Dr. Schankler ultimately used R.A.V.E in that performance of The Duke Of York, though, because its ability to augment an individual performers sound, they said, seemed thematically resonant with the piece. For it to work, the duo needed to train it on a personalized corpus of recordings. I sang and spoke for three hours straight, Wang recalled. I sang every song I could think of.

Antoine Caillon developed R.A.V.E. in 2021, during his graduate studies at IRCAM, the institute founded by the composer Pierre Boulez in Paris. R.A.V.E.s goal is to reconstruct its input, he said. The model compresses the audio signal it receives and tries to extract the sounds salient features in order to resynthesize it properly.

Wang felt comfortable performing with the software because, no matter the sounds it produced in the moment, she could hear herself in R.A.V.E.s synthesized voice. The gestures were surprising, and the textures were surprising, she said, but the timbre was incredibly familiar. And, because R.A.V.E. is compatible with common electronic music software, Dr. Schankler was able to adjust the program in real time, they said, to create this halo of other versions of Jens voice around her.

Tina Tallon, a composer and professor of A.I. and the arts at the University of Florida, said that musicians have used various A.I.-related technologies since the mid-20th century.

There are rule-based systems, which is what artificial intelligence used to be in the 60s, 70s, and 80s, she said, and then there is machine learning, which became more popular and more practical in the 90s, and involves ingesting large amounts of data to infer how a system functions.

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What Happens When A.I. Enters the Concert Hall - The New York Times

Over the last several weeks, investors listened to corporate executives discuss the topic of artificial intelligence (AI) on earnings calls. While AI seems like the new buzzword, there are some legitimate reasons to be bullish.

Perhaps no other company has benefited more from the AI mania than chipmaker Nvidia (NVDA 0.68%). Nvidia's earnings report was solid, but its guidance was so far ahead of Wall Street expectations that the stock jumped well over 30% and the company briefly joined the $1 trillion market cap club. What really stood out on the earnings call was how well the company's data center business performed.

Image source: Nvidia investor presentation.

The chart above is from Nvidia's Q1 fiscal 2024 earnings investor presentation. For the quarter ended April 30, Nvidia's data center revenue grew 14% year over year, achieving a record of $4.3 billion. While setting a record in its database segment is exciting, investors really cheered due to the company's Q2 guidance of $11 billion in total revenue.

On the earnings call, Nvidia CFO Colette Kress attributed this growth to the data center business. She stated: "We expect this sequential growth to largely be driven by data center, reflecting a steep increase in demand related to generative AI and large language models. This demand has extended our data center visibility out a few quarters, and we have procured substantially higher supply for the second half of the year."

Kress' commentary on the data center business should not be taken lightly. While Q1 results were respectable, the more important point Kress makes is that demand for applications that leverage AI, such as quantum computing and machine learning, is so strong that Nvidia has a lot of visibility into future business.

This dynamic is important. For the last couple of years, investors have been told that semiconductor companies like Nvidia and Advanced Micro Devicesare experiencing myriad challenges such as supply chain disruptions and waning consumer demand due to the COVID-19 pandemic.

Now, however, some of these issues seem to be drying up. Nvidia's technology is currently center-stage when it comes to the AI revolution. Demand for its chips and its growing suite of data center products should continue to grow as companies of all sizes and across many different industries are figuring out how generative AI can be a catalyst for their business.

While Nvidia briefly joined $1 trillion stock cohort comprised of Apple, Microsoft, Alphabet, and Amazon, the company's market capitalization has fallen to roughly $955 billion. That's still not bad for a company that's generated $27 billion in revenue over the last 12 months.

Nvidia's current forward price-to-earnings (P/E) ratio is 52. Comparable companies like AMD and Taiwan Semiconductor trade with a forward P/E of 42 and 19, respectively. It's obvious that investors are placing a premium on Nvidia. In fact, there's an argument to be made that the future business from AI is, at least in part, priced into the stock.

Nonetheless, it's crucial for long-term investors to zoom out and consider the broader picture. Nvidia will provide ample exposure to AI for your portfolio, and the company's near-to-intermediate pipeline appears encouraging.

While the stock currently seems pricey compared to other chipmakers, there's something to be said for the fact that the company was the first of its kind to reach the trillion-dollar milestone. Moreover, as its data center business continues to blossom, Nvidia could very well cement itself as a member of the trillion-dollar market cap club in the long term.

Suzanne Frey, an executive at Alphabet, is a member of The Motley Fool's board of directors. Adam Spatacco has positions in Alphabet, Apple, Microsoft, and Nvidia. The Motley Fool has positions in and recommends Advanced Micro Devices, Alphabet, Apple, Microsoft, Nvidia, and Taiwan Semiconductor Manufacturing. The Motley Fool has a disclosure policy.

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This Artificial Intelligence Stock Is Headed Toward $1 Trillion. This ... - The Motley Fool

Automation technologies machines capable of performing productive tasks in place of human workers have played an enormous role in the economic history of humanity since the Industrial Revolution. From the automation of textile production in the 19th century to the mechanisation of agriculture in the early 20th century, historical waves of automation drove huge sectoral reallocations of labour and helped spur urbanisation and massive social change. These waves of automation were far from perfectly benevolent in the short and medium run (Acemoglu and Johnson 2023), but ultimately contributed to immense growth in production and living standards in industrialised countries.

Between the 1970s and early 2020s, the story of automation in high-income countries remained fairly consistent (Autor 2015). Advances in machinery, the rise of computers, and the proliferation of digital technologies led to the gradual automation of middle-skilled tasks ranging from factory-floor assembly-line tasks to clerical bookkeeping and accounting tasks (Autor et al. 2003). These tasks consisting of discrete, formalisable sequences of steps could increasingly be programmed into ever-cheaper computers and machines, displacing humans from many occupations.

These incremental waves of routine-biased automation contributed to a widely discussed polarisation of the labour market: middle-wage manufacturing and clerical jobs slowly melted away while new jobs appeared in low-wage cleaning, retail, and personal care occupations as well as high-wage managerial, technical, and professional occupations. As a consequence, wage and income inequality increased dramatically over this period, with demographic groups once concentrated in automation-stricken occupations falling behind (Acemoglu and Restrepo 2022) while higher-income professionals and capital owners pulled ahead (Moll et al. 2022).

Beginning in the 2010s, economists observed that the burgeoning field of machine learning could steer automation in a new direction. Previously, tasks could only be automated if they could be broken down into explicit sequences of steps that could be formally explained to a computer or machine. Many tasks that required creativity or tacit, hard-to-formalise knowledge from writing to medical diagnosis to graphic design hence avoided automation. But in the 2010s, economists noted that emerging deep learning techniques, which trained computers inductively on large existing datasets rather than providing explicit instructions, might eventually permit automation of even creative or tacit-knowledge-reliant tasks.

The first wave of machine-learning-based automation technologies targeted predictive tasks such as bail decisions, hiring decisions, or medical diagnoses (Kleinberg et al. 2018, Chalfin et al. 2016, Mullainathan and Obermeyer 2022). Machine-learning algorithms became increasingly good at making binary predictions from high-dimensional input data, prompting concerns about the future of occupations like radiology. But creative tasks still seemed securely insulated from the threat of automation.

This changed with the public release of impressive generative artificial intelligence systems in mid-to-late 2022. Trained using deep-learning techniques to generate large coherent bodies of text or well-produced images in response to written prompts, these systems were substantially more capable than any pre-existing chatbot or image generation tool. For the first time, it appeared that creative writing or design tasks might face imminent widespread automation.

In a recent paper (Noy and Zhang 2023), we report the results of an online experiment we conducted that provide a first look at the potential productivity and labour market impacts of text-based generative AI systems, specifically ChatGPT 3.5.

We conducted the experiment on Prolific, a survey platform that is a mainstay of academic social science research. We screened tens of thousands of respondents on the platform to identify a subset of college-educated respondents in our occupations of interest managers, human resource professionals, grant writers, marketers, consultants, and data analysts which were chosen based on our ability to come up with realistic, occupation-specific, 2030 minute writing tasks that we could administer through an online survey. Managers and HR professionals were assigned to write a sensitive email, marketers to write a press release for a hypothetical product, grant writers to write a grant application, consultants to write a short report, and data analysts to write an analysis plan. About 85% of participants rated the tasks as realistic or very realistic imitations of real tasks performed in their occupations.

Prolific respondents who passed our screening stage were invited to complete an hour-long survey involving two occupation-specific writing tasks. Participants were paid a base rate of $10 and were heavily incentivised to perform well on the tasks: their task submissions were graded by other Prolific respondents working in the same occupations, and they received up to $14 in bonus payments based on their grades. The average total payment in our sample was $17/hour, significantly exceeding the typical $12/hour on Prolific. Our combination of above-market pay and high-powered incentives successfully elicited substantial effort from participants, who spent an average of 27 minutes on the first task.

Between the first and second tasks, participants were randomised into a treatment or control group. Treated participants were told to sign up for ChatGPT and enter several sample prompts, showing them how to use the technology. Control participants were told to sign up for Overleaf (to keep survey time as similar as possible between treatment and control and minimise selective attrition, almost no control participants used Overleaf on the second task). Treated participants were told they were permitted to use ChatGPT on the second task if they found it helpful.

The treatment group overwhelmingly chose to use ChatGPT on the second task: 87% of those who successfully signed up for an account used it. Treated participants were very impressed with the technology, giving it an average usefulness score of 4.4 out of 5.0. Almost all users simply pasted the task prompt into ChatGPT and submitted an unedited or lightly edited version of its output. Contrary to expectations, few participants chose to use ChatGPT in other ways, such as using it to edit their own draft, to brainstorm ideas, or to write a rough draft before heavily editing its output.

Consequently, time spent on the second task dropped precipitously for the treatment group compared to the control group on the second task, decreasing by 40% (Figure 1 Panel A). Average grades rose by 18% (Figure 1 Panel B). The increase in grades largely reflected graders high opinion of pure-ChatGPT output compared to pure-human output, and does not seem to have reflected any value-added from the treated participants themselves.

Figure 1 Productivity effects

Why did the participants do so little editing of ChatGPTs output? One possibility is that they recognised clear deficiencies in the output or areas of potential improvement, but wanted to rush through the task as quickly as possible. Under this interpretation, participants were simply using ChatGPT as a time-saving device and ignoring its output quality, reducing the external validity of our experiment to the higher-stakes real world.

Three pieces of evidence contradict this interpretation. First, 40% of our participants were cross-randomised into a convex incentive scheme that promised them a substantial additional bonus payment for receiving a high grade of 6 or 7 out of 7. This provided an extra incentive to fix or improve ChatGPTs raw output, yet respondents in this group spent no more time editing on average than respondents in our main linear incentive group, and did not receive higher grades. Second, respondents who did choose to edit (or spent longer editing) did not receive higher grades than those who submitted unedited output. Third, many respondents clearly judged that ChatGPT was an output-improving device in addition to a time-saving device. At the end of the survey, some treated respondents were given an opportunity to revise or replace their pre-treatment task submission using ChatGPT; 19% fully replaced their entry with ChatGPTs output and a further 17% used ChatGPT as an editor. Our overall interpretation is that participants saw ChatGPTs output as high-quality and lacking obvious areas of improvement.

As a consequence of broadly uniform usage of ChatGPT in the treatment group, inequality in productivity between participants shrank dramatically, as shown in Figure 2. ChatGPT access allowed nearly everyone in the treated group to perform as well as the top humans in the control group.

Figure 2 Grade inequality decreases

How did participants react to being introduced to this startlingly productive technology? We asked participants about their enjoyment of each task; as Figure 3 Panel A shows, enjoyment rose by 0.5 standard deviations in the treatment group compared to the control group. Participants concerns about AI displacing workers in their occupation rose in the treatment group, as did excitement about AI augmenting workers in their occupation, while overall optimism about AI rose slightly. Respondents therefore greeted the technology enthusiastically overall, but not without trepidation. These gaps disappeared in subsequent resurveying.

Figure 3 Job satisfaction, self-efficacy, and beliefs about automation

We resurveyed participants two weeks and then two months after the experiment to track diffusion of ChatGPT into their real jobs. Two weeks out, 34% of treated and 18% of control respondents had used ChatGPT in their job in the past week; two months out, these figures were 42% and 27%. The slow increase in usage and persistent treatment-control gap suggest that diffusion of ChatGPT into real-world jobs remains somewhat slow and hampered by information frictions. Respondents not using ChatGPT in their main job reported a mix of reasons: lack of familiarity, lack of access at work, or lack of usefulness of ChatGPT due to the importance to their work of context-specific knowledge and style.

ChatGPT has a substantial impact on productivity in mid-level professional writing tasks, increasing speed and quality and narrowing the gap between higher- and lower-ability writers. Its aggregate impacts, however, will depend on complex general-equilibrium considerations that our experiment is unable to speak to. As we discuss in the paper, a number of factors ranging from the elasticity of demand for ChatGPT-relevant services, the particular skills ChatGPT best complements, and the nature of optimal production structures with ChatGPT will determine the impacts of ChatGPT-like technologies on employment, occupation, and wage structures.

Acemoglu, D and P Restrepo (2022), Tasks, Automation, and the Rise in US Wage Inequality, Econometrica 90(5).

Acemoglu, D and S Johnson (2023), Power and Progress: Our 1000-Year Struggle Over Technology and Prosperity, New York: Public Affairs.

Autor, D, F Levy and R Murnane (2003), The Skill Content of Recent Technological Change: An Empirical Exploration, Quarterly Journal of Economics 118(4).

Autor, D (2015), Why Are There Still So Many Jobs? The History and Future of Workplace Automation, Journal of Economic Perspectives 29(3).

Chalfin, A, O Danieli, A Hillis, Z Jelveh, M Luca, J Ludwig and S Mullainathan (2016), Productivity and Selection of Human Capital with Machine Learning, American Economic Review 106(5).

Kleinberg, J, H Lakkaraju, J Leskovec, J Ludwig and S Mullainathan (2018), Human Decisions and Machine Predictions, Quarterly Journal of Economics 133(1).

Moll, B, L Rachel and P Restrepo (2022), Uneven Growth: Automations Impact on Income and Wealth Inequality, Econometrica 90(6).

Mullainathan, S and Z Obermeyer (2022), Diagnosing Physician Error: A Machine Learning Approach to Low-Value Healthcare, Quarterly Journal of Economics 137(2).

Noy, S and W Zhang (2023), Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence, working paper.

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The productivity effects of generative artificial intelligence - CEPR

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Dublin, June 08, 2023 (GLOBE NEWSWIRE) -- The "Global Artificial Intelligence Industry - Forecast and Analysis 2023" report has been added to ResearchAndMarkets.com's offering.

The global AI market was valued at around $62.35 billion in 2020, and it is expected to grow at a compound annual growth rate (CAGR) of nearly 40% during the forecast period from 2021 to 2026.

This report serves as a valuable resource for industry stakeholders, investors, researchers, and professionals seeking comprehensive insights into the global AI industry, its market dynamics, and future prospects.

In recent years, the global artificial intelligence (AI) industry has experienced remarkable growth and transformation, revolutionizing various sectors and driving technological advancements. The industry has witnessed significant investments, with both established tech giants and emerging start-ups contributing to its development.

Multiple sectors have been rapidly adopting AI technologies to enhance their operations and decision-making processes. Industries such as healthcare, finance, military, and retail have all shown significant interest and investment in AI applications.

While North America remains a prominent market for AI, with the United States being a key contributor to the industry, Europe and the Asia Pacific region have also witnessed substantial growth and adoption of AI technologies.

Looking ahead, the global AI market is expected to continue its upward trajectory, driven by many emerging technologies. Moreover, the market outlook highlights the potential for AI to reshape industries further, opening up new possibilities and creating exciting opportunities for businesses worldwide.

The publisher presents a comprehensive analysis of the Global Artificial Intelligence (AI) Industry in its research report Global Artificial Intelligence Industry - Forecast and Analysis 2023.

The report contains the following:

It delves into the various aspects of AI, including its definition, evolution, and working principles. The report explores the differentiation between AI, machine learning, and deep learning, while highlighting the significance and potential impact of AI across industries.

With an extensive market analysis, the report offers insights into the current state and future growth of the global AI industry up to 2026. It examines market segmentation, trends, and the impact of COVID-19 on AI adoption.

The technology landscape section outlines the key components such as machine learning algorithms, natural language processing, computer vision, robotics, and emerging technologies in AI.

To provide a holistic view, the report conducts a SWOT analysis, PEST analysis, and Porter's Five Forces Strategy Analysis of the AI industry.

The regulatory landscape and regional analysis sections shed light on the global regulatory environment and the adoption of AI across different regions. The report also presents case studies and examples of real-world applications of AI, ranging from deepfake detection to speech recognition and automatic translation.

The report covers the major AI markets worldwide, including China, Germany, India, Japan, the United States, and others, highlighting their contributions to the AI industry. It examines investment trends, corporate investment, and the rise of AI start-ups.

Strategic recommendations are provided for market entry, growth strategies, partnerships, and collaborations in the AI industry. The report identifies key technology trends such as cloud computing, big data, and the Internet of Things (IoT) that are driving AI advancements. Additionally, it emphasizes the importance of addressing AI bias and ethical considerations.

The research concludes with a market outlook, highlighting future applications and emerging trends in the AI industry.

The competitive landscape section showcases major industry players, including Alibaba Cloud, Amazon Web Services, Baidu, Google, IBM, Nvidia, Microsoft, and others.

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Competitive Landscape

Key Topics Covered:

A. Executive Summary

B. Introduction to the Global Artificial Intelligence IndustryB.1 What is Artificial Intelligence?B.2 Industry Evolution and HistoryB.3 How does AI Work?B.4 What is the difference between AI, Machine Learning, & Deep Learning?B.5 Why is AI so Important?B.6 Pros & Cons of Artificial Intelligence?B.7 Weak AI vs. Strong AIB.8 Stages of Artificial IntelligenceB.8.1 Artificial Narrow IntelligenceB.8.2 Artificial General IntelligenceB.8.3 Artificial Super IntelligenceB.9 Types of Artificial IntelligenceB.10 Rise of Generative AI

C. Global Artificial Intelligence IndustryC.1 Industry DefinitionC.2 Market AnalysisC.3 Market SegmentationC.4 Market Size and GrowthC.5 Market Trends and OutlookC.6 Global Adoption of AIC.7 Impact of COVID-19 on the Industry

D. Global Artificial Intelligence Industry: Technology LandscapeD.1 Overview of Technology LandscapeD.2 Machine Learning AlgorithmsD.3 Natural Language ProcessingD.4 Computer VisionD.5 Robotics and AutomationD.6 Emerging Technologies in AI

E. Global Artificial Intelligence Industry: SWOT AnalysisE.1 Strengths to Build UponE.2 Weaknesses to OvercomeE.3 Opportunities to ExploitE.4 Threats to Overcome

F. Global Artificial Intelligence Industry: Porter's Five Forces Strategy AnalysisF.1 OverviewF.2 Bargaining Power of BuyersF.3 Bargaining Power of SuppliersF.4 Competitive Rivalry in the IndustryF.5 Threat of New EntrantsF.6 Threat of Substitutes

G. Global Artificial Intelligence Industry: PEST AnalysisG.1 Political FactorsG.2 Economic FactorsG.3 Social FactorsG.4 Technological Factors

H. Market DynamicsH.1 Market Trends & ChallengesH.2 Key Success Factors for Companies in AIH.3 Mergers and Acquisitions in the IndustryH.4 Industry Outlook for Profitability in AI

I. Why AI Matters?I.1 Economic Impact of AII.2 Productivity Gains from AII.3 Increase in Consumer DemandI.4 Job Loss or New Job Opportunities?

J. Global Artificial Intelligence Industry: Regulatory Landscape

K. Global Artificial Intelligence Market Analysis by ApplicationK.1 Overview of Applications of AIK.2 AgricultureK.3 Automotive & Autonomous VehiclesK.4 Aviation/AirlinesK.5 ChatbotsK.6 COVID-19 and AI-powered Drug DiscoveryK.7 DefenceK.8 EducationK.9 Finance and BankingK.10 GamingK.11 Industrial ApplicationsK.12 Industrial RobotsK.13 ManufacturingK.14 Media and EntertainmentK.15 Pharmaceuticals and HealthcareK.16 RetailK.17 Space ExplorationK.18 Technology and CommunicationsK.19 Transportation and LogisticsK.20 Utilities & Smart Grid

L. Regional AnalysisL.1 North AmericaL.2 EuropeL.3 Asia PacificL.4 Latin AmericaL.5 Middle East and Africa

M. Major AI MarketsM.1 Overview of the Leading AI MarketsM.2 ChinaM.3 GermanyM.4 IndiaM.5 IsraelM.6 JapanM.7 RussiaM.8 SingaporeM.9 South KoreaM.10 United KingdomM.11 United States

N. Global Investment in AIN.1 Corporate InvestmentN.2 Boom in AI Start-upsN.3 Investment by RegionN.4 Investment by Application AreasN.5 Major Investment Trends

O. Real-World Applications of AI: Case Studies and ExamplesO.1 Deepfake DetectionO.2 Image RecognitionO.3 Estimation of Human PoseO.4 Semantic SegmentationO.5 Embodied VisionO.6 Computer Vision - Video AnalysisO.7 SuperGLUEO.8 Stanford Question Answering Dataset (SQuAD)O.9 OpenAI and ChatGPTO.10 Speech RecognitionO.11 Automatic Translation

P. Strategic RecommendationsP.1 Market Entry StrategiesP.2 Growth StrategiesP.3 Partnerships and CollaborationsP.4 Technology Trends that are Advancing AIP.4.5 Internet of Thing (IoT)P.5 Dealing with AI Bias

Q. Global Artificial Intelligence Industry: Market OutlookQ.1 Global Market OutlookQ.2 Future ApplicationsQ.3 Future AI Market Trends to Watch Out For

R. Global Artificial Intelligence Industry: Competitive LandscapeR.1 Competition in the IndustryR.2 Major Industry Players

For more information about this report visit https://www.researchandmarkets.com/r/7in3dg

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.

Originally posted here:
Global Artificial Intelligence (AI) Industry Report 2023-2026: Economic Impact of AI, Productivity Gains from AI, Increase in Consumer Demand, Job...