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In the old days, books, movies and television shows would have stories of computers that could thing for themselves. They could make assumptions, solve problems, and answer questions.

Fast forward to 2023 and that fiction has now become reality.

It's called ChatGPT.

ChatGPT uses algorithm, artificial intelligence, and other state-of-the-art computer "stuff" to generate human-like text. You can ask it to write an article, answer questions and more. It uses information on the internet to solve and answer.

ChatGPT can also do some creative writing

So, I used ChatGPT to help me with a project centering around my Cat Country Morning Show co-host Jahna.

The results blew me away!

The first "project" I asked for ChatGPT to do was to provide a caption for this photo of Jahna:

Courtesy of Jahna

As of yet, ChatGPT can't "see" the photo, so I couldn't use it directly, Instead I asked: "Provide thee possible captions for a photo of Jahna, the co-host of the radio show, Cat Country Mornings with Joe and Jahna."

Within seconds, Chat spit out this:

I must admit, Chat did pretty good, right?

Then, I decided to go a little further. I asked Chat, "Provide three creative fictional stories about 32-year-old Jahna."

Mind you, that's the only information I provided. The response I received from Chat was absolutely stunning!

Sure, here are three creative fictional stories about 32-year-old Jahna:

Holy crap!

Find out more about ChatGPT here.

From one side of South Jersey to the other, these are some of the best places to grab a hoagie... enjoy!

This Cape May County Home is Amazing!

Originally posted here:
Artificial Intelligence Paints Quite a Picture of Cat Country's Jahna - catcountry1073.com

The firm's new report provides an overview of the eight trends that will continue to drive AI innovation over the coming months.

TORONTO, March 27, 2023 /PRNewswire/ - To help tech leaders and their organizations stay on top of the latest technology shifts, global IT research and advisory firm Info-Tech Research Group has released its AI Trends 2023 report. The report covers various topics, including synthetic data, edge AI, digital twins, and AI reasoning, and is intended to prepare teams for both what is currently happening and what is coming.

The top eight AI trends for 2023 that will shape its future research and define new strategic business opportunities. (CNW Group/Info-Tech Research Group)

With AI technologies evolving rapidly, technology leaders must understand and remain ahead of the latest trends and developments. This forward-thinking approach will allow leaders to plan for future investments in AI-powered technologies and systems, drive new strategic business opportunities, and enable their organization's competitive advantage.

"As the pace of AI innovation accelerates, it is important for organizations to have a business strategy in place to respond to emerging technologies and the transformations AI brings to the organizations," says Irina Sedenko, research director at Info-Tech Research Group. "The consistently changing AI landscape is impacting all areas of business and all industries. Understanding the strategic directions and overall trends of AI innovation will help IT leaders identify the opportunities enabled by new technology and define strategies for new challenges."

Info-Tech's AI Trends 2023 report details the barriers that can slow the initial adoption of AI, such as data readiness and data quality issues, the lack of tools or methodologies, the lack of understanding of AI use cases, and how best to define the business value of AI investments. The report also explores strategies that enterprises have used to overcome these challenges.

In the report, the firm has identified the following eight trends for the coming months:

Design for AI Sustainable AI system design will need to consider aspects such as the business application of the system, data, software and hardware, governance, privacy, and security. It is crucial to define the purpose of AI and set goals for its implementation from the beginning. The approach should cover all stages of the AI lifecycle and enable iterative development. To take advantage of different tools and technologies for AI development, deployment, and monitoring, the AI system design should consider software and hardware needs and seamless integration with other existing systems within the enterprise.

Event-Based Insights AI-driven signal-gathering systems provide insights and predictions for strategic decision making through continuous data analysis. AI enables scenario-based modeling and pattern identification, allowing businesses to understand how events are related. An event-driven architecture that analyzes various data types across multiple channels will be necessary for anticipatory capabilities.

Synthetic Data Synthetic data is used for training machine learning models when real data is insufficient or does not meet specific requirements. Synthetic data can also remove contextual bias from personal data, ensure privacy compliance, and solve data-sharing challenges. Researchers have found that synthetic data sets can outperform real-world data in some cases and can be used in various applications such as language systems, self-driving cars, fraud detection, and clinical research. Moving forward, synthetic data has the potential to enable innovation across other emerging applications.

Edge AI Edge AI allows AI applications to be deployed in physical devices where data is generated, such as IoT devices or healthcare devices, as well as self-driving cars. It offers benefits such as real-time data processing, reduced cost and bandwidth requirements, increased data security, and improved automation. Edge AI can be used for various applications, including computer vision, geospatial intelligence, object detection, drones, and health monitoring devices.

AI in Science and EngineeringAI has numerous uses in science and engineering, including genome sequencing for identifying genetic disorders, modeling physics processes, and understanding planet ecosystems. It is also driving advances in drug discovery by assisting with molecule synthesis and property identification. AI is expected to continue to contribute to scientific understanding by enabling faster innovation, generating new insights and ideas, generalizing scientific concepts, and transferring them between areas of scientific research.

AI Reasoning The majority of machine learning and AI applications today involve predicting future behaviors based on historical data and correlations between different parameters. However, the development of a causal AI that can identify root causes and causal relationships between variables without mistaking correlation and causation is still in its early stages. Researchers are working on causal graph models and algorithms at the intersection of causal inference with decision making and reinforcement learning to address this challenge.

Digital Twin Digital twins enable organizations to predict future failures, perform predictive maintenance, and design and test complex equipment before physically manufacturing it. Digital twins are used in various industries, including manufacturing, architecture, construction, energy, infrastructure, and retail. Combining digital twins with the metaverse provides an immersive, interactive environment with real-time physics capabilities. Future potentialincludes enabling the autonomous behavior of digital twins, which will influence the growth and further advancement of AI.

Combinatorial Optimization Combinatorial optimization is traditionally used in supply chain, scheduling and logistics, and operations optimization. Recently, the integration of deep learning algorithms with classical optimization algorithms has led to significant performance improvements. Research in this area continues to explore the potential of machine learning and AI in solving challenging combinatorial and decision-making problems.

Story continues

Download and read the full AI Trends 2023 report to learn more about each of the trends for the year ahead.

To schedule interviews with an Info-Tech analyst on the topic of AI trends or to capture additional insights via email, contact pr@infotech.com.

About Info-Tech Research Group Info-Tech Research Group is one of the world's leading information technology research and advisory firms, proudly serving over 30,000 IT professionals. The company produces unbiased and highly relevant research to help CIOs and IT leaders make strategic, timely, and well-informed decisions. For 25 years, Info-Tech has partnered closely with IT teams to provide them with everything they need, from actionable tools to analyst guidance, ensuring they deliver measurable results for their organizations.

Media professionals can register for unrestricted access to research across IT, HR, and software and over 200 IT and Industry analysts through the Media Insiders program. To gain access, contactpr@infotech.com.

Info-Tech Research Group Logo (CNW Group/Info-Tech Research Group)

Cision

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SOURCE Info-Tech Research Group

Originally posted here:
The Top Artificial Intelligence Trends for 2023 Published in Info-Tech Research Group's Latest Report - Yahoo Finance

Newswise JMIR Publications published "Examining the Use of an Artificial Intelligence Model to Diagnose Influenza: Development and Validation Study" in the Journal of Medical Internet Research, which reported that it may be possible to diagnose influenza infection by applying deep learning to pharyngeal images given that influenza primarily infects the upper respiratory system.

These authors aimed to develop a deep learning model to diagnose influenza infection using pharyngeal images and clinical information. They recruited patients who visited clinics and hospitals because of influenza-like symptoms.

In the training stage, the authors developed a diagnostic prediction artificial intelligence (AI) model based on deep learning to predict polymerase chain reaction (PCR)confirmed influenza from pharyngeal images and clinical information. In the validation stage, they assessed the diagnostic performance of the AI model. In an additional analysis, the authors compared the diagnostic performance of the AI model with that of 3 physicians and interpreted the AI model using importance heat maps.

This process led to the development of the first AI model that can accurately diagnose influenza.

Dr Sho Okiyama, MD, from Aillis, Inc said, "According to the Global Burden of Disease Study 2016, the global burden of influenza is substantial." Timely and accurate diagnosis of influenza has the potential to prevent widespread transmission of the virus within the population and during subsequent epidemics and pandemics, as well as to prevent the unnecessary prescription of antibiotics in primary care, which is a cause of emerging antibiotic-resistant bacteria.

The COVID-19 pandemic and surge in the use of telemedicine highlighted the importance of accurately diagnosing influenza infection without increasing the risk of spreading the virus through physical interaction. The gold-standard method for diagnosing influenza infection is the reverse transcriptionPCR (RT-PCR) of nasopharyngeal aspirates or swabs; however, RT-PCR is not easily performed in primary care, and the results turnaround time could delay prompt diagnosis and preventive or treatment interventions.

Neither of these tests can be performed through telemedicine, and the sensitivity and specificity of diagnosing influenza using clinical information only are suboptimal. Given the recent increase in the number of patients being diagnosed through telemedicine, an alternative influenza test that can be conducted through telemedicine is warranted.

Dr Okiyama and the research team concluded in their JMIR Publications Research Article, "we developed the first AI-assisted diagnostic camera for influenza and prospectively validated its high performance. We found that the AI model often focused on follicles, which confirmed previous case reports and series suggesting that visual inspection of the pharynx would help in the diagnosis of influenza infection."

About the Journal of Medical Internet Research

The Journal of Medical Internet Research (JMIR) (founded in 1999, now in its 23rd year!), is the pioneer open access eHealth journal and is the flagship journal of JMIR Publications. It is a leading digital health journal globally in terms of quality/visibility (Journal Impact Factor 7.08 (Clarivate, 2022)) and is also the largest journal in the field. The journal focuses on emerging technologies, medical devices, apps, engineering, telehealth and informatics applications for patient education, prevention, population health and clinical care.

JMIR is indexed in all major literature indices including MEDLINE, PubMed/PMC, Scopus, Psycinfo, SCIE, JCR, EBSCO/EBSCO Essentials, DOAJ, GoOA and others. As a leading high-impact journal in its disciplines, ranking Q1 in both the 'Medical Informatics' and 'Health Care Sciences and Services' categories, it is a selective journal complemented by almost 30 specialty JMIR sister journals, which have a broader scope, and which together receive over 6.000 submissions a year.

As an open access journal, we are read by clinicians, allied health professionals, informal caregivers, and patients alike, and have (as with all JMIR journals) a focus on readable and applied science reporting the design and evaluation of health innovations and emerging technologies. We publish original research, viewpoints, and reviews (both literature reviews and medical device/technology/app reviews). Peer-review reports are portable across JMIR journals and papers can be transferred, so authors save time by not having to resubmit a paper to a different journal but can simply transfer it between journals.

We are also a leader in participatory and open science approaches, and offer the option to publish new submissions immediately as preprints, which receive DOIs for immediate citation (eg, in grant proposals), and for open peer-review purposes. We also invite patients to participate (eg, as peer-reviewers) and have patient representatives on editorial boards.

As all JMIR journals, the journal encourages Open Science principles and strongly encourages publication of a protocol before data collection. Authors who have published a protocol in JMIR Research Protocols get a discount of 20% on the Article Processing Fee when publishing a subsequent results paper in any JMIR journal.

Be a widely cited leader in the digital health revolution and submit your paper today!

###

DOI - https://doi.org/10.2196/38751

Full-text - https://www.jmir.org/2022/12/e38751/

Corresponding author - Sho Okiyama, MD, Aillis, Inc, 1-10-1-11F, Yurakucho, Chiyoda-ku, Tokyo, JP

Keywords - influenza, physical examination, pharynx, deep learning, diagnostic prediction

About JMIR Publications

JMIR Publications is a leading, born-digital, open access publisher of 30+ academic journals and other innovative scientific communication products that focus on the intersection of health and technology. Its flagship journal, the Journal of Medical Internet Research, is the leading digital health journal globally in content breadth and visibility, and it is the largest journal in the medical informatics field.

To learn more about JMIR Publications, please visit https://www.JMIRPublications.com or connect with us via:

YouTube - https://www.youtube.com/c/JMIRPublications

Facebook - https://www.facebook.com/JMedInternetRes

Twitter - https://twitter.com/jmirpub

LinkedIn - https://www.linkedin.com/company/jmir-publications

Instagram - https://www.instagram.com/jmirpub/

Head Office - 130 Queens Quay East, Unit 1100 Toronto, ON, M5A 0P6 Canada

Media Contact - [emailprotected]

The content of this communication is licensed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, published by JMIR Publications, is properly cited.

More:
Journal of Medical Internet Research | Can Artificial Intelligence Be Used to Diagnose Influenza? - Newswise

The hot topic of the day seems to Artificial Intelligence or AI for short. More specifically ChatGPT. When it comes to this topic, I am reminded of a quote by Thomas Jefferson, when he said, I'm a great believer in luck, and I find the harder I work the more I have of it. When it comes to AI, one cant help but ask the question, how will AI impact smaller or rural communities in the future?

Regardless of where you stand on this, make no mistake, it will take hard work and not luck to maximize this tool. As for luck, Guy Tasaka, a good friend of mine, and co-founder of MediaFlowAI, recently said when discussing AI, You can be on the train, or under the train.

The impact of AI on smaller communities will depend on various factors such as the availability of resources, infrastructure, and the community's readiness to adopt new technologies. However, there are several potential positive and negative impacts that AI could have on smaller communities. Here are just a few.

On the positive side, AI will allow for much improved access to healthcare. AI can help diagnose diseases more accurately and quickly, especially in areas where there is a shortage of healthcare professionals. AI will lead to increased efficiency in agriculture, it will help farmers optimize crop yields, reduce waste, and increase profits. AI can lead to a much better education, it can help create personalized learning experiences for students, regardless of their location or socioeconomic status. AI can lead to more job creation, it could potentially create new job opportunities in fields such as data analysis, software development, and robotics.

On the negative side of the equation, like many things, it can be a two-edged sword. While AI can create jobs as we mentioned above, it can also potentially automate certain types of jobs, thus leading to job loss in certain industries. AI can widen inequality, this because smaller communities may not have access to the same resources and infrastructure needed to fully adopt AI, potentially leading to a wider digital divide.

AI can intrude on ones privacy, it requires vast amounts of data to operate effectively, and this data could potentially be used for purposes that do not align with community values. AI can create a dependence on technology. When we have an over-reliance on AI, it can potentially lead to a loss of critical thinking skills and decision-making abilities.

Overall, as one might expect, the impact of AI on smaller communities will depend on how it is understood, implemented, and integrated into the community. It is important that communities carefully consider the potential benefits and risks before they select which aspects of AI to adopt or embrace. This allows them to adopt AI where it makes sense, thus ensuring the potential benefits outweigh the potential risks.

Lastly, understand that fear of something tends to paralyze. We cant afford to fear AI, there are far too many positive aspects to AI. It is important that in lieu of ignoring or fearing AI, we strive to understand the many values and positive uses that can be derived from it. Ignoring or fearing AI simple places us under the train. Learning to understand, utilize and even embrace it assures we have the right tools to succeed.

John Newby, from SW Missouri, is a nationally recognized Columnist, Speaker, & Publisher. He consults with Community, Business & Media. His Building Main Street, not Wall Street, column is read by 60+ communities around the country. As founder of Truly-Local, he assists communities, media and business leaders in building synergies that create vibrant communities. He can be reached at: John@Truly-Local.org.

See original here:
How will artificial intelligence affect rural communities? - Alton Telegraph

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IrelandUruguay, Eastern Republic ofUzbekistanVanuatuVenezuela, Bolivarian Republic ofViet Nam, Socialist Republic ofWallis and Futuna IslandsWestern SaharaYemenZambia, Republic ofZimbabwe

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Meet CityRover, the artificial intelligence helping Oregon City battle ... - Oregon City News