3rd April 2017

What is NLG? : White Paper Download


Free download 

Turn your numbers into valuable insights in seconds 

Natural Language Generation (NLG) is a subfield of AI that automates the analysis and interpretation of your data, turning it into actionable written insight reports.

When data moves too fast for you to keep up with, NLG gives your data a voice so it can communicate valuable insights directly to you in next to real-time.


Download this NLG White Paper to discover the power of Natural Language Generation.

Key benefits of NLG include the ability to:

• Generate automated reports in real-time 24/7/365.
• Provide scalable embedded expertise.
• Deliver information efficiently and accurately.
• Increase productivity by freeing experts from laborious repetitive data analysis.
• Capture the knowledge of your most trusted experts in software.
• Tailor reports using the same data for multiple audiences.

What is NLG? White paper
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8th March 2017

Five things you need to know about NLG


Author: Caroline Smart

The trouble with NLG is that even after you find out what the three letters stand for (Natural Language Generation), you may be none the wiser. It’s when you understand what NLG does, that you appreciate the world of possibilities it opens up.  And that’s particularly true as we step off the Information Super Highway (just some of you may be old enough to recognize the reference) onto the Internet of Things.

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23rd February 2017

Automation, employees and the bottom line


Originally featured: CIODive



Though it is often approached with fear, automation doesn’t necessarily mean bad things for employees.

Advancements in technology are not always welcome, particularly to a workforce fearing displacement. This is particularly true with the rise of automation, with the threat that companies could outsource labor to machines. And while experts say artificial intelligence and automation can provide a cheaper and better way to solve problems that previously took up valuable human time and effort, putting numbers to those changes is challenging.

Almost half of knowledge work activity can be automated, according to a recent McKinsey study. Physical tasks "in highly structured and predictable environments, as well as data collection and processing" will be the first to be automated, according to the report. And because those types of jobs make up a little over half of activities in the economy, that equates to almost $2.7 trillion in wages.

McKinsey also acknowledges almost all occupations — blue collar and white collar — have potential for some automation, which could result in a savings of about $16 trillion in wages. Those are big numbers, certainly large enough to garner the attention of businesses looking to trim costs in a competitive environment.

Though it is often approached with fear, automation doesn't necessarily mean bad things for employees. When it comes to replacing workers altogether, McKinsey estimates that could only work in less than 5% of occupations.

Instead, automation is more likely to make employees more productive.

Not all bad news for employees

While some people express concerns about job losses due to automation, others focus on how the gradual displacement in the workforce through automation will aid the economy and drive growth. McKinsey estimates automation could raise productivity growth globally by 0.8% to 1.4% annually.

"Technology such as natural language generation (NLG) — AI technology that can absorb vast quantities of big data and communicate key insights and conclusions into easily digestible reports — will drive our workforce forward by streamlining processes, helping people to do their jobs more efficiently," said Sharon Daniels, CEO of Arria NLG. "The best and brightest will be free to innovate; the engineers to build, the doctors to heal, the scientists to discover."

Only 60% or less of actual work time today is spent productively, according to a report from Atlassian. If employees had access to tools and technology they need to automate their workflow, the amount of time spent on workflow disruptions could be drastically lowered.


"Successful work will require humans and machines working together to better delight customers, better grow the top line, and better improve the bottom line," said Tiger Tyagarajan, CEO of Genpact.

Workers will not only be happier, many are likely to see a bump in salary as well, Tyagarajan predicts. For example, a recent Deloitte study in the U.K. found that AI technology has replaced 800,000 lower-skilled jobs with 3.5 million new ones, which pay on average £10,000 ($12,500) more than the jobs they replaced. Those jobs include engineers and data analysts, who create the machines and analyze the data collected by the them.

"Essentially, as tasks and jobs become increasingly automated, that automation opens the door for employees to work more efficiently and creatively to solve problems in which human knowledge is intrinsically valuable," said Tyagarajan. "Machines are taking over more and more repetitive, time-consuming tasks, meaning humans will have more time to take on higher-skilled roles."

Daniels agreed. For example, in financial services and healthcare, the vast troves of data collected can change as fast as someone can analyze it.

"AI capabilities and the ability to automate reporting actually takes the time-consuming and repetitive mechanical tasks away from the human, freeing them to investigate new ideas and to create new solutions," said Daniels. "We believe that AI will augment knowledge-workers, who will advance to a whole new level of expertise."

The tasks that are taken away by AI are often the time-consuming, repetitive, mundane tasks associated with preparing reports.

"The responsibility of actual reporting remains intact but now can be done more efficiently, in real-time and at scale," said Daniels. "This does not remove the job per se; it optimizes the dynamics of the task, allowing knowledge knowledge-workers and analysts to do more and know more, faster."

Who delivers the bad news?

One question that remains unanswered: If automation is to take away jobs, will the CIO be responsible for making that decision?

While it's still unclear, experts say in some cases, it will likely be the CIO, but the chief data officer (CDO) may also play a role.

It will also depend on the area being automated. For example, financial services and healthcare sectors see a strong ROI from using AI technology. "While the CIO has a responsibility for implementation, the benefits are delivered to multiple departments and stakeholders, so decision-making typically becomes a collective exercise of evaluating and redefining information-related roles," Daniels said.

Either way, experts say enterprise IT leaders need to begin preparing their workers to embrace robots as teammates, not adversaries. McKinsey predicts workers will have to adapt for automation and perhaps learn new, more complex skills that they then perform alongside machines. It will therefore be more a matter of better assisting machines rather than being replaced by them.
"I would advise CEOs and CIOs to stay focused on creating a company culture that equips employees with the tools to succeed in a workplace cohabited by robots," said Tyagarajan. "Pushback — both internal and external — is inevitable during times of transformation, especially at the beginning."

Leaders need to be transparent and accountable. This begins with keeping employees in the loop when it comes to how and when the company plans to apply AI and automated systems. Employees need to know that while the robots are coming for some jobs, it is possible to retrain and reskill to work alongside them.

"Developing reskilling and education programs is absolutely key to helping employees feel empowered — rather than threatened — by the rise of robots at work," said Tyagarajan. "[These] programs should focus on teaching human employees how to create, use and maintain the AI systems they will be working alongside."

Workers should also keep in mind there are many areas where humans still outperform machines — such as any task requiring negotiation, judgment or creativity.

"By helping human employees build on these strengths, leaders will help employees accept machine teammates as valuable supplements to human talent, rather than insidious replacements," said Tyagarajan.

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9th February 2017

NLG Tools Automate Analysis


Originally featured: Eckerson Group


It is often said that a picture is worth a thousand words. But in the era of big data, a paragraph from a natural language generation (NLG) tool might be worth a thousand pictures. 

NLG tools automatically analyze data, interpret it, identify the most significant parts, and generate written reports in plain English. In essence, NLG brings artificial intelligence to business intelligence (BI), automating routine analysis, saving business users time and money.

Although BI products generate visualizations, reports, and dashboards, business users still have to analyze and interpret data. That’s where NLG comes in. It automatically performs the analysis and generates an English language translation of what is significant and meaningful in the data. Business users no longer have to study the data to interpret its meaning; NLG tools do that for them. 

Moreover, NLG tools bypass the need to create visualizations, charts, and reports in the first place. The tools can sift through large volumes of data and generate reports automatically. This is particularly valuable in the age of big data where huge amounts of data can overwhelm business users and IT departments alike. With NLG tools, data analysts can spend 80% of their time analyzing data rather than 80% of their time preparing data. In other words, NLG tools augment the job of business users so they can focus more on high-value tasks and less on menial work.

Job Killer?

Since NLG tools automate analysis and have human-like capabilities, some people worry that NLG and other artificial intelligence products will undermine job security. Although this is a legitimate concern, officials at all three companies I interviewed emphasize that NLG tools augment the job of business users, and don’t replace them. They say NLG tools allow business users to focus on higher value tasks. Although NLG tools generate written analysis, business users still have to read, understand, and act on the reports. Rather than replacing analysts, these tools allow for more analysis to happen more often.

Arria’s NLG Platform and Recount

Arria offers NLG Platform, which works with almost any data source or application. NLG Platform runs on the cloud or on-premises and works in any industry, including financial services, healthcare, and marketing. Soon, Arria will release two cloud versions of the product: an enterprise version called Articulator Pro and Articulator Lite geared to non-NLG programmers that will compete with Yseop Savvy.

Arria also sells Recount, an accounting solution that works with accounting software from Xero. Recount automatically generates reports that identify key trends and issues in their accounting data that they need to pay attention to. The tool helps small and medium-sized business owners focus on the business rather than administrative duties and bookkeeping.

According to Jeff Zie, CMXO and Head of Recount, “Xero is a fantastic accounting platform, but it doesn’t tell owners what’s going on and whether it’s a good thing or a bad thing. Using Recount is like picking up a phone to a financial analyst and asking ‘what’s going on with my business?’”

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2nd February 2017

10 AI companies to watch out for in 2017


Originally featured: Techseen

 

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18th January 2017

Gartner : NLG will become a standard in analytics


Exerpt taken from original article published by Gartner

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10th January 2017

What is NLG, and how does it relate to NLP and other forms of AI?


Author: Ehud Reiter



I sometimes find that people are confused about the difference between NLG, NLP and AI. This blog aims to make the differences clear.  Apologies in advance for all the acronyms!

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9th January 2017

Finding a voice


Originally featured: The Economist 



Language: Finding a voice

Computers have got much better at translation, voice recognition and speech synthesis, says Lane Greene. But they still don’t understand the meaning of language

I’M SORRY, Dave. I’m afraid I can’t do that.” With chilling calm, HAL 9000, the on-board computer in “2001: A Space Odyssey”, refuses to open the doors to Dave Bowman, an astronaut who had ventured outside the ship. HAL’s decision to turn on his human companion reflected a wave of fear about intelligent computers.

When the film came out in 1968, computers that could have proper conversations with humans seemed nearly as far away as manned flight to Jupiter. Since then, humankind has progressed quite a lot farther with building machines that it can talk to, and that can respond with something resembling natural speech. Even so, communication remains difficult. If “2001” had been made to reflect the state of today’s language technology, the conversation might have gone something like this: “Open the pod bay doors, Hal.” “I’m sorry, Dave. I didn’t understand the question.” “Open the pod bay doors, Hal.” “I have a list of eBay results about pod doors, Dave.”

Creative and truly conversational computers able to handle the unexpected are still far off. Artificial-intelligence (AI) researchers can only laugh when asked about the prospect of an intelligent HAL, Terminator or Rosie (the sassy robot housekeeper in “The Jetsons”). Yet although language technologies are nowhere near ready to replace human beings, except in a few highly routine tasks, they are at last about to become good enough to be taken seriously. They can help people spend more time doing interesting things that only humans can do. After six decades of work, much of it with disappointing outcomes, the past few years have produced results much closer to what early pioneers had hoped for.

Digital assistants on personal smartphones can get away with mistakes, but for some business applications the tolerance for error is close to zero, notes Nikita Ivanov. His company, Datalingvo, a Silicon Valley startup, answers questions phrased in natural language about a company’s business data. If a user wants to know which online ads resulted in the most sales in California last month, the software automatically translates his typed question into a database query. But behind the scenes a human working for Datalingvo vets the query to make sure it is correct. This is because the stakes are high: the technology is bound to make mistakes in its early days, and users could make decisions based on bad data.

This process can work the other way round, too: rather than natural-language input producing data, data can produce language. Arria, a company based in London, makes software into which a spreadsheet full of data can be dragged and dropped, to be turned automatically into a written description of the contents, complete with trends. Matt Gould, the company’s chief strategy officer, likes to think that this will free chief financial officers from having to write up the same old routine analyses for the board, giving them time to develop more creative approaches.

Machines that relieve drudgery and allow people to do more interesting jobs are a fine thing. In net terms they may even create extra jobs. But any big adjustment is most painful for those least able to adapt. Upheavals brought about by social changes—like the emancipation of women or the globalisation of labour markets—are already hard for some people to bear. When those changes are wrought by machines, they become even harder, and all the more so when those machines seem to behave more and more like humans. People already treat inanimate objects as if they were alive: who has never shouted at a computer in frustration? The more that machines talk, and the more that they seem to understand people, the more their users will be tempted to attribute human traits to them.

That raises questions about what it means to be human. Language is widely seen as humankind’s most distinguishing trait. AI researchers insist that their machines do not think like people, but if they can listen and talk like humans, what does that make them? As humans teach ever more capable machines to use language, the once-obvious line between them will blur.

Above article is an excerpt from the original article. 

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4th January 2017

NLG – The Cinderella of AI


Originally featured: Inside BigData

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