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Artificial Intelligence Feedback on Physician Notes Improves Patient Care – NYU Langone Health

Artificial intelligence (AI) feedback improved the quality of physician notes written during patient visits, with better documentation improving the ability of care teams to make diagnoses and plan for patients future needs, a new study finds.

Since 2021, NYU Langone Health has been using pattern-recognizing, machine-learning AI systems to grade the quality of doctors clinical notes. At the same time, NYU Langone created data informatics dashboards that monitor hundreds of measures of safety and the effectiveness of care. The informatics team over time trained the AI models to track in dashboards how well doctors notes achieved the 5 Cs: completeness, conciseness, contingency planning, correctness, and clinical assessment.

Now, a new case study, published online April 17 in NEJM Catalyst Innovations in Care Delivery, shows how notes improved by AI, in combination with dashboard innovations and other safety initiatives, resulted in an improvement in care quality across four major medical specialties: internal medicine, pediatrics, general surgery, and the intensive care unit.

This includes improvements across the specialties of up to 45 percent in note-based clinical assessments (that is, determining diagnoses) and reasoning (making predictions when diagnoses are unknown). In addition, contingency planning to address patients future needs saw improvements of up to 34 percent.

Last year, NYU Langone added to this long-standing effort a newer form of AI that develops likely options for the next word in any sentence based on how billions of people used language on the internet over time. A result of this next-word prediction is that generative AI chatbots like GPT-4 can read physician notes and make suggestions.In a pilot within the case study, the research team supercharged their machine-learning AI model, which can only give physicians a grade on their notes, by integrating a chatbot that added an accurate written narrative of issues with any note.

The NYU Langone case study also showed that GPT-4 or other large language models could provide a method for assessing the 5Cs across medical specialties without specialized training in each. Researchers say that the generalizability of GPT-4 for evaluating note quality supports its potential for application at many health systems.

Our study provides evidence that AI can improve the quality of medical notes, a critical part of caring for patients, said lead study author Jonah Feldman, MD, medical director of clinical transformation and informatics within NYU Langones Medical Center Information Technology (MCIT) Department of Health Informatics. This is the first large-scale study to show how a healthcare organization can use a combination of AI models to give note feedback that significantly improves care quality.

Poor note quality in healthcare has been a growing concern since the enactment of the Health Information Technology for Economic and Clinical Health (HITECH) Act in 2009. The act gave incentives to healthcare systems to switch from paper to electronic health records (EHR), enabling improved patient safety and coordination between healthcare providers.

A side effect of EHR adoption, however, has been that physician clinical notes are now four times longer on average in the United States than in other countries. Such note bloat has been shown to make it harder for collaborating clinicians to understand diagnoses described by their colleagues, say the study authors. Issues with note quality has been shown in the field to lead to missed diagnoses and delayed treatments, and there is no universally accepted methodology for measuring it. Further, evaluation of note quality by human peers is time-consuming and hard to scale up to the organizational level, the researchers say.

The effort captured in the new NYU Langone case study outlines a structured approach for organizational development of AI-based note quality measurement, a related system for process improvement, and a demonstration of AI-fostered clinician behavioral change in combination with other safety programs. The study also details how AI-generated note quality measurement helped to foster adoption of standard workflows, a significant driver for quality improvement.

Each of the four medical specialties that participated in the study achieved the institutional goal, which was that more than 75 percent of inpatient history and physical exams and consult notes, were being completed using standardized workflows that drove compliance with quality metrics. This represented an improvement from the previous share of less than 5 percent.

Our study represents the founding stage of what will undoubtedly be a national trend to leverage cutting-edge tools to ensure clinical documentation of the highest qualitymeasurably and reproducibly, said study author Paul A. Testa, MD, JD, MPH, chief medical information officer for NYU Langone. The clinical note can be a foundational toolif accurate, accessible, and effectiveto truly influence clinical outcomes by meaningfully engaging patients while ensuring documentation integrity.

Along with Dr. Feldman and Dr. Testa, the current studys authors from NYU Langone were Katherine Hochman, MD, MBA, Benedict Vincent Guzman, Adam J. Goodman, MD, and Joseph M. Weisstuch, MD.

Greg Williams Phone: 212-404-3500 Gregory.Williams@NYULangone.org

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Small Businesses Face Uphill Battle in AI Race, Says AI Index Head – PYMNTS.com

Small and medium-sized businesses will struggle to keep pace with tech giants like OpenAI in developing their own artificial intelligence (AI) models, according to a new report from Stanford University.

In an interview,Nestor Maslej, the editor-in-chief of Stanfords newly released 2024 AI Index Report, highlighted the studys findings on the growing AI divide between large and small companies. While tech behemoths pour billions into AI R&D, smaller firms lack the resources and talent to compete head-on.

A small or even medium-sized business will not be able to train a frontier foundation model that can compete with the likes of GPT-4, Gemini or Claude, Maslej said. However, there are some fairly competent open-source models, such as Llama 2 and Mistral, that are freely accessible. A lot can be done with these kinds of open-source models, and they are likely to continue improving over time. In a few years, there may be an open, relatively low-parameter model that works as well as GPT-4 does today.

Astudy from PYMNTS last year highlighted that generative AI technologies such as OpenAIs ChatGPT could significantly enhance productivity, yet they also risk disrupting employment patterns.

A major takeaway from the report is the possible disconnect between AI benchmarks and actual business requirements in the real world.

To me, it is less about improving the models on these tasks and more about asking whether the benchmarks we have are even well-suited to evaluate the business utility of these systems, Maslej stated. The current benchmarks may not be well-aligned with the real-world needs of businesses.

The report indicated that while private investment in AI generally declined last year, funding for generative AI experienced a dramatic surge, growing nearly eightfold from 2022 to $25.2 billion. Leading players in the generative AI industry, including OpenAI, Anthropic, Hugging Face and Inflection, reported substantial increases in their fundraising efforts.

Maslej highlighted that while the costs of adopting AI are considerable, they are overshadowed by the expenses associated with training the systems.

Adoption is less of a cost problem because the real cost lies in training the systems. Most companies do not need to worry about training their own models and can instead adopt existing models, which are available either freely through open source or through relatively cost-accessible APIs, he explained.

The report also calls for standardized benchmarks in responsible AI development. Maslej imagines a future where common benchmarks allow businesses to easily compare and choose AI models that match their ethical standards. Standardization would make it simpler for businesses to more confidently ascertain how various AI models compare to one another, he stated.

Balancing profit with ethical concerns emerges as a key challenge. The report shows that while many businesses are concerned about issues like privacy and data governance, fewer are taking concrete steps to mitigate these risks. The more pressing question is whether businesses are actually taking steps to address some of these concerns, Maslej noted.

Measuring AIs impact on worker productivity across different industries remains complex. It is possible to measure productivity within various industries; however, comparing productivity gains across industries is more challenging, Maslej said.

Looking ahead, the report highlights the need for businesses to navigate an increasingly complex regulatory landscape. On Tuesday, Utah Sen. Mitt Romney and several Senate colleagues unveiled a plan to guard against the potential dangers of AI. These include threats in biological, chemical, cyber and nuclear areas by increasing federal regulation of advanced technological developments.

Maslej emphasized the importance of staying vigilant. Navigating this issue will be challenging. The regulatory standards for AI are still unclear.

As public awareness of AI grows, Maslej believes that businesses must address concerns about job displacement and data privacy. As people become more aware of AI, how can businesses proactively address nervousness, especially regarding job displacement and data privacy? he posed as a crucial question for the industry to consider.

The 2024 AI Index Report is meant to guide businesses and society in navigating the rapid advancements in artificial intelligence. Maslej concluded, The AI landscape is evolving at an unprecedented pace, presenting both immense opportunities and daunting challenges.

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NSA Warns of AI Cybersecurity Risks, Urges Businesses to Bolster Defenses – PYMNTS.com

The National Security Agency (NSA) is sounding the alarm on the cybersecurity risks posed by artificial intelligence (AI) systems, releasing new guidance to help businesses protect their AI from hackers.

As AI increasingly integrates into business operations, experts warn that these systems are particularly vulnerable to cyberattacks. The NSAs Cybersecurity Information Sheetprovides insights into AIs unique security challenges and offers steps companies can take to harden their defenses.

AI brings unprecedented opportunity but also can present opportunities for malicious activity. NSA is uniquely positioned to provide cybersecurity guidance, AI expertise, and advanced threat analysis, NSA Cybersecurity Director Dave Luber said Monday (April 15) in anews release.

The report suggested that organizations using AI systems should put strong security measures in place to protect sensitive data and prevent misuse. Key measures include conducting ongoing compromise assessments, hardening the IT deployment environment, enforcing strict access controls, using robust logging and monitoring and limiting access to model weights.

AI is vulnerable to hackers due to its complexity and the vast amounts of data it can process,Jon Clay, vice president of threat intelligence at the cybersecurity company Trend Micro,told PYMNTS. AI is software, and as such, vulnerabilities are likely to exist which can be exploited by adversaries.

Asreported by PYMNTS, AI is revolutionizing how security teams approach cyber threats by accelerating and streamlining their processes. Through its ability to analyze large datasets and identify complex patterns, AI automates the early stages of incident analysis, enabling security experts to start with a clear understanding of the situation and respond more quickly.

Cybercrime continues to rise with the increasing embrace of a connected global economy. According to an FBI report, the U.S. alone saw cyberattack losses exceed $10.3 billion in 2022.

AI systems are particularly prone to attacks due to their dependency on data for training models, according to Clay.

Since AI and machine learning depend on providing and training data to build their models, compromising that data is an obvious way for bad actors to poison AI/ML systems, Clay said.

He emphasized the risks of these hacks, explaining that they can lead to stolen confidential data, harmful commands being inserted and biased results. These issues could upset users and even lead to legal problems.

Clay also pointed out the challenges in detecting vulnerabilities in AI systems.

It can be difficult to identify how they process inputs and make decisions, making vulnerabilities harder to detect, he said.

He noted that hackers are looking for ways to get around AI security to change its results, and this method is being talked about more in secret online forums.

When asked about measures businesses can implement to enhance AI security, Clay emphasized the necessity of a proactive approach.

Its unrealistic to ban AI outright, but organizations need to be able to manage and regulate it, he said.

Clay recommended adopting zero-trust security modelsand using AI to enhance safety measures. This method means AI can help analyze emotions and tones in communications and check web pages to stop fraud. He also stressed the importance of strict access rules andmulti-factor authenticationto protect AI systems from unauthorized access.

As businesses embrace AI for enhanced efficiency and innovation, they also expose themselves to new vulnerabilities, Malcolm Harkins, chief security and trust officer at the cybersecurity firm HiddenLayer, told PYMNTS.

AI was the most vulnerable technology deployed in production systems because it was vulnerable at multiple levels, Harkins added.

Harkins advised businesses to take proactive measures, such as implementing purpose-built security solutions, regularly assessing AI models robustness, continuous monitoring and developing comprehensive incident response plans.

If real-time monitoring and protection were not in place, AI systems would surely be compromised, and the compromise would likely go unnoticed for extended periods, creating the potential for more extensive damage, Harkins said.

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Generative Artificial Intelligence Revolution Heats Up in Asia/Pacific, with IDC expecting a 95.4% CAGR in 2027 … – EMSNow

SINGAPORE IDCs latestWorldwide AI and Generative AI Spending Guidereveals that the Asia/Pacific* region is witnessing an unprecedented surge in Generative AI (GenAI) adoption, including software, services, and hardware for AI-centric** systems with spending projected to soar to $26 billion by 2027, with a compound annual growth rate (CAGR) of 95.4 percent for the period 2022-2027. This surge underscores the regions pivotal role in driving the next wave of AI innovation and technological advancement.

GenAI is a branch of computer science involving unsupervised and semi-supervised algorithms that enable computers to create new content using previously created content, such as text, audio, video, images, and code, in response to short prompts. IDC believes GenAI will be a trigger technology to transition to a new chapter in the move toward automation for both internal and external parties across generic productivity, business functionspecific enhancements, or industry-specific tasks.

We anticipate that Asia/Pacific will experience a surge in the adoption of Generative AI, with growth rates expected to match those of North America, largely due to enterprises investing heavily in developing data and infrastructure platforms tailored for GenAI applications. We forecast that this investment in GenAI will reach its zenith within the next two years, followed by a period of stabilization. China is projected to maintain its position as the dominant market for GenAI, while Japan and India are set to become the most rapidly expanding markets in the forthcoming years,Deepika Giri, Head of Research, Big Data & AI, IDC APJ.

Unlocking the vast potential of GenAI, the Asia/Pacific region is poised for a transformative journey across various sectors. With robust digital infrastructure and growing investments in technology, Asia/Pacific emerges as a pivotal player in this dynamic landscape. Strategic investment in hardware, software, and associated services for GenAI is crucial to sustaining and propelling this progress. From software development to customer service, GenAI is revolutionizing industries, ushering in a new era of innovation in Asia/Pacific.

IT spending in GenAI technology progresses through three distinct stages. Initially, during the GenAI Foundation Build phase, attention is directed towards enhancing core infrastructure, investing in IaaS, and bolstering security software. Subsequently, in the Broad Adoption phase, the focus shifts towards the widespread adoption of open-source AI platforms offered as-a-service, playing a fundamental role in digital business control planes. Finally, the Unified AI Services phase sees a surge in spending as organizations rapidly integrate GenAI to gain a competitive edge, diverging from the typical slower growth observed in new technology markets.

GenAI isnt a fleeting trend. Its capacity to generate entirely new content, across various mediums, such as images, videos, code, and marketing materials, promises substantial efficiency gains and paves the way for innovative creative opportunities, granting a competitive advantage, saysVinayaka Venkatesh, Senior Market Analyst, IT Spending Guides, Customer Insights & Analysis, IDC Asia/Pacific. A significant portion of organizations have either already adopted Generative AI or are in the initial stages of experimenting with models,Vinayaka Venkateshends.

The financial services sector is experiencing rapid growth in Generative AI adoption in Asia. It is projected to reach $4.3 billion by 2027 with a remarkable CAGR of 96.7%. Within this industry, GenAI is being utilized internally to enhance operations efficiency, automate repetitive tasks, and optimize back-office processes such as fraud detection and the creation of intricate documents. Generative AI-powered solutions provide tailored financial services like personalized planning tools and reports, which dynamically adjust to meet customers evolving needs. Furthermore, the integration of GenAI yields substantial benefits to profitability by cutting costs, driving revenue generation, and enhancing productivity across various functions such as DevOps, marketing, and legal compliance.

The software and information services industry stands as the second-largest adopter of GenAI, embracing its versatility across sectors such as marketing, data analytics, and software development. Within marketing, GenAI can streamline content creation for websites, blogs, and social media platforms, optimizing marketing strategies and enhancing audience engagement. In data-driven fields like machine learning and analytics, GenAI proves invaluable for generating synthetic data, enriching existing datasets, and improving model performance and resilience. Additionally, in software development, these tools aid developers by automating coding tasks, generating prototypes, and accelerating the software development lifecycle, leading to heightened productivity and efficiency.

As the third-largest adopter of GenAI, governments across the Asia-Pacific region have a substantial opportunity to transform their operations and service delivery. This technology holds the potential to enhance efficiency, transparency, and citizen engagement. Governments are well-placed to spearhead efforts in advancing education and training in GenAI, thereby catalyzing the creation of new job prospects, and stimulating the growth of technology innovation hubs. These hubs will function as focal points for state-of-the-art training, bolstering skill sets, and nurturing the emergence of future AI professionals, including scientists, engineers, technicians, and specialists.

In the rapidly evolving Asia/Pacific retail market, characterized by diverse consumer preferences and advancing digital technologies, retailers are increasingly turning to GenAI to gain a competitive advantage. GenAI enables enhanced personalization, tailoring experiences to individual preferences, while also boosting efficiency by automating tasks like product design and content creation, thereby accelerating time-to-market. Furthermore, retailers leverage GenAI to create dynamic visual content and interactive experiences, fostering heightened customer engagement and loyalty.

IDCsWorldwide AI and Generative AI Spending Guidemeasures spending for technologies that analyze, organize, access, and provide advisory services based on a range of unstructured information. The Spending Guide quantifies the AI opportunity by providing data for 38 use cases across 27 industries in nine regions and 32 countries. Data is also available for the related hardware, software, and services categories. The AI and Generative AI Spending Guide is produced to provide the latest market developments through an accurate and quality forecast. During the period between updates, IDCs AI and Generative AI analyst teams conduct primary and secondary research to support this data product. Research in the period from August 2023 to February 2024 resulted in multiple additions and enhancements to the data. In this release of the AI and GenAI Spending Guide, we distilled leading forecasts such as IDCs Worldwide Black Book and IDCs Worldwide ICT Spending Guide, as well as AI and generative AI research led by IDCs AI Council of senior researchers globally.

20% of Asia/Pacific organizations are planning to build their own generative AI models. Explore IDCs latest eBook to stay equipped for the GenAI revolution. Download now:bit.ly/ genai -build-buy

**Taxonomy Note:The IDCWorldwide AI and Generative AI Spending Guideuses a precise definition of what constitutes an AI Application in which the application must have an AI component that is crucial to the application without this AI component the application will not function. This distinction enables the Spending Guide to focus on those software applications that are strongly AI-centric. In comparison, the IDCWorldwide Semiannual Artificial Intelligence Trackeruses a broad definition of AI Applications that includes applications where the AI component is non-centric, or not fundamental, to the application. This enables the inclusion of vendors that have incorporated AI capabilities into their software, but the applications are not exclusively used for AI functions. In other words, the application will function without the inclusion of the AI component.

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Artificial Intelligence Amplifies State Tax Audits on High Earners – WebProNews

As fears about artificial intelligence (AI) veer from job displacement to broader societal control, state tax departments harness this potent technology to boost audits on high earners significantly. Robert Frank of CNBC highlights how already high-taxed Democrate-controlled states like New York and California are increasingly deploying AI to scrutinize the tax declarations of the wealthy, intensifying efforts to reclaim unreported income.

In the past year, high-tax states have issued a surge in audit letters, with figures marking a 56% increase from the previous year. The targets? Affluent individuals who have relocated across state lines during the pandemic and remote workers whose physical locations do not align with their companys base.

AIs role in these audits is groundbreaking and unnerving for those it targets. By analyzing vast datasets, AI systems identify patterns and anomalies in tax returns more efficiently than human auditors ever could. This capability is instrumental in tracking high earners who might have underreported their incomes or falsely claimed to have moved permanently to tax-haven states.

Accountants and tax lawyers confirm that the rate of audits has escalated dramatically over the last six months. Tax authorities are challenging the permanence of moves made during the COVID-19 pandemic, insisting that many owe state taxes irrespective of their new residences. Furthermore, states are scrutinizing remote workers who, despite working entirely out-of-state, are employed by companies based in places like New York.

The fiscal implications for states are significant. With California facing a $38 billion deficit and New York bracing for a $10 billion shortfall next year, the financial incentive to pursue wealthy taxpayers is compelling. The infusion of $80 billion into the IRS, earmarked for enforcement, means that high earners are likely to face audits from both state and federal levels.

Questions linger about the efficacy and fairness of AI-driven audits. Critics ask whether these automated systems might overreach or misinterpret complex tax data, potentially leading to wrongful accusations. Yet, proponents argue that AI could revolutionize tax enforcement by uncovering hidden patterns of evasion that would be impossible for human auditors to detect.

As states and the IRS increasingly rely on artificial intelligence to bolster their audits, the landscape of tax enforcement is undergoing a profound transformation. This shift promises greater efficiency but raises important questions about privacy, fairness, and the transparency of AI algorithms in legal and financial contexts. Whether this trend will lead to a more equitable tax system or merely shift the burden more heavily onto certain groups remains to be seen.

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Chamath Palihapitiya Says Voice Will Be the "Front Door" for the Next Phase of Artificial Intelligence (AI): Here Are … – The Motley Fool

Among the most under-the-radar facets of artificial intelligence (AI) are its applications in voice-controlled devices.

Artificial intelligence (AI) has taken the world by storm during the past year and a half. Breakthroughs in AI are leading to sweeping changes in accelerated computing, healthcare, e-commerce, and more. It seems like the possibilities are endless, and the technology is destined to disrupt more and more areas of everyday life.

One application in the AI realm that is often overlooked is voice-recognition technology. But believe it or not, you interact with this feature of AI quite often.

Billionaire venture capitalist Chamath Palihapitiya recently took to X (the social media platform formerly known as Twitter) to predict that applications in voice will be the "front door" to the next frontier of the AI revolution.

Let's break down the market for AI-powered voice software, and explore some investment opportunities in the space.

A number of companies compete in the voice-recognition software market. Apple entered the niche through its acquisitions of Siri and Shazam. The Siri virtual assistant has become integrated throughout Apple's ecosystem and is a staple in the company's devices. Amazon has leveraged the technology in its Echo devices, and Alphabet has done the same in its Google Home smart home appliances.

On top of that, Microsoft (MSFT -0.66%) and Nvidia also entered the voice-recognition software market through a series of savvy investments.

According to Statista, the total addressable market for voice-recognition tools is forecast to reach nearly $50 billion by 2029. Considering the opportunities in this pocket of the AI landscape, it's not surprising that so many big tech enterprises are competing in it.

Image Source: Getty Images.

Two of the more prominent names in the voice-recognition market right now are SoundHound AI (SOUN -2.46%) and Microsoft.

Earlier this year, investors learned that Nvidia has a small ownership stake in SoundHound AI. After that information became public, the shares of SoundHound AI soared by as much as 320%. Although some of that momentum has waned, the company remains a top name in AI.

One of the biggest use cases for SoundHound AI's tech is voice-controlled systems in cars. With clients including Stellantis, Honda, and Hyundai, it's clear that the company has been able to attract some brand recognition.

SoundHound AI recently said that it will be offering Nvidia's DRIVE software to its automaker customers. Some applications for this technology include helping drivers answer questions related to vehicle maintenance, safety features, and car settings.

Shortly after SoundHound AI released details about its partnership with Nvidia, ChatGPT developer OpenAI made an announcement of its own. Namely, it revealed its latest product, Voice Engine, a tool that aims to help in areas such as video translation as well as in clinical settings related to speech therapy.

I agree with Palihapitiya's assertion that voice will play a big role in the further development of artificial intelligence services. In a way, it makes total sense. The sophistication of AI use cases is evolving in real time. Leveraging voice points to a future in which AI becomes even more ingrained in many aspects of daily life.

For this reason, some investors may be eager to get in on the action. As with any investment, however, there are risks.

Sure, SoundHound AI's partnership with Nvidia is exciting on the surface. But with only $46 million in revenue last year, coupled with mounting operating losses, SoundHound AI may not be the most prudent opportunity in voice-recognition technology.

Furthermore, when you layer in that Nvidia DRIVE is also being used by many other customers -- including SoundHound AI's competitor Cerence -- the potential for the partnership between the two appears less lucrative because it's not exclusive.

On the other hand, investing in Microsoft may be a subtle way to benefit from breakthroughs in AI-powered speech technology. The company is a major investor in OpenAI. Moreover, throughout 2023, Microsoft aggressively implemented ChatGPT across its Windows operating system -- a move that has unlocked a new phase of growth.

Although Voice Engine is not yet commercially available, I suspect that OpenAI will release it once it figures out how to best mitigate the risks that come with voice-mimicking technology.

Nevertheless, given Microsoft's close ties to OpenAI, I see it as a major beneficiary of voice-recognition software in the long run, and a much more proven, established opportunity in the AI narrative overall compared to other smaller competitors.

Suzanne Frey, an executive at Alphabet, is a member of The Motley Fool's board of directors. John Mackey, former CEO of Whole Foods Market, an Amazon subsidiary, is a member of The Motley Fool's board of directors. Adam Spatacco has positions in Alphabet, Amazon, Apple, Microsoft, and Nvidia. The Motley Fool has positions in and recommends Alphabet, Amazon, Apple, Microsoft, and Nvidia. The Motley Fool recommends Cerence and Stellantis and recommends the following options: long January 2026 $395 calls on Microsoft and short January 2026 $405 calls on Microsoft. The Motley Fool has a disclosure policy.

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Artificial intelligence in liver cancer new tools for research and patient management – Nature.com

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AI-Powered Apps Streamline Team Collaboration – PYMNTS.com

Artificial intelligence (AI) chatbots are already conversing with you and are now here to enhance teamwork.

Snap, a new app by Swit Technologies, is among a wave of collaboration tools that use generative AI to streamline project management, communication and workflows. Experts say such software can include intelligent meeting schedulers, real-time document collaboration, virtual assistants, and adaptive workflow management systems.

AI can be great for speeding up or automating certain tasks and elements of collaboration that can be tedious or prone to error,Darrin Murriner, the CEO ofCloverleaf.me, told PYMNTS. In the collaboration process, this could include collaborating on documents, writing content, communicating and compiling information.

APYMNTS report from last year suggests that GenAI technologies like OpenAIs ChatGPT could significantly enhance productivity. While they may also disrupt employment landscapes, the chief operations officer at Axios HQ, Jordan Zaslav, expressed optimism about AIs role in fostering collaboration. He predicted the designation AI-powered tools might soon become as commonplace as cloud-based technologies are today, inspiring a new era of productivity.

Snap is a project management system, task manager, and message board rolled into one designed to provide a range of features that extend beyond simple conversation facilitation. The chatbot aims to support collaborative project work by offering functionalities such as converting conversations into tasks, generating checklists, offering contextual responses and summarizing tasks.

Snap is not alone in the realm of AI-powered collaboration tools. Zoom, the well-known video conferencing platform, has recently introducedZoom Workplace, an AI-driven solution aimed at boosting productivity and fostering teamwork within its user-friendly interface. The AI Companion updates feature a range of new tools, most notably Ask AI Companion, a digital assistant that helps users streamline their workday within Zoom Workplace. Other improvements include an AI Companion for Zoom Phone and enhanced capabilities for Team Chat and Whiteboard.

AI note-taking applications such as Otter.ai and Fireflies not only transcribe meeting discussions in real time but also automatically distribute these notes to all participants after the meeting,Kevin LouxofCharlotte Works told PYMNTS. This feature ensures that everyone involved has access to the same information, fostering better communication and collaboration among team members.

AI tools are definitely a booster for collaboration especially with global and remote teams,Harpaul Sambhi, CEO of the AI company Magical, told PYMNTS. By incorporating AI tools into their workflow, teams can increase productivity, improve efficiency and streamline communication. By curating a shared library of top productivity tricks from frequently used messages to common workflow automation teams work more efficiently together.

Magical uses AI to automate repetitive tasks such as messaging, Sambhi said.

With Magical, we can start to understand the common workflows of all of our users and suggest recommendations for automating those tasks, he explained. AI will help us understand those patterns. Similarly, if you think of a large organization with many employees and lots of coordination/collaboration, we can start to narrow in on the repetitive tasks of, lets say, a team or department, and start to automate the tasks between employees.

As AI evolves and as people get more comfortable with its application, the uses of AI in collaboration will evolve as well, Murriner predicted.

There will likely be a move from more routine tasks to higher-order problem-solving and solutions, as well as improving our ability to build relationships and make connections, he added. These can be useful in a multitude of ways, including improving sales performance, recommending new opportunities for collaboration, or identifying who to connect with to improve outcomes.

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Palantir Stock vs. Microsoft Stock: Which Is the Best Artificial Intelligence (AI) Stock to Buy? – The Motley Fool

Palantir might be a smaller company, but that doesn't automatically make Microsoft the better investment.

Fool.com contributor Parkev Tatevosian compares Palantir Technologies (PLTR -2.60%) to Microsoft (MSFT -0.66%) to determine the better stock to buy.

*Stock prices used were the afternoon prices of April 14, 2024. The video was published on April 16, 2024.

Parkev Tatevosian, CFA has no position in any of the stocks mentioned. The Motley Fool has positions in and recommends Microsoft and Palantir Technologies. The Motley Fool recommends the following options: long January 2026 $395 calls on Microsoft and short January 2026 $405 calls on Microsoft. The Motley Fool has a disclosure policy. Parkev Tatevosian is an affiliate of The Motley Fool and may be compensated for promoting its services. If you choose to subscribe through his link, he will earn some extra money that supports his channel. His opinions remain his own and are unaffected by The Motley Fool.

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These 3 Artificial Intelligence (AI) Cryptos Are Rocketing Higher Today – Yahoo Finance

It's been a wild day for cryptocurrency investors, with a number of top tokens seeing outsize volatility in today's session. For AI cryptos, these moves have been even more exaggerated.

As of 2:15 p.m. ET on Monday, The Graph (CRYPTO: GRT), Fetch.ai (CRYPTO: FET), and SingularityNET (CRYPTO:AGIX) are still up meaningfully, surging 5.6%, 2%, and 1.8%, respectively, over the past 24 hours. However, many of these tokens have continued to decline in afternoon trading alongside other risk assets, as Middle East tensions rise.

For AI cryptos, geopolitical concerns shouldn't matter to the same degree as with other assets that are more sensitive to capital flows. That said, capital flows do matter regardless of which niche a given project is pursuing, and selling pressure remains strong today.

Fetch.ai and SingularityNET are two projects uniquely focused on AI that have a shared catalyst that investors are clearly pricing in. Fetch.ai is collaborating with SingularityNET and Ocean Protocol to create what they're calling the "Superintelligence Alliance."

As part of this alliance, some talks around a potential token merger have taken place, with investors now pricing these tokens in high correlation to each other.

That certainly makes sense, given the AI focus of both projects, and their collaborative ties to work together on solving much bigger problems than they likely could on their own. One thing that certainly stands out to me about crypto assets is the relative lack of willingness for projects to merge. If these projects do tie the knot at some point, it will be interesting to see how the market values a token combination.

The demand for blockchain-based AI solutions appears to be strong, and a combination of these two relatively small-cap projects could improve their chances of success in creating meaningful utility for end users.

The Graph's core model as an oracle network, allowing off-blockchain data to be ported on-chain, has seen impressive demand build over time. A number of recent collaborations and partnerships have driven an impressive amount of momentum in this token over the past week. The fact that this momentum has continued is a very positive development for long-term investors, and suggests this AI-related play could have more room to run.

Today's price action certainly implies a dip could be on the horizon, or at least a mellowing out of some rather strong momentum in these tokens in recent days. No rally lasts forever, and a breather can turn out to be a good thing. This year, these three AI-related cryptos have been among the best performers, and I wouldn't be surprised to see that narrative carried through to the end of the year.

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For growth investors seeking some crypto exposure, (and in particular, projects with AI-related headwinds), these are three tokens that I think are worth adding to the watch list to potentially buy on dips. Each project has unique catalysts that could drive value for investors and users over time. That's what this space is supposed to be about, which is what makes assessing these cryptos so compelling.

Before you buy stock in Fetch, consider this:

The Motley Fool Stock Advisor analyst team just identified what they believe are the10 best stocks for investors to buy now and Fetch wasnt one of them. The 10 stocks that made the cut could produce monster returns in the coming years.

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*Stock Advisor returns as of April 15, 2024

Chris MacDonald has no position in any of the stocks mentioned. The Motley Fool has positions in and recommends Fetch and The Graph. The Motley Fool has a disclosure policy.

These 3 Artificial Intelligence (AI) Cryptos Are Rocketing Higher Today was originally published by The Motley Fool

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