Category Archives: Ai

Theres nothing more personal than ones health and wellbeing. While receiving care, patients often face extreme challenges. They rightfully expect personalization, but despite the countless amounts of data, research, and scientific discoveries that have dramatically improved the quality of care, patients often leave healthcare facilities feeling like a number.

According to McKinsey, patients expect the same digital experiences they have grown accustomed to in retail, insurance, and banking. Those sectors long ago adopted innovations and processes that allow them to use technology to enhance the experience and put the customer first. Patients who have grown used to such benefits now demand a similar engagement from their healthcare providers and payers.

Patients expect to be at the center of their care. They want their providers and payers to use omnichannel methods to reach them. They are demanding transparency in decision-making and the ability to manage administrative actions and finances independently. They expect interactions to be frictionless across their preferred methods of communication, regardless of the industry. Patients demand quick and efficient service without repeating or resharing information.

Labor shortages and Covid-19 surges continue to pressure providers and payers. They are having trouble meeting these demands with available staff and tools. But voice AI technology has the potential to help. It can drive the next wave of healthcare innovation by making each patient advocate or service representative smarter and nimbler to meet the healthcare consumers growing expectations, with the added benefit that AI technology is always on and unbiased.

Shifting customer expectations

When the first wave of Covid-19 taxed care facilities, patients became more tolerant. According to the latest Pega Healthcare Engagement Survey, which surveyed more than 2,000 U.S. consumers and 200 healthcare industry leaders, 63% of patients said they would switch doctors due to poor communication and engagement (down 23% from the previous year). This year, however, personalized healthcare has become a higher priority.

Health experts know that better patient engagement can lead to better health outcomes. With attention, patients become active members of their care team. They regularly communicate throughout the process as they manage their health. They ultimately become better informed and more involved in their treatment options and care decisions.

Because each patient has varying levels of both health literacy and willingness to participate in the care process, engagement can be a challenge. However, AI-driven service and care solutions can remove barriers and bias, personalize interactions, improve health access and equity, and automate access to knowledge and insight. Benefits such as these can free service agents to serve and focus on the patient experience instead of processes.

Conversational AI solutions operate as copilots for the agent. The technology takes in keywords and phrases, interprets them, and then recommends steps to resolution and additionally eliminates tedious manual processes, such as error-prone data entry or data searches. They also analyze intent to guide caregivers (and agents) in the most efficient and empathetic service possible.

With voice AI capturing data and guiding the conversation, agents can mitigate distracting back-end tasks and better focus on listening and delivering quality services. In addition, care providers are armed with the tools and resources to better answer questions rapidly and accurately in real-time, from human to human.

Putting the care back into healthcare

In the past, AI has often been thought of as cold and impersonal, but over time, AI capabilities have become usable in a more intuitive and empathetic way. With continuous advancements and powerful algorithms, AI can interpret the unique circumstances and patient preferences that play a critical role in care delivery and service.

AI has the power to be used to minimize care gaps, improve early prevention, and give patients the best chance of getting the right treatments quickly while avoiding complications. The technology can predict and coordinate with patients to proactively set up appointments on a regular cadence through their preferred communication channels. It can also adapt over time. For example, if a patient doesnt reply to an email but answers text messages, AI will learn these preferences and automatically update them. It can also continually configure messaging, incorporating empathetic language for the appropriate situation. Imagine a patient is in an area with forest fires; the agent can be prompted to ask how the person is doing and if they are safe before continuing the call, thereby personalizing the customers experience.

The intersection of human connection and technology through voice AI holds the power to revolutionize how patients interact with their healthcare providers and payers. As a result, healthcare organizations across the value chain that prioritize high-quality personalized service and care will improve both business and health outcomes and will be better positioned to retain and increase their patient populations, even as the pandemic persists and beyond. We need personalization, and a fundamental way forward is with voice AI technology.

Photo: berya113, Getty Images

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The healthcare industry has a problem with personalization: How voice AI can help - MedCity News

TOKYO--(BUSINESS WIRE)--Mitsubishi Electric Corporation (TOKYO: 6503) announced today that it has developed a technology based on its Maisart1 AI technology that automatically constructs knowledge graphs by acquiring key phrases, authors, citation relationships and whole-part relationships of elements in various materials, including figures and tables, and then visualizes the relevance of the information so that users can identify and understand the most necessary information quickly and intuitively. The new technology is expected to greatly reduce the amount of time users spend gathering information.

Conventionally, it can take much time to collect necessary information from the overload of information people are exposed to these days. Moreover, to quickly find necessary or interesting information, the information must not only be digitalized but also managed based on information relationships within or among the materials. Mitsubishi Electrics new AI technology structurally digitalizes materials and data by extracting important information and estimating the interrelationships in advance. Thereafter, when users explore the information, they can grasp the strengths of relationships based on the varying widths of bands in diagrams (see figure above) to quickly and intuitively find necessary, interesting and even previously unnoticed information.

For the full text, please visit: http://www.MitsubishiElectric.com/news/

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Mitsubishi Electric's AI Creates Knowledge Graphs from Text and Graphics to Visualize Information Relevance - Business Wire

By Joseph Wang, Junior Data Scientist at VRGL

The wealth management business has traditionally been late to adopt new technology, primarily because of caution about security. It took nearly a decade for firms to trust putting data in the cloud. Today, many other industries are leveraging artificial intelligence (AI) to improve processes. The wealth management industry is still using processes that are tedious and manual. But that is about to change.

AI will not only accelerate but automate both client acquisition and service in the wealth management space. Acquiring clients is very relationship-driven, often consisting of numerous dinners and rounds of golf. Today, when a relationship between the prospective client and wealth manager is established, the wealth manager will begin the manual process of entering the clients statement data into analytics software to become familiar with the portfolio. After that, a proposal is generated to bring forth ways to improve the current portfolio.

Unlock hidden potential

Once a decision is made to hire the wealth manager, the process begins of maintaining the ongoing relationship, managing the portfolio, trading, compliance, among other responsibilities. Described above is an example of the traditional prospecting journey for wealth managers. It can take several months to even years as it is challenging to build trust and time intensive to extract, process, and analyze statement data manually. How can AI accelerate this process and simplify decision-making for wealth managers?

In addition to all the process improvements AI can bring to wealth managers, it can also simplify decisions in both client acquisition and investment management. As mentioned above, the client acquisition process for a wealth manager can be long and arduous, but AI can provide solutions as to whether a prospect should be pursued at all. Using AI, a recommender system can be built to suggest wealth managers to clients based on the clients risk tolerance, ESG beliefs, and other characteristics.

Recommender systems have been in use for decades and are how Google suggests ads to you based on your search history or how Netflix highlights movies you may want to watch based on your history. Based on a clients past investments and current characteristics, a recommender system could provide a list of recommended wealth managers who are suitable. Not only does this save the prospect time in finding an appropriate wealth manager, but also allows the wealth manager to save time not pursuing incompatible prospects.

Once hired by the client, the wealth manager often selects an investment strategy for the client in the form of a model portfolio based on the clients characteristics. Using AI for this selection allows the wealth manager to be more confident that the decision is backed by quantitative reasoning. Many unsupervised learning methods, such as clustering or expectation-maximizing (EM) algorithms, can find patterns in data and group similar data points. By applying such methods to a group of clients, wealth managers would not only be able to better understand similar clients, but also quickly and confidently categorize a new client. By selecting model portfolios using unsupervised learning, wealth managers would reduce the time analyzing client portfolio, as well as be confident in the quantitative validity of the result.

Combatting challenges

AI implementation in the wealth management business is not without challenges. The requirement for data accuracy is a continued pain point in statement parsing. Tuning the confidence level required for OOD data (Out-of-Distribution; that is, data or formats never seen before) would be challenging and may pose the risk of leaking confidential client information.

Privacy and security become a top priority when utilizing AI, and wealth management firms should develop their internal framework and preemptively address client concerns about the use of sensitive information. Additionally, even with significant inroad, decisions made by AI are still relatively poorly explainable, sometimes even unreliable when facing new data, and the regulatory landscape is constantly evolving. Practitioners are best served using a mosaic approach, retain a healthy suspicion for AI-provided recommendations as to any other source of information, and be ready to intervene.

Heightened efficiency with AI

AI has already positively affected many industries from automating tedious and manual tasks to bringing to light new information in formerly collected data. As AI solutions continue to be adopted by wealth managers, it will only become clearer just how much more efficient the process can be.

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Wealth Management Is a Tech Late Bloomer, But AI Will Change That - Global Banking And Finance Review

Organised crime that involves indentured labour remains big business, amassing an estimated $50-150 billion annually. And the problem is far closer to home than many realise.

Modern AI approaches, NLP and data fusion have a role to play in tackling criminal activity

Despite Britain having banned slavery outright in 1833, as recently as 2020, the Centre for Social Justice estimated that there were 100,000 or more modern slaves in the UK alone.

To profit from this exploitation of human rights, criminal gangs use various sophisticated psychological, financial and physical techniques to maintain control over their workforce. Often targeting vulnerable people who have nowhere to turn such as homeless or substance-dependent individuals and those who may not even be aware they are being exploited. An example is debt bondage where people end up working for little or no wages in order to repay debt. This is one of the most common forms of modern slavery, according to the UN.

This all makes detecting and disrupting modern slavery extremely problematic. Cases reported to the police arerising, but perhaps more than 90% of modern slavery still goes undetected. This makes the crime as hidden as it is pervasive, deepening consequences for business, government and society which must all work together to spot the red flags.

Laws such as The Modern Slavery Act (2015) and the EUs SixthAnti-Money Laundering Directive are raising the topic of modern slavery to board-level, but questions remain surrounding implementation and accountability.

It is not easy, but as a global community, we have the capacity to end slavery. Bold management, social policies and control frameworks will all be essential. Unfortunately, legacy technology and institutional structures both complicate the path ahead.

Organisations have a regulatory duty to identify anti-money laundering (AML) and enforce sanctions through strong customer due diligence. While successful detection of money laundering will ultimately identify many involved in human trafficking and modern slavery, we know that detection is limited. The UN estimates that $1.6 trillion is laundered through the global financial system annually and only 1% of illicit financial flows are intercepted globally.

The types of payments used to commit human trafficking can be particularly hard to detect. Low-value payments made to a few dozen migrant workers all sharing a few addresses is unlikely to trigger suspicion.

Fortunately, there is significantly enhanced awareness of the issues, and technology can now provide some vital support to companies, banks and governments.

Modern artificial intelligence (AI) approaches, such as utilising machine learning to grapple with huge quantities of data to identify patterns, provide a new opportunity to tackle slavery and trafficking which was simply not practical manually. Even with an army of compliance staff.

Advanced data analytics help companies automatically detect risk in their supply chain and among their customers. It can scale their understanding of available data, joining the dots automatically to detect and thwart human trafficking and modern slavery.

We are now seeing a step change in how entity resolution and natural language processing (NLP) are helping suppliers and partners across the entire supply chain network make substantial leaps forward in tackling this crime. Using AI to sift through vast amounts of data, technology is now being deployed on the front lines of the fight against modern slavery in the following ways:

Enhancing due diligence Traffickers and criminal gangs have adopted stratagems to avoid detection such as acquiring legitimate enabling assets such as bank accounts, national insurance numbers and tax details. These allow funds to be deposited, withdrawn and laundered, and all the while avoiding scrutiny by the police and employers.

Critically, it allows criminals to put victims in well-paying jobs in the supply chain. Criminal gangs also maintain control of victims earnings by opening bank accounts in their names but not in their control, providing the bank with the required onboarding documents, such as proof of address and utility statements.

Analytics can spot hidden connections between otherwise seemingly disconnected individuals, meaning that next generation KYC checks can more reliably monitor for deception and violations and involve the law, if necessary.

Screening can also identify those perpetrating slavery and trafficking. Banks and other organisations can get early indication of clients who have been associated with illicit activity before either through exposure in the news media or through specialist watchlists.

At the root of all criminal activity is profit. Making these crimes difficult or impossible to commit profitably will ultimately benefit the potential victims.

Employment vetting It is a widespread misconception to think that victims are mostly paid cash or off-the-books. With legitimate assets and tax codes, victims can unwittingly earn tens of thousands of pounds a year while only receiving a small stipend on top of their food and accommodation.

But times are a changing. Prompted by the Modern Slavery Act and Anti-Money Laundering Directives, employment agencies and supply chain partners such as factories and warehouses are increasingly looking at the details provided by workers, conducting their own due diligence.

Sophisticated entity resolution that can uniquely identify individuals from ambiguous and sparse datasets can detect the tell-tale red flags of exploitation such as unusual numbers of employees sharing the same address or bank details.

Complex investigations Data fusion technologies are now helping resource-constrained teams of intelligence analysts to more easily extract data from any source whether structured or unstructured. Natural language processing (NLP) and entity resolution combined with flexible link-analysis help investigators build up a single, centralised knowledge graph for a case or network of criminal gangs. The graph helps connect the dots automatically between victims, suspects phone numbers, bank accounts, transactions, flight records or any other evidence collected during an investigation.

The application of AI, data fusion and other techniques is a key development in the fight against modern slavery and human trafficking. It can automatically isolate risks at any point in the client or employee lifecycle and help the entire ecosystem of businesses, agencies, financial institutions and authorities understand the tell-tale signs of human trafficking and exploitation.

Entity resolution, automatic prioritisation, NLP and data fusion all play a role in ensuring that pertinent data is not missed. Enforcement analysts can then more easily uncover these hidden crimes bringing about a step-change in the way that modern slavery is detected and prevented.

Originally posted here:

How AI and data fusion are aiding the battle against modern slavery - FinTech Futures

I can still remember the first time I used Alexa.

Its crystal clear in my head: I said, Alexa, playTake On Meand a few seconds later A-has synthy drums kicked in. It was one of the few times in my life that artificial intelligence has left me speechless.

It was a real-world version of the technology I grew up seeing in countless science fiction movies and TV shows. Giving that command, I felt like Captain Kirk talking to the computers on the USS Enterprise.

This was in 2013. I recall wondering where the technology would be in another five or 10 years -- which is roughly where we are right now. I imagined myself having full-blown conversations with personal AI assistants and giving complex voice instructions to my computer. That all seemed achievable, even probable. After all, technology advances exponentially.

With the benefit of hindsight, I can see that I was too optimistic. Were still a long way from bona fide human-to-AI conversation.

Human imaginations always outpace technology. What I can imagine in a minute takes a decade to become something tangible. Left unchecked, our perceptions race away from facts. Every so often, we have to recalibrate our expectations.

We need to swap out our science fiction dreams for technological fact.

How Advanced is AI -- Really?

For as long as I can remember, people have claimed that fully self-driving cars are just over the horizon. Tesla, Toyota, General Motors, and Google all promised us self-driving cars by the end of 2020, but were still waiting. The technology seems to always be just out of reach.

Its the same in most other industries.

Take cloud communication. People have long dreamed of autonomous AI agents that handle the bulk of contact center communication. Some have even promised theyre on the way. But like building an autonomous car, crafting an artificial agent is a big challenge. I have no doubt that we can get there, just that it will take more time than expected.

Think about two small parts: speech recognition (transcribing speech into text) and natural language processing (understanding text and spoken word).

Today, technology transcribes calls instantaneously, with far better accuracy than I could manage if I had to become a stenographer for a day. And, natural language processing technology for enterprises is good, too. It can analyze transcripts and provide some basic understanding of topics, questions, sentiment, action items, and so on.

But what AI cant do just yet is understand what a conversation is actually about. Systems can transcribe a conversation about puppies. It can pull out questions about breeds and highlight an unanswered question about Labrador veterinary care. But it doesnt know what a Labrador is or what a flea treatment entails. It doesnt even know what a dog is. Is that kind of tragic, and a little creepy? Sure, but its also true.

Todays AI systems are great for simple, repeatable functions. Because they perform those functions so well, they can give a false impression of its potential. The leap from simple function to fully autonomous agent or self-driving car is a chasm. I feel confident saying that we wont see a fully autonomous smart agent replacing a human agent in the next five to ten years.

Theres a gap between what we believe AI can do and what its capable of in the real world. Its up to companies to fix the discrepancy. Because if we let rumor run wild, itll undermine all the breakthroughs we have made.

Its tempting to tweak the truth and embellish functionality, especially when it comes to something as opaque as AI. But a lot of companies do just that. According to venture firm MMC, four in 10 European startups classified as AI companies dont use AI technology in a way thats material to their business. In a lot of cases, their AI powers things like chatbots or fraud prevention. Both are useful applications, but theyre more of an auxiliary service than a central selling point.

Small embellishments or overpromises probably help in the short term. A company can generate media buzz, win over some customers, and pad its bottom line. But after people start using their product, those small wins turn into big losses.

When you overpromise and underdeliver, people get frustrated. They complain. They cancel. They bad-mouth your company to their network. I know thats true because Ive been that consumer.

In the mid-1990s, I was captivated by an ad for a speech-to-text program. They promised the whole science fiction experience: speaking out loud, giving voice commands, and perfect transcription. It sounded amazing, so I downloaded the program and spent 60 hours training it on my voice. Prep work done, I sat down to narrate a college essay.

Lets just say it failed to live up to any semblance of expectations.

It missed commands, transcribed poorly, and was far more frustrating than just writing my college papers with a pen, paper, and Bic Wite-Out. It was all hype and no substance. I ditched the tool and never came back. Its only now, decades later -- and with the development of personal assistants -- that Im finally coming back to voice commands.

Heres the wild part: Theres no regulation around this whatsoever. Companies have to read cautionary tales like this and decide to regulate themselves. For those leaders and organizations willing to hold themselves accountable, there are some basic rules.

First, businesses should be upfront about how they source their training data. Companies like Google and Facebook have rightly caught flack for being cagey around their data gathering methods. Where does it come from? Is it representative? How do you manipulate it after collection?

If youre an AI practitioner or youre part of the go-to-market team for an AI product, you need to be open. Theres nothing sensitive you can share. What happens when you tell your competitors how you find your data? Nothing. Owning the data is the important bit, not your data gathering process.

Second, be clear about how youre using that data. Data is the lifeblood of AI systems. Its what makes them work, so theres no sidestepping the question. When youre upfront, people are usually happy to opt into sharing their anonymized data to a collective pool, especially when you tell them its to help improve the product.

Last, describe your AI products accurately and honestly. Be upfront about what you can do and, when its appropriate, what you cant. You might lose an inch to your competitors in the short term, but ethical companies stand to win out in the long term. Theyll retain happy customers, enjoy sustainable growth, and blow past organizations playing fast and loose with the truth.

The human imagination is a brilliant thing. But we cant let it rewrite our technological reality. By all means, imagine, daydream, and ponder. Think up dozens of new AI applications and products. Use those ideas to fuel your work.

But dont let your ideas write checks your technology cant cash.

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It's Time to Recalibrate Our AI Expectations - InformationWeek

3D analysis, right, of a 2D video image, left, in the Sportsbox AI app. (Sportsbox AI Image)

Before you take yet another mulligan, consider that the tool to improving your golf game might not be found in your bag of clubs.

The makers of an app that employs artificial intelligence and 3D motion analysis say theyve created a revolutionary way to improve your golf swing through a video shot on your phone.

Sportsbox AI is a startup that launched in 2021 out of AI Thinktank, a Bellevue, Wash.-based incubator for AI ideas founded by tech veteransMike KennewickandRich Kennewick. Theyre the brothers who founded Voicebox Technologies, an early leader in speech recognition and natural language tech that sold in 2018.

Along with Samuel Menaker, former VP of engineering at Voicebox, AI Thinktanks brain trust has been applying its experience and knowledge in voice AI and machine learning to visual understanding, and specifically how to translate 2D video into 3D information.

The golf expertise in the equation is provided by Sportsbox co-founder and CEO Jeehae Lee, who played more than five years on the LPGA tour before transitioning to a career in sports media entertainment. Lee led strategy and new business development at Topgolf, the high-tech golf and entertainment company.

Meeting Menaker and seeing what AI Thinktank was already working on changed her view of what was possible when it comes to teaching the game.

Im a student of the game first and foremost, said Lee, who now coaches friends who are getting into golf. I felt like a lot of what I know about learning the game and refining the game could translate to helping to build a better product.

Key to that product is the data it collects, which is important for tracking progress and assessment.

The main problem we want to solve is: how a single cell phone camera can let people accurately measure any activity in 3D from any reasonable angle, height and distance, Menaker said. We can calculate hundreds of different types of body movements (joints, limbs) in degrees, inches, velocities and accelerations. This can be applied to any activity.

Sportsbox still relies on experienced coaches to translate whats being captured and help students work through whats being suggested as a fix. The company offers tiered subscription levels for those coaches.

The core of what we want to stand for is 3D everywhere, Lee said. Its accessible and not some motion-capture studio that requires eight cameras and $100,000 to set up, but its available on your phone.

The company counts several leading instructors among its advisors including David Leadbetter, an investor who, according to Lee, said the apps tech compared to current video practices is like comparing an MRI to barely an X-ray.

At Topgolf, Lee was involved with Toptracer, technology that tracks ball trajectory, speed, height and so on. At Sportsbox, the tech brings it all back to the body mechanics that made the ball do its thing.

Its almost like were completing the picture of the why, Lee said. We know what happened, but why?

The company is working on partnerships with broadcasters and golf centers. And, after golf, the company envisions answering that question of why for a variety of sports such as tennis or baseball, as well as exercise such as yoga or for movements involved in physical therapy.

Sportsbox now employs about 15 people full time. Lee, who is based in San Francisco, joined Sportsbox at the urging of co-founder Stephanie Wei, the former Wei Under Par blogger who played golf with Lee at Yale University and serves as head of marketing. Dr. Phil Cheetham the so-called 3D Guy was previously the director of Sports Technology and Innovation at the United States Olympic and Paralympic Committee and is chief science officer at Sportsbox.

The startup is completing a seed funding round and investors include Elysian Park Ventures, PGA of America, Leadbetter, instructor Sean Foley and pro golfer Michelle Wie.

The team in Bellevue is working on ML models, AI and computer vision problems, application (cloud and mobile) and UI development, integration of all components, backend development and infrastructure, according to Menaker.

We also built our own tools and infrastructure to generate and manage data to train our own ML models, he said.

Asked whether his own golf game is improving thanks to Sportsbox, Menaker said absolutely.

When we started I had no clue how to hit a golf ball, how to move my hands, shoulders. Now I know how to do it, he said. It does not mean I do it correctly all the time, but I am making good progress.

Link:

Ex-golf pro links with Seattle-area AI experts on app that uses 3D motion analysis to improve game - GeekWire

In-brief Governments around the world should pass intellectual property laws that grant rights to AI systems, two academics at the University of New South Wales in Australia argued.

Alexandra George, and Toby Walsh, professors of law and AI, respectively, believe failing to recognize machines as inventors could have long-lasting impacts on economies and societies.

"If courts and governments decide that AI-made inventions cannot be patented, the implications could be huge," they wrote in a comment article published in Nature. "Funders and businesses would be less incentivized to pursue useful research using AI inventors when a return on their investment could be limited. Society could miss out on the development of worthwhile and life-saving inventions."

Today's laws pretty much only recognize humans as inventors with IP rights protecting them from patent infringement. Attempts to overturn the human-centric laws have failed. Stephen Thaler, a developer who insists AI invented his company's products, has sued trademark offices in multiple countries, including the US and UK to no avail.

George and Walsh are siding with Thaler's position. "Creating bespoke law and an international treaty will not be easy, but not creating them will be worse. AI is changing the way that science is done and inventions are made. We need fit-for-purpose IP law to ensure it serves the public good," they wrote.

A video clip with the face of a 13-year-old boy, who was shot dead outside a metro station in the Netherlands, swapped onto a body using AI technology was released by police.

Sedar Soares died in 2003. Officers have not managed to solve the case, and with Soares' family's permission, they have generated a deepfake of his image on a kid playing football in a field presumably to help jog anyone's memory. The cops have reportedly received dozens of potential leads since, according to The Guardian.

It's the first time AI-generated images have been used to try and solve a criminal case, it seems. "We haven't yet checked if these leads are usable," said Lillian van Duijvenbode, a Rotterdam police spokesperson.

You can watch the video here.

America's National Artificial Intelligence Research Resource (NAIRR) urged Congress to launch a "shared research cyberinfrastructure" to better provide academics with hardware and data resources for developing machine-learning tech.

The playing field of AI research is unequal. State-of-the-art models are often packed with billions of parameters; developers need access to lots of computer chips to train them. It's why research at private companies seems to dominate, while academics at universities lag behind.

"We must ensure that everyone throughout the Nation has the ability to pursue cutting-edge AI research," the NAIRR wrote in a report. "This growing resource divide has the potential to adversely skew our AI research ecosystem, and, in the process, threaten our nation's ability to cultivate an AI research community and workforce that reflect America's rich diversity and harness AI in a manner that serves all Americans."

If AI progress is driven by private companies, it could mean other types of research areas are left out and underdeveloped. "Growing and diversifying approaches to and applications of AI and opening up opportunities for progress across all scientific fields and disciplines, including in critical areas such as AI auditing, testing and evaluation, trustworthy AI, bias mitigation, and AI safety," the task force argued.

You can read the full report here [PDF].

Researchers at Meta AI released Myosuite, a set of musculoskeletal models and tasks to simulate biomechanical movement of limbs for a whole range of applications.

"The more intelligent an organism is, the more complex the motor behavior it can exhibit," they said in a blog post."So an important question to consider, then, is what enables such complex decision-making and the motor control to execute those decisions? To explore this question, we've developed MyoSuite."

Myosuite was built in collaboration with researchers at the University of Twente in the Netherlands, and aims to arm developers studying prosthetics and could help rehabilitate patients. There's another potential useful application for Meta, however: building more realistic avatars that can move more naturally in the metaverse.

The models only simulate the movements of arms and hands so far. Tasks include using machine learning to simulate the manipulation of die or rotation of two balls. The application of Myosuite in Meta's metaverse is a little ironic given that there's no touching allowed there along with restrictions on hands to deter harassment.

Excerpt from:

Experts: AI should be recognized as inventors in patent law - The Register

The pace of digitalization in healthcare is accelerating. AI-based reading solutions that support radiologists in managing their growing workload and delivering confident diagnoses will become particularly more important over the next years.

Usage of AI is on the rise

In healthcare, the demand for diagnostic services is steadily growing while the number of available experts is decreasing. Additionally, diagnostics and treatment are becoming increasingly complex. As a result, software solutions using artificial intelligence (AI) are on the rise. In fact, AI in the healthcare market is projected to grow from USD 6.9 billion in 2021 to USD 67.4 billion by 2027 translating into a compound annual growth rate (CAGR) of 46.2% from 2021 to 2027.1 Especially in diagnostics, where radiologists must examine ever larger amounts of data, AI has demonstrated its value by supporting radiologists in image reading pushing healthcare more to the cloud and cloud computing.

Cloud computing becomes essential

The dramatic increase in AI software calls for the implementation of cloud computing. It is essential for tapping the full potential of AI algorithms and maintaining flexibility. Some countries or even individual clinical institutions, however, have strict regulations that prohibit sending data to the cloud because patient data are particularly sensitive. This remains the case even though cloud computing has proved to be safe. So, does this mean that some radiologists will be left out in the cold, unable to use AI-based reading software?

Making full use of AI algorithms with a hybrid solution an example

Offering automatic postprocessing of imaging data sets through AI-powered algorithms, AI-Rad Companion from Siemens Healthineers is an analytical tool that uses patient data to support radiologists in fast reading and confident diagnostic decision-making. To support customers in making full use of AI algorithms without having to go to the cloud, Siemens Healthineers offers the Edge functionality of its teamplay digital health platform. With this hybrid computing solution, patient data stays on the local server of the clinical institution, while only the AI algorithms of the reading software are managed from the cloud, so they can be reliably updated and maintained.

Deeper insights into how the Edge functionality works

This hybrid computing solution combines essential capabilities of the cloud with the need for local data storage. By activating the Edge functionality, a closed environment is downloaded that is controlled by Siemens Healthineers via the cloud. This allows Siemens Healthineers to fully manage its applications like AI-Rad Companion locally, update AI algorithms within the defined regulatory framework and share data that are relevant for updating the AI based on the preferences of the clinical institution.

The draw of the hybrid solution is that the Edge functionality is a one-way data street. It allows the cloud to send data into the closed environment only to interact with the AI algorithms and allows the cloud to retrieve only the data needed for servicing the AI, leaving the privacy settings up to the customer and keeping patient data on premises.

Taking a first step toward cloud computing in healthcare

Clinical institutions that are restricted in using cloud computing may have to look for other solutions that allow them to stay current with innovations. With new hybrid computing solutions like the Edge functionality, radiologists have the chance to fully benefit from AI-based reading solutions while complying with strict data protection regulations.

Reference

Visit our site teamplay digital health platform

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AI-based reading solutions: Pointing the way to the cloud - Healthcare IT News

In their discussion section, the authors comment that engagement with stakeholders remains important if AI tools are to be used to positive effect in pediatric healthcare.

Further, although AI-assisted care is viewed favorably by parents for their children in the acute care setting, the selection of stakeholder groups for its development and implementation requires a diverse representation, Ramgopal and co-authors write. This is particularly important with respect to age and race/ethnicity, the two demographics associated with discomfort in our multivariable model.

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Our findings offer promise in the use of AI in acute care settings and indicate how parent perspectives on AI use may differ in ways that can inform clinicians discussions of AI-based decision-making with parents.

In a news item published by the hospitals communications team, Ramgopal says AI will sooner or later become standard in routine pediatric practice.

In the ED, we already use computer-based decision supports systems, which are precursors to AI, he adds. [T]hese systems dont dictate a particular course of action, but rather inform a physicians approach to care in situations where a human might easily miss an important pattern in how illness presents itself.

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Some 78% of parents OK with AI reading their child's chest X-rays - Radiology Business

Liberty Mutual is one of the most experienced and advanced cloud adopters in the nation. And that is in no small part thanks to the vision of James McGlennon, who in his role as CIO of Liberty Mutual for past 17 years has led the charge to the cloud, analytics, and AI with a budget north of $2 billion.

Eight years ago, McGlennon hosted an off-site think tank with his staff and came up with a technology manifesto document that defined in those early days the importance of exploiting cloud-based services, becoming more agile, and instituting cultural changes to drive the companys digital transformation.

Today, Liberty Mutual, which has 45,000 employees across 29 countries, has a robust hybrid cloud infrastructure built primarily on Amazon Web Services but with specific uses of Microsoft Azure and, lesser so, Google Cloud Platform. Liberty Mutuals cloud infrastructure runs an array of business applications and analytics dashboards that yield real-time insights and predictions, as well as machine learning models that streamline claims processing.

As the Boston-based insurance companys journey to the cloud has unfolded, it has also maintained a select set of datacenters from which to run legacy applications more economically than they would on the cloud, as well as software from vendors that make licensing on the cloud less attractive.

And while McGlennon believes that will change over time, he is far more focused on technologies that will define the next generation of applications.

Were really trying to understand the metaverse and what it might mean for us, says McGlennon, whose mild Irish brogue bares his Galway, Ireland, upbringing. Were focused on augmented reality and virtual reality. Were doing a lot on AI and machine learning and robotics. Weve already built up blockchain and well continue with all those.

And that ability to push the envelope, especially around machine learning and AI, finds its foundation in Liberty Mutuals rich cloud capabilities.

Despite his laser focus on embracing emerging technologies, McGlennon remains highly enthusiastic about Liberty Mutuals use of and expertise in the cloud. Sixty percent of the insurers global workloads run in the cloud, delivering significant savings in hardware and software purchasing, but the big benefit comes in the form of business insights from analytics on the cloud that are immeasurable, he says.

The cloud has been a huge positive impact on us economically and surely you hear this story all the time, but it didnt necessarily start out that way, he says. It tended to be additive to our legacy platforms when we started building out our cloud initially, but more recently, weve become far more mature in our use of the cloud and in our ability to optimize it to make sure that every single cycle of a CPU that we use out in the cloud is adding value.

Here, McGlennon says governing controls, instrumentation, and observability metrics are key. The CIO would not specify how much the multinational company has saved by deploying workloads to the cloud but estimated it has saved about 5% over the past two and a half years. Its a big number, he says.

Implementing cloud-native architectures for autoscaling and instrumenting Liberty Mutuals applications to control how theyre performing have been crucial to realizing those savings, McGlennon says.

Like many other early cloud adopters, Liberty Mutual deploys off-the-shelf tools such as Apptio to monitor costs and automate scaling depending on workloads, he says.

Weve worked with our cloud partners to better instrument our applications and better understand how theyre performing, says McGlennon, who was a finalist for the MIT Sloan CIO Leadership Award for 2022. That gives us greater insight into where we are potentially wasting resources and where we can optimize such as moving workloads to smaller cloud platforms.

McGlennon is proud of his teams use of Apptio, for example, to best exploit its consumption-oriented model for not just its data on the cloud but for its internal services, software, and SaaS offerings, which, when linked to Liberty Mutuals business portfolio, essentially provides the insurers partners with a bill of materials for all of the resources used.

Over the past eight years, the Liberty Mutual IT team, which consists of 5,000 internal IT employees and about 5,000 outside contractors, has used a variety of development platforms and analytical tools as part of its cloud journey, spanning from IBM Rational and .NET in the early days to Java and tools such as New Relic, Datadog, and Splunk.

Liberty Mutuals data scientists employ Tableau and Python extensively to deploy models into production. To expedite this, the insurers technical team built an API pipeline, called Runway, that packages models and deploys them as Python, as opposed to requiring the companys data scientists to go back and rebuild them in Java or another language, McGlennon says.

Its really critical that we can deploy models quickly without having to rebuild them in another platform or language, he adds. And to be able to track the effectiveness of those machine learning models such that we can retrain them should the data sets change as they often do.

The insurer also uses Amazon Sage Maker to build machine learning models, but the core models are based on Python.

Liberty Mutuals IT team has also created a set of components called Cortex to enable its data scientists to instantiate the workstations they need to build a new model so the data scientist doesnt have to worry about how to build out the infrastructure to start the modeling process, McGlennon says.

With Cortex, Liberty Mutuals data scientists can simply set their technical and data-set requirements, and a modeling workstation will be created on AWS with the right data and tools in an appropriately sized GPU environment, McGlennon explains.

The insurer also deploys software bots in its claims model to enable customers to initiate a claim, e-mail a digitized photograph of their damaged vehicle, answer a few questions, and arrange a car rental quickly. On the back end, a machine learning model analyzes the photograph of the damaged vehicle to detect whether its airbag has been deployed, for instance, and to determine immediately whether a vehicle is totaled or the damage is limited to a fender bender.

The insurers computer vision models may also tap into IoT devices and sensors deployed outside to generate more data for the claim.

Liberty Mutual has come a long way from its technology manifesto to its advanced use of the cloud and AI, and embracing next-generation technologies such as augmented reality and blockchain will yield further advances, McGlennon notes.

But this CIO is happy enough with the cloud and AI platform of today.

Weve already seen significant economic payback from being able to use machine learning models to fine-tune quotes and pricing, in fraud detection, and our coding process to make it easier for customers to do business with us, McGlennon says, pointing to advanced cloud applications benefits in its core business of processing claims. We use it all over the place.

Although his is a property and casualty company, McGlennon believes CIOs must drive innovation and take risks to create a culture where people feel there is the latitude to try something.

Risk is our business, McGlennon said during a panel at the MIT Sloan CIO Symposium this week, adding that CIOs need to show that when things go wrong, and sometimes they will, no one is going to be made to feel that the risk wasnt worth it.

You have to incubate something, nurture it, give it support, he said.

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AI in the cloud pays dividends for Liberty Mutual - CIO