cultivate it:

Dedicate time to not thinking about the problem. Research suggests that a period of incubation helps produce more creative solutions. When you set aside a problem for a period, you distance yourself from its current framing, making room for restructuring and spontaneous insights. So after you initiate the process of solving a problem, go and do something completely different for a while, letting it cook slowly on your back burner.

Make hidden assumptions explicit. Were mostly unaware of the limiting, self-imposed assumptions with which we approach situations. Group processes that are designed to induce cognitive conflict can help surface them. You can make one group argue against another groups solution (devils advocacy) or make two groups develop opposing solutions to a problem (dialectical inquiry). Building a mathematical model of the problem can also be helpful, because it forces you to spell out assumptions about what is causing the problem and how proposed remedies are supposed to work. Modeling often reveals unanticipated dynamics, triggering shifts in mindsets about how to best manage certain things. When Fluor Corporation introduced simulation modeling to help predict changes in the costs and schedules of complex projects, managers started to see that those changes could be managed proactively rather than dealt with the retrospectively, as was industry practice at the time.

Engage in playful exploration. Injecting an element of the imaginative into decision making can help managers mentally distance themselves from tacit assumptions and industry recipes what everyone who knows the industry understands and unleash creativity. This liberation from ordinary constraints can be accomplished by, for example, asking teams to build Lego models of their business ideas in order to communicate them to others.

Leverage (surprising) analogies. Analogies are powerful tools for reframing familiar problems. Ideas and practices from one industry can be used to reshape another. Berry Gordy Jr., for instance, made Motown Records into a hit factory by modeling it after the Ford Motor Companys assembly line, where he had previously worked. In some cases, exposing yourself to something completely different like combat sports, opera, or superhero comics can be a great way to gain fresh insights that other insiders lack. Apples minimalist design, for instance, was inspired by the calligraphy classes, Zen Buddhism lessons, and Bauhaus architecture Steve Jobs was exposed to. Even when the analogy is imperfect, it may provide the rough outlines of a novel framing of a vexing problem.

While managers can add these practices to their tool kits to enhance their own reframing capabilities, they also have a responsibility to ensure that the broader organization supports reframing. The first step is to build channels and foster a culture where the in-house devils advocates and visionaries can voice their concerns and ideas and employees have time for playful exploration and incubation. Though such efforts may not result in tangible benefits immediately, they may be essential for the renewal and long-term prosperity of the organization and its stakeholders.

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Why AI Will Never Replace Managers - Harvard Business Review

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Register now!

This article was written by Bob Friday, Vice President and Chief Technology Officer of Junipers AI-Driven Enterprise Business.

In a recent survey of 700 IT pros around the world, 95% said they believe their companies would benefit from embedding artificial intelligence (AI) into daily operations, products, and services, and 88% want to use AI as much as possible. When was the last time you heard that many people agree on anything?

Yes, AI is all the rage because it is the next step in the evolution of automation in doing tasks on par with human domain experts whether it is driving a car or helping doctors diagnose disease. But make no mistake while we are starting to see the fruits of AI here and there: By and large, the industry and most organizations are still in the early days of AI adoption. And as with any new momentous technology, organizations need to develop an adoption strategy specific to their organization to get the full benefits of AI automation and deep learning technology.

The complication as Gartner put it: How to make AI a core IT competency still eludes most organizations.

But failing to learn how to leverage the benefits AI/ML will leave an organization at a competitive disadvantage in terms of customer experience and operational efficiency. So, whats the way to get there? Here are three common traps that companies should steer clear of as they get themselves AI-ready.

Great wine requires good grapes and great AI starts with good data, but great AI also needs a clear business ROI. The business benefit ROI and the data needed to automate the domain expert task must be clearly defined at the outset of the project if the AI solution is to deliver real value and continue receiving the resources to grow from pilot to production.

AI ingredients, like algorithms and machine learning, sound very science-y, but business AI projects should never resemble science experiments. The Shiny New Toy Syndrome is a real pitfall for AI. To avoid succumbing to it, organizations should tie every AI project to specific business outcomes and know the business outcome question and what task you are trying to do on par with a domain expert.

For example, is the objective of using intelligent automation to relieve IT team members of mundane, routine tasks so they can focus on higher-value activities? Beyond the IT department, is it to help the marketing department gain competitive advantage by delivering more personalized experiences to customers? Is it automating more of the sales process to boost lead volume and close rate?

C-suite leaders would have to be living under a rock at this point not to recognize AIs potential and the fact that investment is required for AI-ready technology stacks, but theyre going to want to understand how its good for the business. Everyone in a company needs to recognize this reality, and ward off any squishiness in an AI projects reason for being.

The AI talent shortage is often cited as one of the tech industrys toughest challenges. It has even been called a national security threat amid Chinas ambitions to become the world leader in AI.

According to OReillys 2021 AI Adoption in the Enterprise report, which surveyed more than 3,500 business leaders, a lack of skilled people and difficulty hiring tops the list of AI challenges.

To make sure their companies have the talent to fully leverage the benefits of AI/ML they should start both a hiring and training program.

On the hiring side, companies should look for talent beyond the typical data science degree and look at adjacent degrees such as physics, math and self-taught computer science. But hiring talent is not enough for a companies strategy to build their AI workforces, especially when theyre competing with behemoths like Amazon and Facebook. Another good solution to consider: If you cant hire them, train them.

While its unreasonable to expect someone to become a data scientist after taking a couple of online Coursera classes. Engineers with Physics, Math and Computer Science backgrounds have the foundation to master data science and deep learning.

Sources of talent may exist inside the organization in unexpected places. Take, for example, the large business intelligence (BI) ecosystems that many companies have. These have talent that is familiar with using Bayesian statistical analysis that is common to most machine learning algorithms.

In making sure they have the right skills to support AI initiatives, it makes sense for companies to re-train existing employees as much as possible in addition to having an AI/ML hiring strategy. Companies need to get creative in pinpointing those employees and AI/ML talent.

Ive seen companies get bogged down by trying to build their own AI tools and solutions from scratch rather than buying them or leveraging open source. The algorithms being used to develop AI solutions are fast evolving and companies should look to partner with vendors in their industry who are leading the AI wave. Unless it happens to be one of the companys core competencies, building AI solutions is usually an overreach. Why reinvent the wheel when you can buy one of the many commercial AI tools on the market?

Deloittes most recent State of the AI in the Enterprise report, which surveyed 2,737 IT and line-of-business executives worldwide, found that seasoned and skilled AI adopters are more likely than starters to buy the AI systems they need.

This suggests that many organizations may go through a period of internal learning and experimentation before they know whats necessary and then seek it from the market, the report said.

Companies that avoid these three traps will have a much easier time accelerating their AI adoption and enjoying the benefits of revenue growth, lower operating costs, and improved customer experience.

Bob Friday is Vice President and Chief Technology Officer of Junipers AI-Driven Enterprise Business.

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3 traps companies should avoid in their AI journeys - VentureBeat

AV8 Ventures unveiled its AV8 Ventures II fund with $180 million from Allianz Group, an insurance and asset management giant, aimed at supporting entrepreneurs developing artificial intelligence-driven technologies in the areas of health, mobility, enterprise and deep tech.

Since the Palo Alto venture firms launch in 2018, it has invested in 20 seed-stage companies, with another four in the pipeline. Its first fund was also $180 million and backed by Allianz, George Ugras, managing director at AV8, told TechCrunch. The new fund will also invest in seed stage and some Series A and will aim to go into 25 companies.

The idea is to operate as a financial VC with the support of the world largest insurance company and asset manager behind us, Ugras said.

Some of the technologies the firm is excited about include how chronic diseases are managed. Ugras believes the lack of access to swaths of data and alignment of interest around the table are prohibiting many of the right solutions from bubbling up. In enterprise, AV8 is looking at management around cyberattacks, predicting vulnerabilities and the impact they have on enterprises, so that companies can be proactive in securing their vulnerabilities versus reactive.

Meanwhile, the driver for the second fund was to ensure continuity in deal activity. AV8 is seeing so many deals right now, and the competition to get into a VC deal makes it difficult to project how fast a fund will be able to deploy the capital. Even if a firm gets excited and issues terms sheets, there is always uncertainty, he added.

With venture capital being abundant these days, Ugras noted that the velocity is the fastest he has seen in 22 years. The competitiveness in the market is such that if a startup has a decent team, there is no issue raising capital. However, on the investor side, they have to do things better than ever.

In terms of the key diligence, you need domain expertise to be very clear on how you can add value and key execution milestones going forward, he added. Healthcare and insurance more so than others because the business models are complicated. If you have the startups educating you on the front end, it is going to be difficult for the fund.

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Allianz backs AV8 Ventures second fund focused on AI technologies - TechCrunch

Computers can sometimes show a surprising lack of common sense. Thats why asking the right questions, using the right data and guarding against the introduction of bias are keys to making augmented intelligence (AI) a valuable decision-support tool that is often called artificial intelligence.

Your clinicians can program the protocol you want for the alerts and predictions you want, said Ben Maisano, chief digital and innovation officer for New Jerseys Atlantic Health System, an AMA Health System Programmember.

If we're trying to reduce hospital stays, or we're trying to understand if our accountable care organization is profitable, or if social determinants of health data helps us better take care of someone, or predict a risk for readmission, youve got to understand what problems are you trying to solve and mapping that to the outcome you wantand then go fill in the blanks, said Maisano, who is a co-founder of CareDox, a platform that connected schools, pediatric practices and families in 38 states.

Maisano spoke during a virtual meeting of the AMA Insight Network that covered how to get a health care AI program up and running, and how to use it properly.

The network aims to helpAMA Health System Programmembers gain early access to innovative ideas, get feedback from their peers, network and learn about pilot opportunities.Learn more.

Dont start with, We want to use AI for radiology triage, because youre starting in the middle, Maisano explained. We take the approach of: What are our problems? And: Where are we well-positioned to execute?

Asking the right questions is fundamental, said Edward Lee, MD, the executive vice president for information technology and chief information officer for the Permanente Federation, and associate executive director of the Permanente Medical Group, an AMA Health System Program member.

I think Ben said it really well actually: What is the problem that we're trying to solve? Dr. Lee said. That's the first question we need to ask and answer before you embark on any program.

Other must-ask questions include:

Another important point to remember, Dr. Lee said, was that AI is intended to enhance, assist, complement and augment human intelligence and not necessarily to replace human intelligence.

He outlined these three main buckets of opportunity for health care AI.

Computer vision, which includes and specialty that deals with digitized imagessuch as radiology, dermatology, ophthalmology and pathologyis considered fertile ground for health care AI.

Predictive analytics, which involves using hundreds or thousands of data points to understand the likelihood of a particular event occurring. It can be used to predict hospital readmissions, fall risks and emerging COVID-19 hot spots.

Natural language processing, which involves interpreting unstructured data and can be used, for example, to search records for patients who have not had needed follow-up care.

Scale is also important, Dr. Lee said. He explained that a system needs to have a diverse and robust team processing a diverse and robust stream of data that is representative of the patient population it serves.

You want a diverse group of people with diversity of thought, because if you do things in a very narrow or potential tunnel vision way, the risk of bias can be introduced much more, Dr. Lee said. If you don't look for bias, you'll never find it.

He described a New England Journal of Medicine study, Automated Identification of Adults at Risk for In-Hospital Clinical Deterioration, by researchers from Kaiser Permanente. They examined a health care AI program that alerted clinicians when it appeared a high-risk patients condition was deteriorating.

The intervention group had a 16% lower mortality rate, and lower rates for intensive-care unit admission and shorter hospital stays. Patients were also less likely to die without a palliate care referral.

The panelists agreed that developing an effective health care AI program takes time.

You dont jump into the future, said Maisano. You have one foot in the present and one foot in the future and youve got to bring people along at their comfort level.

Learn more about theAMAs commitment to helping physicians harness health care AIin ways that safely and effectively improve patient care.

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For smart use of health care AI, start with the right questions - American Medical Association

Whatever kind of music you listen to, the art of remixing is an integral part of popular music today. From its earliest roots in musique concrte and dancehall artists in 60s Jamaica to the latest Cardi B remix, repurposing and rearranging songs to create new material has long been a way for musicians to discover new and exciting sounds.

In the early days of electronic music production, music was remixed by means of physical tape manipulation, a process mastered by pioneering sound engineers like Delia Derbyshire, King Tubby and Lee Scratch Perry. And the process largely remained unchanged until the advent of digital music.

Nw, remixing is on the verge of another big transformation and AI company Audioshake is leading the charge. We spoke to Audioshake co-founder Jessica Powell about how the company is using a sophisticated algorithm to help music makers mine the songs of the past to create new material, and about potential future applications for the tech in soundtracking funny Tik-Tok videos, advertising, and making virtual live music concerts sound great.

Speaking to TechRadar in between appearances at a conference in Italy, Powell told us how Audioshakes technology works.

We use AI to break songs into their parts, which are known by producers as stems and stems are relevant because theres already lots you can do with them, like in movies and commercials, she explained.

Working with these stems allows producers to manipulate individual elements of a song or soundtrack for instance, lowering the volume of the vocal when a character on screen begins speaking. Stems are also used in everything from creating karaoke tracks, which cut out the lead vocal completely so that you can front your favorite band for three minutes, to the remixing of an Ed Sheeran song to a reggaeton beat.

And, as Powell explains, stems are being used in even more ways today. Spatial audio technologies like Dolby Atmos take individual parts of a track and place them in a 3D sphere and when youre listening with the right speakers or a great soundbar, it sounds like the music is coming from you at all angles.

So, if stems are used so widely in the music industry and beyond, why is Audioshake even needed? Well, record labels dont always have access to a tracks stems and before the 1960s, most popular music was made using monophonic and two-track recording techniques. And that means the individual parts of these songs the vocals, the guitars, the drums couldnt be separated.

Thats where Audioshake comes in. Take any song, upload it to the companys database, and its algorithm analyses the track, and splits it into any number of stems that you specify all you have to do is select the instruments it should be listening out for.

We tried it for ourselves with David Bowies Life on Mars. After selecting the approximate instruments we wanted the algorithm to listen out for (in this case, vocals, guitar, bass, and drums), it took all of 30 seconds for it to analyze the song and break it up into its constituent parts.

From there you can hear each instrument separately: the drums, the droning bass notes, the iconic whining guitar solo, Rick Wakemans flamboyant piano playing, or just Bowies vocal track. And the speed in which Audioshake is able to do this is breathtaking.

If youre a record label or music publisher, you can kind of create an instrumental on the fly, Powell explains. You dont have to go into a DAW (Digital Audio Workstation) like Ableton or Pro Tools to reassemble the song to create the instrumental its just right here on demand.

So, how does it work? Well, the algorithm has been trained to recognize and isolate the different parts of a song. Its surprisingly accurate, especially when you consider that the algorithm isnt technically aware of the difference between, say, a cello and a low-frequency synth. There are areas that do trip it up, though.

Heavy autotune Powell uses the example of artists like T-Pain will be identified as a sound effect as opposed to a vocal stem. The algorithm cant yet learn from user feedback, so this is something that needs to be addressed by developers, but the fact that these stems can be separated at all is seriously impressive.

Sadly, Audioshakes technology isnt currently available to the humble bedroom producer. Right now, the companys clients are mainly rights holders like record labels or publishers and while that might be disappointing to anyone whod love to break apart an Abba classic ahead of the groups upcoming virtual residency in London, the tech is being utilized in some really interesting ways.

One song management company, Hipgnosis, which sees songs as investment opportunities as much as works of art, owns the rights to an enormous back catalogue of iconic songs by artists ranging from Fleetwood Mac to Shakira.

Take Van Goghs Sunflowers. Were not just going to go and pop out a sunflower if you dont want us to.

Using Audioshake, Hipgnosis is creating stems for these old songs and then giving them to its stable of songwriters to try to reimagine those songs for the future, and introduce them to a new generation, as Powell puts it, adding You can imagine some of those beats in the hands of the right person that can do really cool things with them.

Owning the rights to these songs makes these things possible and opening up the technology to the public could be a legal quagmire, with people using and disseminating artistic creations that dont belong to them. Its not just a legal issue, though; for Audioshake its an ethical issue too, and Powell makes it clear that the technology should work for the artists, not against them.

She says the company really wanted to make sure that we respected the artists wishes. If they want to break open their songs and find these new ways to monetize them, we want to be there to help them do that. And if theyre not cool with that, were not going to be the ones helping someone to break open their work without permission.

Take Van Goghs Sunflowers, she adds. Were not just going to go and pop out a sunflower if you dont want us to.

Traditional pop remixes are just the start, though. There are lots of potential applications for Audioshake that could be opened up in the future and TikTok could be one of the more lucrative.

The possibilities created by giving TikTok creators the opportunity to work with stems to mash up tracks in entertaining ways could be an invaluable tool for a social media platform thats based on short snippets of audio and video.

Theres also the potential to improve the sound quality of livestreamed music. When an artist livestreams one of their concerts on a platform like Instagram, unless theyre able to use a direct feed from the sound desk, the listener is going to hear a whole load of crowd noise and distortion.

Watch something on Instagram Live and you dont even stick around youd almost prefer to watch the music video because its bad audio, says Powell. Using Audioshake (and with a small delay) you could feasibly turn down the crowd noise, bring the bass down, and bring the vocals up for a clearer audio experience.

Looking even further into the future, theres the potential to use the technology to produce adaptive music that is, music that changes depending on your activities.

This is more futuristic, but imagine youre walking down the street listening to Drake, says Powell. And then you start running and that song transforms its still the Drake song, but its now almost like a different genre, and that comes from working with the parts of the song, like increasing the intensity of the drumbeat as you exercise.

It sounds like adaptive music is a little way off, but we know that audio can already be manipulated based on your environment. Just look at adaptive noise-cancelling headphones like the Sony WH-1000XM4, which can turn the level of noise cancellation up as you enter noisy environments and other headphones models have similar features that automatically adjust the volume of your music based on your surroundings. The XM4s Speak-to-Chat feature is another example, with the headphones listening out for the sound of your voice.

The applications for running headphones could go even further than this. With the Apple AirPods 3 rumored to have biometric sensors that will measure everything from your breathing rate to how accurately you can recreate a yoga pose, adaptive music could even be used to bolster your workouts when your headphones detect a drop-off in effort and stem-mining technologies like Audioshake could make it easier for artists to monetize their music in this way.

While adaptive music is unlikely to reach our ears for a few years yet, the idea of breaking open songs in order to make them more interactive and to personalize them is just as exciting as the next generation of musicians mining the songs of the past to create new sounds. Heres hoping that one day, humble bedroom musicians will be able to mine these songs too, like plucking flowers from a Van Gogh vase.

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Meet the AI that busts open classic songs to make new tunes - TechRadar

Published on Tuesday, September 7, 2021

The AI system improves the prognosis of surviving patients with Hypoxic Ischemic Brain Injury (HIBI) after cardiac arrest by allowing and facilitating earlier treatment. (Image: iStock/monsitj)

University of Chicago researchers have developed a patent-pending technique using deep learning, a form of artificial intelligence (AI), to better assess hypoxic-ischemic brain injury in survivors of cardiac arrest.

Over the past three decades, Maryellen Giger, A.N. Pritzker Distinguished Service Professor of Radiology, has been conducting research on computer-aided diagnosis, including computer vision, machine learning, and deep learning, in the areas of breast cancer, lung cancer, prostate cancer, lupus, and bone diseases.

She also is a cofounder of Quantitative Insights, which started through the 2010 New Venture Challenge at the Polsky Center. The company produced QuantX, which in 2017 became the first FDA-cleared machine-learning-driven system to aid in cancer diagnosis (CADx). In 2019, it was named one of TIME magazines inventions of the year and was bought by Qlarity Imaging.

Backed by this wealth of knowledge, she is today applying her research to neuro-imaging in collaboration with Fernando Goldenberg, a professor of neurology and neurosurgery, as well as the co-director of the comprehensive stroke center and director of neuroscience critical care at UChicago Medicine. The research team is enhanced with collaborators Jordan Fuhrman, a PhD student in Gigers lab in the Committee on Medical Physics and the Department of Radiology, and Ali Mansour, an assistant professor of neurology and neurosurgery with expertise in advanced clinical neuroimaging and machine learning.

The goal of this multi-department research was to see if machine-learning could help clinicians at the hospital better assess hypoxic-ischemic brain injury (HIBI), which can occur when the brain does not receive enough oxygen during cardiac arrest. The extent of this damage depends on several variables, including the baseline characteristics of the brain and its vascular supply, duration of oxygen deprivation, and cessation of blood flow.

While the neurological injury that follows cardiac arrest is largely a function of HIBI, the process of determining a patients projected long-term neurological function is a multifaceted endeavor that involves multiple clinical and diagnostic tools. In addition to bedside clinical exam, head CT (HCT) is often the earliest and most readily available imaging tool, explained Goldenberg.

In their work, the researchers hypothesized that the progression of HIBI could be identified in scans completed on average within the first three hours after the heart resumes normal activity.

To test this, the team used machine learning, specifically, a deep transfer learning approach (which Fuhrman had been using to assess COVID-19 in thoracic CTs) to predict from the first normal-appearing HCT scan whether or not HIBI would progress. The deep learning technique, for which there is a patent-pending, automatically assessed the first HCT scan to identify the progression of HIBI.

This is important as currently there is no imaging-based method/analyses to identify early on whether or not a patient will exhibit HIBI, and while more data is needed to further confirm the efficacy of the AI-based method, the results to date are very promising, said Fuhrman.

The findings in patients first HCT may be too subtle to be picked up by the human eye, said Giger. However, a computer looking at the complete image may be able to determine between those patients who will progress and eventually show evidence of HIBI and those who will not.

According to the researchers, the AI system can help in the process of prognostication in survivors of cardiac arrest by identifying patients who may differentially benefit from early interventions a step along precision medicine in this patient population. If prospectively validated, it could also allow for the neuroprognostic process to start sooner than the current standard timeline, said Mansour. Additionally, the AI algorithm is expected to be easily integrated into various commercially available image analysis software packages that are already deployed in clinical settings.

//Polsky Patentedis a column highlighting research and inventions from University of Chicago faculty. For more information about available technologies,click here.

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AI Helps to Earlier Detect Brain Injury in Survivors of Cardiac Arrest - Polsky Center for Entrepreneurship and Innovation - Polsky Center for...

You will work with the AI. The use of artificial intelligence is becoming increasingly commonplace in the marketing field. There are also a lot of buzzwords flying around in regards to AI: machine learning, Google AI, Skynet, etc. None of them explains the most critical aspects of AI in marketing and why you should get used to working with your cyber colleagues.

Looking at AI in the present, the picture is overall positive:

The global market value of AI $93.53 billion and is expected to grow at CAGR 40% until 2028;

Marketing agencies & in-housing companies are raising their investments into AI;

The outlook on AI is overall a positive one, expecting to create more jobs.

Talking AI is complicated. There is no one singular way to define an AI that we are using today. Rather, its a list of types of artificial intelligence that are each good at some tasks. And not all of them are relevant to marketers.

Currently, we have these MOST COMMONLY used AIs some of them you already heard about:

Machine learning (ML) the most popular, widely used type of AI.

Natural language processing (NLP) a type of AI that processes and interprets human language.

Expert systems (ES) an AI that is trained to store data in a single field and extract information based on inference rules.

A BIG disclaimer incoming: there are more types of AI in varying stages of development. These are only examples and the 3 most popular and relevant to marketing. If you want to go down the rabbit hole and learn more about AI, heres a good place to start.

Machine learning in marketing. Thats what really interests us, and brings highest benefits. We have covered what its like now in the present. ML needs a lot of data to test its algorithms against. And while we are already seeing a lot of positive impact on data analytics, digital advertising planning and other instances there are future hurdles ML AI has to overcome.

AI use in creative marketing fields.

Creativity takes a human. Even though this article is quite technical and uses a lot of sources, it still took creativity to construct. Your social media post took inspiration from a picture you took, a video you filmed or a text you read. It still takes a human that instinctively understands context in order to produce creative work.

That doesnt mean that AI cant participate. In the world of journalism, news giants like BBC, Forbes, The Washington Post, MSN and others are using some form of AI to help them get first drafts of stories a human later fixes. An AI here saves time by providing grounds instead of a blank page.

Still, its a long way to go until true creativity. While ML can extrapolate from existing information, it would take many more algorithmic connections to truly produce creative work. Were not there yet. The question also remains: do we need to go that far?

Increased AI use in data processing and how 3rd-party cookie removal will affect that.

Right now, the most popular uses of AI in marketing are all technical and largely numerical. Based on studies, there are 5 categories where the use of ML and NLP AIs is prevalent (and somewhat successful). Some examples Im sure youre familiar with are:

Notice that 4 out of 5 categories rely heavily on user data. Thats why we all have cookies helping algorithms watch our behavior online: so ML can learn about us and help adjust business strategies accordingly.

What happens when cookies will no longer apply? It wont be quite the apocalypse that we tend to imagine, but it will get harder to track users. GDPR already curbed individual user tracking significantly and this trend is unlikely to stop. Googles set to introduce cohort tracking as opposed to personal, but that story will unravel in 2022 at the earliest.

All we know now is that AI needs data to learn. It will still be learning for sure, but it might do so in different ways.

Move towards hyperautomation.

Hyperautomation is a term that describes constructive and planned automation of as many business processes as possible. The use of AI is only one of many possible tools to attain the status of hyperautomated company.

Why is this a hurdle? The biggest question is not the fear of employees losing their job. Earlier we saw that AI is in fact a job-creating force. The issue is training and readiness for working hand-in-hand with AIs.

70% of participants in the study done by Drift and Marketing Artificial Intelligence Institute say they dont have the necessary knowledge or training to adopt AI. Not to mention fully automate their business processes.

Marketing employers will have to contend with this fact, train their employees AND themselves if they want to keep up with the competition.

AIs are here to stay. Our cyber partners are helping us achieve productivity and to keep up with the pace of modern digital marketing. Our greatest challenge right now is to prepare for them adequately. Keep up with the creativity, but do it in a smart, organized and productive fashion with AI helping you.

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Use of AI in marketing: present and future - The Drum