Mediaboss Marketing

Important among the factors responsible for the rise of the Left in India was the exploitative character of colonial British rule, which impacted adversity upon the Indian economy and the various socio economic sections of Society. The land-based system had led to unfair treatment of agricultural labour and resulted in the emergence of the Kisan Sabha movement. The rise of modern industries, based on capitalistic ideology, and exploitation of factory workers, only added fuel to the fire. The financial burden on account of the World War-I, rising prices, famine conditions, manipulative practices by businessmen, the romantic appeal of Marxist ideas, the formation of the new regime in the USSR and its success, showing the triumph of the power and struggle of the people, were the last straw.

Jawaharlal Nehru paid his first ever visit to Russia, the land of socialism in 1961. He studied the works of Marx and Lenin, and was greatly influenced by their ideologies. He then applied his knowledge to the ongoing struggle for achieving equitable growth and sustainable development, of India.

Around the second decade of the twentieth century, India began to be influenced by the Gandhian philosophy of peaceful confrontation as a means of securing freedom from the colonial rulers. The Marxist ideology of the working class overthrowing the propertied exploiter by sheer force, struck a chord deep within the hearts of the agitators of nationalism. The Russian revolution, however, set in a new course in the trajectory of nationalist struggle. People began looking for an alternative idea to the Gandhian constructive programme which caused disillusionment.

Many leaders such as M N Roy, (the founder of the Communist Party of India - CPI), who was personally mentored by Lenin in Russia to prepare Indian soil for revolution against the foreign colonisers), S C Bose, Bipin Chandra Pal and Nehru developed Leftist persuasions.

Student and youth associations were organised all over the country from 1927 onwards. Hundreds of youth conferences were organised during 1928 and 1929, with speakers advocating radical solutions for the political, economic and social ills from which the country was suffering from. Jawaharlal Nehru and Subhas Bose toured the country, making speeches attacking imperialism, capitalism, and landlordism and preaching the ideology of socialism.

These developments also had a desirable impact on women's movements. Various legislations and decisions of those times can be credited to Socialist workers' agitations; such as the Unions Act, the Fundamental Rights and Economic Programme at Karachi Session, and the National Economic Policy in Faizapur session. Socialist ideas began to spread rapidly especially because many young persons who had participated actively in the non-cooperation movement were unhappy with its outcome.

There was also broad acceptance of socialist principles and adoption of the Socialist outlook by INC, post-independence; the reason why the (erstwhile) Planning Commission was often called a Soviet era hangover. Still, the Communists have always had a love hate relationship with the Congress.

Bhagat Singh, the celebrated revolutionary, was noted to have studied in detail the life of Lenin and the Communist Manifesto during his time in jail. Periyar, who started the 'self-respect movement' and also the Dravida Kazhagham, is known to have drawn inspiration from the Russian Communist method of bringing social justice, which he thought was best applicable to the plight of the lower castes in India.

The All India Trade Union Congress (AITUC) the oldest trade union federation in India, is associated with the CPI. It was created originally by moderates associated with the INC. Founded on 31 October 1920, it had Lala Lajpat Rai as its first President.

During its initial days, the CPI focused on mobilising peasants and workers towards a revolutionary cause, while at the same time influencing the Congress in developing a sturdy Left leaning ideology. Trouble arose when, in the 1940s, Gandhi launched the Quit India movement against the British almost at the same time when the Soviet Union urged the CPI to back the British war efforts in the fight against Fascism. In their efforts to please the Russians, they alienated themselves from the nationalist struggle.

Post-Independence, the Party sprung back to form, leading armed struggles in several principalities where the princely rulers were reluctant to give up power. Most noteworthy among these was the rebellion against the Nizam of Hyderabad. In Manipur and Bihar too, the Party made its ideological impact felt strong. Having been successful in garnering enough support among some sections of the Indian population, it soon emerged as the first leading opposition Party that the Congress faced. The Party experienced its first-ever electoral success in the state of Kerala in the 1957 Legislative Assembly elections. Two decades later the Party gained a footing in West Bengal and soon after in Tripura.

By the early 1960s, however, international conditions affecting Communism had altered again, the ripples of which were felt strongly in the Left politics of India. The Soviet Union and China (two most important Communist powers of the world) at that time, were at daggers drawn over ideological implications of Left politics. The Chinese, led by Mao Zedong, denounced the Russians for leaning towards the West as a diplomatic means of spreading Communism, rather than leading to an armed struggle. The ideological conflict between the two countries had its immediate effect on the CPI, drawing sturdy lines between those who leaned towards a Soviet philosophy and those who supported the Chinese. The Indo-China border war in 1962 affected the politics within the Party with one section backing Nehru, while the other radical section opposed what they believed was an unqualified aggression towards China.

Another drawback of the Party was that it mostly looked outside India for political guidance. The internal politics within the CPI soon manifested themselves in the famous split of 1964, when the radical section leaning towards China walked out of a meeting held in Delhi, calling themselves the 'real Communist Party'. Soon after they formed the Communist Party of India (Marxist) (CPI-M), which eventually overshadowed the CPI.

Cultural tradition and ideological positioning of a certain community can never decide the fate of a party in electoral politics especially given the Indian ethos. This was why the Left suffered a drastic loss of power in West Bengal in 2011 to the All India Trinamool Congress led by Mamata Banerjee, an experience that was to repeat itself in succeeding elections.

(The writer is former Chief Secretary, Government of Andhra Pradesh)

Link:
The influence of Communism in India - The Hans India

Grandma and grandpa communists, the lucky ones in the Netherlands. (Sison and wife)

AND I mean both: as a failed revolutionary organization and in terms of its leaders. The party is 52 years old, nearly four decades more than the average 15 years it took successful revolutionary organizations elsewhere to grab state power.

The party's founder Jose Ma. Sison is 82 his life obviously extended by the comforts and health services of the Netherlands. Only Sison is left among the original dozen or so central committee members, all of whom but two Kumander Dante and former party chairman Rodolfo Salas - died violently in their youth or of old age. Its core of idealistic young people, who built it up in 1970, are all now past 70 years old.

Its last known chairman, Julius Giron, who was killed last year, was 69 years old. He had replaced the captured Benito Tiamzon, 68 years. From a party of young idealistic students barely out of their teens, the party is now a party of septuagenarians.

As happened in failed revolutions, there is no capable second generation of leaders that has emerged. Those who would have had the intellect to lead the revolution, have instead chosen to be party-list representatives reveling in public attention and enjoying taxpayers' money as "congressmen."

The party has become pathetic, as the baby boomers that led it are apparently unaware, they are in the twilight of their lives, and there is no proletarian heaven where they can be with Lenin and Mao. Many are unable to live with their grown-up children who detest them for having abandoned them to their grandparents, or fear being involved with a fugitive from the law.

Those who have become useless to the party because of old age and sickness find themselves without "retirement" pay, and of course no savings, as the organization's sources of funding from loggers and miners in the 1970s ad telecom firms have dried up. There are no homes for the elderly for retired cadres.

While I detest this organization for the crimes it has and continues to commit against the nation, I still felt a tinge of sadness reading recently of the death of three communist leaders I had met when I was still with the organization.

Antonio Cabanatan and his wife Florenda Yap were killed back in November although it was only announced as no relatives claimed his body and of his wife in March when the military found out who he was. "Tonycab" was 74, his wife 65. The police claimed they were killed by burglars. The party claimed they were tortured and killed by the military, and they had "retired" from the organization in 2017. Cabanatan was nearly legendary in Central Visayas in the 1970s. He was a hunchback five feet tall, yet was an agile NPA commander.

The other day the military announced the killing - also in the Visayas, which appears to be the dwindling epicenter of the communist insurgency - of Rey Bocala and former priest Rustico Tan, known to have been ranking communist leaders but which the party claimed had already retired; Bocala was 75, Tan, 80 years.

Revolutionary organizations are living organisms, having their periods of youth and adulthood. After that, they either die if they lose to the government, or if they win, they transform into a different entity, the core of governments.

Lenin joined the Russian Social Democratic Labor Party in 1901: in 17 years, he was in power. The Chinese Communist Party fought for 22 years to win power in 1949. Fidel Castro's guerrillas toppled the Batista regime in six years. The Vietnamese Workers Party fought for 16 years, even against the most powerful nation on earth, the US, and won in 1975. The longest running Marxist revolutionary organization has been the Fuerzas Armadas Revolucionarias de Colombia which emerged in 1964. It entered into a peace agreement with the government only in 2013, and in 2017 ceased to be an armed group.

Sison's Communist Party has been organizing and killing Filipinos for 52 years. Can't these communists find some sanity left in their brains to realize they are defeated, and have become a zombie organization, surviving because the past four administrations had given it hope? Aren't the 50,000 Filipinos they slaughtered on the altar of the failed god communism enough?

If Sison were to find in his heart a shred of patriotism and morality or in his mind an ember of reason, he should call for the disbandment of the party, or at least the total cessation of its armed struggle. The old 1950s leaders of the old pro-Soviet Partido Komunista ng Pilipinas did.

Spare the lives of young people who you know will only die in some godforsaken jungle or in prisons while you enjoy the affluence of Utrech, Mr. Sison.

Email: tiglao.manilatimes@gmail.com

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The Communist Party is dying of old age - The Manila Times

Health care is at a junction, a point where artificial intelligence tools are being introduced to all areas of the space. This introduction comes with great expectations: AI has the potential to greatly improve existing technologies, sharpen personalized medicines, and, with an influx of big data, benefit historically underserved populations.

But in order to do those things, the health care community must ensure that AI tools are trustworthy, and that they dont end up perpetuating biases that exist in the current system. Researchers at the MIT Abdul Latif Jameel Clinic for Machine Learning in Health (Jameel Clinic), an initiative to support AI research in health care, call for creating a robust infrastructure that can aid scientists and clinicians in pursuing this mission.

Fair and equitable AI for health care

The Jameel Clinic recently hosted the AI for Health Care Equity Conference to assess current state-of-the-art work in this space, including new machine learning techniques that support fairness, personalization, and inclusiveness; identify key areas of impact in health care delivery; and discuss regulatory and policy implications.

Nearly 1,400 people virtually attended the conference to hear from thought leaders in academia, industry, and government who are working to improve health care equity and further understand the technical challenges in this space and paths forward.

During the event, Regina Barzilay, the School of Engineering Distinguished Professor of AI and Health and the AI faculty lead for Jameel Clinic, and Bilal Mateen, clinical technology lead at the Wellcome Trust, announced the Wellcome Fund grant conferred to Jameel Clinic to create a community platform supporting equitable AI tools in health care.

The projects ultimate goal is not to solve an academic question or reach a specific research benchmark, but to actually improve the lives of patients worldwide. Researchers at Jameel Clinic insist that AI tools should not be designed with a single population in mind, but instead be crafted to be reiterative and inclusive, to serve any community or subpopulation. To do this, a given AI tool needs to be studied and validated across many populations, usually in multiple cities and countries. Also on the project wish list is to create open access for the scientific community at large, while honoring patient privacy, to democratize the effort.

What became increasingly evident to us as a funder is that the nature of science has fundamentally changed over the last few years, and is substantially more computational by design than it ever was previously, says Mateen.

The clinical perspective

This call to action is a response to health care in 2020. At the conference, Collin Stultz, a professor of electrical engineering and computer science and a cardiologist at Massachusetts General Hospital, spoke on how health care providers typically prescribe treatments and why these treatments are often incorrect.

In simplistic terms, a doctor collects information on their patient, then uses that information to create a treatment plan. The decisions providers make can improve the quality of patients lives or make them live longer, but this does not happen in a vacuum, says Stultz.

Instead, he says that a complex web of forces can influence how a patient receives treatment. These forces go from being hyper-specific to universal, ranging from factors unique to an individual patient, to bias from a provider, such as knowledge gleaned from flawed clinical trials, to broad structural problems, like uneven access to care.

Datasets and algorithms

A central question of the conference revolved around how race is represented in datasets, since its a variable that can be fluid, self-reported, and defined in non-specific terms.

The inequities were trying to address are large, striking, and persistent, says Sharrelle Barber, an assistant professor of epidemiology and biostatistics at Drexel University. We have to think about what that variable really is. Really, its a marker of structural racism, says Barber. Its not biological, its not genetic. Weve been saying that over and over again.

Some aspects of health are purely determined by biology, such as hereditary conditions like cystic fibrosis, but the majority of conditions are not straightforward. According to Massachusetts General Hospital oncologist T. Salewa Oseni, when it comes to patient health and outcomes, research tends to assume biological factors have outsized influence, but socioeconomic factors should be considered just as seriously.

Even as machine learning researchers detect preexisting biases in the health care system, they must also address weaknesses in algorithms themselves, as highlighted by a series of speakers at the conference. They must grapple with important questions that arise in all stages of development, from the initial framing of what the technology is trying to solve to overseeing deployment in the real world.

Irene Chen, a PhD student at MIT studying machine learning, examines all steps of the development pipeline through the lens of ethics. As a first-year doctoral student, Chen was alarmed to find an out-of-the-box algorithm, which happened to project patient mortality, churning out significantly different predictions based on race. This kind of algorithm can have real impacts, too; it guides how hospitals allocate resources to patients.

Chen set about understanding why this algorithm produced such uneven results. In later work, she defined three specific sources of bias that could be detangled from any model. The first is bias, but in a statistical sense maybe the model is not a good fit for the research question. The second is variance, which is controlled by sample size. The last source is noise, which has nothing to do with tweaking the model or increasing the sample size. Instead, it indicates that something has happened during the data collection process, a step way before model development. Many systemic inequities, such as limited health insurance or a historic mistrust of medicine in certain groups, get rolled up into noise.

Once you identify which component it is, you can propose a fix, says Chen.

Marzyeh Ghassemi, an assistant professor at the University of Toronto and an incoming professor at MIT, has studied the trade-off between anonymizing highly personal health data and ensuring that all patients are fairly represented. In cases like differential privacy, a machine-learning tool that guarantees the same level of privacy for every data point, individuals who are too unique in their cohort started to lose predictive influence in the model. In health data, where trials often underrepresent certain populations, minorities are the ones that look unique, says Ghassemi.

We need to create more data, it needs to be diverse data, she says. These robust, private, fair, high-quality algorithms we're trying to train require large-scale data sets for research use.

Beyond Jameel Clinic, other organizations are recognizing the power of harnessing diverse data to create more equitable health care. Anthony Philippakis, chief data officer at the Broad Institute of MIT and Harvard, presented on the All of Us research program, an unprecedented project from the National Institutes of Health that aims to bridge the gap for historically under-recognized populations by collecting observational and longitudinal health data on over 1 million Americans. The database is meant to uncover how diseases present across different sub-populations.

One of the largest questions of the conference, and of AI in general, revolves around policy. Kadija Ferryman, a cultural anthropologist and bioethicist at New York University, points out that AI regulation is in its infancy, which can be a good thing. Theres a lot of opportunities for policy to be created with these ideas around fairness and justice, as opposed to having policies that have been developed, and then working to try to undo some of the policy regulations, says Ferryman.

Even before policy comes into play, there are certain best practices for developers to keep in mind. Najat Khan, chief data science officer at Janssen R&D, encourages researchers to be extremely systematic and thorough up front when choosing datasets and algorithms; detailed feasibility on data source, types, missingness, diversity, and other considerations are key. Even large, common datasets contain inherent bias.

Even more fundamental is opening the door to a diverse group of future researchers.

We have to ensure that we are developing and investing back in data science talent that are diverse in both their backgrounds and experiences and ensuring they have opportunities to work on really important problems for patients that they care about, says Khan. If we do this right, youll see ... and we are already starting to see ... a fundamental shift in the talent that we have a more bilingual, diverse talent pool.

The AI for Health Care Equity Conference was co-organized by MITs Jameel Clinic; Department of Electrical Engineering and Computer Science; Institute for Data, Systems, and Society; Institute for Medical Engineering and Science; and the MIT Schwarzman College of Computing.

Go here to read the rest:
The potential of artificial intelligence to bring equity in health care - MIT News

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

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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