Pros and cons of automation

dean wang

Feb 08, 2015

Automation drives forward the economy. It allows businesses to scale and service large groups of customers. Automation first appeared in traditional industries, such as cotton production in England in 18th century or car conveyors in the US in early 20th century. The automation replaced physical labor.

With the invention of computers automated systems began to replace intellectual labour such as math calculations. Most of the software applications we use today can be described as automation. Online payments processing, online tickets purchasing, tax returns software, computer games, search engines, and endless other programs are all examples of software automation system.

As a next step we are now aiming at automating human decision making processing and high-level intellectual activities, historically considered to be sole domain of humans.

One interesting aspect of automation is lesser quality of service compared to manual service.

This is to be expected. If we gain in quantity we lose in quality.The gain in quantity is what automation is about - it allows to reach out to a large number of customers. Manual product or service can reach out to individuals only. The price we pay for the ability to deliver product or provide service en masse is the drop in quality.

Sometimes automation is an obvious choice. This is when the gain, the scalability, hugely outweighs the costs, lower quality. Search engine is a popular successful example. In other cases, the advantage in not so obvious. Online travel booking offers fast service without leaving the comforts of the home, but it does not often deliver the best option, such as finding the cheapest flight, and therefore many people still use ‘manual’ travel agents.

Interested in reading more? Check out our other blogs:

MAKING AI MAINSTREAM

We are experiencing a strong demand for conversational AI solutions. It is coming from every corner of the B2C market. It is growing by the day.

Conversational AI is becoming increasingly popular among the consumer facing business community. It is easy to see why - AI offers sales and customer service scalability and therefore is critical for the long-term success of a business.

Conversational AI solutions such as chatbots, voice bots, and virtual assistants provide much needed speed and efficiency, in an age where the rapid advancement of technology makes them virtually the only sustainable customer service solution.

Bu there is a catch - AI is complicated. Mainstream businesses do not have in house AI expertise. And it is not part of their business model to develop such expertise.

Today’s market offer several good conversational AI solutions, such as IBM Watson or Google DialogFlow. However, getting a business value out of them requires the very AI expertise that mainstream companies do not possess.

So what can be done?

Any AI solution should follow these three steps in order for the mainstream business community to fully benefit from it:

Conversational AI should come as a service,
The service should be available in natural language,
The service should be fully personalized.

In the next several posts we will explore how the AI industry, including nmodes, is moving towards achieving these goals.

Towards smarter data - accuracy and precision

There is a huge amount of information out there. And it is growing. To make it efficient and increase our competitive advantage we need to evolve and start using information in a smart way, by concentrating on data that drives business value because it is accurate, actionable, and agile. Accuracy is an important measure that determines the quality of data processing solutions.

How accuracy is calculated?

It is easy to do with structured data, because the requirements are formalizable. It is less obvious with unstructured data, e.g. a stream of social feeds, or any data set that involves natural language. Indeed, the sentences of natural language are subject to multiple interpretations, and therefore allow a degree of subjectivity. For example, should a sentence ‘I haven’t been on a sea cruise for a long time’ be qualified for a data set of people interested in going on a cruise? Both answers, yes and no, seem valid.

In these cases an argument was put forward endorsing a consensus approach which polls data providers is the best way to judge data accuracy. This approach essentially claims that attributes with the highest consensus across data providers is the most accurate.

At nmodes we deal with unstructured data all the time because we process natural language messages, primarily from social networks. We do not favor this simplistic approach, as it is considered biased, inviting people to make assumptions based on what they already believe to be true, and making no distinction between precision and accuracy. Obviously the difference is that precision measures what you got right, and accuracy measures both what you got right and what you got wrong. Accuracy is a more inclusive and therefore more valuable characteristic.

Our approach is

a) to validate data against third party independent sources (typically of academic origin) that contain trusted sets and reliable demography. Validating nmodes data against third party sources allows us to verify that our data achieves the greatest possible balance of scale and accuracy.

b) to enrich upon the existing test sets by purposefully including examples ambiguous in meaning and intent, and providing additional levels of categorization to cover these examples.

Accuracy is becoming important when businesses move from rudimentary data use, typical of the first Big Data years, to a more measured and careful approach of today. Understanding how it is calculated and the value it brings helps in achieving long-term sustainability and success.