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Airlines aiming for total revenue optimization need intelligent solutions. While artificial intelligence and deep learning algorithms promise better forecasting capabilities, those systems can only truly shine when coupled with the flexibility and human touch of a data analyst.

FLYR

Commercial airlines and other travel and transportation leaders are facing significant challenges in managing pricing, demand, and logistics in todays volatile environment. This summers travel disruptions have laid bare the potential for intermittent hiccups in post-pandemic operations to have drastic effects on customer satisfaction and revenue opportunities.

With travelers patience wearing thin, airlines need to reinforce their people, processes, and technologies. By building artificial intelligence (AI) solutions into processes across their organizations, airlines can leverage their data, analysts, and revenue management opportunities to take advantage of new business fundamentals in this changing environment.

Artificial intelligence isnt replacing the airline data analysts job, said Alex Mans, founder and CEO of FLYR Labs, a technology company driving commercial optimization for airlines. But it is changing their role and hopefully unlocking their potential to drive revenue and improve operational efficiencies.

Airline data can be difficult to parse, but artificial intelligence makes it easier. Its impossible for humans to manually process all the information airlines are collecting from digital sources, but deep learning neural networks can provide effective forecasts that give analysts the confidence to make better decisions.

Historically, the leading forecasting type has been linear, regression-based models, where analysts look at very concentrated year-over-year patterns, Mans said. The problem is that theres just not enough data on any single flight for a given point in time to drive accurate forecasts in a volatile environment. Legacy systems are really bad at determining whether booking one seat on a given flight has a meaningful impact on the outcome.

To power deep learning algorithms, airlines feed neural networks vast amounts of historical data such as bookings, searches, events, promotions, and competitive prices resulting in forecasts that keep analysts better informed on revenue and load-factor performance into the future.

They can compare how the actual performance builds against the forecast data as the departure date approaches, Mans said. The real value comes when, with a platform such as ours, analysts come to trust the forecast and can start using it to inform strategic decisions instead of viewing it as a loose guideline.

According to Mans, artificial intelligence should be thought of as an airline data analysts smart sidekick its not replacing the analysts job, but rather enhancing the analysts ability to make coordinated operational decisions in areas where automation alone is insufficient.

In the past, analysts have not had accurate forecasts, so most of their decisions were based on instinct, Mans said. On top of that, they havent had good user interfaces for consuming that data. We generate much better forecasts, enable smarter workflows, and provide a dedicated user interface where analysts can easily access and filter the data and then use the resulting information. With better load and revenue forecasts at any level of granularity across the network, they can do amazing things.

For example, if an analyst sees a cluster of flights months into the future with a 99-percent forecasted load factor, they can alert colleagues in charge of scheduling and suggest adding capacity. At most airlines, where functions across the organizations are typically siloed, this kind of cross-functional collaboration between commercial teams isnt common.

All it takes to start breaking down those silos is for other teams to have access to the same information that the revenue management team has access to, Mans said. At the end of the day, different departments are trying to achieve the same results: maximize revenue and contain costs.

In addition to playing gatekeeper of that information, the analysts role will evolve to support a variety of critical functions across the organization.

For one thing, they can look around corners that the data itself cant see, Mans said. The analyst might know that a schedule change is coming, but unless that information is passed to our system, we have no awareness. Artificial intelligence doesnt know everything. Another thing to note is that optimizing for maximum revenue isnt always the goal. An airline entering a new market may want to follow a non-revenue-optimal strategy focused on market control or market share, so the analyst is needed to fine-tune that strategy. Or consider promotions every airline runs tons of promotions throughout the year, whether tied to their credit card program, certain destinations, or other variables that require the analyst to actively work with our platform and their marketing team to achieve the best results.

For airlines to weather the storm of todays unprecedented industry disruptions, dynamic pricing powered by deep learning algorithms is essential. FLYR was built to provide a singular platform that helps airlines manage data, break past data silos with consistently accurate forecasts that can be accessed by anybody in the organization, and achieve total revenue management across all of their products.

Our job is to help airlines effectively price everything they want to sell, including ancillaries like seat selection, extra baggage, priority boarding, and other upsells, Mans said. FLYRs operating system provides a vertically integrated SaaS platform across data management, forecasting, pricing, automation, business intelligence, and reporting which includes simulation and scenario evaluation while also removing constraints within e-commerce and fulfillment thanks to acquisitions such as Newshore so airlines can get stuff done faster and more efficiently. Thats what were building as a company, and thats the future of work within this industry.

Join us on October 19th at 11:00 a.m. ET for a webinar featuring FLYR founder and CEO Alex Mans, How Artificial Intelligence Is Reshaping the Travel Business. Register today

For more information on how FLYR is helping airlines achieve total revenue optimization, check out their latest whitepaper with IATA.

This content was created collaboratively by FLYR and Skifts branded content studio, SkiftX.

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How Artificial Intelligence Is Influencing the Future of Work in the Airline Industry - Skift Travel News

PALM BEACH, Fla., Sept. 15, 2022 /PRNewswire/ -- FinancialNewsMedia.com News Commentary - Artificial Intelligence (AI) is crossing over to new markets and brings additional revenues along with it. Increasing demand for Intelligent Tutoring Systems (ITSs) is fueling the AI in education industry growth. ITSs have a common aim of enabling learning effectively. The growing integration of ITS into the learning process has assisted in improving students learning styles, offering personalized tutoring and high-quality education to the students, thereby accelerating industry statistics. Cloud computing technology is increasingly being used by educators, faculties, facilitators, and students at schools & higher education institutes to improve productivity and the overall learning experience. Cloud computing allows schools and universities to upgrade their prevailing infrastructure with advanced technologies without any substantial increase in their capital costs. A reportfrom Global Market Insights projected that AI in Education Marketsize is set to surpass USD 80 billion by 2030. The report said: "There is a growing demand for outsourcing the management of AI in educational platforms. AI in education managed service segment is estimated to grow at a CAGR of over 55% through 2030 impelled by the increasing usage of intelligent algorithms among managed service providers. The cost-effectiveness of AI solutions is a major factor promoting the acceptance of AI among MSPs." Active Tech Companies in the markets today include: Amesite Inc. (NASDAQ: AMST), Microsoft Corporation (NASDAQ: MSFT), 2U, Inc. (NASDAQ: TWOU), Blackbaud(NASDAQ: BLKB),Instructure Holdings, Inc. (NYSE: INST).

The report added: "Natural Language Processing (NLP) held a market share of nearly 60% in 2021 owing to the growing adoption of NLP technology across educational institutes. NLP is a branch of AI, which assists computers in understanding, interpreting, and operating human language, filling human communication & computer interpretation gaps. Learning platforms and virtual facilitators accounted for about 50% of the artificial intelligence in education market share in 2021 led by the growing usage of various technologies by educational institutions to improve the quality of education for students. On-premise deployment held a market share of around 80% in 2021 credited to the security offered by on-premise deployment models. The on-premise storage of this data helps in providing instant access to the data as and when required with complete control over the system. These solutions also enable privacy and security of the student's data, accelerating their demand in the market."

Amesite Inc.(NASDAQ: AMST) BREAKING NEWS: Amesite and NAFEO Announce Addition of New Member Universities to Alliance - Amesite Inc., a leading artificial intelligence software company offering a cloud-based learning platform for business and education markets, announces that five new member universities have joined their collaborative alliance with the National Association for Equal Opportunity in Higher Education (NAFEO). New members include:

which have agreed to join NAFEO's Center for Opportunity and Equity (COE), a vehicle that NAFEO intends to support with a $30M fundraising effort to bring online learning resources to a first group of NAFEO's constituents, including Historically Black Colleges and Universities (HBCUs) and Predominantly Black Institutions (PBIs). Members of NAFEO's COE will have the opportunity to utilize NAFEO's planned Learning Community Environment, powered by Amesite, to deliver eLearning to build professional skills. The NAFEO members collectively enroll more than 700,000 students, and have over 7 million living alumni, all of whom are anticipated to benefit from the COE.

Lezli Baskerville, Esq., CEO of NAFEO, stated,"We are growing this alliance to deliver critical online learning infrastructure to our members through NAFEO's Center for Opportunity and Equity because it is the most efficient way to build enrollments and impact. Each university building its own infrastructure would take a great deal of time and be an inefficient use of resources. By working together and leveraging the strong history of trust and collaboration within NAFEO to engage and uplift our members, we ultimately intend to deliver millions of effective and affordable elearning opportunities. These will advance our constituencies economically, while enabling our universities to retain their storied brands and build greater impact." CONTINUED Read this full release for Amesite at: https://ir.amesite.com/

Other recent developments in the tech industry include:

Instructure Holdings, Inc. (NYSE: INST) recently announced it has earned a renewed Ed-Fi Managed Operational Data Store and API Platform Badge for Elevate K-12 Analytics and the Ed-Fi API Provider Badge for Elevate Standards Alignment (formerly Academic Benchmarks), both for another two years. The distinction validates Instructure as an ongoing, trusted Ed-Fi Operational Data Store, Analytics provider and API partner.

Instructure's renewed badges reaffirm the ongoing contributions the SaaS platform is making as part of the collaborative Ed-Fi Community, which advocates for the effective use of data at scale in education agencies of all sizes through a data standard. Because Instructure's Elevate products leverage the Ed-Fi standard and technology suite, any state or local education agency can more effectively leverage their own data to support administrators, educators, students and parents. Districts have hundreds of applications in use and typically this data remains siloed and unused. By seamlessly and securely bringing these data sources together through a data standard; administrators, educators and parents can more clearly see important data in real-time to support student success.

Blackbaud(NASDAQ: BLKB),the world's leading cloud softwarecompany powering social good, recently announced that it has acquiredKilter. The acquisition will allow Blackbaud toexpand activity-based peer-to-peer fundraising engagement, to support activity-based health and wellness initiatives for socially responsible companies, and to grow the ways individuals can connect with the causes they care about most through the activities they love.

Kilter is an intuitive, gamified, activity-based engagement app boasting virtually limitless activity type choices. Kilter expands activity-based engagement beyond the familiar options of running, walking and cycling, enabling users to track new, popular and personally relevant activities, from pickleball to meditation to motorcycling and more.

2U, Inc. (NASDAQ: TWOU),the parent company of leading global online learning platformedX, recently announced a significant update to its partnership model as part of its transition to a platform company, unveiling new revenue share options that give universities greater flexibility, as well as new tools to assist partners in lowering the tuition of their online degree programs at their discretion.

"As we embrace our future as a platform company under the edX brand, we're taking bold and important steps to support our university partners in transforming their institutions, expanding access, and helping bring down the cost of higher education,"said 2U Co-Founder and CEO Christopher "Chip" Paucek. "Higher education is at an inflection point, with learners demanding more flexibility and better ROI. 2U and edX strongly believe that greater affordability is better for students, better for universities, better for the company, and critical to the future of higher education."

Microsoft Corporation (NASDAQ: MSFT) recently Barclays Bank PLC (Barclays) and Microsoft Corp. announced that Barclays has deployed Microsoft Teams as its preferred collaboration platform, powering collaboration for more than 120,000 colleagues and service partners in key locations around the globe. Under the agreement, Barclays is streamlining its existing communications and collaboration solutions, with Teams replacing several point solutions previously in use across the company.

As part of its efforts to better connect employees across its business units and functions, Barclays and Microsoft jointly executed a deployment plan for the use of Teams across the company.This plan included enhancing the data retention, search and retrieval capabilities available within Microsoft Purview to meet Barclays' needs.

DISCLAIMER: FN Media Group LLC (FNM), which owns and operates FinancialNewsMedia.com and MarketNewsUpdates.com, is a third party publisher and news dissemination service provider, which disseminates electronic information through multiple online media channels. FNM is NOT affiliated in any manner with any company mentioned herein. FNM and its affiliated companies are a news dissemination solutions provider and are NOT a registered broker/dealer/analyst/adviser, holds no investment licenses and may NOT sell, offer to sell or offer to buy any security. FNM's market updates, news alerts and corporate profiles are NOT a solicitation or recommendation to buy, sell or hold securities. The material in this release is intended to be strictly informational and is NEVER to be construed or interpreted as research material. All readers are strongly urged to perform research and due diligence on their own and consult =a licensed financial professional before considering any level of investing in stocks. All material included herein is republished content and details which were previously disseminated by the companies mentioned in this release. FNM is not liable for any investment decisions by its readers or subscribers. Investors are cautioned that they may lose all or a portion of their investment when investing in stocks. For current services performed FNM has been compensated twenty six hundred dollars for news coverage of the current press releases issued by Amesite Inc. by a non-affiliated third party. FNM HOLDS NO SHARES OF ANY COMPANY NAMED IN THIS RELEASE.

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Artificial Intelligence (AI) In Education Market Size Is Expected to Hit $80 Billion By 2030 - PR Newswire

When I was a kid, my favorite poem was Shel Silversteins The Homework Machine, which summed up my childhood fantasy: a machine that could do my homework at the press of a button. Decades later that technology, the innocuously titled GPT-3, has arrived. It threatens many aspects of university education above all, college writing.

The web-based GPT-3 software program, which was developed by an Elon Musk-backed nonprofit called OpenAI, is a kind of omniscient Siri or Alexa that can turn any prompt into prose. You type in a query say, a list of ingredients (what can I make with eggs, garlic, mushrooms, butter, and feta cheese?) or a genre and prompt (write an inspiring TED Talk on the ways in which authentic leaders can change the world) and GPT-3 spits out a written response. These outputs can be astonishingly specific and tailored. When asked to write a song protesting inhumane treatment of animals in the style of Bob Dylan, the program clearly draws on themes from Dylans Blowin in the Wind:

How many more creatures must suffer?How many more must die?Before we open up our eyesAnd see the harm were causing?

When asked to treat the same issue in the style of Shakespeare, it produces stanzas of iambic tetrameter in appropriately archaic English:

By all the gods that guide this EarthBy all the stars that fill the skyI swear to end this wretched dearthThis blight of blood and butchery.

GPT-3 can write essays, op-eds, Tweets, jokes (admittedly just dad jokes for now), dialogue, advertisements, text messages, and restaurant reviews, to give just a few examples. Each time you click the submit button, the machine learning algorithm pulls from the wisdom of the entire internet and generates a unique output, so that no two end products are the same.

The quality of GPT-3s writing is often striking. I asked the AI to discuss how free speech threatens a dictatorship, by drawing on free speech battles in China and Russia and how these relate to the First Amendment of the U.S. Constitution. The resulting text begins, Free speech is vital to the success of any democracy, but it can also be a thorn in the side of autocrats who seek to control the flow of information and quash dissent. Impressive.

From an essay written by the GPT-3 software program

The current iteration of GPT-3 has its quirks and limitations, to be sure. Most notably, it will write absolutely anything. It will generate a full essay on how George Washington invented the internet or an eerily informed response to 10 steps a serial killer can take to get away with murder. In addition, it stumbles over complex writing tasks. It cannot craft a novel or even a decent short story. Its attempts at scholarly writing I asked it to generate an article on social-role theory and negotiation outcomes are laughable. But how long before the capability is there? Six months ago, GPT-3 struggled with rudimentary queries, and today it can write a reasonable blog post discussing ways an employee can get a promotion from a reluctant boss.

Since the output of every inquiry is original, GPT-3s products cannot be detected by anti-plagiarism software. Anyone can create an account for GPT-3. Each inquiry comes at a cost, but its usually less than a penny and the turnaround is instantaneous. Hiring someone to write a college-level essay, in contrast, currently costs $15 to $35 per page. The near-free price point of GPT-3 is likely to entice many students who would otherwise be priced out of essay-writing services.

It wont be long before GPT-3, and the inevitable copycats, infiltrate the university. The technology is just too good and too cheap not to make its way into the hands of students who would prefer not to spend an evening perfecting the essay I routinely assign on the leadership style of Elon Musk. Ironic that he has bankrolled the technology that makes this evasion possible.

To help me think through what the collision of AI and higher ed might entail, I naturally asked GPT-3 to write an op-ed exploring the ramifications of GPT-3 threatening the integrity of college essays. GPT-3 noted, with mechanical unself-consciousness, that it threatened to undermine the value of a college education. If anyone can produce a high-quality essay using an AI system, it continued, then whats the point of spending four years (and often a lot of money) getting a degree? College degrees would become little more than pieces of paper if they can be easily replicated by machines.

The effects on college students themselves, the algorithm wrote, would be mixed: On the positive side, students would be able to focus on other aspects of their studies and would not have to spend time worrying about writing essays. On the negative side, however, they will not be able to communicate effectively and will have trouble in their future careers. Here GPT-3 may actually be understating the threat to writing: Given the rapid development of AI, what percent of college freshmen today will have jobs that require writing at all by the time they graduate? Some who would once have pursued writing-focused careers will find themselves instead managing the inputs and outputs of AI. And once AI can automate that, even those employees may become redundant. In this new world, the argument for writing as a practical necessity looks decidedly weaker. Even business schools may soon take a liberal-arts approach, framing writing not as career prep but as the foundation of a rich and meaningful life.

So what is a college professor to do? I put the question to GPT-3, which acknowledged that there is no easy answer to this question. Still, I think we can take some sensible measures to reduce the use of GPT-3 or at least push back the clock on its adoption by students. Professors can require students to draw on in-class material in their essays, and to revise their work in response to instructor feedback. We can insist that students cite their sources fully and accurately (something that GPT-3 currently cant do well). We can ask students to produce work in forms that AI cannot (yet) effectively create, such as podcasts, PowerPoints, and verbal presentations. And we can design writing prompts that GPT-3 wont be able to effectively address, such as those that focus on local or university-specific challenges that are not widely discussed online. If necessary, we could even require students to write assignments in an offline, proctored computer lab.

Eventually, we might enter the if you cant beat em, join em phase, in which professors ask students to use AI as a tool and assess their ability to analyze and improve the output. (I am currently experimenting with a minor assignment along these lines.) A recent project on Beethovens 10th symphony suggests how such projects might work. When he died, Beethoven had composed only 5 percent of his 10th symphony. A handful of Beethoven scholars fed the short, completed section into an AI that generated thousands of potential versions of the rest of the symphony. The scholars then sifted through the AI-generated material, identified the best parts, and pieced them together to create a complete symphony. To my somewhat limited ear, it sounds just like Beethoven.

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Will Artificial Intelligence Kill College Writing? - The Chronicle of Higher Education

SEATTLE, Wash.-

For over two years, protein structure prediction has been changed by machine learning. On Sept. 15, two science related research talk about a similar idea in the revolution of protein design.

The findings show how machine learning can create protein molecules that are more accurate and made quicker than before.

With these new software tools, we should be able to find solutions to long-standing challenges in medicine, energy, and technology, said senior author David Baker, professor of biochemistry at the University of Washington School of Medicine.

The algorithm used in machine learning which includes RoseTTAFold have been trained to predict the smaller detailed shapes if natural proteins based on their amino acid sequences.

Machine learning is a type of artificial intelligence that allows computers to learn from data without having to be programmed.

A.I. has the ability to generate protein in two ways. One being akin to DALL-E or other A.I. tools that produce an output from simple prompts. The second is the autocomplete feature we can find in a search bar.

As a way of making things go by faster the A.I. team created a new algorithm that creates amino acid sequences. This tool, called ProteinMPNN, creates the sequence in one second. That's over 200 minutes faster than previous best software.

The Baker Lab also says combining new machine learning tools could reliably generate new proteins that functioned in the laboratory. Among those were the nanoscale ring that could make up part of a custom nanomachines.

Read the rest here:
University of Washington graduates use artificial intelligence to create new proteins - NBC Right Now

DUBLIN, Sept. 14, 2022 /PRNewswire/ --The "Artificial Intelligence Market Size, Share & Trends Analysis Report by Solution, by Technology (Deep Learning, Machine Learning, Natural Language Processing, Machine Vision), by End Use, by Region, and Segment Forecasts, 2022-2030" report has been added to ResearchAndMarkets.com's offering.

The global artificial intelligence market size is expected to reach USD 1,811.8 billion by 2030. The market is anticipated to expand at a CAGR of 38.1% from 2022 to 2030.

Artificial Intelligence (AI) denotes the concept and development of computing systems capable of performing tasks customarily requiring human assistance, such as decision-making, speech recognition, visual perception, and language translation. AI uses algorithms to understand human speech, visually recognize objects, and process information. These algorithms are used for data processing, calculation, and automated reasoning.

Artificial intelligence researchers continuously improve algorithms for various aspects, as conventional algorithms have drawbacks regarding accuracy and efficiency. These advancements have led manufacturers and technology developers to focus on developing standard algorithms. Recently, several developments have been carried out for enhancing artificial intelligence algorithms. For instance, in May 2020, International Business Machines Corporation announced a wide range of new AI-powered services and capabilities, namely IBM Watson AIOps, for enterprise automation. These services are designed to help automate the IT infrastructures and make them more resilient and cost reduction.

Various companies are implementing AI-based solutions such as RPA (Robotic Process Automation) to enhance the process workflows to handle and automate repetitive tasks. AI-based solutions are also being coupled with the IoT (Internet of Things) to provide robust results for various business processes. For Instance, Microsoft announced to invest USD 1 billion in OpenAI, a San Francisco-based company. The two businesses teamed up to create AI supercomputing technology on Microsoft's Azure cloud.

The COVID-19 pandemic has emerged as an opportunity for AI-enabled computer systems to fight against the epidemic as several tech companies are working on preventing, mitigating, and containing the virus. For instance, LeewayHertz, a U.S.-based custom software development company, offers technology solutions using AI tools and techniques, including the Face Mask Detection System to identify individuals without a mask and the Human Presence System to monitor patients remotely. Besides, Voxel51 Inc., a U.S.-based artificial intelligence start-up, has developed Voxel51 PDI (Physical Distancing Index) to measure the impact of the global pandemic on social behavior across the world.

AI-powered computer platforms or solutions are being used to fight against COVID - 19 in numerous applications, such as early alerts, tracking and prediction, data dashboards, diagnosis and prognosis, treatments and cures, and maintaining social control. Data dashboards that can visualize the pandemic have emerged with the need for coronavirus tracking and prediction. For instance, Microsoft Corporation's Bing's AI tracker gives a global overview of the pandemic's current statistics.

Artificial Intelligence Market Report Highlights

Key Topics Covered:

Chapter 1 Methodology and Scope

Chapter 2 Executive Summary

Chapter 3 Market Variables, Trends & Scope3.1 Market Trends & Outlook3.2 Market Segmentation & Scope3.3 Artificial Intelligence Size and Growth Prospects3.4 Artificial Intelligence-Value Chain Analysis3.5 Artificial Intelligence Market Dynamics3.5.1 Market Drivers3.5.1.1 Economical parallel processing set-up3.5.1.2 Potential R&D in artificial intelligence systems3.5.1.3 Big data fueling AI and Machine Learning profoundly3.5.1.4 Increasing Cross-Industry Partnerships and Collaborations3.5.1.5 AI to counter unmet clinical demand3.5.2 Market Restraint3.5.2.1 Vast demonstrative data requirement3.6 Penetration & Growth Prospect Mapping3.7 Industry Analysis-Porter's3.7.1 Supplier Power3.7.2 Buyer Power3.7.3 Substitution Threat3.7.4 Threat From New Entrant3.7.5 Competitive Rivalry3.8 Company Market Share Analysis, 20213.9 Artificial Intelligence-PEST Analysis3.9.1 Political3.9.2 Economic3.9.3 Social3.9.4 Technology3.10 Artificial Intelligence-COVID-19 Impact Analysis

Chapter 4 Artificial Intelligence Market: Solution Estimates & Trend Analysis4.1 Artificial Intelligence Market: Solution Movement Analysis4.1.1 Hardware4.1.1.1. Hardware Artificial Intelligence Market, by Region, 2017-20304.1.1.1.1. Market Estimates and Forecasts, 2017-2030 (USD Billion)4.1.2 Software4.1.2.1. Software Artificial Intelligence Market, by Region, 2017-20304.1.2.1.1. Market Estimates and Forecasts, 2017-2030 (USD Billion)4.1.3 Services4.1.3.1. Services Artificial Intelligence Market, by Region, 2017-20304.1.3.1.1. Market Estimates and Forecasts, 2017-2030 (USD Billion)

Chapter 5 Artificial Intelligence Market: Technology Estimates & Trend Analysis5.1 Artificial Intelligence Market: Technology Movement Analysis5.1.1 Deep Learning5.1.1.1. Deep Learning Artificial Intelligence System Market, by Region, 2017-20305.1.1.1.1. Market Estimates and Forecasts, 2017-2030 (USD Billion)5.1.2 Machine Learning5.1.2.1. Machine Learning Artificial Intelligence System Market, by Region, 2017-20305.1.2.1.1. Market Estimates and Forecasts, 2017-2030 (USD Billion)5.1.3 Nlp5.1.3.1. Nlp Artificial Intelligence System Market, by Region, 2017-20305.1.3.1.1. Market Estimates and Forecasts, 2017-2030 (USD Billion)5.1.4 Machine Vision 5.1.4.1. Machine Vision Artificial Intelligence System Market, by Region, 2017-20305.1.4.1.1. Market Estimates and Forecasts, 2017-2030 (USD Billion)

Chapter 6 Artificial Intelligence Market: End-Use Estimates & Trend Analysis6.1 Artificial Intelligence Market: End-Use Movement Analysis6.2 Artificial Intelligence Market: End-Use Trends6.2.1 Healthcare6.2.1.1. Healthcare Artificial Intelligence Market, by Region, 2017-20306.2.1.1.1. Market Estimates and Forecasts, 2017-2030 (USD Billion)6.2.1.2. Healthcare by Use Case6.2.1.2.1. Global Ai Healthcare Market, by Use Case, 2017-2030 (USD Billion)6.2.2 Bfsi6.2.2.1. Bfsi Artificial Intelligence Market, by Region, 2017-20306.2.2.1.1. Market Estimates and Forecasts, 2017-2030 (USD Billion)6.2.2.2. Bfsi by End Use6.2.2.2.1. Global Ai Bfsi Market, by End Use, 2017-2030 (USD Billion)6.2.3 Law6.2.3.1. Law Artificial Intelligence Market, by Region, 2017-20306.2.3.1.1. Market Estimates and Forecasts, 2017-2030 (USD Billion)6.2.4 Retail6.2.4.1. Retail Artificial Intelligence Market, by Region, 2017-20306.2.4.1.1. Market Estimates and Forecasts, 2017-2030 (USD Billion)6.2.5 Advertising & Media6.2.5.1. Advertising & Media Artificial Intelligence Market, by Region, 2017-20306.2.5.1.1. Market Estimates and Forecasts, 2017-2030 (USD Billion)6.2.6 Automotive & Transportation6.2.6.1. Automotive & Transportation Artificial Intelligence Market, by Region, 2017-20306.2.6.1.1. Market Estimates and Forecasts, 2017-2030 (USD Billion)6.2.7 Agriculture6.2.7.1. Agriculture Artificial Intelligence Market, by Region, 2017-20306.2.7.1.1. Market Estimates and Forecasts, 2017-2030 (USD Billion)6.2.8 Manufacturing6.2.8.1. Manufacturing Artificial Intelligence Market, by Region, 2017-20306.2.8.1.1. Market Estimates and Forecasts, 2017-2030 (USD Billion)6.2.9 Others6.2.9.1. Others Artificial Intelligence Market, by Region, 2017-20306.2.9.1.1. Market Estimates and Forecasts, 2017-2030 (USD Billion)

Chapter 7 Artificial Intelligence Market: Regional Estimates & Trend Analysis

Chapter 8 Competitive Landscape8.1 Company Profiles8.1.1 Advanced Micro Devices8.1.1.1 Company overview8.1.1.2 Financial performance8.1.1.3 Product benchmarking8.1.1.4 Strategic Initiatives8.1.2 AiCure8.1.2.1 Company overview8.1.2.2 Product benchmarking8.1.2.3 Strategic Initiatives8.1.3 Arm Limited8.1.3.1 Company overview8.1.3.2 Product benchmarking8.1.3.3 Strategic Initiatives8.1.4 Atomwise, Inc.8.1.4.1 Company overview8.1.4.2 Product benchmarking8.1.4.3 Strategic Initiatives8.1.5 Ayasdi AI LLC8.1.5.1 Company overview8.1.5.2 Product benchmarking8.1.5.3 Strategic Initiatives8.1.6 Baidu, Inc.8.1.6.1 Company overview8.1.6.2 Financial performance8.1.6.3 Product benchmarking8.1.6.4 Strategic Initiatives8.1.7 Clarifai, Inc8.1.7.1 Company overview8.1.7.2 Product benchmarking8.1.7.3 Strategic Initiatives8.1.8 Cyrcadia Health8.1.8.1 Company overview8.1.8.2 Product benchmarking8.1.8.3 Strategic Initiatives8.1.9 Enlitic, Inc.8.1.9.1 Company overview8.1.9.2 Product benchmarking8.1.9.3 Strategic Initiatives8.1.10 Google LLC8.1.10.1 Company overview8.1.10.2 Financial performance8.1.10.3 Product benchmarking8.1.10.4 Strategic Initiatives8.1.11 H2O.ai.8.1.11.1 Company overview8.1.11.2 Product benchmarking8.1.11.3 Strategic Initiatives8.1.12 HyperVerge, Inc.8.1.12.1 Company overview8.1.12.2 Product benchmarking8.1.12.3 Strategic Initiatives8.1.13 International Business Machines Corporation8.1.13.1 Company overview8.1.13.2 Financial performance8.1.13.3 Product benchmarking8.1.13.4 Strategic Initiatives8.1.14 IBM Watson Health8.1.14.1 Company overview8.1.14.2 Financial performance8.1.14.3 Product benchmarking8.1.14.4 Strategic Initiatives8.1.15 Intel Corporation8.1.15.1 Company overview8.1.15.2 Financial performance8.1.15.3 Product benchmarking8.1.15.4 Strategic Initiatives8.1.16 Iris.ai AS.8.1.16.1 Company overview8.1.16.2 Product benchmarking8.1.16.3 Strategic Initiatives8.1.17 Lifegraph8.1.17.1 Company overview8.1.17.2 Product benchmarking8.1.17.3 Strategic Initiatives8.1.18 Microsoft8.1.18.1 Company overview8.1.18.2 Financial performance8.1.18.3 Product benchmarking8.1.18.4 Strategic Initiatives8.1.19 NVIDIA Corporation8.1.19.1 Company overview8.1.19.2 Financial performance8.1.19.3 Product benchmarking8.1.14.4 Strategic Initiatives8.1.20 Sensely, Inc.8.1.20.1 Company overview8.1.20.2 Product benchmarking8.1.20.3 Strategic Initiatives8.1.21 Zebra Medical Vision, Inc.8.1.21.1 Company overview8.1.21.2 Product benchmarking8.1.21.3 Strategic Initiatives

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Insights on the Artificial Intelligence Global Market to 2030 - Featuring Baidu, Clarifai and Google Among Others - PR Newswire