Involvement of AI in Writing - Write Your Imagination with AI Tools

Professional writing isn’t easy. As a blogger, journalist or reporter, you have to meet several challenges to stay at the top of your trade. You have to stay up to date with the latest developments and at the same time write timely, compelling and unique content.The same goes for scientists, researchers and analysts and other professionals whose job involves a lot of writing.

Fortunately, Artificial Intelligence, which is fast permeating every aspect of human life, has a few tricks up its sleeve to boost the efforts of professional writers.

Introducing AI based new Smart proofreading tool: 

In 2014, George R. R. Martin, the acclaimed writer of the Song of Ice and Fire saga, explained in an interview how he avoids modern word processors because of their pesky auto-correct and spell checkers.

Software vendors have always tried to assist writers by adding proofreading features to their tools. But as writers like Martin will attest, those efforts can be a nuisance to anyone with more-than-moderate writing skills.

However, that is changing as AI is getting better at understanding the context and intent of written text. One example is Microsoft word new editor feature, a tool that uses AI to provide more than simple proofreading.

Editor can understand different nuances in your prose much better than code-and-logic tools do. It flags not only to grammatical errors and style mistakes, but also the use of unnecessarily complex words and overused terms. For instance, it knows when you’re using the word “really” to emphasize a point or to pose a question.

It also gives eloquent descriptions of its decisions and provides smart suggestions when it deems something as incorrect. For example if it marks a sentence as passive, it will provide a reworded version in active voice.

Editor has been well received by professional writers (passive voice intended), though it’s still far from perfect.

Nonetheless AI-powered writing assistance is fast becoming a competitive market. Grammarly , a freemium grammar checker that installs as a browser extension, uses AI to help with all writing tasks on the web. Atomic Reach is another player in the space, which uses machine learning to provide feedback on the readability of written content.

Impact of Machine Learning and Robotics on Society and global market - Dr. Kunal Singh Berwar

Over the past few years, artificial intelligence has rapidly matured as a viable field of technology. Machines that learn from experience, adjust to new inputs, and perform tasks once uniquely the domain of humans, have entered our daily lives in ways seen and unseen. Given the current breakneck pace of change and innovation, the question for governments and policymakers is how to harness the benefits of artificial intelligence, and not be trampled by the robot takeover of our nightmares. The answer is simple: make them work for us.

There are four areas of artificial intelligence and machine learning of importance 

1. Governance: The need to address the provenance of data, as well as matters of privacy and informed consent before basing analysis or grounding policy advice on Big Data or the algorithms used to generate findings. Big Data is dynamic, heterogeneous, and may originate in sectors that do not map cleanly to the existing lines of responsibility or expertise. For example, data generated from e-commerce, the Internet of Things, satellite data, or supply-chain and logistics data are not yet well understood or integrated in how we assess the health of a country’s economy. Countries will need to develop expertise in the use of such micro-level data.
2. Labor markets: Labor markets will look different in the next few years. There will be fewer middle-skilled jobs, such as insurance claims processing or jobs performed in a constrained physical space, like fork-lift operator or order expediter. These sorts of jobs have been more resistant to offshoring or automation so far. But they may disappear soon, as artificial intelligence improves and robots are more able to make decisions based on ambiguous situations. This has implications for education, retirement, and social welfare programs. Large numbers of middle-class jobs may be eliminated, leading to unemployment or underemployment. Some jobs will require extensive retraining to ensure that workers can perform the work. Many countries are already facing rapidly aging populations. Should large numbers of workers leave the labor market prematurely, governments will find it even more difficult to fund social-welfare and retirement benefits.  
3. Taxes: By implication, should labor markets rapidly shed middle-skilled or low-skilled jobs as many predict, the tax structures of many countries will need to reflect the decreasing share of GDP attributable to wages and salaries. Among the Organisation for Economic Cooperation and Development countries, roughly half of government revenue is derived from individual income or social insurance taxes. If labor becomes a smaller part of developed economies, tax structures will need to change to sustain government revenues near current levels, and to avoid creating further disincentives to the creation of jobs. For example, Microsoft founder Bill Gates suggested that a tax might be levied on robots.
4. Social equity: Computer-driven decision-making should be open to scrutiny and inspection, and must not simply be automated versions of mental models that embed legacies of social inequality. For instance, some businesses make use of data to offer personalized pricing, based on predictive models about the future revenue stream that a potential customer might provide.  Some customers who do not match an optimal profile might be “invited to leave quietly.” Such redlining of particular groups of customers may lead to further marginalization, leading to a self-fulfilling prophesy.  

Economists generally build models and then refine them to reduce error and improve robustness. Many artificial intelligence methods are impervious to external analysis, because software based on artificial intelligence learns and adapts as it encounters new data. After millions of iterations, the algorithm itself will have changed substantially. “The algorithm told me to do it,” is unlikely to withstand public inquiry as the basis for policy development.

Automation in AI and Insecurity of Loosing Jobs - Dr. Kunal Singh Berwar

I’ve been noticing how articles about how AI, robots, or automation will impact the future job outlook. All seem to reuse the same terms, like “disrupt”, “steal”, or “threaten”. The thesaurus has only so many terms to go around I suppose. I got to wondering which terms were most popular, and then how they’ve changed over time.

So I ran some queries. My goal was not as much sentiment tracking as it was hyperbole tracking. By hyperbole I mean the breathless, panicky articles that tend to track progress. The worst of them bet on winners (few and unworthy) and losers (all of us!), cherry pick the most extreme studies that seem to prove their point, and extrapolate long straight lines from nascent trends.

Just as Gartner tracks the hype cycles of technologies, there is a “hyperbole cycle” for scary technologies. I first noticed it covering “enterprise attention management” (a more actionable, business-focused version of the “information overload” meme). 2004-05 seemed to be peak terror about digital distractions making our caveman brains explode. A term hits the mainstream with “sky is falling articles”, they acquire academic cred as long time academics whose work connects to the narrative find sudden fame, then counter-articles appear popping the bubble, and finally it settles into a low buzz.

Which brings me back to my current work on how organizations can planfully adopt and vendors and service providers can responsibly sell AI, robotic, and automation technology. I wanted to see how the terms associated with these technologies has evolved in headlines over time. I searched for all headlines since 2013 that involved the term, work/jobs, and AI/robots/automation (and variants – see Boring Details below). I used a scale of terms from the neutral “transform” (which could be good or bad, and doesn’t make a statement about losing your job) to “kill” (’nuff said). Read the next publication to know the results !

AI and the World - Basic Category of AI

AI can be categorized as either weak or strong. Weak AI, also known as narrow AI, is an AI system that is designed and trained for a particular task. Virtual personal assistants, such as Apple's Siri, are a form of weak AI. Strong AI, also known as artificial general intelligence, is an AI system with generalized human cognitive abilities. When presented with an unfamiliar task, a strong AI system is able to find a solution without human intervention.

Because hardware, software and staffing costs for AI can be expensive, many vendors are including AI components in their standard offerings, as well as access to Artificial Intelligence as a Service platforms. AI as a Service allows individuals and companies to experiment with AI for various business purposes and sample multiple platforms before making a commitment.