Since the coronavirus pandemic took hold of our lives, virtual events have taken centre stage. With evolving time, virtual events are becoming the new normal, and AI is no longer something that business owners, marketers or organisers can afford to ignore. There is no denying that AI is proving to be one of the most important resources available for creating top-notch, successful events.

But before we go any further, let’s understand, what is AI?

Most of us relate Artificial intelligence (AI) to sci-fi movies like Matrix or Inception. But AI is simply the human-like intelligence of a computer. One of the most intriguing aspects of AI technology is its ability to process vast amounts of data in the blink of an eye.

According to Grand View Research, global virtual events industry was worth USD 77.98 billion in 2019, and is estimated to expand at a compound annual growth rate (CAGR) of 23.2 percent from 2020 to 2027, reaching USD 404 billion. Artificial Intelligence will be an integral part of this progress. From matching-making, personalised recommendations, automated updates and alerts, to pre and post-event analytics, AI plays a significant role in making the entire process of virtual events smoother and more efficient.

Here’s a breakdown of some of the key aspects of AI technology that can assist you in planning and organising successful virtual events:

Valuable Event Insights

Manually processing data of each event participant can take days or even weeks, and it is more vulnerable to errors. AI technology, on the other hand, processes all the information (name, interests, contact information, business of an individual etc) with 100 percent accuracy in seconds. Event platforms can be enabled to use this data to extract meaningful event reports and insights.

For example, based on data from pre-event activities and previous events, an AI-enabled event platform can predict the traction of the upcoming event. These predictions can help the organiser in evaluating their marketing strategies and allocation of resources, and overall effective event planning.

Matchmaking

What do you think of finding the right sellers of a product you’re looking at buying in an exhibition of hundreds and thousands of exhibitors? Well, AI can come to your rescue in formats like large exhibitions and buyer seller meets.

Event platforms that offer AI-powered matchmaking between buyers and sellers can help connect participants with the right opportunities and people, allowing them to make the best of the experience and achieve their goals in the shortest period of time.

Now, imagine the system telling you from the shortlisted sellers, which are the ones that are offering discounts! Possible. With AI-enabled event platforms.

Automated Alerts & Teleportation

An AI-enabled virtual platform can make the whole event experience enriching for participants. For instance, if you wish to visit a virtual event with a friend, it can send you an alert and help you visit specific areas together.

It can record and remind you of the important events that you don’t wish to miss in a virtual show and teleport your virtual self from an inauguration hall to the sponsor booth or a networking lounge within seconds, making sure that you miss out on nothing important.

AI is the future of the events industry. AI technology is making events more organised, targeted, customised in order to offer ideal experiences. Each event participant is unique and therefore should be and can be given a custom experience using artificial intelligence.

The Covid era is the prime time for companies and organisers to leverage the advantages and make the most of AI technology. Event trends will keep on evolving as technology advances. Business and event organisers will have to adapt to changing needs and demands of the audiences to survive and succeed.

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Businesses experienced the merits of virtual events first-hand, thanks to artificial intelligence.

Since the time coronavirus gripped our lives, artificial intelligence has played a key role in facilitating the work-from-home and also the leisure-from-home culture. From online meetings, virtual events, press releases, product launches, and client conferences,  AI made sure we stayed sane in the most uncertain times.

2020 saw a big shift in the way businesses work. In a way, businesses took the most hit and stole all the spotlight when technology proved to be the savior for not just businesses but also governments by helping everyone stay connected. Online events, conferences, and webinars continued to become increasingly popular with businesses and the general public rapidly adopting collaboration tools like Zoom, Skype, Google Meet, Microsoft Teams, etc. 2020 brought the focus on online event platforms to organize, plan, and execute virtual meetings, exhibitions, and whatnot. Though this was an emergency last resort, it proved to be effective, more than traditional methods in some ways as it added value like cost savings, measurable ROI, and quick audience insights. These events also saw the overall audience experience improve drastically with several engagement interventions and immersive virtual 3D elements.

More Power To Artificial Intelligence

According to a market report by Grand View Research, the virtual events market worldwide stood at $77.98 billion in 2019 and grew at such a rapid rate in 2020 that it is expected to hit the mark of $404 billion by 2027. As the world is still being struck by the pandemic, several industries like IT, retail, healthcare, automotive, and education have actively transitioned to hosting virtual events. Activities like internal training, press releases, product launches, team meetings, etc. are taking the virtual route and it’s slowly becoming a norm rather than an exception.

With artificial intelligence driving virtual events, AI bots are facilitating virtual companionship for audiences, improved customer engagement, personalized experience, and increase in demand. For example, companies can use machine learning technology to observe and learn behavior patterns of engagement.

Virtual Events In The Future

Before COVID-19, around 40% of the total market spend was allotted to physical events. But as the global environment for businesses became increasingly challenging due to COVID-19, on-ground event spends saw a drastic decline as companies welcomed virtual events. Many businesses believe that events will continue to be a big part of their growth and about 80% of marketers claim that businesses are more supportive of online events because adoption of technology is no longer a barrier but a necessity.

As artificial intelligence continues to power virtual event platforms, companies are leveraging the advantage of data and analytics to add value to their work. Seeing ROI improving and leads getting generated in bigger numbers is resulting in better market presence and industry standing. The trajectory of the events industry suggests that virtual events are being perceived as a viable alternative for C-level executives as it saves them travel time and cost.

Another advantage of virtual events that are here to stay is accessibility. With platforms compatible with all devices, events can be experienced and hosted from anywhere. This opens up many opportunities for virtual event marketers and companies who have started to conduct internal events online. The healthcare industry and pharmaceutical industry are using virtual event platforms to host medical conferences that are used for knowledge sharing, networking sharing, training, and discussions.

Augmented reality, virtual reality, and intelligence AI-driven bots are extracting maximum data from virtual events which are proving to be a gold mine. This is making virtual events more streamlined, focused, targeted, and personalized to deliver experiences that are just perfect.

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With technology new and old, instructors try to recreate the interactivity of their pre-Covid classroom.

Deep learning is advancing at lightning speed, and Alexander Amini ’17 and Ava Soleimany ’16 want to make sure they have your attention as they dive deep on the math behind the algorithms and the ways that deep learning is transforming daily life.

Last year, their blockbuster course, 6.S191 (Introduction to Deep Learning) opened with a fake video welcome from former President Barack Obama. This year, the pair delivered their lectures “live” from Stata Center — after taping them weeks in advance from their kitchen, outfitted for the occasion with studio lights, a podium, and a green screen for projecting the blackboard in Kirsch Auditorium on their Zoom backgrounds.

“It’s hard for students to stay engaged when they’re looking at a static image of an instructor,” says Amini. “We wanted to recreate the dynamic of a real classroom.”

Amini is a graduate student in MIT’s Department of Electrical Engineering and Computer Science (EECS), and Soleimany a graduate student at MIT and Harvard University. They co-developed 6.S191’s curriculum and have taught it during MIT’s Independent Activities Period (IAP) for four of the last five years. Their lectures and software labs are updated each year, but this year’s pandemic edition posed a special challenge. They responded with a mix of low- and high-tech solutions, from filming the lectures in advance to holding help sessions on a Minecraft-like platform that mimics the feel of socializing in person.

Some students realized the lectures weren’t live after noticing clues like the abrupt wardrobe change as the instructors shifted from lecture mode to the help session immediately after class. Those who caught on congratulated the pair in their course evaluations. Those who didn’t reacted with amazement. “You mean they weren’t livestreamed?” asked PhD student Nada Tarkhan, after a long pause. “It absolutely felt like one instructor was giving the lecture, while the other was answering questions in the chat box.”

The growing popularity of 6.S191 — both as a for-credit class at MIT, and a self-paced course online — mirrors the rise of deep neural networks for everything from language translation to facial recognition. In a series of clear and engaging lectures, Amini and Soleimany cover the technical foundations of deep nets, and how the algorithms pick out patterns in reams of data to make predictions. They also explore deep learning’s myriad applications, and how students can evaluate a model’s predictions for accuracy and bias.

Responding to student feedback, Amini and Soleimany this year extended the course from one week to two, giving students more time to absorb the material and put together final projects. They also added two new lectures: one on uncertainty estimation, the other on algorithmic bias and fairness. By moving the class online, they were also able to admit an extra 200 students who would have been turned away by Kirsch Auditorium’s 350-seat limit.

To make it easier for students to connect with teaching assistants and each other, Amini and Soleimany introduced Gather.Town, a platform they discovered at a machine learning conference this past fall. Students moved their avatars about in the virtual 6.S191auditorium to ask homework questions, or find collaborators and troubleshoot problems tied to their final projects.

Students gave the course high marks for its breadth and organization. “I knew the buzzwords like reinforcement learning and RNNs, but I never really grasped the details, like creating parameters in TensorFlow and setting activation functions,” says sophomore Claire Dong. “I came out of the class clearer and more energized about the field.”

This year, 50 teams presented final projects, twice as many as the year before, and they covered an even broader range of applications, say Amini and Soleimany, from trading cryptocurrencies to predicting forest fires to simulating protein folding in a cell.

“The extra week really helped them craft their idea, create some of it, code it up, and put together the pieces into a presentation,” says Amini.

“They were just brilliant,” adds Soleimany. “The quality and organization of their ideas, the talks.”

Four projects were picked for prizes.

The first was a proposal for classifying brain signals to differentiate right hand movements from the left. Before transferring to MIT from Miami-Dade Community College, Nelson Hidalgo had worked on brain-computer interfaces to help people with paralysis regain control of their limbs. For his final project, Hidalgo, a sophomore in EECS, used EEG brain wave recordings to build a model for sorting the signals of someone attempting to move their right hand and left.

His neural network architecture featured a combined convolutional and recurrent neural net working in parallel to extract sequential and spatial patterns in the data. The result was a model that improved on other methods for predicting the brain’s intention to move either hand, he says. “A more accurate classifier could really make this technology accessible to patients on a daily basis.”

A second project explored the potential of AI-based forestry.Tree planting has become a popular way for companies to offset their carbon emissions, but tracking how much carbon dioxide those trees actually absorb is still an inexact science. Peter McHale, a master’s student at MIT Sloan School of Management, proposed that his recently launched startup, Gaia AI, could fly drones over forests to take detailed images of the canopy above and below.

Those high-resolution pictures could help forest managers better estimate tree growth, he says, and calculate how much carbon they’ve soaked up from the air. The footage could also provide clues about what kinds of trees grow best in certain climates and conditions. “Drones can take measurements more cheaply and accurately than humans can,” he says.

Under Gaia AI’s first phase of development, McHale says he plans to focus on selling high-quality, drone-gathered sensor data to timber companies in need of cheaper, more accurate surveying methods, as well as companies providing third-party validation for carbon offsets. In phase two, McHale envisions turning those data, and the profits they generate, to attack climate change through drone-based tree-planting.

A third project explored the state of the art for encoding intelligent behavior into robots. As a SuperUROP in Professor Sangbae Kim’s lab, Savva Morozov works with the mini cheetah and is interested in figuring out ways that robots like it might learn how to learn.

For his project, Morozov, a junior in the Department of Aeronautics and Astronautics, presented a scenario: a mini cheetah-like robot is struggling to scale a pile of rubble. It spots a wooden plank that could be picked up with its robotic arm and turned into a ramp. But it has neither the imagination nor repertoire of skills to build a tool to reach the summit. Morozov explained how different learning-to-learn methods could help to solve the problem.

A fourth project proposed the use of deep learning to make it easier to analyze street-view images of buildings to model an entire city’s energy consumption. An algorithm developed by MIT’s Sustainable Design Lab and PhD student Jakub Szczesniak estimates the window-to-wall ratio for a building based on details captured in the photo, but processing the image requires a lot of tedious work at the front end.

Nada Tarkhan, a PhD student in the School of Architecture and Planning, proposed adding an image-processing convolutional neural net to the workflow to make the analysis faster and more reliable. “We hope it can help us gather more accurate data to understand building features in our cities — the façade characteristics, materials, and wall-to-window ratios,” she says. “The ultimate goal is to improve our understanding of how buildings perform citywide.”

Based on student feedback, Amini and Soleimany say they plan to keep the added focus on uncertainty and bias while pushing the course into new areas. “We love hearing that students were inspired to take further AI/ML classes after taking 6.S191,” says Soleimany. “We hope to continue innovating to keep the course relevant.”

Funding for the class was provided by Ernst & Young, Google, the MIT-IBM Watson AI Lab, and NVIDIA.

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Women in Data Science (WiDS) will host its fourth WiDS Livermore regional event “to encourage our community of women in computing.” Attendees will watch WiDS Stanford Livestream as well as feature Lab-focused technical talks, mentoring breakout sessions and a career panel.  The program for this one-day virtual technical conference is an opportunity to hear about the latest data science-related research and applications in a number of domains, and to connect with others in the field. 

WiDS Livermore is an independent event organized by Lawrence Livermore National Laboratory (LLNL) Ambassadors as part of the annual Women in Data Science (WiDS) Worldwide conference organized by Stanford University at an estimated 150+ locations worldwideWiDS said ll genders are invited to attend all WiDS Worldwide events.

The Women in HPC (WHPC) Mentoring Programme connects women from around the world with mentorship and support that will help them to achieve their full potential as HPC professionals. This will primarily be a professionals’ network. However, where appropriate the mentoring program will be open to students as well (for example when the host organization does not provide an appropriate scheme or HPC related mentor).

The WHPC Mentoring Programme is accepting applications for the next cohort, running during 1st March–30th June 2021. See below for more information and the application form.

The WHPC mentoring program will be open three times a year and will last four months. During the prescribed mentoring program, both mentors and mentees will receive training on how to make the most of mentoring, as well as the opportunity to participate in webinars on key skills. Mentoring will be provided one-to-one with mentors and mentees from different organizations and career-levels. Each formal mentor-mentee engagement session lasts four months. WiDS accepts mentor/mentee applications year long and we will pool you into the next cohort.

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