How AI is changing the work landscape

sasha uritsky

Jan 11, 2021

"For better or worse, robots are going to replace many humans in their jobs,” says analysts from BBC, and the coronavirus is speeding up the process. Consumer preferences are evolving and recently consumer behavior demonstrates that we as a society have become more tolerant accepting of using automation in our daily routines.

In the professional workspace, most if not all companies have moved towards working from home. Given the unprecedented times, recruitment, the employees management, and the corporate governance processes and communication have moved online. As a result of pandemics many companies are experiencing hiring freezes, but many others have moved their recruitment efforts online. A few companies have begun piloting recruitment with the help of artificial intelligence. They are now leveraging AI to conduct online interviews and assessments and deliver data back to the employer. Now more than ever, companies are realizing the importance of moving towards a remote-friendly workforce. Being able to scale human capital on a larger scale online has definitely been accelerated recently.

I know for myself, as a current student who recently had their internship offers rescinded due to COVID-19, I’ve put myself back into the market. I’ve seen both small businesses and corporations utilize screening questions, video pitches, and unique riddles to test students’ critical thinking and how they fit into the company culture. This experience in itself has been revealing – after so many years of in-person interviews to suddenly having to emulate the same energy online or via video. Given the adjustment, at times it definitely felt unnatural to sit in front of my computer camera and pitch myself or answer video questions. However, going forward, I can see how automation and online platforms will become more explored given the time it saves and the bias it could remove during the recruitment process.

Yet it is not just a change in the recruitment process that we are seeing. The customer service environment, as I have seen first-hand, is under large stress. One of the first calls I had made was to an online retailer, to try and put in a return order. What seemed to be an idea that everyone else had as well, I was put into a queue that lasted more than 30 minutes. After hitting that 30-minute mark, I gave up and put off the task for a later date. Now, a month later, more and more companies are adopting chatbots and artificial intelligence into their customer service processes. These companies are beginning to provide information in a more efficient manner, and with less human capital.

Moving forward, in the next few months and post-COVID-19, it would be interesting to see which companies are focusing more on their digital transformation efforts. I believe that a larger number of universities and educational institutions will partner with tech companies to help digitize their working environments. And private businesses will continue to implement some of the already existing practices and produce products that cater to the remote working lifestyle and online interactions.

Interested in reading more? Check out our other blogs:

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.

Building Facebook Messenger chatbot: what they forgot to tell you.

There are lots of written tutorials and online videos on this subject.

Yet many of them omit important details of the bot building process. These details may vary from one user to another and are difficult to describe in a unilateral fashion. Consequently it is easier for tutorial writers not to mention them at all. We try here to fill the gap and provide some additional clarity.

1. Creating Facebook app.

One of the first steps in building a Facebook Messenger bot is creating a Facebook App. It requires a business Facebook page. This might seem obvious to avid social users yet worth mentioning: a business Facebook page can only be created from a personal Facebook page. If you already have a business Facebook page move on to the next step. If you have a personal Facebook page go on and create a business page. If you are among the lucky ones that live without Facebook presence now is your chance to become like everybody else.

2. Getting SSL certificate.

Next you need to setup a webhook. Your web application is hosted on a web server and the webhook’s role is to establish connection between Facebook and your web application via your web server. In order for the webhook to work you need SSL certificate because Facebook supports only secure connections (HTTPS) to external web servers. So first, you need to purchase it. The costs change from one company to another but it is important to buy a reliable certificate otherwise Facebook might reject it. All major ISP companies offer SSL products. Second, you need to install it on your web server. The installation process can be tricky. Sometimes you can get technical help from the ISP company that sold you the certificate (as a rule of thumb, the bigger the brand the better their technical support is supposed to be. But the cost may be higher too). You can also rely on popular tools, such as keytool command utility, assuming you know how to use them. In any case, it might be a good idea to allocate several days, up to a week, for this step when planning your project.

3. Choosing the server environment.

Your options are (almost) unlimited. Many online tutorials use Heroku which is a cloud-based web application platform, but a simple Tomcat web server would suffice too. Your decisions should be based on your business requirements. A lightweight server such as Tomcat is a good fit when it comes to web centric, user facing applications. If backend integration comes into play, a web application server should be considered.

Your choice of programming languages is also broad. PHP is one popular option, Java is another but the list by no means ends here. Your chatbot app communicates with Facebook using POST requests, so any language that supports web protocols will work. Again, make decisions having your business goals in mind.