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The Use of Artificial Intelligence in the Business World – Fagen wasanni

Not much is known about the use of artificial intelligence (AI) in the business world, as it is a rapidly evolving technology. The most recent data collected by the government pertains to 2020, and since then, AI systems have grown dramatically.

According to the Census Bureaus Annual Business Survey, in 2020, the adoption of advanced computer-based technologies, including AI, was not common among US businesses. Approximately nine-in-ten businesses either did not use or were uncertain about their use of AI-related technologies such as natural language processing, machine learning, machine vision software, or augmented reality.

Only about 1% to 3% of businesses reported using or testing these technologies to supply goods or services, apart from touchscreens and kiosks for customer interaction.

However, the businesses that did utilize these technologies had a significant impact on the labor market. For example, firms utilizing robotics accounted for 20% of US employment, despite only 1% of firms using or testing this technology. Firms using machine learning, machine vision, or natural language processing accounted for about 11% to 16% of employment, while firms using touchscreens and kiosks affected 27% of the workforce.

These figures indicate that the adoption of advanced technologies, including AI, was primarily led by large businesses, and their decisions regarding the use of AI had a significant influence on employment rates.

Additional data from the 2019 Annual Business Survey revealed that 3% of firms were using AI and 2% were using robotics, lagging behind the use of other technologies such as specialized software, cloud computing, and dedicated equipment. However, firms employing AI or robotics represented approximately 13% and 15% of US employment, respectively.

Furthermore, AI usage was more prevalent in the information sector, professional, scientific, and technical services sector, and finance and insurance sector. These sectors were also found to have a higher share of workers most exposed to AI.

Overall, the use of AI in the business world is still evolving, with larger businesses leading the way in adoption.

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How Artificial Intelligence is Improving the Auto Dealership – Dealer Marketing Magazine

A survey done by CDK Global highlights the automotive retail market shift toward Artificial Intelligence (AI). More than half of the survey sample reports successful improvements from AI and anticipate further positive outcomes. In this survey, CDK Global illustrates that AI can have a domino effect on automotive dealership success, beginning with applying technologies such as Machine Learning and Natural Language Processing (NLP) to solve issues within their sales, service, and marketing departments. By improving employee productivity and satisfaction through procedural efficiencies and better guidance, dealerships can not only enhance employee retention rates and revenue growth but also provide quality service to their customers.

According to CDK Global, the department that benefits the most from AI is sales. The Sales department can use AI to improve employee efficiency and productivity through various methods. Fifty-six percent of dealerships are already using AI to attract new customers by identifying, targeting, and converting sales leads. Then using an AI-powered Virtual Assistant (VA), sales can routinely rekindle interest in dormant leads. Furthermore, VA allows prospective and current customers to contact dealers 24/7 through email, text, and internet chat. Meanwhile, AI technology like NLP is helping dealerships handle customer calls by creating a more efficient method to identify customer needs through voice and language, leading to higher rates of service and sales appointments. From a marketing perspective, AI can convert customer data into profiles that can help dealerships target their marketing efforts and quantify their ROI. All these procedural efficiencies made possible by AI create opportunities for automotive dealerships to increase their profits. In fact, 47% of automotive dealerships are already using AI to improve their revenue growth and customer service.

These technologies prove the effectiveness of AI to not only increase the profitability of automotive dealerships, but to create an overall higher level of employee satisfaction. The survey showed that 59% of automotive dealerships agree that finding and retaining quality employees is a monumental challenge in creating a successful business. With high rates of employee turnover due to burnout and inadequate guidance, AI offers various avenues of solutions. In the service department, AI technology can be used to organize vehicle repair-related data and use it to properly guide technicians through the inspection process more effectively and efficiently. Another effective use of AI is to simplify workflows by automating repetitive and mundane tasks, and enabling intelligent searches can improve employee productivity and satisfaction, leading to better employee retention.

According to an IDC Employee Experience Survey, when employees feel productive and engaged in their roles, it leads to a higher quality experience for their customers. If AI enhances the work experience for dealers and dealership employees by effectively and efficiently assisting them in their day-to-day tasks and long-term endeavors, then it empowers dealerships to deliver better customer service and obtain higher levels of customer satisfaction.

Improved employee satisfaction translates to better overall performance and engagement, which can also help with marketing efforts and customer service in front of the customer.

Dealership workers in various departments (such as service, sales, and F&I) are utilizing AI tools to enhance their productivity. These tools are becoming more precise, which ensures that marketing strategies and technologies can appropriately match the correct vehicle to the correct customer at an earlier stage of the process. With third-party repair shops dominating the market, dealerships can also use AI to differentiate their customers experiences and achieve better satisfaction.

This ultimately leads to customer loyalty and retention, a trending concern for dealers.

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Best Machine Learning Books: Inspire Your Technological Journey with Expert Knowledge – Economic Times

Embark on an extraordinary journey into the world of machine learning with our carefully curated selection of the best machine learning books. Discover the power of cutting-edge algorithms, delve into the realm of artificial intelligence, and unlock the secrets of data-driven decision-making. Whether you're an aspiring data scientist, a seasoned programmer, or a curious mind eager to explore the limitless possibilities of this transformative field, these books offer unparalleled insights and practical guidance. From foundational principles to advanced techniques, each page is a treasure trove of knowledge that will empower you to create intelligent solutions, gain a competitive edge, and embrace the future of technology. Elevate your skills and stay ahead in the dynamic landscape of machine learning with the best machine book.List of the best machine learning booksName and authorAmazon RatingsAmazon PriceDeep Learning by Aaron Courville, Ian Goodfellow, Yoshua Bengio4.7 / 5Rs. 5,737Advances in Financial Machine Learning by Marcos Lopez de Prado4.4 / 5Rs. 3,406Machine Learning for Algorithmic Trading by Stefan Jansen4.4 / 5Rs. 3,385Python Machine Learning by Sebastian Raschka , Vahid Mirjalili4.5 / 5Rs. 3,020MATHEMATICS FOR MACHINE LEARNING by Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong4.5 / 5Rs. 1,569Introduction to Machine Learning with Python by Andreas Muller4.5 / 5Rs. 1,300Machine Learning For Absolute Beginners by Oliver Theobald4.4 / 5Rs. 1,151Machine Learning by Tom M. Mitchell4.3 / 5Rs. 999Machine Learning using Python by Manaranjan Pradhan, U Dinesh Kumar4.3 / 5Rs. 579The Hundred-Page Machine Learning Book by Andriy Burkov4.6 / 5Rs. 4421. Deep Learning by Aaron Courville, Ian Goodfellow, Yoshua Bengio"Deep Learning (Adaptive Computation and Machine Learning series) Hardcover" by Aaron Courville, Ian Goodfellow, and Yoshua Bengio is a comprehensive guide to the fascinating world of machine learning. Exploring the hierarchy of concepts in deep learning, the book covers mathematical foundations, practical methodologies, and real-world applications. Whether you're a student, researcher, or software engineer, this book equips you with the tools to harness the power of deep learning in your projects and career pursuits.Buy Deep Learning by Aaron Courville, Ian Goodfellow, Yoshua Bengio2. Advances in Financial Machine Learning by Marcos Lopez de Prado"Advances in Financial Machine Learning" by Marcos Lopez de Prado offers a practical and scientifically backed approach to real-world financial challenges. Through math, code, and examples, readers gain valuable insights to implement effective solutions in their contexts. As a trusted expert and portfolio manager, the author equips investment professionals with groundbreaking tools essential for thriving in the dynamic landscape of modern finance.Buy Advances in Financial Machine Learning by Marcos Lopez de Prado3. Machine Learning for Algorithmic Trading by Stefan Jansen"Machine Learning for Algorithmic Trading: Master Systematic Strategies with Python, 2nd Edition" by Stefan Jansen equips traders and investment professionals with end-to-end machine learning techniques. From idea to backtesting, the book covers market, fundamental, and alternative data usage for predictive modelling and strategy design. Readers learn to evaluate alpha factors, optimize portfolios, and implement trading strategies with Python. Suitable for data analysts, Python developers, and traders seeking hands-on machine learning expertise for systematic trading success. Prior Python and ML knowledge is recommended.Buy Machine Learning for Algorithmic Trading by Stefan Jansen4. Python Machine Learning by Sebastian Raschka , Vahid Mirjalili"Python Machine Learning: ML and Deep Learning with scikit-learn" by Raschka and Mirjalili is a comprehensive guide to machine learning techniques, principles and applications. With updated content on TensorFlow 2.0 and new Keras API features, the book delves into cutting-edge reinforcement learning techniques and introduces GANs and sentiment analysis in NLP. Ideal for Python developers and data scientists, this resource-packed book empowers readers to build, train, and evaluate ML models, making it essential for those seeking to harness the power of machine learning and deep learning in real-world projects.Buy Python Machine Learning by Sebastian Raschka , Vahid Mirjalili5. MATHEMATICS FOR MACHINE LEARNING by Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong"Mathematics for Machine Learning" by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong is a comprehensive textbook that seamlessly integrates mathematical principles with machine learning techniques and concepts. Suitable for both students with a mathematical background and those new to the subject, the book presents four key machine-learning methods: linear regression, principal component analysis, Gaussian mixture models, and support vector machines. With work examples and exercises in each chapter, readers gain valuable hands-on experience and a solid understanding of applying mathematical concepts in the field of machine learning.Buy MATHEMATICS FOR MACHINE LEARNING by Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong6. Introduction to Machine Learning with Python by Andreas Muller"Introduction to Machine Learning with Python: A Guide for Data Scientists" by Andreas Muller provides a comprehensive learning experience. Delve into fundamental concepts and applications of machine learning, understanding the strengths and weaknesses of popular algorithms. Learn data representation and explore advanced techniques for model evaluation and parameter tuning. Discover the power of pipelines for workflow organization. Enhance your skills by mastering text data processing and gain valuable insights to excel in the field of machine learning and data science.Buy Introduction to Machine Learning with Python by Andreas Muller7. Machine Learning For Absolute Beginners by Oliver Theobald"Machine Learning For Absolute Beginners: A Plain English Introduction" by Oliver Theobald offers a practical and beginner-friendly approach to machine learning. While no programming experience is needed, two later chapters introduce Python to demonstrate a machine-learning model. Designed for newcomers, this book lays the foundation for understanding machine learning, but further learning is recommended for mastering this exciting field.Buy Machine Learning For Absolute Beginners by Oliver Theobald8. Machine Learning by Tom M. Mitchell"Machine Learning" by Tom M. Mitchell is a comprehensive textbook catering to advanced undergraduate and graduate students, developers, and researchers interested in machine learning. Without assuming prior knowledge in AI or statistics, the book offers a unified introduction to primary machine learning approaches. It includes accessible algorithms, example datasets, and project-oriented homework assignments accessible through the World Wide Web. A clear, precise, and explanatory writing style ensures a seamless understanding of concepts and techniques from various fields, encompassing recent topics like genetic algorithms, reinforcement learning, and inductive logic programming.Buy Machine Learning by Tom M. Mitchell9. Machine Learning using Python by Manaranjan Pradhan, U Dinesh Kumar"Machine Learning using Python" offers a robust introduction to machine learning with Python libraries, enriched by real-life case studies and examples. It spans essential topics, including Python fundamentals, and descriptive and predictive analytics. Advanced concepts like decision tree learning, random forest, boosting, recommended systems, and text analytics are explored with a balanced focus on theory and practical applications. Through real-world examples and step-by-step guidance, readers gain proficiency in exploring, building, evaluating, and optimizing machine learning models. This book serves as an invaluable resource for learners seeking a solid foundation in machine learning and its Python implementations.Buy Machine Learning using Python by Manaranjan Pradhan, U Dinesh Kumar10. The Hundred-Page Machine Learning Book by Andriy Burkov"The Hundred-Page Machine Learning Book" by Andriy Burkov caters to both beginners and experienced practitioners, making it an excellent resource for anyone seeking to delve into or expand their knowledge of machine learning. It's particularly beneficial for engineers looking to seamlessly integrate ML into their daily tasks without investing excessive time. With concise yet comprehensive content, this book provides valuable insights and practical guidance for all levels of learners in the field of machine learning.Buy The Hundred-Page Machine Learning Book by Andriy BurkovSimilar products for youFAQs related to the best machine learning books1. What are the best machine-learning books?Ans. Deep Learning by Aaron Courville, Ian Goodfellow, Yoshua Bengio, Advances in Financial Machine Learning by Marcos Lopez de Prado, Python Machine Learning by Sebastian Raschka, Vahid Mirjalili, and Machine Learning using Python by Manaranjan Pradhan, U Dinesh Kumar are a few of the best machine learning books.2. What are the 4 basics of machine learning?Ans. The four basics of machine learning are data collection, data preprocessing, model building, and model evaluation. These fundamental steps form the foundation for developing effective machine learning systems.3. Is machine learning better for AI?Ans. Machine learning is a crucial subset of artificial intelligence (AI) that enables systems to learn from data and improve their performance without being explicitly programmed, making it a fundamental aspect of AI.

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Join a fierce debate on the promises and perils of artificial intelligence. – Monterey County Weekly

We all have questions. Some of us wonder if artificial intelligence will take our jobs, while others fear AI will take over the world. Part of the issue is that AI is meta enough to be applicable to so many aspects of our human experience, from medicine to entertainment. We crave and fear this revolution, sensing that we dont really control it and that we probably will not be able to stop it and yet we press the accelerator pedal at the very same time, seeking solutions to other things we fear, like climate change.

AI is very in-our-face, says C. Michael Hogan, a physicist who, after years at Stanford University, retired to Carmel Highlands and now chairs the California Arts & Sciences Institute, a nonprofit that connects local scientists and artists for educational purposes. The subject has monumental implications, he adds. All Im saying is, lets not trust it too much.

Hogan is one member of a panel that will meet at Sand Citys SandBox on Friday, July 28 to discuss the implications of AI across various fields.

While Hogan, an expert in the field of environmental science and U.S. energy policy, will be presenting on the subject from the position of utmost skepticism, there are other, more AI-enthusiastic members of the panel. One is Francois Melese, emeritus professor of economics at the Naval Postgraduate School, who will shed light on AIs costs and benefits, like job disruption versus productivity gain.

He is brilliant, Hogan says of Melese, excited about the prospect of healthy disagreement.

Other panel members include Tom Atchison, an early pioneer in AI development, who will provide insights into the various forms of AI technology. Artist Jennifer Perlmutter will represent the Central Coasts creative community on issues relating to intellectual property and artist livelihoods. Radio host Edward King hosts.

This event is the first in a series; subsequent presentations will invite experts to dive into how AI is impacting various fields including filmmaking, journalism, nonprofits and for-profit businesses.

This event is co-sponsored by the Ethics Department of UC Santa Cruz.

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No, a bot didn’t write this story. How artificial intelligence will expand … – News 5 Cleveland WEWS

CLEVELAND Over the last year, artificial intelligence has opened our eyes to new possibilities in the way we live our lives, prompting a seismic change not seen since the advent of the internet.

This week, about 700 people are set to attend MAICON, the Marketing Artificial Intelligence Conference, at the Huntington Convention Center. The event is organized by the Marketing AI Institute, which is a media, event and education company focused on making AI accessible and actionable for marketers and business leaders.

This is the fourth year the event has been held in Cleveland, however, organizer Cathy McPhillips told News 5 the crowd is set to double in size compared to years past.

"Ever since November 30th, when ChaptGPTcame out, people are like, 'Oh, thats what AI is,'" McPhillips, the chief growth officer at the Marketing AI Institute, said. "Its given people the opportunity to see whats possible. Its not this big scary thing anymore in many respects. It's given people a chance to say, 'I think this is something helpful for my business and my marketing.'"

Even before the advent of ChatGPT and other similar bots, artificial intelligence has already permeated many facets of daily life.

Artificial intelligence, which is computers and machines that have the ability to think and solve problems as well as react to its surrounding environment, is already used in adaptive cruise control in cars, facial recognition to unlock a phone, tools used to help teach children how to read, and virtual assistants such as Amazon Alexa.

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RELATED: From A.I. to puppets: How Cleveland Metropolitan School District is testing new ways to teach kids how to read

"This is a whole new world right now, and people are coming from all over the world to our event," McPhillips explained. "It's exciting to see how much things have advanced already with so little we already know. As these tools get better and things start to improve, I think it will make us more efficient, more resourceful in what we're doing and make us love what we're doing more and not doing a lot of the repetitive things that take up much of our week."

Just last week, leaders from the Cleveland Clinic and University Hospitals, along with business leaders and educators, met with Lt. Gov. Jon Husted to explore what AI looks like going forward in Ohio.

"Society's cultures and nations throughout history, they have all become dominant or submissive based on how they've used technologies," Husted said.

"Generative AI is different in that it is actually synthesizing information from a variety of patterns and generating new information," Dr. Rohit Chandra, Cleveland Clinic's chief digital officer, said. "It is in the hands of everyday consumers, and it is going to affect everything. I think it cannot be overemphasized that the seismic shifts in every industry are going to happen."

Intellectual property and marketing attorney Sharon Toerek is among those set to speak at MAICON and admits she's encountering more and more questions when it comes to AI. However, her firm has not started to use it when it comes to drafting legal documents.

"We certainly reviewed things that clients provided us that AI had a hand in creating, but were not there yet, no," she said. "I think we have a ton to learn. These are early days of dealing with the technology and what it means for the content thats created with it."

Toerek does see how past the concerns of plagiarism and replacement, harnessing the collection of information AI pulls from can change not just her industry but her entire world.

"Educate yourself about what it means for you, your work on a daily basis," she said. "The more you know, the less reason you have to be afraid of it. I'm choosing for my firm to embrace the positive possibilities of it. I am exhibiting a healthy amount of skepticism for now about the speed with which it's going to replace human involvement and knowledge work."

"People think AI is out to replace all of us, and I don't think it is," McPhillips added. "I think it's here to help us do better work. None of these can run without human intervention. Humans need to make sure the outputs are right; we need to practice what we're inputting so the output is accurate and make sure we're using the machines the right way to augment what we're doing and not replace it."

MAICON runs through Friday, and you can learn more about the conference by CLICKING HERE.

Clay LePard is a special projects reporter at News 5 Cleveland. Follow him on Twitter@ClayLePard or on Facebook Clay LePard News 5. Only content gathered, created and written by humans was used in the reporting for this story.

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New Developments in Artificial Intelligence and Education – Fagen wasanni

Developments in artificial intelligence (AI) technology have gained widespread attention in recent years. AI has the potential to revolutionize various sectors, including government, society, and education. However, a national review conducted by the Center on Reinventing Public Education (CRPE) found that most state education departments have not publicly acknowledged the implications of AI in teaching and learning.

CRPE conducted a search of all US state and territory education department websites to gather information on AI policy guidance or mentions of AI developments. Apart from the Hawaii state Department of Education, which called for a working group to recommend AI usage in the upcoming school year, none of the other 58 departments addressed AI in a policy context.

A few states have indicated that they will leave AI-related decision-making up to individual districts. States like New York, Rhode Island, and Wyoming have mentioned similar approaches. Other states, such as Massachusetts and New Hampshire, have discussed AI with their education boards but have not proposed regulations related to its use in schools. In Idaho, state officials have started conversations about AI in schools.

Approximately half of the departments have made key references to AI on their websites since August 2021. Only four states Georgia, Florida, South Carolina, and Arkansas have posted curriculum standards or courses related to AI. These states have taken steps to integrate AI education into their systems, with Georgia introducing an AI pathway in its career, technical, and agriculture standards. Florida has partnered with the University of Florida to provide professional development and coaching on AI to districts. South Carolina plans to adopt a statewide AI framework and is seeking feedback on draft standards. Arkansas has incorporated AI and machine learning into its computer science courses.

The potential of AI to reshape teaching, learning, assessment, and management in education is still evolving. CRPEs research has found that states often delay providing crucial guidance during moments of crisis or ambiguity. However, offering early guidance, as seen with a few states actions regarding COVID relief funding, can have positive outcomes.

To prepare for the future, state education departments can consider providing guidance on addressing inherent biases in AI and offer suggestions on integrating AI into the curriculum. By doing so, they can ensure that students are better prepared to navigate and succeed in a technology-driven society.

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Artificial Intelligence (AI) and Machine Learning (ML) Integration to Drive Revenue Management System Market Growth – Yahoo Finance

DUBLIN, July 26, 2023 /PRNewswire/ -- The "Revenue Management System Market Forecast to 2028: Global Analysis by Component, Deployment, and Verticals" report has been added toResearchAndMarkets.com's offering.

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The report provides a comprehensive analysis of the global revenue management system market with key insights and growth projections for the period 2022-2028.

Key Findings:

The revenue management system market is witnessing significant transformation, driven by increasing demand for revenue and channel management solutions.

Upgrading existing legacy systems and adopting competitive pricing strategies are key drivers of market growth.

Subscription-based billing and revenue management platforms experience growing adoption due to increased mobile device penetration.

Challenges and Opportunities:

Compatibility issues with existing infrastructure present challenges to market adoption and growth.

Opportunities arise from cross-department collaboration and the need for personalized revenue management solutions.

Trends and Future Outlook:

Integration of Artificial Intelligence (AI) and Machine Learning (ML) in billing and revenue management systems is a prominent future trend.

The hospitality industry shows potential for advanced revenue management system adoption.

Market Projections:

Industries Driving Growth:

The market's growth is driven by the following key industries:

Constantly developing digital solutions to enable seamless operations.

Increased demand for billing automation for postpaid and prepaid clients.

Adoption of competitive pricing strategies to stay ahead in the market.

Travel & Tourism:

Rising global standard of living boosts the industry.

Demand for price management and revenue assurance solutions to cater to diverse customer needs.

Personalized offers based on customer behavior analysis.

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Market Segmentation:

The revenue management system market is segmented based on:

Components: Solutions and services.

Deployment Types: On-premise and cloud-based solutions.

Industry Verticals: Banking, IT & telecom, hospitality, healthcare, retail & e-commerce, insurance, travel & tourism, media & entertainment, and others.

Geography: North America, Europe, Asia Pacific, Middle East & Africa, and South America.

Company Profiles:

The report features profiles of prominent companies operating in the revenue management system market, including:

Accelya Solutions India Ltd

Amdocs Ltd

Cerillion Plc

CSG Systems International Inc

Telefonaktiebolaget LM Ericsson

Netcracker Technology Corp

Optiva Inc

Oracle Corp

SAP SE

Sage Group Plc

The "Revenue Management System Market Forecast to 2028" report offers valuable insights into the current status, growth prospects, and future trends of the market. Businesses in various industries can leverage this analysis to make informed decisions about adopting revenue management systems, enabling them to optimize their revenue streams effectively.

For more information about this report visit https://www.researchandmarkets.com/r/5ql24b

About ResearchAndMarkets.comResearchAndMarkets.com is the world's leading source for international market research reports and market data. We provide you with the latest data on international and regional markets, key industries, the top companies, new products and the latest trends.

Media Contact:Research and MarketsLaura Wood, Senior Managerpress@researchandmarkets.com

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Education unions grappling with Artificial Intelligence: we snooze … – Education International

There is a lot of speculation regarding the future of teaching and learning in compulsory education, especially with recent developments in technology. The world-wide release of ChatGPT and other generative artificial intelligence platforms have created a plethora of new, even more difficult, questions. What role will these AI systems play in classrooms? What do students need to learn to succeed in an AI saturated world? How will teaching change? How do you strike a balance between teachers and machines? And obviously, will AI replace teachers or other education workers?

Although teacher unions may not have the answers, they cannot stand by and let others determine the answers. The worlds education unions must come together and proactively shape the way emerging technologies are used in our schools and classrooms. If we snooze, we lose.

Before we dig a little deeper, I will start where every school system must startpersonal safety, online security, and ensuring equity. Although some countries and jurisdictions have strong policies to protect privacy and data, they are the exception and not the rule. Moreover, the rate at which new technologies are being developed, previous regulations and even some current proposed regulations will likely fall short. The rabbit-like way new technology comes online will always outpace the tortoise we call governmental action.

Just like we see with corporate driven privatization, schools in systems that are not run democratically and lack resources will be seen as frontiers of profit for big tech. The power of multinational corporations will overwhelm the rights of students and citizens alike.

This is where unions must step in. Yes, we must continually push for even stronger regulations, but waiting for others to defend and protect our professions is a losing strategy. And our profession cannot afford to lose.

Undoubtedly, the role of teachers will change in the future. Most agree teachers will not be replaced by AI, but the power of generative AI will require teachers to become less the deliverers of content and more coaches of learningdeeper learning. This new role is not necessarily a bad one, but that all depends on us. Even more than what is happening today, it will require teachers to develop increased higher-order thinking skills in their students. Skills such as collaborative learning, critical thinking, problem solving, multi-disciplinary approaches, and real-world application. For sure, all of these are part classroom lessons today but, using a common American saying, this part of teaching and learning needs to go on steroids. Whatever teachers were doing in the past needs to expand and be greatly enhanced. That is the reality of preparing students for an AI world.

AI-led instruction may allow students to learn at their own pace using adaptive algorithms that continuously adjust the content and difficulty based on student interaction. However, AI cannot teach the thinking skills that will become an integral part of future success. That is the role teachers must play.

This focus on deeper learning and advanced thinking will become part of every curriculum. Classes that emphasize understanding, motivation, and problem solving will become an essential part of educating for the future. AI-led instruction may allow students to learn at their own pace using adaptive algorithms that continuously adjust the content and difficulty based on student interaction. However, AI cannot teach the thinking skills that will become an integral part of future success. That is the role teachers must play.

Teachers like to say that they dont teach kids what to think, they teach them how to think. With the infusion of AI in every part of society, this belief system will be put to the test. The future needs strong independent thinkers who can determine good information from bad and apply that knowledge in a comprehensive way, not to solve a problem or two on a standardized test, but to solve todays complex real-world problems, and whatever the world throws at them in the future.

The implications of AI on teaching and learning starts with ensuring current teachers have the support and learning opportunities that, frankly, do not exist todayat least not at scale. Teacher preparation programs will also need to profoundly change. The interaction between administration, schools, and teachers will need to be reimagined. And importantly, the way learning will be assessed will fundamentally change. The obsession with standardized tests and other large-scale assessments will slowly come to an end. With AI in the classroom assessment will be built-in, and a fundamental part of the personalized learning process. Success will be measured in terms of individual students and not across the board averages.

The days of the one teacher, one classroom model is slowly also coming to an end. There will be a mix of traditional face-to-face instruction, team teaching online learning, project-based lessons, and an emphasis on collaboration and student team-based learning. This will require not only a change in teaching, but a major rethinking of schools physical space. This new model will require school buildings to be reconfigured away from a hallway of single purpose classrooms we see today to a more interactive environment that allows both small and large group work. These new schools will allow teams of teachers to leverage the strengths of each in a learning format that will maximize instruction.

This does not mean that all countries will see schools change in the same way. Schools in democratic countries will be able to leverage the power of regulation and political expediency to help ensure AI-based changes to schools protectto the extent possiblethe privacy and security of students and adults. But that will not be the case in other parts of the world.

Just like we see with corporate driven privatization, schools in systems that are not run democratically and lack resources will be seen as frontiers of profit for big tech. The power of multinational corporations will overwhelm the rights of students and citizens alike. Governments will be forced to accept corporate mandates or risk being left behind. And even worse, international monetary funds may play a significant role in this process. We have seen this type of educational blackmail before, and with the AI revolution in education, we are likely to see it again.

What does all this mean? It means a lot of things are going to change in and around schools. Artificial intelligence has created both challenges and opportunities that will require lots of decisions about the future of the teaching profession in the next few years. Teacher unions must lead addressing the challenges and leveraging the opportunities. Just as the pandemic was a once-in-a-lifetime event, the AI revolution in education is a unique point in timethe outcome will influence education for decades.

The artificial intelligence revolution is at the schoolhouse doorstep. As teacher unions, we must be proactive and respond. If not, we will see even more de-professionalization and commercialization of our work. All of us see a brighter future where AI can aid our educators, but we must prevent it from devaluing the educational experience, and those who work and learn in schools. Teacher unions must be at the forefront of this revolution. If not, others will determine the nature of schools, the teaching profession, and the future of education for us. In a real sense, if we snooze, we lose.

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Philanthropy Has Been Involved in Artificial Intelligence for Years … – Inside Philanthropy

Artificial intelligence has been advancing rapidly and working away in the background of any number of business and academic applications for decades now. But its recently gone a lot more mainstream, with a few regular-person tools like OpenAI's ChatGPT and Google's Bard out in the wild and driving a charged combination of fear and excitement.

Even as experts and not-so-experts weigh in on the technology's potential for good and harm, no one knows for sure how AI will impact all of us in the coming years. One thing we can say is that, as with just about any big society-shifting development, philanthropy is involved behind the scenes. And when it comes to AI, the sector seems just as conflicted as the rest of us.

Depending on who is cutting the checks, the developing field of artificial intelligence represents a technological boon with valuable benefits in healthcare and other areas, something that's going to put a lot of people out of work, an existential threat to humanity, or some combination of the above.

More than 1,000 technology leaders signed an open letter expressing concerns of the potential risks to society from AI, calling for a "pause" on its development until those risks could be better understood and possibly regulated. Among the best-known of the signatories is Elon Musk, certainly no technophobe. Just last week, the Biden administration announced that seven AI companies, including Amazon, Google and Meta, have agreed to voluntary safeguards on AI development, such as the technology's ability to create and spread misinformation. It will not be the last we hear of legislation or public oversight of AI.

Despite the many concerns, substantial money is being poured into AI. During the past decade, private investment has totaled about $92 billion globally, with U.S. investors providing the largest share at $47.4 billion in that one year, according to Stanford University's A.I. Index 2023 Report. The Stanford report said that the AI focus area with the most investment in 2022 was medical and healthcare, at $6.1 billion, followed by data management, processing and cloud at $5.9 billion and fintech at $5.5 billion. Meanwhile, by one measure used in the Stanford report, U.S. government spending on AI research was $3.3 billion in 2022.

So with all this funding from business and venture sources, not to mention academia, what's philanthropy's role in AI? Pretty substantial, actually. In fact, weve been following the sectors surging interest in AI since at least 2015, when we saw several wealthy Silicon Valley donors starting to crank out grants driven by their growing concerns about the technology. Weve also covered a handful of science research funders making grants to advance AI, eager for what breakthroughs it might enable. And of course, plenty of higher ed donors have jumped on board, seeking to turn their institutions of choice into leaders within this growing field.

Here are some of the big players in recent years:

Eric and Wendy Schmidt

The former top Google executive and his wife Wendy have demonstrated a truly huge commitment to philanthropic support for science: IP has covered the couple's mega-giving (backed by their estimated $20 billion net worth) in climate and the environment, and ocean science and ocean conservation.

Eric's interest in AI is no surprise. He chaired the U.S. National Commission on Artificial Intelligence from 2018 to 2021, and coauthored "The Age of AI: And our Human Future" with Dan Huttenlocher and Henry Kissinger (maybe not be the first person you'd think of in connection with this topic).In 2022, the Schmidts committed $125 million over five years to launch AI2050, through their Schmidt Futures organization. The initiative will support people working on opportunities and problems "that are critical to get right for society to benefit from AI," according to the announcement of the initiative. Eric evidently sees AI as more than a useful tool: AI will cause us to rethink what it means to be human, he said at the time of the announcement.

The organization also committed $148 million to create the Eric and Wendy Schmidt Postdoctoral Fellowship to advance AI in STEM fields; when the fellowship was announced in October of 2022, Schmidt Futures said its support for AI totaled $400 million. No doubt the Schmidts are true believers in the potential of AI, but Eric also owns millions of shares of Google, which has invested heavily in AI, so the technologys broader adoption benefits him personally, as well.

Paul Allen

The late billionaire cofounder of Microsoft was one of philanthropy's biggest backers of AI research until his death in 2018, but his influence continues through the Allen Institute for AI (AI2), which he established in 2014. Only a few months before he passed away, Allen increased support for AI2 with an additional $125 million, doubling the organization's previous budget.

The nonprofit research institute employs a couple of hundred scientists studying a wide range of AI applications, some of which will sound familiar, such as natural language processing and computer vision, as well as less immediately obvious applications, like climate modeling and wildlife protection, among others. In 2019, AI2 opened an Israel office, which employs an additional team of AI researchers.

Allen, who obviously benefitted greatly from the personal computer revolution that Microsoft fueled, was by all evidence a true believer in the power of computer technology to be a tool for the good of society, even beyond useful applications like spreadsheets and data management enabled in the business world.

Thomas Siebel

Thomas Siebel is a tech financier and founder of the software company Siebel Systems, which develops customer relationship management systems for large enterprises. That company was acquired by Oracle, which made Siebel a billionaire. He comes to AI funding from the business side, as founder the C3.ai software company, which develops AI applications for large enterprises in business, government and defense. Siebel's primary philanthropic vehicle, the Thomas and Stacey Siebel Foundation has supported a range of topics at research universities, including energy, stem cell research and computer science all unsurprising interests for a funder who spent his life in the technology industry.

Seibel is also cofounder, along with Microsoft and 10 top science research universities and laboratories, of the C3.ai Digital Transformation Institute. The philanthropic research consortium launched in early 2020, making grants to accelerate "the benefits of artificial intelligence for business, government and society." The first round of grants out of C3.ai Digital Transformation Institute were announced in mid-2020, which happened to be the first year of the COVID-19 epidemic, and those grants focused on the use of AI to find meaning in large datasets, the better to understand the epidemic.

More recent research awards, in 2022, went to scientists working on cybersecurity for financial and other key types of information infrastructure, such as energy. Siebel, something of a computer industry legend, is clearly a believer in AI's potential benefits, and someone who personally benefits from the technology. But he has also acknowledged that AI will put some people out of work, just as previous disruptive technologies have done, and that AI carries potential for abuse by creating false content with the power to reach and manipulate countless people.

Stephen Schwarzman

The founder and CEO of the Blackstone private equity outfit, Giving Pledge signatory Stephen Schwarzman is worth an estimated $20.7 billion. He's supported education, particularly his alma maters, and has taken criticism in the process, such as for his demands for naming rights attached to big gifts. But his gifts regularly run to tens and hundreds of millions, like a $25 million gift to his alma mater Abington High school, a $150 million gift to Yale and several other major sums.

But those were all topped by his 2018 donation of $350 million to MIT, establishing the MIT Stephen A. Schwarzman College of Computing, which will focus on AI and the integration of AI across all fields of study at the university. MIT has long been a leader in the development of AI, but the school said the Schwarzman gift would enable it to nearly double its academic capability in computing and AI.

The AI gift was something of a departure for Schwarzman, whose philanthropy had typically been general in nature, to legacy institutions, rather than in the service of any grander vision. But in the big investment circles that Schwarzman runs in, he would assuredly be deeply immersed in talk of AI and its potential just remember the study I cited above that estimated business investment in AI totaling $92 billion in 2022 alone. Schwarzman's AI-focused gift to MIT may represent both a genuine desire to advance the technology's ability to solve problems and improve the human condition, as well as a desire to solidify the returns on his own investment interests. But perhaps we needn't be cynical: If you're an investor and philanthropist and you believe in the social and business value of AI, it would make sense to both support it via philanthropy and your investment bets.

Fred Luddy

Another tech billionaire seeding AI research in academia, Indiana native Fred Luddy in 2019 donated $60 million to establish an AI research initiative at his alma mater, Indiana University. Renamed the IU Luddy School of Informatics, Computing and Engineering, the initiative will initially focus on the use of AI in digital health. Luddy founded ServiceNow, a provider of cloud-based IT help desk services. IU has announced a strategic shift toward emphasis on AI and machine learning, particularly in digital health. Given the heavy investment from the private sector on AI in digital health again, as noted in the Stanford report on AI Luddy's gift will help IU feed technology and human talent into the healthcare segment, where it will affect essentially everyone.

Patrick J. McGovern Foundation

The Patrick J. McGovern Foundation was established after the death of its namesake, who built a fortune in technology-related publishing and industry research. In 2021, the foundation merged with the Cloudera Foundation, a philanthropy created by Silicon Valley data and AI software company Cloudera Inc. to bring data analytics technology to the nonprofit sector. The organization's Data and Society team was to create and share solutions and examples of whats possible and practical in the field of data and AI, promoting the technology's uses for social change, equity and ethical use of data.

Reid Hoffman

Billionaire venture capitalist and LinkedIn co-founder Reid Hoffman in 2018 gave $2.3 million to establish a chair in AI at the University of Torontos iSchool to study the broad impacts of AI on humanity. Hoffman, who has been interested in AI since his student days at Stanford University, contributed $10 million to back the Ethics and Governance of Artificial Intelligence Fund, a philanthropic joint venture of MIT and Harvard. Hes also on the board of the Stanford Institute for Human-Centered Artificial Intelligence, where he funds the Hoffman-Yee Research Grants program to back interdisciplinary research to understand the human and societal impact of AI and to develop AI technologies. Also backing the Ethics and Governance fund is the Omidyar Network, with a $10 million contribution; other supporters include the Knight Foundation, the Hewlett Foundation and investor Jim Pallotta.

Open Philanthropy

Open Philanthropy, known for its practice of effective altruism, and the study of what it calls global catastrophic risks, such as biosecurity, has invested considerably in the study of unanticipated serious dangers of AI that may accompany progress in the field. According to Open Philanthropys website, the organization has to date made $310 million in grants to support technical, strategic and policy research around AI. Among the largest of the grants was $55 million to Georgetown University to create the Center for Security and Emerging Technology, a think tank dedicated to policy around technology and national and international security.

Elon Musk

It is not surprising that rocket ship builder Elon Musk, though lately outspoken in his calls for oversight of AI, has long been involved in AI and does not seem to be shying away from the technology. He backed ChatGPT developer OpenAI in its early nonprofit days; he said he kicked in $100 million, but other reports said the sum could not have been more than about $57 million, which is still a lot. And just this month, because he doesnt run enough companies, Musk announced the launch of his own AI company, xAI, intended to work on complex scientific and mathematical problems, and to understand the true nature of the universe. Linda Yaccarino, the CEO of Musk-owned social media app X, formerly known as Twitter, has said that a reshaped X would be powered by AI.

And the list goes on. Other philanthropists and billionaires active in AI include Amin and Julie Khoury, who gave alma mater Northeastern University $50 million to advance AI at the schools renamed Khoury College of Computer and Information Sciences.

The Kavli Foundation funds efforts to understand the ethical implications of science and technology, and has named AI as a topic of focus. In 2021, the foundation announced two institutes at UC Berkeley and the University of Cambridge, devoted to top-of-mind ethical concerns within science including artificial intelligence.

Earlier this year, Ford Foundation president Darren Walker co-authored an op-ed in the Washington Post calling upon AI developers from all sectors of society to tread carefully into the future of AI. The time has come for new rules and tools that provide greater transparency on both the data sets used to train AI systems and the values built into their decision-making calculus, Walker wrote. We are also calling for more action to address the economic dislocation that will follow the rapid redefinition of work.

The Rockefeller Foundation operates a residency program for scholars studying the social impact and responsible use of AI, and has also supported efforts to use AI in global health initiatives, such as this one, aimed at infectious disease outbreaks. The MacArthur Foundation has for several years been providing grant support to the Partnership on Artificial Intelligence to Benefit People and Society, a nonprofit that brings together organizations from business, academia and civil society to consider collective standards to use AI in safe and socially beneficial ways.

Funders that may not have had specific interests in AI will likely step up giving as the technology develops in fields in which they already have programs, as does Rockefeller. Health/biomedical research is clearly one such area: The Chan Zuckerberg Initiative, for example, the ambitious philanthropy created by Facebook founder Mark Zuckerberg and his wife, physician Priscilla Chan, has made several grants to biomedical researchers seeking to use AI.

The above list is incomplete and will likely become far more incomplete in the coming decade, as the field gains momentum and expands, hopefully for better rather than for worse, throughout society.

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Philanthropy Has Been Involved in Artificial Intelligence for Years ... - Inside Philanthropy

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The Advancement of Generative Artificial Intelligence: ChatGPT vs … – Fagen wasanni

The advancement of generative artificial intelligence (GenAI) has led to the emergence of two popular language models, namely ChatGPT and Bard. OpenAI developed ChatGPT, which was launched in November 2022, while Google AI created Bard, released in March 2023. Both models have the ability to generate creative and informative text, making them valuable tools for individuals and companies alike.

ChatGPT, powered by a Generative Pre-trained Transformer (GPT), gained popularity for its high-quality responses across different topics. It is pre-trained on a massive text corpus, enabling it to generate text efficiently. On the other hand, Bard utilizes a pre-trained language model called BERT, fine-tuned specifically for natural language processing (NLP) tasks.

Both ChatGPT and Bard possess similar traits and functionalities. They can understand and respond to prompts with human-like text, thanks to their extensive training on diverse data. These models take into account previous prompts to generate contextually relevant responses. However, there are notable differences between the two.

ChatGPT appeals to non-technical users with its user-friendly nature, while Bard is more suitable for individuals with technical knowledge. Moreover, Bard offers integration capabilities with other Google products, whereas ChatGPT has limited integration options. In terms of data, ChatGPTs information is restricted to events before 2021, whereas Bards data includes the present year.

When it comes to size and versatility, ChatGPT stands out with its 175 billion parameters, requiring more computational power. On the other hand, Bard is trained on a smaller text corpus and is more suitable for specific NLP tasks.

Considering cost, the GPT-3.5-based version of ChatGPT is available for free, while Bard is accessible to all without any access fee as it is part of the TensorFlow library.

Organizations should consider various factors when choosing between ChatGPT and Bard, such as data availability, security, integration, accuracy, and responsiveness. ChatGPT is suitable for generating social media posts and business articles, while Bard excels in answering complex technical queries. However, its important for organizations to note that GenAI is still in its early stages, and both models have faced challenges with accuracy and misinformation.

Ultimately, the choice between ChatGPT and Bard depends on the specific needs and use cases of the organization.

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