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What Problems Are Virtual Assistants Intended To Solve?

By Author: James Church
Total Articles: 4

Artificial intelligence (AI) is storming into every walk of life.

Can you see it? It’s very much here, inventing something valuable.
If you like to know a few ones, check the quickly respond and nudging features in your Gmail. Or, type dogs or hugs in your Google photos library. Its integrated AI assists to pop up the images of furry dogs on the screen. Likewise, personalized recommendations in Google Play Music, automatic captions with transcription facility in more than one billion YouTube videos, assistance in navigation and searching through Google Lens epitomize remote assistance.

Is it a new kind of assistance- Let’s understand it, first.

What is a virtual assistant?
Certainly, there are many bright and a few dark shades of the artificial intelligence. If consider its upside, it emerges in the role of a virtual assistant. It’s the one that is self-employed, providing professional, administrative, technical or creative assistance from a comfort zone.

However, many outsourcing companies appear in this virtual assistant role, especially from India, Malaysia and many other Asian countries. But, the assistance is matchless. A human brain can’t predict the location, for example, of an earthquake aftershock. But, AI has proven its worth. In association with machine learning, AI has helped to successfully evolve a model. It perceives that location accurately after the tremor. Thereby, a catastrophe can be prevented with its vision.

Why do businesses require a virtual assistant?
• AI assistance in healthcare industry:
The machine intelligence virtually elevates a business. Let’s consider a few examples. If you talk about healthcare domain, a bot service, viz. Quest Diagnostics, is discovered. Now, the healthcare industry doesn’t need to begin health screening from the scratch. The bot can tackle prescreening through triage complex medical questions, a set of handoff features and a symptom checker. While embedding with the Apple Health App, it securely shares diagnostic results.

• AI’s assistive technology:
The days have gone when you had to rely on a business development officer. It’s the era wherein AI rules. When the idea of Duplex shaped up in 2018, people were amazed. Its voice recognition, natural language & dialog understanding and text-to-speech technology surprisingly took away hearts. What human-like projections and comprehensibility it showed while taking up a demo call in I/O conference, it was indeed incredible.

The always-on AI-based virtual helpdesk is frequently and consistently evolving. The business community is fond of hiring this virtual assistant because it aims at shortening turnaround time and unifying convenience in diverse business operations.
Challenges that remote assistants are facing: Can virtual assistants solve complex issues? It’s hard to answer. The assistance of machine intelligence is, to a certain extent, emerging as a breakthrough. But, the day is far when it could be able to encapsulate end-to-end business puzzles.

Let’s have a glance over a few challenges that AI-based virtual assistant can’t defeat:

• Scarcity of adequate niche data: An app or software works on an incredible phenomenon of embedding niche data in the form of queries to parse through. But, the problem of inadequacy hampers its outcome. Consequently, the flaw overtakes efforts.

Amazon, for example, introduced an AI screening program for evaluating tech-job applicants. Whatever data its developers fed to the application, it was incomplete. Because of this inadequacy, several eligible candidates got a rejection. Reuter highlighted that Amazon’s AI recruiting tool scraped data that showed it biased against women. Later, a spokeswoman rejected this allegation.

• Ignoring diverse data: Machine learning is based on diverse facts. It means that diversity is in the epicenter. It’s a key to derive an exact sense. Otherwise, the discrepancy will creep in. The research paper from MIT and Stanford University confirm that ignoring diversity in data can lead to fatality. Three AI-based facial recognition tools misled law-enforcement agencies in 2018. However, its algorithms were trained on 1,270 samples. But still, the flaws emerge in programs when it incorrectly recognized dark-skinned women as men. Its errors scrolled up more than 20 percent in one case and 34 percent in two other programs. For a human-like assistance, these virtual assistant programs should have unified dark-skinned women’s images while undergoing machine learning.

• Intensive research: The CEO of a finance company in Los Angeles pressed that the deep research is a need of the hour. His company’s virtual assistant app filters criteria to grant credit on the basis of researched conditions. Initially, its algorithm anticipated candidates having poor credit risks by analyzing their car mileage and location. But, these were not the only factors to reject a credit request. Therefore, it broadened that research adding more criteria, such as regularly paid rent and cell phone bills on time, to segment more creditworthy.

Besides, there are many other challenges that are modeled on emotional and psychological aspects. But, the developers hold the competency to deal with these logical challenges.

More About the Author

James is a business analyst with over five years of experience. He inclines toward big data for deriving incredible intelligence. Their implementation injects breakthroughs, which steer an operation from loss-bearing to profit-making scenario. He has written several success stories during outsourcing data solutions for innumerable clients.

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