DEV Community: namynot

Examples of Conversational AI In Action

namynot — Tue, 25 Jul 2023 19:34:06 +0000

As you navigate the swiftly changing landscape of artificial intelligence, you'll find Conversational AI, with its suite of technologies, including chatbots and virtual agents, becoming an integral part of many services.

This technology harnesses vast data, machine learning, and natural language processing to enable intuitive, human-like interaction through speech or text while also offering the remarkable capability to interpret and translate inputs across multiple languages. This breakthrough allows for a seamless communication pathway between you and various digital systems.

In this article, you'll journey through the dynamic world of Conversational AI, encountering a multitude of real-world examples across diverse industries where this game-changing technology is truly coming to life.

What is Conversational AI?

Conversational AI encompassing technologies like chatbots and virtual agents, leverages vast data, machine learning, and natural language processing to interact with users through speech or text. It can interpret and translate inputs across multiple languages, promoting seamless communication between humans and digital systems.

Real-World Examples of Conversational AI

Here are interesting examples of companies using Conversational AI:

Customer Service

HSBC Bank uses a chatbot called Amy to answer customer questions and resolve issues.

Air.ai is a new service that powers other businesses to use an AI agent that sounds like a real person. Check out this impressive video of a car sales agent conversing with a prospect. (Once the pauses and tonal changes are ironed out, it would be difficult to determine you are speaking with a machine!)

Companies like ServiceNow use it to help other businesses create chatbots using low code to improve customer service.

Sales and Marketing

Companies are using it to improve sales and marketing. For example, Sephora uses a chatbot to help customers find products and purchase them.

Education

Conversational AI is being used in education to provide personalized learning experiences. For example, Duolingo uses a chatbot to help students learn new languages.

Healthcare

In the healthcare industry, it is used to provide patient care. For example, Buoy Health uses a chatbot to help patients assess their symptoms and connect with medical professionals.

Finance

It's used to help customers manage their finances. For example, Schwab Intelligent Assistant uses a chatbot to help customers check their account balances and make transfers.

Banking

Banks use Conversational AI to provide customer service, fraud detection, and other services. For example, Bank of America uses a chatbot called Erica to help customers with their banking needs.

Retail

Retailers provide customer service, product recommendations, and other services using chatbots. Walmart uses a chatbot called Ask Sam to help customers find products and make purchases.

Travel

Travel companies use conversational AI to provide customer service, flight booking, and other services. For example, Kayak uses a chatbot called Kayakbot to help customers book flights and hotels.

Media

Media companies use it to provide news, weather, and other information. For example, The Washington Post uses it to connect to Alexa to provide news and weather updates.

Government

Governments provide services such as tax filing, benefits enrollment, and other services. For example, the Internal Revenue Service (IRS) uses a chatbot called Taxbot to help taxpayers file their taxes.

These are just a few examples of how Conversational AI is used in the real world today. As the technology continues to develop, we can expect to see even more innovative and transformative uses of Conversational AI in the future.

The riveting examples explored in this article highlight the profound versatility and immense potential of Conversational AI in our world today. This transformative technology is making waves in sectors ranging from customer service to education and healthcare to government services, altering how businesses function and interact with digital platforms. As Conversational AI evolves, we can look forward to a future brimming with even more innovative and efficient applications that will continue redefining our interactions with the digital world. The ongoing journey of Conversational AI is nothing short of exciting, promising groundbreaking advancements that will shape our tomorrow.

If you enjoyed these examples, please react, comment, and follow. Antonio Wells is a veteran content creator with interests in Tech, AI, Marketing, and Entertainment.

The cover image was created with Midjourney.

How to integrate test automation into software development cycles?

namynot — Fri, 22 Jan 2021 14:02:32 +0000

There are many challenges with integrating test automation into software development cycles as all team members may not be proficient in the scripting languages and/or tools necessary to succeed. For a Quality Control manager, throwing more people at the problem may not necessarily be the solution. In some instances, it could hinder the QA team and end up costing more in the longterm.

How do you overcome these challenges while still keeping up with swift Agile development cycles? In this article, we will discuss how our test automation software helped one client conquer these typical challenges.

We had the pleasure to interview one of our clients DeNA Co., Ltd. DeNA is one of the leading venture firms in Japan. They provide a mobile portal as well as e-commerce websites based in Japan. It owns the Mobage platform, which is one of the most popular cell phone platforms in Japan. We had the opportunity to interview their Quality Control testing leads; Kenji Serizawa and Naoki Kashiwakura.

DeNA has a specialized team of testers who are considered Software Engineers in Test (SWET). These are software developers whose focus is on testing. The benefit of having a software engineer be a tester is that they can code the bugs they find rather than pointing out errors alone. At DeNA (and similar to many other software testing departments) not all team members are fluent in using software like Selenium as it requires writing code scripts for running automated tests. Their engineers who used it was at their own will, however, the usage was not mandated department-wide.

What was required of the department, unfortunately, were many manual tests.

Challenges with manual testing

“We were overwhelmed with daily manual testing.”

The QA team at DeNA would manually take screenshots of bugs, then catalog and manage them in Excel. Their focus shifted away from testing but more on the tedious tasks of creating screenshots explains Kenji Serizawa. With the introduction of Autify, this requirement vanished. It also allowed their team to increase the range of tests rather than reducing workload according to Naoki Kashiwakura. With Autify, there was no need for manually logging screenshots as the software does this for you.

How to integrate test automation?

It is wise to start small at first. Then increase automated tasks. This is what many of our clients have done, including DeNA.

They illustrate how they started with automating small tasks, “we started creating scenarios for short test cases such as checking the display and page transitions.” They then started automating some test items one by one. This was great for repetitive tasks. “We’ve raised the priority of repetitive tests, gradually turned it into scenarios, and run it on a regular basis,” according to Kenji.

The team would have regular meetings with the team leads to discuss test automation using Autify. From the strategic planning phase their tactics consisted of:

Starting with small tasks
Ponder how to efficiently use Autify’s functions in the test workflow
Design test scenarios with data-driven test functions
Collaborating knowledge with team members accelerated usage at scale after starting small

Benefits of integration test automation software

There are plenty of benefits of integrating test automation software including; more time spent testing, more people for testing, which means more man-hours and an inflated total cost of ownership in software development.

“By automating simple items we were able to spend time on other tasks,” explains Kenji regarding when the team introduced test automation. Their greatest benefit materialized with the ability to automate repetitive tests at scale. “At our scale, doing it manually is impractical. I can’t imagine how many people we would need.”

“Autify is amazingly simple and extremely easy to get into,” describes Naoki. “For example, it’s easy to use even without any coding skills, and that point alone made it viable to introduce it to the whole team,” the QC lead elaborates.

In our client’s case study, they realized a bump in productivity. “This is still experimental, but when I tried it for about a month with a product with a quick-release cycle, I was able to reduce workload by about 10%,” details Naoki.

Conclusion

We have interviewed many QA testers and a wealth of them dislike the manual repetitive nature of their jobs. They are also new to integrating automated testing solutions. Until the entire department mandates automation, they must deal with their challenges individually. However, what if an AI-based automation solution was present to alleviate the frustrations of manual testing? Would your team try it?

This article was originally posted here.

Can AI Solve Test Automation Engineer Shortages?

namynot — Tue, 08 Dec 2020 19:33:38 +0000

The test automation industry is rapidly transitioning towards artificial intelligence to tackle challenging issues. AI can decipher subtle changes in a GUI, learn patterns for recommendations, consume massive data, take on repetitive tasks, and can potentially reduce development costs. But can it address the shortage of skilled test automation engineers?

“Artificial Intelligence is transforming software testing by not only automating manual tasks but learning of changes and automatically adapting. This helps save time which reduces costs. It can also point out signals for managers to make better data-driven decisions.” Source: How AI is transforming software testing

There are a lot of tools and programming languages test automation engineers need to possess. There are plenty of jobs of this ilk, however, most go unfilled or take a long time to fulfill. Most companies are seeking specialists rather than generalists. According to a hiring manager, the majority of those prospects he’s interviewed either lack the required technical skills or have no comprehension of QA and testing principles.

In a study, one of the greatest struggles companies face with test automation is finding the right tools, followed by training/acquiring skilled automation engineers. Tools and people are common obstacles here. Regarding the latter, artificial intelligence is one of the key indicators for overcoming the challenges of humans. We don’t anticipate it to eliminate jobs, but used as a tool to produce superior software, faster.

Conclusion

It is evident there is a test automation engineer shortage. However, the industry is making significant strides in test automation. Based on stats, between 10 percent and 49 percent of a company’s overall QA budget is invested in test automation related expenditures. And larger companies — those with 500 or more employees — are more than twice as likely to spend over 75 percent of their QA budget on test automation.

So, can artificial intelligence solve test automation engineer shortages? I believe it can, as the investment prioritization is there for many companies. DevOps teams are always exploring better tools. Since new tools are introducing AI learning algorithms into their platforms, this will rid the need for such a scarce role, and place the responsibility back on testers. Some codeless AI-based testing platforms are so user-friendly, they require no coding of test scripts, nor maintaining them. This means less skilled individuals on the QA team can learn and apply the testing software to the lifecycle.

Are you a test automation engineer? Are you a tester? Or apart of a software quality assurance team? I’d like to hear your thoughts on this perplexing question in the comments!

References:

Kobiton-Infostretch State of Test Automation (2020-2021)
Autify’s Mind-Blowing DevOps Testing Stats (2020)