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Current trends in AI are nothing if not remarkable. Day after day, we hear stories about systems and machines taking on tasks that, until very recently, we saw as the exclusive and permanent preserve of humankind: making medical diagnoses, drafting legal documents, designing buildings, and even composing music.

Our concern here, though, is with something even more striking: the prospect of high-level machine intelligence systems that outperform human beings at essentially every task. This is not science fiction. In a recent survey the median estimate among leading computer scientists reported a 50% chance that this technology would arrive within 45 years.

Importantly, that survey also revealed considerable disagreement. Some see high-level machine intelligence arriving much more quickly, others far more slowly, if at all. Such differences of opinion abound in the recent literature on the future of AI, from popular commentary to more expert analysis.

Yet despite these conflicting views, one thing is clear: if we think this kind of outcome might be possible, then it ought to demand our attention. Continued progress in these technologies could have extraordinarily disruptive effects it would exacerbate recent trends in inequality, undermine work as a force for social integration, and weaken a source of purpose and fulfilment for many people.

In April 2020, an ambitious initiative called Positive AI Economic Futures was launched by Stuart Russell and Charles-Edouard Boue, both members of the World Economic Forums Global AI Council (GAIC). In a series of workshops and interviews, over 150 experts from a wide variety of backgrounds gathered virtually to discuss these challenges, as well as possible positive Artificial Intelligence visions and their implications for policymakers.

Those included Madeline Ashby (science fiction author and expert in strategic foresight), Ken Liu (Hugo Award-winning science fiction and fantasy author), and economists Daron Acemoglu (MIT) and Anna Salomons (Utrecht), among many others. What follows is a summary of these conversations, developed in the Forum's report Positive AI Economic Futures.

Participants were divided on this question. One camp thought that, freed from the shackles of traditional work, humans could use their new freedom to engage in exploration, self-improvement, volunteering, or whatever else they find satisfying. Proponents of this view usually supported some form of universal basic income (UBI), while acknowledging that our current system of education hardly prepares people to fashion their own lives, free of any economic constraints.

The second camp in our workshops and interviews believed the opposite: traditional work might still be essential. To them, UBI is an admission of failure it assumes that most people will have nothing of economic value to contribute to society. They can be fed, housed, and entertained mostly by machines but otherwise left to their own devices.

People will be engaged in supplying interpersonal services that can be provided or which we prefer to be provided only by humans. These include therapy, tutoring, life coaching, and community-building. That is, if we can no longer supply routine physical labour and routine mental labour, we can still supply our humanity. For these kinds of jobs to generate real value, we will need to be much better at being human an area where our education system and scientific research base is notoriously weak.

So, whether we think that the end of traditional work would be a good thing or a bad thing, it seems that we need a radical redirection of education and science to equip individuals to live fulfilling lives or to support an economy based largely on high-value-added interpersonal services. We also need to ensure that the economic gains born of AI-enabled automation will be fairly distributed in society.

One of the greatest obstacles to action is that, at present, there is no consensus on what future we should target, perhaps because there is hardly any conversation about what might be desirable. This lack of vision is a problem because, if high-level machine intelligence does arrive, we could quickly find ourselves overwhelmed by unprecedented technological change and implacable economic forces. This would be a vast opportunity squandered.

For this reason, the workshop attendees and interview participants, from science-fiction writers to economists and AI experts, attempted to articulate positive visions of a future where Artificial Intelligence can do most of what we currently call work.

These scenarios represent possible trajectories for humanity. None of them, though, is unambiguously achievable or desirable. And while there are elements of important agreement and consensus among the visions, there are often revealing clashes, too.

The economic benefits of technological progress are widely shared around the world. The global economy is 10 times larger because AI has massively boosted productivity. Humans can do more and achieve more by sharing this prosperity. This vision could be pursued by adopting various interventions, from introducing a global tax regime to improving insurance against unemployment.

Large companies focus on developing AI that benefits humanity, and they do so without holding excessive economic or political power. This could be pursued by changing corporate ownership structures and updating antitrust policies.

Human creativity and hands-on support give people time to find new roles. People adapt to technological change and find work in newly created professions. Policies would focus on improving educational and retraining opportunities, as well as strengthening social safety nets for those who would otherwise be worse off due to automation.

The World Economic Forums Centre for the Fourth Industrial Revolution, in partnership with the UK government, has developed guidelines for more ethical and efficient government procurement of artificial intelligence (AI) technology. Governments across Europe, Latin America and the Middle East are piloting these guidelines to improve their AI procurement processes.

Our guidelines not only serve as a handy reference tool for governments looking to adopt AI technology, but also set baseline standards for effective, responsible public procurement and deployment of AI standards that can be eventually adopted by industries.

We invite organizations that are interested in the future of AI and machine learning to get involved in this initiative. Read more about our impact.

Society decides against excessive automation. Business leaders, computer scientists, and policymakers choose to develop technologies that increase rather than decrease the demand for workers. Incentives to develop human-centric AI would be strengthened and automation taxed where necessary.

New jobs are more fulfilling than those that came before. Machines handle unsafe and boring tasks, while humans move into more productive, fulfilling, and flexible jobs with greater human interaction. Policies to achieve this include strengthening labour unions and increasing worker involvement on corporate boards.

In a world with less need to work and basic needs met by UBI, well-being increasingly comes from meaningful unpaid activities. People can engage in exploration, self-improvement, volunteering or whatever else they find satisfying. Greater social engagement would be supported.

The intention is that this report starts a broader discussion about what sort of future we want and the challenges that will have to be confronted to achieve it. If technological progress continues its relentless advance, the world will look very different for our children and grandchildren. Far more debate, research, and policy engagement are needed on these questions they are now too important for us to ignore.

Written by

Stuart Russell, Professor of Computer Science and Director of the Center for Human-Compatible AI, University of California, Berkeley

Daniel Susskind, Fellow in Economics, Oxford University, and Visiting Professor, Kings College, London

The views expressed in this article are those of the author alone and not the World Economic Forum.

Read more from the original source:
6 positive AI visions for the future of work - World Economic Forum

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?"

See the article here:
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.

See the original post:
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