The Automation Is Coming

sasha uritsky

Jan 27, 2017

A close look at the history of humanity leaves us with no choice but to admit that the majority of jobs as we know them now will be transferred to automated systems. This is part of the technological and scientific progress our civilization is undertaking and it is irreversible.

Artificial intelligence became mainstream in 2016. For the first time artificial intelligence is not only available to big companies like Google, Amazon or Apple, but to the majority of businesses worldwide. Startups have started building products and services using artificial intelligence en masse.

The essence of artificial intelligence is massive, intuitive computing power: machines so smart that they can learn and become even smarter. The machines are becoming quicker and more nimble. They cover wider range of conversation topics. They now connect to robotic systems and online interactive systems. There is literally very little they cannot, or will not be able to, do as applied to industrial workforce.

With all the good that’s going to come with automation, we are suddenly faced with a new problem: the elimination of many low and middle class jobs. Many jobs that have already been severely impacted by computers (manufacturing, administrative support, retail, and transportation) will continue to diminish. In the nearest future routine-based jobs (telemarketing, sewing) and work that can be solved by smart algorithms (tax preparation, data entry keyers and insurance underwriters) are most likely to be eliminated.

What to do? It is fruitless to fight automation, we need to find ways to work with automation rather than against it.

The solution is to become more creative as species. Creativity is the natural advantage of humans over machines. Automation is about to change the course of the world, it’s going to be a great disruptor and impact the workforce like nothing we’ve seen before. We can sit around and gradually become obsolete, or accept the challenge and use the tool of creativity, which we are in unique possession of, to maintain our superiority.

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Crowdsourced data can often be inconsistent, messy or downright wrong

We all like something for nothing, that’s why open source software is so popular. (It’s also why the Pirate Bay exists). But sometimes things that seem too good to be true are just that.

Repustate is in the text analytics game which means we needs lots and lots of data to model certain characteristics of written text. We need common words, grammar constructs, human-annotated corpora of text etc. to make our various language models work as quickly and as well as they do.

We recently embarked on the next phase of our text analytics adventure: semantic analysis. Semantic analysis the process of taking arbitrary text and assigning meaning to the individual, relevant components. For example, being able to identify “apple” as a fruit in the sentence “I went apple picking yesterday” but to identify “Apple’ the company when saying “I can’t wait for the new Apple product announcement” (note: even though I used title case for the latter example, casing should not matter)

nmodes Technology - Overview

nmodes ability to accurately deliver relevant messages and conversations to businesses is based on its ability to understand these messages and conversations. Once a system understands a sentence or text, it can easily perform a necessary action, i.e. bring a sentence about buying a car to the car dealership, or a complaint about purchased furniture to the customer service department of the furniture company.

Understanding sentences is called semantics. nmodes has developed a strong semantic technology that stand out in a number of ways.

Here is how nmodes technology is different:

1. Low computational power. We don’t use methods and algorithms deployed by almost everyone else in this space. The algorithms we are using allow us to achieve high level of accuracy while significantly reducing the computational power. Most accurate semantic systems, e.g. Google’s, or IBM’s, rely on supercomputers. By comparison our computational requirements are modest to the extreme, yet we successfully compete with these powerhouses in terms accuracy and quality of results.

2. Private data sources. We work extensively with Twitter and other social networks, yet at the same time we process enterprise data. Working with private data sources means system should know details specific only to this particular data source. For example, when if a system handles web self-service solution for online electronics store it learns the names, prices, and other details of all products available at this store.

3. User driven solution. Our system learns from user’s input. Which makes it extremely flexible and as granular as needed. It supports both generic topics, for example car purchasing, and conversations concentrating on specific type of car, or a model.