Mediaboss Marketing

Artificial Intelligence Software Market 2022-2028

A New Market Study, Titled Artificial Intelligence Software Market Upcoming Trends, Growth Drivers and Challenges has been featured on fusionmarketresearch.

Description

Artificial intelligence (AI, also machine intelligence, MI) is intelligence displayed by machines, in contrast with the natural intelligence (NI) displayed by humans and other animals. Artificial intelligence software is Software that is capable of intelligent behavior. In creating intelligent software, this involves simulating a number of capabilities, including reasoning, learning, problem solving, perception, and knowledge representation.

Request Free Sample Report @ https://www.fusionmarketresearch.com/sample_request/(COVID-Version)-Global-Artificial-Intelligence-Software-Market-2021/75354

This global study of theArtificial Intelligence SoftwareMarketoffers an overview of the existing market trends, drivers, restrictions, and metrics and also offers a viewpoint for important segments. The report also tracks product and services demand growth forecasts for the market. There is also to the study approach a detailed segmental review. A regional study of the globalArtificial Intelligence Softwareindustryis also carried out in North America, Latin America, Asia-Pacific, Europe, and the Near East & Africa.The report mentions growth parameters in the regional markets along with major players dominating the regional growth.

This research covers COVID-19 impacts on the upstream, midstream and downstream industries. Moreover, this research provides an in-depth market evaluation by highlighting information on various aspects covering market dynamics like drivers, barriers, opportunities, threats, and industry news & trends. In the end, this report also provides in-depth analysis and professional advices on how to face the post COIVD-19 period.

The report offers detailed coverage of Artificial Intelligence Software industry and main market trends with impact of coronavirus. The market research includes historical and forecast market data, demand, application details, price trends, and company shares of the leading Artificial Intelligence Software by geography. The report splits the market size, by volume and value, on the basis of application type and geography.First, this report covers the present status and the future prospects of the global Artificial Intelligence Software market for 2016-2025.

And in this report, we analyze global market from 5 geographies: Asia-Pacific[China, Southeast Asia, India, Japan, Korea, Western Asia], Europe[Germany, UK, France, Italy, Russia, Spain, Netherlands, Turkey, Switzerland], North America[United States, Canada, Mexico], Middle East & Africa[GCC, North Africa, South Africa], South America[Brazil, Argentina, Columbia, Chile, Peru].At the same time, we classify Artificial Intelligence Software according to the type, application by geography. More importantly, the report includes major countries market based on the type and application.

Key CompaniesBaiduGoogleIBMMicrosoftSAPIntelSalesforceBrighterionKITT.AIIFlyTekMegvii TechnologyAlbert TechnologiesH2O.aiBrainasoftYseopIpsoftNanoRep(LogMeIn)Ada SupportAstute SolutionsIDEAL.comWipro

Market by TypeOn-PremiseCloud-based

Market by ApplicationVoice ProcessingText ProcessingImage Processing

Ask Queries @ https://www.fusionmarketresearch.com/enquiry.php/(COVID-Version)-Global-Artificial-Intelligence-Software-Market-2021/75354

Table of Contents

Part 1 Market Overview1.1 Market Definition1.2 Market Development1.2.1 Current Situation1.2.2 Aspects of COVID-19 Impact1.3 By TypeTable Type of Artificial Intelligence SoftwareFigure Global Artificial Intelligence Software Market Share by Type in 20201.4 By ApplicationTable Application of Artificial Intelligence SoftwareFigure Global Artificial Intelligence Software Market Share by Application in 20201.5 By RegionFigure Global Artificial Intelligence Software Market Share by Region in 2020Figure Asia Artificial Intelligence Software Market Share by Region in 2020

Part 2 Key Companies2.1 Baidu2.1.1 Company ProfileTable Baidu Overview List2.1.2 Products & Services Overview2.1.3 Sales Data ListTable Artificial Intelligence Software Business Operation of Baidu (Sales Revenue, Sales Volume, Price, Cost, Gross Margin)2.2 Google2.3 IBM2.4 Microsoft2.5 SAP2.6 Intel2.7 Salesforce2.8 Brighterion2.9 KITT.AI2.10 IFlyTek2.11 Megvii Technology2.12 Albert Technologies2.13 H2O.ai2.14 Brainasoft2.15 Yseop2.16 Ipsoft2.17 NanoRep(LogMeIn)2.18 Ada Support2.19 Astute Solutions2.20 IDEAL.com2.21 Wipro

Part 3 Global Market Status and Future Forecast3.1 Global Market by RegionTable Global Artificial Intelligence Software Market by Region, 2016-2020 (Million USD)Figure Global Artificial Intelligence Software Market Share by Region in 2020 (Million USD)Table Global Artificial Intelligence Software Market by Region, 2016-2020 (Volume)Figure Global Artificial Intelligence Software Market Share by Region in 2020 (Volume)Table Price List by Region, 2016-20203.2 Global Market by CompanyTable Global Artificial Intelligence Software Market by Company, 2016-2020 (Million USD)Figure Global Artificial Intelligence Software Market Share by Company in 2020 (Million USD)Table Global Artificial Intelligence Software Market by Company, 2016-2020 (Volume)Figure Global Artificial Intelligence Software Market Share by Company in 2020 (Volume)Table Price List by Company, 2016-20203.3 Global Market by TypeTable Global Artificial Intelligence Software Market by Type, 2016-2020 (Million USD)Figure Global Artificial Intelligence Software Market Share by Type in 2020 (Million USD)Table Global Artificial Intelligence Software Market by Type, 2016-2020 (Volume)Figure Global Artificial Intelligence Software Market Share by Type in 2020 (Volume)Table Price List by Type, 2016-20203.4 Global Market by ApplicationTable Global Artificial Intelligence Software Market by Application, 2016-2020 (Million USD)Figure Global Artificial Intelligence Software Market Share by Application in 2020 (Million USD)Table Global Artificial Intelligence Software Market by Application, 2016-2020 (Volume)Figure Global Artificial Intelligence Software Market Share by Application in 2020 (Volume)Table Price List by Application, 2016-20203.5 Global Market by ForecastFigure Global Artificial Intelligence Software Market Forecast, 2021E-2028F (Million USD)Figure Global Artificial Intelligence Software Market Forecast, 2021E-2028F (Volume)

Continue

ABOUT US:

Fusion Market Research is one of the largest collections of market research reports from numerous publishers. We have a team of industry specialists providing unbiased insights on reports to best meet the requirements of our clients. We offer a comprehensive collection of competitive market research reports from a number of global leaders across industry segments.

CONTACT US

sales@fusionmarketresearch.com

Phone:+ (210) 775-2636 (USA)+ (91) 853 060 7487

Tags: Global Artificial Intelligence Software Market Trends, Artificial Intelligence Software Market Trends, Artificial Intelligence Software Market Size, Artificial Intelligence Software Market Share, Artificial Intelligence Software, Artificial Intelligence Software Industry, Artificial Intelligence Software Industry Trends, Artificial Intelligence Software Market Growth, Global Artificial Intelligence Software Industry

sales@fusionmarketresearch.comPhone:+ (210) 775-2636 (USA)+ (91) 853 060 7487

See the original post here:
Artificial Intelligence Software Market 2022, Industry Size, Trends, Share, Growth, Analysis and Forecast to 2028 - Inter Press Service

We have accepted the use of artificial intelligence (AI) in complex processes from health care to our daily use of social media often without critical investigation, until it is too late. The use of AI is inescapable in our modern society, and it may perpetuate discrimination without its users being aware of any prejudice. When health-care providers rely on biased technology, there are real and harmful impacts.

This became clear recently when a study showed that pulse oximeters which measure the amount of oxygen in the blood and have been an essential tool for clinical management of COVID-19 are less accurate on people with darker skin than lighter skin. The findings resulted in a sweeping racial bias review now underway, in an attempt to create international standards for testing medical devices.

There are examples in health care, business, government and everyday life where biased algorithms have led to problems, like sexist searches and racist predictions of an offenders likelihood of re-offending.

AI is often assumed to be more objective than humans. In reality, however, AI algorithms make decisions based on human-annotated data, which can be biased and exclusionary. Current research on bias in AI focuses mainly on gender and race. But what about age-related bias can AI be ageist?

In 2021, the World Health Organization released a global report on aging, which called for urgent action to combat ageism because of its widespread impacts on health and well-being.

Ageism is defined as a process of systematic stereotyping of and discrimination against people because they are old. It can be explicit or implicit, and can take the form of negative attitudes, discriminatory activities, or institutional practices.

The pervasiveness of ageism has been brought to the forefront throughout the COVID-19 pandemic. Older adults have been labelled as burdens to societies, and in some jurisdictions, age has been used as the sole criterion for lifesaving treatments.

Digital ageism exists when age-based bias and discrimination are created or supported by technology. A recent report indicates that a digital world of more than 2.5 quintillion bytes of data is produced each day. Yet even though older adults are using technology in greater numbers and benefiting from that use they continue to be the age cohort least likely to have access to a computer and the internet.

Read more: Online arts programming improves quality of life for isolated seniors

Digital ageism can arise when ageist attitudes influence technology design, or when ageism makes it more difficult for older adults to access and enjoy the full benefits of digital technologies.

There are several intertwined cycles of injustice where technological, individual and social biases interact to produce, reinforce and contribute to digital ageism.

Barriers to technological access can exclude older adults from the research, design and development process of digital technologies. Their absence in technology design and development may also be rationalized with the ageist belief that older adults are incapable of using technology. As such, older adults and their perspectives are rarely involved in the development of AI and related policies, funding and support services.

The unique experiences and needs of older adults are overlooked, despite age being a more powerful predictor of technology use than other demographic characteristics including race and gender.

AI is trained by data, and the absence of older adults could reproduce or even amplify the above ageist assumptions in its output. Many AI technologies are focused on a stereotypical image of an older adult in poor health a narrow segment of the population that ignores healthy aging. This creates a negative feedback loop that not only discourages older adults from using AI, but also results in further data loss from these demographics that would improve AI accuracy.

Even when older adults are included in large datasets, they are often grouped according to arbitrary divisions by developers. For example, older adults may be defined as everyone aged 50 and older, despite younger age cohorts being divided into narrower age ranges. As a result, older adults and their needs can become invisible to AI systems.

In this way, AI systems reinforce inequality and magnify societal exclusion for sections of the population, creating a digital underclass primarily made up of older, poor, racialized and marginalized groups.

We must understand the risks and harms associated with age-related biases as more older adults turn to technology.

The first step is for researchers and developers to acknowledge the existence of digital ageism alongside other forms of algorithmic biases, such as racism and sexism. They need to direct efforts towards identifying and measuring it. The next step is to develop safeguards for AI systems to mitigate ageist outcomes.

There is currently very little training, auditing or oversight of AI-driven activities from a regulatory or legal perspective. For instance, Canadas current AI regulatory regime is sorely lacking.

This presents a challenge, but also an opportunity to include ageism alongside other forms of biases and discrimination in need of excision. To combat digital ageism, older adults must be included in a meaningful and collaborative way in designing new technologies.

With bias in AI now recognized as a critical problem in need of urgent action, it is time to consider the experience of digital ageism for older adults, and understand how growing old in an increasingly digital world may reinforce social inequalities, exclusion and marginalization.

Follow this link:
Artificial intelligence can discriminate on the basis of race and gender, and also age - The Conversation CA

Early diagnosis of Alzheimers disease (AD) using analysis of brain networks

AD-related neurological degeneration begins long before the appearance of clinical symptoms. Information provided by functional MRI (fMRI) neuroimaging data, which can detect changes in brain tissue during the early phases of AD, holds potential for early detection and treatment. The researchers are combining the ability of fMRI to detect subtle brain changes with the ability of machine learning to analyze multiple brain changes over time. This approach aims to improve early detection of AD, as well as other neurological disorders including schizophrenia, autism, and multiple sclerosis.

NIBIB-funded researchers are building machine learning models to better manage blood glucose levels by using data obtained from wearable sensors. New portable sensing technologies provide continuous measurements that include heart rate, skin conductance, temperature, and body movements. The data will be used to train an artificial intelligence network to help predict changes in blood glucose levels before they occur. Anticipating and preventing blood glucose control problems will enhance patient safety and reduce costly complications.

This project aims to develop an advanced image scanning system with high detection sensitivity and specificity for colon cancers. The researchers will develop deep neural networks that can analyze a wider field on the radiographic images obtained during surgery. The wider scans will include the suspected lesion areas and more surrounding tissue. The neural networks will compare patient images with images of past diagnosed cases. The system is expected to outperform current computer-aided systems in the diagnosis of colorectal lesions. Broad adoption could advance the prevention and early diagnosis of cancer.

Smart, cyber-physically assistive clothing (CPAC) is being developed in an effort to reduce the high prevalence of low back pain. Forces on back muscles and discs that occur during daily tasks are major risk factors for back pain and injury. The researchers are gathering a public data set of more than 500 movements measured from each subject to inform a machine learning algorithm. The information will be used to develop assistive clothing that can detect unsafe conditions and intervene to protect low back health. The long-term vision is to create smart clothing that can monitor lumbar loading; train safe movement patterns; directly assist wearers to reduce incidence of low back pain;and reduce costs related to health care expenses and missed work.

View original post here:
Artificial Intelligence (AI)

With the expertise of the Johns Hopkins Applied Physics Lab, weve developed one of the nations first online artificial intelligence masters programs to prepare engineers like you to take full advantage of opportunities in this field. The highly advanced curriculum is designed to deeply explore AI areas, including computer robotics, natural language processing, image processing, and more.

We have assembled a team of top-level researchers, scientists, and engineers to guide you through our rigorous online academic courses. Because we are a hub and frontrunner in artificial intelligence, we can tailor our artificial intelligence online masters content to include the most up-to-date practices and offer core courses that address the AI-driven technologies, techniques, and issues that power our modern world.

The online masters in Artificial Intelligence program balances theoretical concepts with the practical knowledge you can apply to real-world systems and processes. Courses deeply explore areas of AI, including robotics, natural language processing, image processing, and morefully online.

At the programs completion, you will:

Original post:
Master's in Artificial Intelligence | Hopkins EP Online

Lately, Artificial Intelligence and Machine Learning is a hot topic in the tech industry. Perhaps more than our daily lives Artificial Intelligence (AI) is impacting the business world more. There was about $300 million in venture capital invested in AI startups in 2014, a 300% increase than a year before (Bloomberg).

Hey there! This blog is almost about 1000+ words long and may take ~5 mins to go through the whole thing. We understand that you might not have that much time.

This is precisely why we made a short video on the topic. It is less than 2 mins, and simplifies Artificial intelligence & Machine learning. We hope this helps you learn more and save your time. Cheers!

AI is everywhere, from gaming stations to maintaining complex information at work. Computer Engineers and Scientists are working hard to impart intelligent behavior in the machines making them think and respond to real-time situations. AI is transiting from just a research topic to the early stages of enterprise adoption. Tech giants like Google and Facebook have placed huge bets on Artificial Intelligence and Machine Learning and are already using it in their products. But this is just the beginning, over the next few years, we may see AI steadily glide into one product after another.

According to Stanford Researcher, John McCarthy, Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. Artificial Intelligence is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.

Simply put, AIs goal is to make computers/computer programs smart enough to imitate the human mind behaviour.

Knowledge Engineering is an essential part of AI research. Machines and programs need to have bountiful information related to the world to often act and react like human beings. AI must have access to properties, categories, objects and relations between all of them to implement knowledge engineering. AI initiates common sense, problem-solving and analytical reasoning power in machines, which is much difficult and a tedious job.

AI services can be classified into Vertical or Horizontal AI

These are services focus on the single job, whether thats scheduling meeting, automating repetitive work, etc. Vertical AI Bots performs just one job for you and do it so well, that we might mistake them for a human.

These services are such that they are able to handle multiple tasks. There is no single job to be done. Cortana, Siri and Alexa are some of the examples of Horizontal AI. These services work more massively as the question and answer settings, such as What is the temperature in New York? or Call Alex. They work for multiple tasks and not just for a particular task entirely.

AI is achieved by analysing how the human brain works while solving an issue and then using that analytical problem-solving techniques to build complex algorithms to perform similar tasks. AI is an automated decision-making system, which continuously learn, adapt, suggest and take actions automatically. At the core, they require algorithms which are able to learn from their experience. This is where Machine Learning comes into the picture.

Artificial Intelligence and Machine Learning are much trending and also confused terms nowadays. Machine Learning (ML) is a subset of Artificial Intelligence. ML is a science of designing and applying algorithms that are able to learn things from past cases. If some behaviour exists in past, then you may predict if or it can happen again. Means if there are no past cases then there is no prediction.

ML can be applied to solve tough issues like credit card fraud detection, enable self-driving cars and face detection and recognition. ML uses complex algorithms that constantly iterate over large data sets, analyzing the patterns in data and facilitating machines to respond different situations for which they have not been explicitly programmed. The machines learn from the history to produce reliable results. The ML algorithms use Computer Science and Statistics to predict rational outputs.

There are 3 major areas of ML:

In supervised learning, training datasets are provided to the system. Supervised learning algorithms analyse the data and produce an inferred function. The correct solution thus produced can be used for mapping new examples. Credit card fraud detection is one of the examples of Supervised Learning algorithm.

Supervised Learning and Unsupervised Learning (Reference: http://dataconomy.com/whats-the-difference-between-supervised-and-unsupervised-learning/)

Unsupervised Learning algorithms are much harder because the data to be fed is unclustered instead of datasets. Here the goal is to have the machine learn on its own without any supervision. The correct solution of any problem is not provided. The algorithm itself finds the patterns in the data. One of the examples of supervised learning is Recommendation engines which are there on all e-commerce sites or also on Facebook friend request suggestion mechanism.

Recommendation Engine

This type of Machine Learning algorithms allows software agents and machines to automatically determine the ideal behaviour within a specific context, to maximise its performance. Reinforcement learning is defined by characterising a learning problem and not by characterising learning methods. Any method which is well suited to solve the problem, we consider it to be the reinforcement learning method. Reinforcement learning assumes that a software agent i.e. a robot, or a computer program or a bot, connect with a dynamic environment to attain a definite goal. This technique selects the action that would give expected output efficiently and rapidly.

Artificial Intelligence and Machine Learning always interests and surprises us with their innovations. AI and Ml have reached industries like Customer Service, E-commerce, Finance and where not. By 2020, 85% of the customer interactions will be managed without a human (Gartner). There are certain implications of AI and ML to incorporate data analysis like Descriptive analytics, Prescriptive analytics and Predictive analytics, discussed in our next blog: How Machine Learning can boost your Predictive Analytics?

Continue reading here:
Artificial Intelligence and Machine Learning made simple