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Dublin, June 02, 2021 (GLOBE NEWSWIRE) -- The "Artificial Intelligence in Marketing Market Forecast to 2028 - COVID-19 Impact and Global Analysis By Offering, Application, End-Use Industry, and Geography" report has been added to ResearchAndMarkets.com's offering.

The global artificial intelligence in marketing market was valued at US$ 12,044.46 million in 2020 and is projected to reach US$ 107,535.57 million by 2028; it is expected to grow at a CAGR of 31.4% from 2020 to 2028.

The rising adoption of customer-centric marketing strategies and increasing use of social media platforms for advertising are among the factors boosting the artificial intelligence in marketing market growth. However, scarcity of personnel well-versed with AI knowledge hinders the market growth. Further, surge in the adoption of cloud-based applications and services creates notable opportunities for the artificial intelligence in marketing market players.

The use of artificial intelligence in marketing helps the marketers to use customer's data to draw important insights of their buying behavior and preferences, among others. It is used in applications such as dynamic pricing, social media advertising, and sales & marketing automation. Artificial intelligence uses concepts such as machine learning to know these patterns, which helps companies to plan their next move accordingly. In the recent years, there has been an unprecedented increase in social media engagement . According to DIGITAL 2021, ~0.5 billion new users joined the world's social media networks in the beginning of 2021. Moreover, in January 2021, there were 4.20 billion social media users worldwide. This number has increased by 490 million in the last year, representing year-on-year growth of more than 13%. During 2020, more than 1.3 million new users joined the social media streams on average every day, i.e., ~15 new users every second.

Many companies have realized the platform's tremendous potential and are using it for ecommerce, customer support, marketing, and public relations, among others. Artificial intelligence have become an unintegral part social media networks today. Social networks such as Facebook, LinkedIn, Instagram, and Snapchat allow marketers to run paid advertising to platform users based on demographic and behavioral targeting. For instance, according to DIGITAL 2020, in January 2020, the potential number of people that marketers can reach using advertisements was 1.95 billion on Facebook, 928.5 million on Instagram, 663.3 million on LinkedIn, 381.5 million on Snapchat, 339.6 million on Twitter, and 169.0 million on Pinterest. Moreover, in January 2019, a total of US$ 89.91 billion was spent on social media ads. In the same month, the total global digital ad spend was US$ 333.3 billion, which accounts for 50.1% of the total global ad expenditure. Of the total digital ad spend, Google, Facebook, Alibaba, and Amazon accounted for 31.1%, 20.2%, 8.8%, and 4.2%, respectively. Thus, the increasing use of social media for advertising is bolstering the AI in marketing market growth.

Based on offering, the artificial intelligence in marketing market is segmented into solutions and services. In 2020, the solutions segment held the larger market share, and it is further projected to account for a larger share during 2021-2028. However, the services segment is expected to register a higher CAGR in the market during the forecast period.

The COVID-19 virus outbreak has been affecting every business globally since December 2019. The continuous growth in the number of virus-infected patients has governments to put a bar on transportation of humans and goods. However, on the contrary, COVID-19 on the other side is anticipated to accelerate private 5G and LTE adoption. Among B2C and consumer, the data consumption is expected to grow as social distancing continues. Also, the enterprises pivot to digital models and function virtually, the rate of data consumption will endure to boom and as result creating demand for establishing connectivity-centric ecosystem.

The Industrial Bank of Korea (IBK), European Association for Artificial Intelligence (EurAI), European Lab for Learning & Intelligent Systems (ELLIS), Organization for Economic Co-operation and Development, and Association for the Advancement of Artificial Intelligence (AAAI) are among the prime secondary sources referred to while preparing this report.

Key Topics Covered:

1. Introduction

2. Key Takeaways

3. Research Methodology3.1 Coverage3.2 Secondary Research3.3 Primary Research

4. Artificial Intelligence in Marketing Market Landscape4.1 Market Overview4.2 Ecosystem Analysis4.3 Expert Opinion4.4 PEST Analysis4.4.1 Artificial Intelligence in Marketing Market - North America PEST Analysis4.4.2 Artificial Intelligence in Marketing Market - Europe PEST Analysis4.4.3 Artificial Intelligence in Marketing Market - APAC PEST Analysis4.4.4 Artificial Intelligence in Marketing Market - MEA PEST Analysis4.4.5 Artificial Intelligence in Marketing Market - SAM PEST Analysis

5. Artificial Intelligence in Marketing Market - Key Industry Dynamics5.1 Market Drivers5.1.1 Rising Adoption of Customer-Centric Marketing Strategies5.1.2 Increasing Use of Social Media for Advertising5.2 Market Restraints5.2.1 Limited Number of Artificial Intelligence (AI) Experts5.3 Market Opportunities5.3.1 Growth in Adoption of Cloud-Based Applications and Services5.4 Future Trends5.4.1 Dynamic Personalized Ad Serving5.5 Impact Analysis of Drivers and Restraints

6. Artificial Intelligence in Marketing Market - Global Market Analysis

7. Artificial Intelligence in Marketing Market - By Offering

8. Artificial Intelligence in Marketing Market - By Application

9. Artificial Intelligence in Marketing Market - By End-Use Industry

10. Artificial Intelligence in Marketing Market - Geographic Analysis

11. Impact of COVID-19 Pandemic11.1 Overview11.2 Impact of COVID-19 Pandemic on Global Artificial Intelligence in Marketing Market11.2.1 North America: Impact Assessment of COVID-19 Pandemic11.2.2 Europe: Impact Assessment of COVID-19 Pandemic11.2.3 Asia-Pacific: Impact Assessment of COVID-19 Pandemic11.2.4 Middle East and Africa: Impact Assessment of COVID-19 Pandemic11.2.5 South America: Impact Assessment of COVID-19 Pandemic

12. Artificial Intelligence in Marketing Market - Industry Landscape12.1 Overview12.2 Growth Strategies Done by the Companies in the Market, (%)12.3 Organic Developments12.3.1 Overview12.4 Inorganic Developments12.4.1 Overview

13. Company Profiles13.1 Affectiva13.1.1 Key Facts13.1.2 Business Description13.1.3 Products and Services13.1.4 Financial Overview13.1.5 SWOT Analysis13.1.6 Key Developments13.2 Appier Inc.13.2.1 Key Facts13.2.2 Business Description13.2.3 Products and Services13.2.4 Financial Overview13.2.5 SWOT Analysis13.2.6 Key Developments13.3 Bidalgo13.3.1 Key Facts13.3.2 Business Description13.3.3 Products and Services13.3.4 Financial Overview13.3.5 SWOT Analysis13.3.6 Key Developments13.4 Novantas (Amplero), Inc.13.4.1 Key Facts13.4.2 Business Description13.4.3 Products and Services13.4.4 Financial Overview13.4.5 SWOT Analysis13.4.6 Key Developments13.5 CognitiveScale13.5.1 Key Facts13.5.2 Business Description13.5.3 Products and Services13.5.4 Financial Overview13.5.5 SWOT Analysis13.5.6 Key Developments13.6 SAS Institute Inc.13.6.1 Key Facts13.6.2 Business Description13.6.3 Products and Services13.6.4 Financial Overview13.6.5 SWOT Analysis13.6.6 Key Developments13.7 SAP SE13.7.1 Key Facts13.7.2 Business Description13.7.3 Products and Services13.7.4 Financial Overview13.7.5 SWOT Analysis13.7.6 Key Developments13.8 Salesforce.com, inc.13.8.1 Key Facts13.8.2 Business Description13.8.3 Products and Services13.8.4 Financial Overview13.8.5 SWOT Analysis13.8.6 Key Developments13.9 Oracle Corporation13.9.1 Key Facts13.9.2 Business Description13.9.3 Products and Services13.9.4 Financial Overview13.9.5 SWOT Analysis13.9.6 Key Developments13.10 IBM Corporation13.10.1 Key Facts13.10.2 Business Description13.10.3 Products and Services13.10.4 Financial Overview13.10.5 SWOT Analysis13.10.6 Key Developments13.11 Amazon Web Services13.11.1 Key Facts13.11.2 Business Description13.11.3 Products and Services13.11.4 Financial Overview13.11.5 SWOT Analysis13.11.6 Key Developments13.12 Adobe13.12.1 Key Facts13.12.2 Business Description13.12.3 Products and Services13.12.4 Financial Overview13.12.5 SWOT Analysis13.12.6 Key Developments13.13 Accenture13.13.1 Key Facts13.13.2 Business Description13.13.3 Products and Services13.13.4 Financial Overview13.13.5 SWOT Analysis13.13.6 Key Developments13.14 Microsoft Corporation13.14.1 Key Facts13.14.2 Business Description13.14.3 Products and Services13.14.4 Financial Overview13.14.5 SWOT Analysis13.14.6 Key Developments13.15 Xilinx, Inc.13.15.1 Key Facts13.15.2 Business Description13.15.3 Products and Services13.15.4 Financial Overview13.15.5 SWOT Analysis13.15.6 Key Developments

14. Artificial Intelligence in Marketing Market- Company Profiles

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

More:
Insights on the Artificial Intelligence in Marketing Global Market to 2028 - by Offering, Application, End-use - GlobeNewswire

As US businesses begin to emerge from Covid, many are now facing a labor shortage crisis. After nearly 18 months of being locked down and vaccination rates increase, Americans are heading out in droves to their favorite restaurants, bars, and retail establishments.While this is a positive sign, its presenting a big problem for businesses across the country as they struggle to keep up with the surge in demand.

According to a May 6th, 2021 Department of Labor Report, 16.2 million are claiming unemployment benefits.Not all news is negative.Aprils ADP payroll report states that 742,000 jobs had been created. iCIMS April report indicates that job openings are up 22%, hiring is up 18%, and job applications have decreased by 23%.

Some economists are attributing the labor shortage to the federal governments expanded unemployment benefits of $300.As we hear about positive trends in the job market, frustrated business owners are left wondering if the federal government has gone too far with unemployment assistance programs.Are capable Americans content sitting at home collecting unemployment than finding work?

While many restaurant and retail establishments employ high school and college students, most are staffed by adults outside of those demographics.The United States Census Bureau study indicates that these low-skilled workers are younger, less likely to have a college degree, and live in poverty. According to a report by Data USA, the average salary for restaurant workers is $22,426.

While the $300 in additional benefits was instituted at the start of the pandemic, is it still necessary as the economy comes roaring back to life?

At $45,188 or$40,976 in annualized benefits for Kentucky and Kansas, what would motivate anyone to find work until benefits expire, given current pay in these low-skilled jobs?

On March 4th of this year, Tech Talks published an article on How AI can help SMBs and workers make the $15 minimum wage transition.The current administrations push to raise the minimum wage fell flat on March 5th.When presented with the dichotomy of not working or working, most will go with the former when the pay is significantly higher.

Two ways out of this conundrum: reduce unemployment benefits or raise the minimum wage.Its not an easy answer as there are many complexities involved like virus concerns, access to childcare, social unrest, etc.This comes at a time when America is getting back on its feet.Many businesses will not service their clientele as we head into the busy spring and summer months.

Its not just restaurants and retail.We see staffing issues in the manufacturing and supply chain arenas.If not addressed, this labor issue can lead to higher prices for consumers, product shortages, or worse, the businesses that were lucky enough to survive Covid will be forced to shut down.

Talk to any small to the mid-size business owner, and theyll say their biggest expense is labor.Oftentimes, this represents 20-30 percent of their gross earnings.According to JP Morgan Chase, outside of the big brands like Walmart, McDonalds, and Amazon, these fearless entrepreneurs represent nearly 99 percent of Americas 28.7 million firms.

Artificial Intelligence is the ability for a computer to think and act like a human, which has become more prominent in recent years.Businesses accelerated their rate of technological adoption to survive during the pandemic.AI-driven platforms are proving to be adequate replacements for repetitive tasks that can easily be automated:

AI will not replace the need for humans in these lines of work. It can, however, significantly reduce the need for labor.Consider a business that would need 5 workers in each of these situations.With properly placed AI platforms, the need for these types of employees can be reduced by as much as 60-70 percent.

A full-time employee paid a minimum wage salary will earn $600/$2400 in a given week/month.Multiply this by 3 employees, and your labor costs total $7,200 a month plus benefits.Many of these AI tools that can help drive top, and bottom-line growth are a fraction of your labor expense.

Labor Shortage + Higher Wages = Inflationary Pressures

Theres no end to the labor uprising dilemma.Businesses will need to turn to AI-driven automation to remain competitive to keep both labor and prices in check.

Read the original post:
Artificial Intelligence and the Labor Shortage Crisis in the US - IoT For All

An artificial intelligence system trained on almost 40 years of the scientific literature correctly identified 19 out of 20 research papers that have had the greatest scientific impact on biotechnology and has selected 50 recent papers it predicts will be among the top 5% of biotechnology papers in the future.1

Scientists say the system could be used to find hidden gems of research overlooked by other methods, and even to guide decisions on funding allocations so that it will be most likely to target promising research.

But its sparked outrage among some members of the scientific community, who claim it will entrench existing biases.

Our goal is to build tools that help us discover the most interesting, exciting and impactful research especially research that might be overlooked with existing publication metrics, says James Weis, a computer scientist at the Massachusetts Institute of Technology and the lead author of a new study about the system.

The study describes a machine-learning system called Delphi Dynamic Early-warning by Learning to Predict High Impact that was trained with metrics drawn from more than 1.6 million papers published in 42 biotechnology-related journals between 1982 and 2019.

The system assessed 29 different features of the papers in the journals, which resulted in more than 7.8 million individual machine-learning nodes and 201 million relationships.

The features included regular metrics, such as the h-index of an authors research productivity and the number of citations a research paper generated in the five years since its publication. But they also included things like how an authors h-index had changed over time, the number and rankings of a papers co-authors, and several metrics about the journals themselves.

The researchers then used the system to correctly identify 19 of the 20 seminal biotechnology papers from 1980 to 2014 in a blinded study, and to select another 50 papers published in 2018 that they predict will be among the top 5% of impactful biotechnology research papers in the years to come.

Weis says the important paper that the Delphi system missed involved the foundational development of chromosome conformation capture methods for analysing the spatial organisation of chromosomes within a cell in part because a large number of the citations that resulted were in non-biotechnology journals and so were not in their database.

We dont expect to be able to identify all foundational technologies early, Weis says. Our hope is primarily to find technologies that have been overlooked by current metrics.

As with all machine learning systems, due care needs to be taken to reduce systemic biases and to ensure that malicious actors cannot manipulate it, he says. But by considering a broad range of features and using only those that hold real signal about future impact, we think that Delphi holds the potential to reduce bias by obviating reliance on simpler metrics, he says. Weis adds that this will also make Delphi harder to game.

Weis says the Delphi prototype can be easily expanded into other scientific fields, initially by including additional disciplines and academic journals, and potentially other sources of high quality research like the online preprint archive arXiv.

The intent is not to create a replacement for existing methods for judging the importance of research, but to improve them, he says. We view Delphi as an additional tool to be integrated into the researchers toolkit not as a replacement for human-level expertise and intuition.

The system has already attracted some criticism. Andreas Bender, a chemist at the University of Cambridge, wrote on Twitter that Delphi will only serve to perpetuate existing academic biases, while Daniel Koch, a molecular biophysicist at Kings College London, tweeted:Unfortunately, once again impactful is defined mostly by citation-based metrics, so whats optimized is scientific self-reference.

Lutz Bornmann, a sociologist of science at the Max Planck Society headquarters in Munich who has studied how research impacts can be measured2 notes that many of the publication features assessed by the Delphi system rely heavily on the quantification of the research citations that result from them. However, the proposed method sounds interesting and led to first promising empirical results, he says. Further extensive empirical tests are necessary to confirm these first results.

See more here:
Artificial intelligence system can predict the impact of research - Chemistry World

Disinformation campaigns are not new think of wartime propaganda used to sway public opinion against an enemy. What is new, however, is the use of the internet and social media to spread these campaigns. The spread of disinformation via social media has the power to change elections, strengthen conspiracy theories, and sow discord.

Steven Smith, a staff member from MIT Lincoln Laboratorys Artificial Intelligence Software Architectures and Algorithms Group, is part of a team that set out to better understand these campaigns by launching the Reconnaissance of Influence Operations (RIO) program. Their goal was to create a system that would automatically detect disinformation narratives as well as those individuals who are spreading the narratives within social media networks. Earlier this year, the team published a paper on their work in the Proceedings of the National Academy of Sciences and they received an R&D 100 award last fall.

The project originated in 2014 when Smith and colleagues were studying how malicious groups could exploit social media. They noticed increased and unusual activity in social media data from accounts that had the appearance of pushing pro-Russian narratives.

"We were kind of scratching our heads," Smith says of the data. So the team applied for internal funding through the laboratorys Technology Office and launched the program in order to study whether similar techniques would be used in the 2017 French elections.

In the 30 days leading up to the election, the RIO team collected real-time social media data to search for and analyze the spread of disinformation. In total, they compiled 28 million Twitter posts from 1 million accounts. Then, using the RIO system, they were able to detect disinformation accounts with 96 percent precision.

What makes the RIO system unique is that it combines multiple analytics techniques in order to create a comprehensive view of where and how the disinformation narratives are spreading.

"If you are trying to answer the question of who is influential on a social network, traditionally, people look at activity counts," says Edward Kao, who is another member of the research team. On Twitter, for example, analysts would consider the number of tweets and retweets. "What we found is that in many cases this is not sufficient. It doesnt actually tell you the impact of the accounts on the social network."

As part of Kaos PhD work in the laboratorys Lincoln Scholars program, a tuition fellowship program, he developed a statistical approach now used in RIO to help determine not only whether a social media account is spreading disinformation but also how much the account causes the network as a whole to change and amplify the message.

Erika Mackin, another research team member, also applied a new machine learning approach that helps RIO to classify these accounts by looking into data related to behaviors such as whether the account interacts with foreign media and what languages it uses. This approach allows RIO to detect hostile accounts that are active in diverse campaigns, ranging from the 2017 French presidential elections to the spread of Covid-19 disinformation.

Another unique aspect of RIO is that it can detect and quantify the impact of accounts operated by both bots and humans, whereas most automated systems in use today detect bots only. RIO also has the ability to help those using the system to forecast how different countermeasures might halt the spread of a particular disinformation campaign.

The team envisions RIO being used by both government and industry as well as beyond social media and in the realm of traditional media such as newspapers and television. Currently, they are working with West Point student Joseph Schlessinger, who is also a graduate student at MIT and a military fellow at Lincoln Laboratory, to understand how narratives spread across European media outlets. A new follow-on program is also underway to dive into the cognitive aspects of influence operations and how individual attitudes and behaviors are affected by disinformation.

Defending against disinformation is not only a matter of national security, but also about protecting democracy, says Kao.

See the rest here:
Artificial intelligence system could help counter the spread of disinformation - MIT News

The European Commission recently published a proposal for a regulation on artificial intelligence (AI). This is the first document of its kind to attempt to tame the multi-tentacled beast that is artificial intelligence.

The sun is starting to set on the Wild West days of artificial intelligence, writes Jeremy Kahn. He may have a point.

When this regulation comes into effect, it will change the way that we conduct AI research and development. In the last few years of AI, there were few rules or regulations: if you could think it, you could build it. That is no longer the case, at least in the European Union.

There is, however, a notable exception in the regulation, which is that is does not apply to international organizations like the United Nations.

Naturally, the European Union does not have jurisdiction over the United Nations, which is governed by international law. The exclusion therefore does not come as a surprise, but does point to a gap in AI regulation. The United Nations therefore needs its own regulation for artificial intelligence, and urgently so.

Artificial intelligence technologies have been used increasingly by the United Nations. Several research and development labs, including the Global Pulse Lab, the Jetson initiative by the UN High Commissioner for Refugees , UNICEFs Innovation Labs and the Centre for Humanitarian Data have focused their work on developing artificial intelligence solutions that would support the UNs mission, notably in terms of anticipating and responding to humanitarian crises.

United Nations agencies have also used biometric identification to manage humanitarian logistics and refugee claims. The UNHCR developed a biometrics database which contained the information of 7.1 million refugees. The World Food Program has also used biometric identification in aid distribution to refugees, coming under some criticism in 2019 for its use of this technology in Yemen.

In parallel, the United Nations has partnered with private companies that provide analytical services. A notable example is the World Food Programme, which in 2019 signed a contract worth US$45 million with Palantir, an American firm specializing in data collection and artificial intelligence modelling.

In 2014, the United States Bureau of Immigration and Customs Enforcement (ICE) awarded a US$20 billion-dollar contract to Palantir to track undocumented immigrants in the U.S., especially family members of children who had crossed the border alone. Several human rights watchdogs, including Amnesty International, have raised concerns about Palantir for human rights violations.

Like most AI initiatives developed in recent years, this work has happened largely without regulatory oversight. There have been many attempts to set up ethical modes of operation, such as the Office for the Co-ordination of Humanitarian Affairs Peer Review Framework, which sets out a method for overseeing the technical development and implementation of AI models.

In the absence of regulation, however, tools such as these, without legal backing, are merely best practices with no means of enforcement.

In the European Commissions AI regulation proposal, developers of high-risk systems must go through an authorization process before going to market, just like a new drug or car. They are required to put together a detailed package before the AI is available for use, involving a description of the models and data used, along with an explanation of how accuracy, privacy and discriminatory impacts will be addressed.

The AI applications in question include biometric identification, categorization and evaluation of the eligibility of people for public assistance benefits and services. They may also be used to dispatch of emergency first response services all of these are current uses of AI by the United Nations.

Conversely, the lack of regulation at the United Nations can be considered a challenge for agencies seeking to adopt more effective and novel technologies. As such, many systems seem to have been developed and later abandoned without being integrated into actual decision-making systems.

An example of this is the Jetson tool, which was developed by UNHCR to predict the arrival of internally displaced persons to refugee camps in Somalia. The tool does not appear to have been updated since 2019, and seems unlikely to transition into the humanitarian organizations operations. Unless, that is, it can be properly certified by a new regulatory system.

Trust in AI is difficult to obtain, particularly in United Nations work, which is highly political and affects very vulnerable populations. The onus has largely been on data scientists to develop the credibility of their tools.

A regulatory framework like the one proposed by the European Commission would take the pressure off data scientists in the humanitarian sector to individually justify their activities. Instead, agencies or research labs who wanted to develop an AI solution would work within a regulated system with built-in accountability. This would produce more effective, safer and more just applications and uses of AI technology.

Read this article:
The United Nations needs to start regulating the 'Wild West' of artificial intelligence - The Conversation CA