Mediaboss Marketing

ST. PETERSBURG, Fla. There have been plenty of news reports about labor shortages and businesses unable to fill positions throughout the pandemic. But, there is another side of this story that hasn't gotten enough attention; millions of people looking for jobs and can't get hired because of online algorithms, artificial intelligence, and more.

ABC Action News reporter Michael Paluska sat down with St. Petersburg resident Elizabeth Longden. She showed us all of the jobs she's applied for on LinkedIn and Indeed. More than a thousand applications were filed on LinkedIn and more than 140 on Indeed.

"So, business data strategy, talent and culture recruiter, diversity, equity and inclusion specialist, human resources," Longden said as she named off a few of the jobs she's applied for. "There are 128 pages with eight applications per page."

"That's a lot of jobs," Paluska said.

"Yeah, a lot," Longden replied with a half-smile that was more of an acknowledgment of her job woes.

"How do you process 1,000 plus rejections?" Paluska asked.

"It's discouraging, and fortunately, there haven't been 1,000 rejections. Most of the places don't even get back to you one way or the other," Longden said. "So yeah, we're looking at less than that. But it's still a big, you know, it's a big confidence blow, especially when you hear, oh, there's a labor crisis. And nobody wants to work. And like, hi, I would like to work."

According to the Bureau of Labor, a record 4.4 million people quit their jobs in September. That's a new all-time high. So, you would think millions of openings would help Longden. But, that's not the case.

Longden has a college degree, an insurance license, and a decade of work experience in human resources. In May, like many Americans throughout this pandemic, she was laid off from her company. So she took about a month off to reset and started the search in her field as an operations specialist, people ops, HR, and businesses operations.

"Have you ever been in a hole where you lost a job, and you couldn't get another one in the past?" Paluska asked.

"Not where I had lost one and couldn't get another one. I'd had times where I'd moved, you know, and had had trouble finding a job for maybe a month or two. But I was always able to find something," Longden said.

In September, the Harvard Business School released a study called Untapped Workers: Hidden Talent. The study explains this lack of hiring phenomenon. The lead author, Joseph Fuller, estimating millions of Americans are in the same position as Longden.

"So, you have this, this system that systematically excludes people that may not check every box in the employer's description of what they're looking for, but can be highly qualified on multiple parameters, even those the most important for job success, but they still get excluded," Fuller, professor of management practice at Harvard Business School said. "But what happens is, the employer in setting up these filters and ranking systems emphasizes some skills over others, intended to rely on two factors to make a decision."

The job search algorithms and artificial intelligence filter out candidates based on keywords before someone like Longden ever talks to a human being.

"And, the algorithms are unforgiving," Fuller said. "If you don't, if you don't have the right keywords, if you're just missing one of those attributes, you can get excluded from consideration even though you check every box on every other attribute they're looking for."

"Whose fault is that the company or LinkedIn or Indeed?" Paluska asked.

"You know, no company sets out to have a failed hiring process," Fuller said. "They provide the tools that their customers regularly ask for. So I think this is a tragedy, without a villain. It's the way companies have gone about it is optimized around minimizing the time it takes to find candidates in minimizing the cost of finding someone to hire. There's some kind of killer variable that is causing the system to say not qualified or not attractive relative to other applicants. The vast majority of those candidates never hear back anything just ghosted."

Longden has been ghosted a lot. One recruiter called her three times in a week asking for her to apply and when she thought she got the job, radio silence. Longden thought he was dead.

"I even was like, 'Are you alive?' You know, like, I just want to know, you're okay, you've just totally gone dark," Longden said.

Longden's job search hell has her skeptical of the entire process.

"I've also discovered that there's been a huge uptick in companies wanting pre-work from people. So all in all, I've probably done about 25 hours worth of pre-work for various companies, none of which has been compensated, and none of which I've even gotten a roll-out of," Longden said.

"Do you think they are using your work for their benefit?" Paluska asked.

"Oh, I'm sure," Longden said. "One of the things I was asked to create was an onboarding process for new employees. So that's what the role at the company would have been doing was onboarding their new employees as they came in. And so, one of the pre-work examples was to create an onboarding process from the offer to the 90-day mark of employment. And I did that. And I'm certain that they're having multiple people do that and pulling what they like best from everyone."

We reached out to LinkedIn and Indeed for comments but did not get a response back.

"Two or three quick suggestions for Elizabeth, the first is be very, very aware of language terms, and make your submission. Match what's being asked for, to the greatest degree you can with integrity," Fuller said. "The second thing I would say is, go on something like LinkedIn and look at the profiles of people who got the job you want. And what are they saying they do? What keywords are they using? Is there a regularly referenced tool that they claim expertise in that she doesn't have?"

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Job hunting nightmare: 1,000 plus job applications and still no offers - ABC Action News

Artificial Intelligence in Farming

Prof Raj Khosla of Kansas State University in the United States, who stressed that digital intelligence in farming was the need of the hour, said that a public-private partnership was essential for digital agriculture and that all farm operations could be digitised using GPS technology because precision input usage would increase farm productivity.

Prof Khosla stressed the importance of artificial intelligence-enabled digital tools for increasing farm income and productivity during a lecture on 'Future of Farming: Big Data, Analytics, and Precision Agriculture' during the plenary session at Prof Jayashankar Telangana State Agriculture University (PJTSAU) on Thursday.

Prof Khosla also stressed the importance of artificial intelligence-enabled digital tools for increasing farm income and productivity during a lecture on 'Future of Farming: Big Data, Analytics, and Precision Agriculture' during the same session.

Lectures on 'Conservation Agriculture a Global Perspective,' were presented by Dr Bruno Gerrad and Ben Guerir from Morocco. They stated in the lectures that switching conventional agriculture to conservation agriculture helps save natural resources and mitigate crop losses caused by climate change.

Furthermore, designing appropriate farm machinery for small and marginal farm holdings can have a significant impact on conservation agriculture adoption.

They also stated that Axial flow pumps should be utilised to preserve moisture during droughts.

Dr Simon Cook of Murdoch University's Future Food Institute who was the keynote speaker gave an enlightening discourse on 'Digital Agriculture for Smart Agriculture,' emphasising the necessity of digitisation in agriculture for precision input application for marginal and small farmers.

In his lectures, he stated emphatically that "In India, the use of digital technology has accelerated in recent decades. From the standpoint of agricultural output, financial gains, consumers, and government, digital-agritech promotes more reforms ".

In the meanwhile, the third-day plenary featured four keynote lectures from national and international experts, as well as 15 lead papers, 15 oral presentations, and 17 fast presentations.

Dear patron, thank you for being our reader. Readers like you are an inspiration for us to move Agri Journalism forward. We need your support to keep delivering quality Agri Journalism and reach the farmers and people in every corner of rural India. Every contribution is valuable for our future.

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Scientists Advocate for the Application of Artificial Intelligence in Agriculture in hyderabad - Krishi Jagran

Amazon and Alphabet are among the companies best positioned to take advantage of future artificial intelligence disruption in the technology industry, a GlobalData analysis shows.

The assessment comes from GlobalDatas Thematic Research ecosystem, which ranks companies on a scale of one to five based on their likelihood to tackle challenges like artificial intelligence and emerge as long-term winners of the technology sector.

According to our analysis, Amazon, Alphabet, Microsoft, IBM, Alibaba, Apple, Baidu, Huawei, Yandex, Z Holdings, Airbnb, ByteDance, Nvidia, Inspur Electronic, Tesla, ABB, TSMC, GE, Expedia, Siemens, Alibaba Pictures, Darktrace, AMD, Wayfair, iFlytek, Nuance, Suning.com, Cambricon and Graphcore are the companies best positioned to benefit from investments in artificial intelligence, all of them recording scores of five out of five in GlobalDatas Advertising, Application software, Cloud services, Consumer electronics, Ecommerce, Industrial automation, IT infrastructure, Music, Film, & TV, Publishing, Semiconductors and Social media Thematic Scorecards.

Amazon, for example, has advertised for 18,116 new artificial intelligence jobs from October 2020 to September 2021; and mentioned artificial intelligence in company filings 86 times.

Alphabet indicated good levels of AI investment, with the company looking for 2,349 new artificial intelligence jobs since October 2020; and mentioning artificial intelligence in filings 137 times.

The table below shows how GlobalData analysts scored the biggest companies in the technology industry on their artificial intelligence performance, as well as the number of new artificial intelligence jobs, deals, patents and mentions in company reports since October 2020.

Higher numbers usually indicate that a company has spent more time and resources on improving its artificial intelligence performance, or that artificial intelligence is at least at the top of executives minds. However, it may not always mean that it is doing better than the competition.

A high number of mentions of artificial intelligence in quarterly company filings could indicate either that the company is reaping the rewards of previous investments, or that it needs to invest more to catch up with the rest of the industry. Similarly, a high number of deals could indicate that a company is dominating the market, or that it is using mergers and acquisitions to fill in gaps in its offering.

Nevertheless, these trends are useful in showing us the extent to which top executives in the technology sector and at specific organisations think about artificial intelligence, and the extent to which they stake their future on it.

This article is based on GlobalData research figures as of 10 November 2021. For more up-to-date figures, check the GlobalData website.

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Amazon and Alphabet lead the way in artificial intelligence, data reveals - Verdict

Artificial intelligence technologies have reached impressive levels of adoption, and are seen as a competitive differentiator. But there comes a point when technology becomes so ubiquitous that it is no longer a competitive differentiator -- think of the cloud. Going forward, those organizations succeeding with AI, then, will be those that apply human innovation and business sense to their AI foundations.

Such is the challenge identified in astudy released by RELX, which finds the use of AI technologies, at least in the United States, has reached 81% of enterprises, up 33 percentage points from 48% since a previous RELX survey in 2018. They're also bullish on AI delivering the goods -- 93% report that AI makes their business more competitive. This ubiquity may be the reason 95% are also reporting that finding the skills to build out their AI systems is a challenge. Plus, these systems could be potentially flawed: 75% worry that AI systems may potentially introduce the risk of bias in the workplace, and 65% admit their systems are biased.

So there's still much work to be done. It comes down to the people that can make AI happen, and make it as fair and accurate as possible.

"While many AI and machine learning deployments fail, in most cases, it's less of a problem with the actual technology and more about the environment around it," says Harish Doddi, CEO of Datatron. Moving to AI "requires the right skills, resources,andsystems."

It takes a well-developed understanding of AI and ML to deliver visible benefits to the business. While AI and ML have been around for many years, "we are still barely scratching the surface of uncovering their true capabilities," says Usman Shuja, general manager of connected buildings for Honeywell. "That said, there are many valuable lessons to be gleaned from others' missteps. While it's arguably true that AI can add significant value to practically any department across any business, one of the biggest mistakes a business can make is to implement AI for the sake of implementing AI, without a clear understanding of the business value they hope to achieve."

In addition, AI requires adroit change management, Shuja continues. "You can install the most cutting-edge AI solutions available, but if your employees can't or won't change their behaviors to adapt to a new way of doing things, you will see no value."

Another challenge is bias, as expressed by many executives in the RELX survey. "Algorithms can easily become biased based on the people who write them and the data they are providing, and bias can happen more with ML as it can be built in the base code," says Shuja. "While large amounts of data can ensure accuracy, it's virtually impossible to have enough data to mimic real-world use cases."

For example, he illustrates, "if I was looking into recruiting collegiate athletes for my professional lacrosse team, and I discovered that most of the players I am hearing about are Texas Longhorns, that might lead me to conclude that the best lacrosse players attend the University of Texas. However, this could just be because the algorithm has received too much data from one university, thus creating a bias."

The way the data is set up and who sets it up "can inadvertently sneak bias into the algorithms," Shuja says. "Companies that are not yet thinking through these implications need to put this to the forefront of their AI and ML technology efforts to build integrity into their solutions."

Another issue is that AI and ML models simply become outdated too soon, as many companies found out, and continue to find out as a result of Covid and supply chain issues. "Having good documentation that shows the model lifecyclehelps, butit'sstill insufficient when models become unreliable," says Doddi, "AI model governance helps bring accountability and traceability to machine learning models by having practitioners ask questions such as 'What were the previous versions like?' and 'What input variables are coming into the model?''" Governance is key. During development,Doddi explains, "MLmodels are bound by certain assumptions, rules, and expectations. Once deployed into production, the results can differ significantly from results in development environments.This is where governance is critical once a model is operationalized.There needs to be a way to keep track of various models and versions."

In some cases with AI, "less is more," says Shuja. "AI tends to be most successful when it is paired with mature, well-formatted data. This is mostly within the realm of IT/enterprise data, such as CRM, ERP, and marketing. However, when we move into areas where the data is less cohesive, such as with operational technology data, this is where achieving AI success becomes a bit more challenging. There is a tremendous need for scalable AI within an industrial environment, for example using AI to reduce energy consumption in a building or industrial plant -- an area of great potential for AI. One day soon, entire businesses -- from the factory floor to the board room -- will be connected; constantly learning and improving from the data it is processing. This will be the next major milestone for AI in the enterprise."

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Artificial intelligence: Everyone wants it, but not everyone is ready - ZDNet

What to Know

Job candidates rarely know when hidden artificial intelligence tools are rejecting their resumes or analyzing their video interviews. But New York City residents could soon get more say over the computers making behind-the-scenes decisions about their careers.

A bill passed by the city council in early November would ban employers from using automated hiring tools unless a yearly bias audit can show they wont discriminate based on an applicant's race or gender. It would also force makers of those AI tools to disclose more about their opaque workings and give candidates the option of choosing an alternative process such as a human to review their application.

Proponents liken it to another pioneering New York City rule that became a national standard-bearer earlier this century one that required chain restaurants to slap a calorie count on their menu items.

Instead of measuring hamburger health, though, this measure aims to open a window into the complex algorithms that rank the skills and personalities of job applicants based on how they speak or what they write. More employers, from fast food chains to Wall Street banks, are relying on such tools to speed up recruitment, hiring and workplace evaluations.

I believe this technology is incredibly positive but it can produce a lot of harms if there isnt more transparency, said Frida Polli, co-founder and CEO of New York startup Pymetrics, which uses AI to assess job skills through game-like online assessments. Her company lobbied for the legislation, which favors firms like Pymetrics that already publish fairness audits.

But some AI experts and digital rights activists are concerned that it doesnt go far enough to curb bias, and say it could set a weak standard for federal regulators and lawmakers to ponder as they examine ways to rein in harmful AI applications that exacerbate inequities in society.

The approach of auditing for bias is a good one. The problem is New York City took a very weak and vague standard for what that looks like, said Alexandra Givens, president of the Center for Democracy & Technology. She said the audits could end up giving AI vendors a fig leaf for building risky products with the city's imprimatur.

Givens said it's also a problem that the proposal only aims to protect against racial or gender bias, leaving out the trickier-to-detect bias against disabilities or age. She said the bill was recently watered down so that it effectively just asks employers to meet existing requirements under U.S. civil rights laws prohibiting hiring practices that have a disparate impact based on race, ethnicity or gender. The legislation would impose fines on employers or employment agencies of up to $1,500 per violation though it will be left up to the vendors to conduct the audits and show employers that their tools meet the city's requirements.

The City Council voted 38-4 to pass the bill on Nov. 10, giving a month for outgoing Mayor Bill De Blasio to sign or veto it or let it go into law unsigned. De Blasio's office says he supports the bill but hasn't said if he will sign it. If enacted, it would take effect in 2023 under the administration of Mayor-elect Eric Adams.

Julia Stoyanovich, an associate professor of computer science who directs New York University's Center for Responsible AI, said the best parts of the proposal are its disclosure requirements to let people know they're being evaluated by a computer and where their data is going.

This will shine a light on the features that these tools are using, she said.

But Stoyanovich said she was also concerned about the effectiveness of bias audits of high-risk AI tools a concept that's also being examined by the White House, federal agencies such as the Equal Employment Opportunity Commission and lawmakers in Congress and the European Parliament.

The burden of these audits falls on the vendors of the tools to show that they comply with some rudimentary set of requirements that are very easy to meet, she said.

The audits wont likely affect in-house hiring tools used by tech giants like Amazon. The company several years ago abandoned its use of a resume-scanning tool after finding it favored men for technical roles in part because it was comparing job candidates against the companys own male-dominated tech workforce.

There's been little vocal opposition to the bill from the AI hiring vendors most commonly used by employers. One of those, HireVue, a platform for video-based job interviews, said in a statement this week that it welcomed legislation that demands that all vendors meet the high standards that HireVue has supported since the beginning.

The Greater New York Chamber of Commerce said the city's employers are also unlikely to see the new rules as a burden.

Its all about transparency and employers should know that hiring firms are using these algorithms and software, and employees should also be aware of it, said Helana Natt, the chamber's executive director.

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NYC Aims to Be First to Rein in Artificial Intelligence Hiring Tools - NBC New York