Automation in artificial intelligence has an extensive effect on the economy.

Industrialists and giant companies all over the world are further adapting to the idea of automation in artificial intelligence. In India, technological progress, is the main driver of growth of GDP per capita, allowing output to increase faster than labor and capital. Technology increases productivity by decreasing the number of labor hours needed to create a unit of output. An increment in labor productivity generally translates into increases in average wages, allowing workers to cut back on work hours and to afford more goods and services. AI should be welcomed for its potential economic benefits. Those economic benefits, however, will not necessarily be evenly distributed across society.

At present, it may be challenging to predict exactly which jobs will be most immediately affected by AI-driven automation. Because AI is not a single technology, but rather a collection of technologies that are applied to specific tasks, the effects of AI will be felt unevenly throughout the economy. Some tasks will be more easily automated than others, and some jobs will be affected more than others—both negatively and positively. Some jobs may be automated away, while for others, AI-driven automation will make many workers more productive and increase demand for certain skills. Finally, new jobs are likely to be directly created in areas such as the development

and supervision of AI as well as indirectly created in a range of areas throughout the economy as higher incomes lead to expanded demand.

Recent research suggests that the effects of AI on the labor market in the near term will continue the trend that computerization and communication innovations have driven in recent decades. Researchers’ estimates on the scale of threatened jobs over the next decade or two range from 9 to 47 percent. For context, every 3 months about 6 percent of jobs in the economy are destroyed by shrinking or closing businesses, while a slightly larger percentage of jobs are added resulting in rising employment and a roughly constant unemployment rate. The economy has

repeatedly proven itself capable of handling this scale of change, although it would depend on how rapidly the changes happen and how concentrated the losses are in specific occupations that are hard to shift from.

Effect of AI Automation in Macroeconomy

Productivity

Technology increases productivity by decreasing the number of labor hours needed to create a unit of output. Labor productivity increases generally translate into increases in average wages, allowing workers to cut back on work hours and to afford more goods and services. Living standards and leisure hours could both increase, although

to the degree that inequality increases—as it has in recent decades—it offsets some of those gains.

Labor Market

Recent research suggests that the effects of AI on the labor market in the decade ahead will continue the trend toward skill-biased change that computerization and communication innovations have driven in recent decades. Researchers differ on the possible magnitude of this effect. Experts declare that 47 percent of U.S. jobs are at risk of being replaced by AI technologies and computerization in this period.

The Jobs Created by AI

Four categories of jobs are likely to rise during AI-driven growth in the future. Employment in areas where humans engage with existing AI technologies, develop new AI technologies, supervise AI technologies in practice, and facilitate societal shifts that accompany new AI technologies will likely grow.

Technological advancement is generated and adopted into the economy as the product of choices of entrepreneurs, workers, and firms looking to better serve a market or streamline a production process, in the context created by public investments in basic and applied research, infrastructure, and other public goods. In a process of directed technical change, incentives draw investment towards more potentially profitable innovations and so the types of technological change that are likely to occur.

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A new distance learning project that will enable more people to develop skills in robotics and autonomous systems, to help close the skills gap and drive forward productivity in the UK, is being launched by researchers from the University of Sheffield.

The project, led by Professor Tony Prescott from the University of Sheffield’s Department of Computer Science, will use cloud computing and state-of-the-art robots to develop distance learning activities in robotics for people across all educational levels.

Piloting a new way of learning in robotics education, the project will enable students to write programs from home, test them using a simulation, then run the program on a remote robot to see if it works in the real-world – a first for robotics teaching.

The project will also focus on addressing inequalities in the robotics and autonomous systems industry. It will provide positive and diverse role models together with the resources needed to help young people from disadvantaged backgrounds to go on and develop the skills to study and establish a career in the industry. 

With the UK economy needing to improve productivity to help recover from Covid-19 and remain competitive on an international level, the Sheffield-led team is developing a state-of-the-art robotic arm and manufacturing cell that engineering students at apprentice and degree-levels can use to develop skills in using robotics and autonomous systems in manufacturing. 

Engineering apprentices at the University of Sheffield AMRC Training Centre and the University’s degree-level computer science and systems engineering students will be able to reprogramme the robotic arm and manufacturing cell remotely.

Beginners can program a robotic simulation using the graphical language Google Blockly while more advanced students can program with the widely-used Python language. Students will be able to test their programs on physical hardware, remotely and in real-time using robot platforms located at the University. This is a new approach to robotics teaching that is being piloted for the first time in Sheffield.

The project will work with the University of Sheffield Advanced Manufacturing Research Centre (AMRC) to understand the skills gap experienced by industry in the Sheffield City Region and beyond in order to develop appropriate training strategies. 

Resources that will enable people outside of education to retrain and learn new skills in robotics and autonomous systems are also being developed by the researchers.

Professor Tony Prescott, Professor of Cognitive Robotics at the University of Sheffield, said: “The Covid-19 pandemic has left the UK with an urgent need to kick start its economy and boost productivity. A key to doing this will be increased automation and deploying the next generation of robotics and autonomous systems in the industry. 

“If we are to use more robots and autonomous systems then we need more people who have the skills to use these technologies. We need to encourage people from a young age to consider studying and developing careers in robotics while also providing the resources and the systems that will inspire young people and give them the platform they need to succeed. We also need to help the current workforce retrain and develop the skills they need to secure employment after the COVID-19 pandemic.” 

For primary and secondary schools, the project will trial a cloud-based simulation of the robot MiRo – a fully programmable autonomous robotic pet, created by the University spin-out company Consequential Robotics. MiRo is currently being used across the UK and abroad to pilot new applications for robotics in education and healthcare. 

The cloud-based simulation, MiRoCloud, will enable school pupils at both primary and high school levels to program a MiRo robot from home with the help of distance learning materials, tests, resources and lesson plans that are being developed by the University of Sheffield-led team. 

The project is working with schools that have high numbers of children from disadvantaged backgrounds and pupils with special educational needs to help widen access to the robotics industry. 

With equality and diversity being a key priority for the project, the educational resources are being developed with the aim of helping more of those who are currently under-represented in robotics to see where they can access learning opportunities and make their own contribution.

The research team will work with DiscoveryStem.org, a Sheffield-based educational consultancy that is at the forefront in developing schools teaching in robotics, to develop and share learning resources with primary and secondary schools across the UK. In the Sheffield City Region, the researchers will work in partnership with Beck Primary School – a large inclusive Sheffield school. 

Dr Becky Parry from the University’s School of Education is leading on ensuring the inclusivity outcomes of the project. Dr Parry said: “As a society, and in response to the rapid development of new technologies, we need new voices to help us imagine the potential value they will have to our lives. This project will be designed so that children can experience working in robotics as coders, designers or researchers and therefore imagine themselves in these roles in the future. 

“The researchers and students involved in running the project will seek to provide positive and diverse role models which we know are key to children making decisions about the subjects they are interested in. In the current climate, it is heartening to be part of a project which attempts to address structural inequalities in the short-term but also in terms of imagining the potential of robotics in the future to help us address the big issues of our times.” 

Professor Prescott added: “We hope that the new distance learning project will help us to develop scalable approaches for plugging the skills gap, then we can work together with schools, universities, industry and policymakers to roll this out across the UK.”

The distance learning project is linked to a national task group – Skills and Education in Robotics and Autonomous Systems (www.seras.org.uk), supported by the UK EPSRC Robotics and Autonomous Systems Network, that is preparing a White Paper on how to transform the UK’s skills in robotics and autonomous systems.

Led by the University of Sheffield, the White Paper is being developed in partnership with universities across the UK, the Institute of Coding, the Royal Academy of Engineering and the National STEM Centre.

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Revenue inequality is among the overarching issues of economics. Some of the efficient instruments policymakers have to handle its taxation: governments acquire cash from folks in line with what they earn and redistribute it both immediately, by way of welfare schemes, or not directly, by utilizing it to pay for public initiatives. However, although extra taxation can result in better equality, taxing folks an excessive amount of can discourage them from working or inspire them to seek out methods to keep away from paying—which reduces the general pot.

Getting the steadiness proper just isn’t straightforward. Economists usually depend on assumptions that are laborious to validate. Individuals’ financial conduct is advanced, and gathering information about it’s laborious. A long time of financial analysis has wrestled with designing the perfect tax coverage; however, it stays an open downside.

Scientists on the US enterprise technology firm Salesforce assume AI will help. Led by Richard Socher, the group has developed a system referred to as the AI Economist that makes use of reinforcement learning—the identical kind of approach behind DeepMind’s AlphaGo and AlpahZero—to establish optimum tax insurance policies for a simulated economic system. The instrument remains to be comparatively easy (there’s no means it may embrace all of the complexities of the actual world or human conduct); however, it’s a promising first step towards evaluating insurance policies in a wholly new means. “It could be superb to make tax coverage much less political and more information-pushed,” says group member Alex Trott.

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With disruptive technologies like Artificial Intelligence (AI) driving businesses, the IT sector wants the Union Budget for fiscal 2020-21 to ensure greater use of these to spur a sluggish economy among other measures for the sector, industry experts said on Friday.

“The budget should announce a fund like Singapore’s Temasek that will invest only in early-stage Indian AI start-ups and lower long-term capital gain’s tax for investing in AI-based firms,” digital intelligence firm Germin8 founder and chief executive Ranjit Nair told IANS.

With the US and China racing ahead of India in AI research, AI entrepreneurship and government investment in AI, he said the budget should make it easier for start-ups to access capital, as they face an uphill task in early-stage funding.

“The government must bring policies that encourage AI companies. Ease of doing business means less bureaucracy so that entrepreneurs can build solutions without distractions,” he said.

AI is expected to have a huge impact not only in commerce, but also in health, national security, cybersecurity, food security, education and global warming.

“The government should announce AI challenges, which make academia and industry solve an important problem in the country. The state’s role should give a crisp problem definition, provide access to data and provide a good cash prize,” Nair said.

Though thousands of engineering graduates pass out every year across the country, India is behind other nations in AI PhDs and AI research. Hence, the budget should allot more grants for AI research and offer incentives to institutes investing in AI training, he added.

Noting that IT was one of the few sectors that remained growth-driven despite the economic slowdown since the last fiscal, Cigniti Technologies chairman C.V. Subramanyam said the budget should give relief or reduce dividend distribution tax (DDT) for IT firms operating in the country.

Anti-virus leader Kaspersky’s General Manager, South Asia, Dipesh Kaura said: “As the budget for the ensuing fiscal is crucial for businesses across the country, we are expecting higher allocations for cyber security from the government.”

Investment in cyber security will accelerate the digital transformation. Funds should be spent on supporting skill development and training students keen to become cyber security professionals, he said.

“Cyber security awareness campaign for consumers should be a major focus of the budget,” Kaura said in a statement.

Cybage Software chief executive Arun Nathani said the budget should take steps to roll back DDT across the board to attract FDI and incentivise the sector to ramp up the capex /opex spend once the demand is back.

“Rationalising the GST rates and compliance processes will outgrow the consumption rates. The Indian IT industry will welcome specific incentives like weighted deductions for investing in R&D of AI/BI technology tools to facilitate IT firms, companies, universities and research institutes,” Nathani added.

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Much as robots have transformed entire swaths of the manufacturing economy, artificial intelligence and automation are now changing information work, letting humans offload cognitive labor to computers. In journalism, for instance, data mining systems alert reporters to potential news stories, while newsbots offer new ways for audiences to explore information. Automated writing systems generate financial, sports and elections coverage.

A common question as these intelligent technologies infiltrate various industries is how work and labor will be affected. In this case, who – or what – will do journalism in this AI-enhanced and automated world, and how will they do it?

The evidence I’ve assembled in my new book “Automating the New: How Algorithms are Rewriting the Media” suggests that the future of AI-enabled journalism will still have plenty of people around. However, the jobs, roles and tasks of those people will evolve and look a bit different. Human work will be hybridized – blended together with algorithms – to suit AI’s capabilities and accommodate its limitations.

Augmenting, not substituting

Some estimates suggest that current levels of AI technology could automate only about 15% of a reporter’s job and 9% of an editor’s job. Humans still have an edge over non-Hollywood AI in several key areas that are essential to journalism, including complex communication, expert thinking, adaptability and creativity.

Reporting, listening, responding and pushing back, negotiating with sources, and then having the creativity to put it together – AI can do none of these indispensable journalistic tasks. It can often augment human work, though, to help people work faster or with improved quality. And it can create new opportunities for deepening news coverage and making it more personalized for an individual reader or viewer.

Newsroom work has always adapted to waves of new technology, including photography, telephones, computers – or even just the copy machine. Journalists will adapt to work with AI, too. As a technology, it is already and will continue to change newswork, often complementing but rarely substituting for a trained journalist.

New work

I’ve found that more often than not, AI technologies appear to actually be creating new types of work in journalism.

Take for instance the Associated Press, which in 2017 introduced the use of computer vision AI techniques to label the thousands of news photos it handles every day. The system can tag photos with information about what or who is in an image, its photographic style, and whether an image is depicting graphic violence.

The system gives photo editors more time to think about what they should publish and frees them from spending lots of time just labeling what they have. But developing it took a ton of work, both editorial and technical: Editors had to figure out what to tag and whether the algorithms were up to the task, then develop new test data sets to evaluate performance. When all that was done, they still had to supervise the system, manually approving the suggested tags for each image to ensure high accuracy.

Stuart Myles, the AP executive who oversees the project, told me it took about 36 person-months of work, spread over a couple of years and more than a dozen editorial, technical and administrative staff. About a third of the work, he told me, involved journalistic expertise and judgment that is especially hard to automate. While some of the human supervision may be reduced in the future, he thinks that people will still need to do ongoing editorial work as the system evolves and expands.

Semi-automated content production

In the United Kingdom, the RADAR project semi-automatically pumps out around 8,000 localized news articles per month. The system relies on a stable of six journalists who find government data sets tabulated by geographic area, identify interesting and newsworthy angles, and then develop those ideas into data-driven templates. The templates encode how to automatically tailor bits of the text to the geographic locations identified in the data. For instance, a story could talk about aging populations across Britain, and show readers in Luton how their community is changing, with different localized statistics for Bristol. The stories then go out by wire service to local media who choose which to publish.

The approach marries journalists and automation into an effective and productive process. The journalists use their expertise and communication skills to lay out options for storylines the data might follow. They also talk to sources to gather national context, and write the template. The automation then acts as a production assistant, adapting the text for different locations.

RADAR journalists use a tool called Arria Studio, which offers a glimpse of what writing automated content looks like in practice. It’s really just a more complex interface for word processing. The author writes fragments of text controlled by data-driven if-then-else rules. For instance, in an earthquake report you might want a different adjective to talk about a quake that is magnitude 8 than one that is magnitude 3. So you’d have a rule like, IF magnitude > 7 THEN text = “strong earthquake,” ELSE IF magnitude < 4 THEN text = “minor earthquake.” Tools like Arria also contain linguistic functionality to automatically conjugate verbs or decline nouns, making it easier to work with bits of text that need to change based on data.

Authoring interfaces like Arria allow people to do what they’re good at: logically structuring compelling storylines and crafting creative, nonrepetitive text. But they also require some new ways of thinking about writing. For instance, template writers need to approach a story with an understanding of what the available data could say – to imagine how the data could give rise to different angles and stories, and delineate the logic to drive those variations.

Supervision, management or what journalists might call “editing” of automated content systems are also increasingly occupying people in the newsroom. Maintaining quality and accuracy is of the utmost concern in journalism.

RADAR has developed a three-stage quality assurance process. First, a journalist will read a sample of all of the articles produced. Then another journalist traces claims in the story back to their original data source. As a third check, an editor will go through the logic of the template to try to spot any errors or omissions. It’s almost like the work a team of software engineers might do in debugging a script – and it’s all work humans must do, to ensure the automation is doing its job accurately.

Developing human resources

Initiatives like those at the Associated Press and at RADAR demonstrate that AI and automation are far from destroying jobs in journalism. They’re creating new work – as well as changing existing jobs. The journalists of tomorrow will need to be trained to design, update, tweak, validate, correct, supervise and generally maintain these systems. Many may need skills for working with data and formal logical thinking to act on that data. Fluency with the basics of computer programming wouldn’t hurt either.

As these new jobs evolve, it will be important to ensure they’re good jobs – that people don’t just become cogs in a much larger machine process. Managers and designers of this new hybrid labor will need to consider the human concerns of autonomy, effectiveness and usability. But I’m optimistic that focusing on the human experience in these systems will allow journalists to flourish, and society to reap the rewards of speed, breadth of coverage and increased quality that AI and automation can offer.

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