Technology is increasingly making its presence felt in today’s business world. Mounting data volumes, availability of cheaper computational processing, and affordable data storage have led to increased interest in machine learning models in recent times.
More than ever before, it’s now possible to create models that can analyze complex data on a large scale while delivering quicker and more accurate results. With such models, organizations can spot profitable opportunities and avoid unknown risks.
With machine learning, organizations use algorithms to set up models that uncover connections and use these to make better decisions – even without human intervention. Most industries, particularly those that handle vast amounts of data have leveraged machine technology to increase efficiency and outdo their competition. Examples of such are in:
- Financial services
- Health services
- Transport services
Machine Learning in the Workplace
In many organizations, communication and collaboration pose a great challenge. This brings in the need to integrate intelligent technology within workflows in a seamless process. Such technology enables teams to leverage their synergies and talents, enhance engagement, and speed up work processes.
This approach to workplace productivity uses artificial intelligence and machine learning in ways that make work-life more productive and profitable. Besides, the use of wireless technologies, high-bandwidth internet, and the cloud already make it possible to work in spaces other than the conventional meeting rooms.
With advances in both memory and processing power, apps and devices now hold higher levels of intelligence and help create communication and collaboration tools. As such, smart meeting rooms can now give workers access to different conferencing technologies that are key to improved business processes.
Types of Machine Learning Models
a) Supervised Learning
Given that machine learning predicts an output by using sample inputs, it’s referred to as supervised learning. An example of this is facial recognition whereby a dataset composed of images of meeting attendees helps to predict the names of future meeting attendees.
b) Unsupervised Learning
While supervised learning uses pre-existing data, unsupervised learning builds its knowledge base through observation of activities such as user behavior or anomaly detection.
c) Other Variations
This form of machine learning covers many categories but reinforcement learning is the most predominant. It features a decision-making policy that gets enhanced with time, based on the outcomes of decisions taken previously. In web conferencing, it can be used in the optimal framing of users in a video or in anticipating a user’s wish to start a meeting.
How Does Machine Learning Enhance Web Conferencing and Workflow?
Machine learning is a subset of Artificial intelligence. It relies on quality data, patterns, and inference and uses statistical models and algorithms to perform assigned tasks – usually performed by humans.
Online meetings can be tedious and often, one half doesn’t know the other half. At times, an attendee forgets to hit the mute button and lets out a big yawn. But, machine learning models bring a new dimension to virtual meeting experiences.
Web conferencing provides an ideal forum to discuss perspectives, exchange ideas, share documents, and provide solutions to problems. It also includes the use of video conferencing and screen share. Users can also hold virtual classes through webinars.
An unlimited number of people can participate in these forums. Video conferencing, for example, allows many meeting attendants to participate without losing relevance. Meeting members also get a fair share of attention – all without affecting the bandwidth.
The use of machine learning models assists in:
- Creating an easy to implement working environment – employees can join meetings at the touch of a button
- Predicting the most ideal meeting location for teams based on previous data
- Connecting scheduling apps to the most ideal meeting venues
- Automatic prediction of the apps most likely used in meetings
- Creating a better web meeting experience using automated artificial intelligence-based API syncing
- Helping to make meetings much more intelligent and contextual
Benefits of Using Machine Learning Models in Web Conferencing
Web conferencing is quickly becoming integral to the modern workplace. Companies, both big and small are embracing more innovative, collaborative, economical, and productive ways of working.
Machine learning models help to provide:
1. Intelligent Meetings Tools
Close collaboration helps in creating strong and dynamic business relationships. This calls for effective meetings. Aira is an artificial Intelligence-based meeting software that:
- Records all meetings proceedings and transcribes the recordings for you
- Handles mundane but time-sucking tasks like calling all meeting participants before the meeting commences and adding people to video calls
- Helps you keep track of all your meetings and notes
- Helps you to identify meeting participants and usher them to the meeting room
- Notes down all meeting records and then shares them out to all meeting participants
- Enables you to optimize your processes by providing meeting analytics
Aira is the go-to meeting companion that helps you focus on solving bigger issues by taking care of menial meeting tasks like minute taking. Besides, the tool connects your meetings to your firm’s knowledge assets which helps to organize meeting notes and set meeting agendas.
2. An Efficient and Cost-effective Way of Hosting Your Meetings
Combining web conferencing with teleconferencing makes hosting a meeting both efficient and cost-effective. Web conferences are both visual and interactive and some allow you audio access via the internet.
A successful machine learning web conferencing solution needs to cover all your company needs. You also need to ensure that the technology is based on the right foundation. Modern conferencing technologies call for a robust, reliable, and highly secure communication infrastructure to help power web conferencing services.
3. Strict Privacy Regulations
While there was a recent public outcry over Zoom’s privacy and security settings ( which was later addressed), developers build responsible artificial intelligence tools for web conferencing. They also make sure that the tools meet the needs of different groups of people and protect private information.
Data privacy can be enhanced with the use of differential privacy techniques. Also, confidential computing can help to secure the organization’s machine learning assets while policies can be applied to maintain resource control and comply with regulatory standards.
4. Increased Employee Responsiveness
For businesses that leverage BYOD (bring your own device), employees show a notable improvement in responsiveness and decision-making. Such businesses can utilize machine learning-based models that offer video conferencing platforms to enable their users to attend video meetings on the go.
5. Replacing Cumbersome Workflows with Automated Chatbots
Managing cumbersome workflows is made easy by the use of virtual assistants for video conferencing. AI-powered chatbots can also intersect your email marketing and CRM for easy workflow follow-up after meetings.
6. Trust and Collaboration
A heightened sense of camaraderie, trust, and collaboration emerges when team members use machine learning meeting tools. An elevated meeting experience also increases engagement and helps participants to stay focused during the entire meeting. This is a big win for the business.
Machine learning models can also enhance your team’s collaboration experience and help build better and stronger relationships. With reduced meeting friction points, employees can strengthen their interactions and build high-performance teams.
What You Need For Successful Web Conferencing
For successful web conferencing, your preferred meeting tool needs to offer the following capabilities:
1. Ease of Use
Your web conferencing technology ought to make this experience smooth and enjoyable. If the solution is hard to use, makes processes like setting up meetings or signing in participants tedious, holding meetings will always be a nightmare.
Go for technology that offers you great user experience, can work from any device, and one that requires no plug-ins or downloads. It should also be flexible and easy to integrate into your work processes.
2. High Security Levels
Does your meeting assistant protect your communication from unauthorized access? Ensure that your meeting solution features in-built multi-layer security. This will allow you to have an uncompromised user experience.
3. Multiple and Advanced Features
Is holding an audio call all you can do? How about screen sharing, video conferencing, or holding audience polls? Demand more from your solution provider and elevate your meetings with enhanced collaboration. Your participants will thank you and your meetings will be more engaged.
Furthermore, meeting participants should also be able to hold web conferences via their desktop, laptop, smartphone, or tablet. Multiple meeting features also help to solve many of the challenges you might face while communicating with peers.
It’s no secret that machine learning has a huge impact on business. More and more organizations are taking advantage of this technology to completely transform how their teams communicate.
As many dull administrative activities get handled by machine learning models, employees will have ample time and resources to leverage their collaborative efforts, intelligence, and creativity.
Machine Learning models are dynamic. They are continually evolving and improving. Increased technological advances in both computing and mobile ability will serve to make workplace communication and collaboration more effective and streamlined.
If you want your business to keep ahead, your teams to work smarter, and to have more productive meetings, integrate machine learning into your web conferencing. This is the future of meetings.