AI-induced Resurgence for the Travel Industry Amidst COVID-19

Presently, travelers have to toggle between multiple apps to gather each set of information. Leading travel app development companies in the industry …

The airline industry is probably the worst hit of all sectors due to COVID-19. As per figures released by the International Air Transport Association, a collective loss of US $84 billion will be inflicted on the industry in 2020. This is more than double the extent of loss (US$30 billion) that it suffered due to the global financial crisis in 2008.

Amidst an uncertain future, massive lay-offs, and carrier bankruptcies, experts place the turn-around time for recovery at 4-6 years, while some put it even further. In such an unpredictable air of gloom one thing is certain, the status quo has changed forever. The industry as a unit needs serious introspection combined with the power of technology to make travel contactless and safer than ever.

AI in the Aviation Industry – A Dire Need

Artificial Intelligence technology could be pivotal in transforming the face of travel. From the outset, AI rests on a solid footing of 4 key pillars namely:

  • Machine Learning
  • Deep Learning
  • Natural Language Processing
  • Computer Vision

In the pre-covid era, there were numerous use cases of airlines using Artificial Intelligence. While it was predominantly used to optimize digital operations, the technology has to step out of its mold and offer a scope of work for AI in airport operations to become a reality. If initial signages are to be believed, the role of AI in the travel industry will be broadened to acquiesce travelers to new standards of safety.

percentage airlines with AI

There is no plan B. Over 100 million jobs have succumbed to covid-induced lay-offs, and the travel industry is likely to bear losses worth $1 trillion. It’s time to explore the applications of AI in the aviation industry.

AI in Aviation to Quell Future-Shock

The need for AI-driven customer experience in the travel industry in the post COVID world is huge. At the same time, it should not be looked upon as the panacea, but rather a pain-killer, to mitigate losses and welcome passengers back. The following are some of the realistic scenarios that are just as conceivable, as they are implementable when it comes to the future of AI in the aviation industry.

A point to note is that irrespective of the use case or the state of COVID19 driven economic condition, AI has found a permanent place in the aviation industry.

global AI in aviation market

Flying Optimized Routes

A lot of long-duration flights tend to have a mid-range landing spot, where often the passengers are required to undergo formal security procedures to check-in to a new flight. In formal terms, this is called a layover. The process is too discomforting from a traveler experience standpoint, forces human-human contact, and invariably increases the risk of community transmission.

Not to mention the fuel-refilling and the per capita resource consumption by passengers at the layover spot. One of the benefits of AI in the aviation industry in the post COVID world is that it can re-route and optimize long-duration flights. Till such time when the carriers reach full-capacity the shortest transit routes can be recommended by AI saving fuel and other capital-intensive resources.

Digitalized Check-ins

People are downright scared to get out of their homes let alone travel. For those mustering the fortitude to step foot on a plane, do so after ensuring the details about their boarding pass, baggage submission, weather updates, and flight status among other things. Presently, travelers have to toggle between multiple apps to gather each set of information. Leading travel app development companies in the industry are foraging ways through which AI helps in revamping the aviation industry.

Lufthansa, for instance, has provisioned for iterations to its mobile app so boarding passes could be stored digitally. An increasing number of pre-market trials suggest that smartphones could act as a one-stop-shop wallet storing necessary travel documentation. There could even be facial recognition to safeguard the app and ensure the best in class privacy. To roll the red carpet for an all-encompassing paperless travel experience, the International Air Transport Association (IATA) has initiated OneID, an identity management solution that will possibly incorporate AI-powered biometrics.

Baggage Assistance

Breakdown of customer complaint stats

** Other data includes complaints related to frequent flyers, smoking, tours credit, cargo problems, security, airport facilities, claims for bodily injury.

Baggage has always been a challenging area for the aviation sector. A challenge that is going to worsen in the COVID19 era. There is the consideration to be made for baggage deposits, wherein the luggage changes hands and multiplies possibilities of community transmission.

To tackle this, the airport concierge could innovate e-commerce apps operating to and fro between customer abodes and the airport. Empowering their architecture with RFID tags, and AI-enabled tracking systems, chances of not just baggage mishandling but also contact tracing can be mitigated in instances of virus transfer.

Not all of us would feel the safety net in trusting an unknown driver to take cost expensive items and dutifully deposit the same at airports. Therefore, for people hell-bent on doing things on their own, self-drop baggage lanes could save the day. In addition to reducing human dependency, they also cut short baggage processing times. Robots could be deployed in such lanes with AI-powered facial recognition software that would recognize the rightful owner of the items.

The airport operations staff must resolutely work towards increasing social distancing. One alternative for this emerged in the pre-pandemic era when JFK airport introduced Google Assistant’s interpreter mode. It supports 29 languages and will help international passengers with typical queries including airport navigation, luggage location, etc.

No doubt, AI is transforming the aviation industry in the post-COVID era. Another example of this would be in thermal imaging cameras. Made super efficient with passenger flow analytics and social distancing software, the cameras would scan body temperatures in real-time informing officials of doubtful cases that can be managed as per protocol.

AI-Fastened Security

One of the most cumbersome and inconvenient instances in the course to board a flight is security checks. All major airports mandate passengers to take off wearables and empty hand-bags so they can be thoroughly checked. Think we all can agree, the process is profoundly annoying. Not to mention the strict levels of distancing required to be maintained are not sustained when officials inspect travelers closely.

All this will be a thing of the past as Artificial Intelligence in aviation safety sees light at the end of the tunnel. State-of-the-art scanners would debut at the airports, infused with capabilities like X-Ray mapping, 3D image processing, and/or anomaly protection algorithms. Body scanners will be remodeled to incorporate AI technology.

AI-enabled automated target recognition algorithms synced into millimeter-wave scanners will make identifying rogue actors a click of the finger.

Digital Entertainment

Airport lounges see a significant number of people walk-in for entertainment/relaxation while waiting for the onboarding to commence. They are often empaneled with public computers and accessory booths used (and touched) by many. This needs to change. Carriers such as Delta Airlines are experimenting with a Parallel Reality experience that would facilitate multiple passengers, all simultaneously looking at the same screen, to view their respective flight information.

We have reason to believe that AI chatbot development is in full swing to complement the mass deployment of robots at airports. Chatbots in the airline industry will be fitted with facial recognition algorithms that would bring a wee bit of personal touch to machine-to-human interaction. Machines will be programmed to sing aloud the advantages of personal hygiene and sanitization. Lately, some of our partners have expressed interest in airline chatbot development. Such conversations are more than food for thought and if pursued with real purpose and a judicious budget, profitable advances can be made in a short period.

Robots for Product Delivery

Duty-free stores attract a lot of travelers thanks to unparalleled prices. But who said we need to risk public safety at such times for purposes of shopping. Store owners are realizing the significance of standardizing new norms to practice social distancing. For instance, Dubai Duty-Free while resuming operations posts the lockdown made customers use their concierge service to fill the cart.

Just so we leave nothing to the imagination, the carts were delivered to the customers by robots. The advantages of AI in the travel industry post Coronavirus are evident from such use cases.

In other locations, click-and-collect app models are establishing relatable grounds for business. Even before arriving at the airport, customers can order items waiting for them when they board-off the plane.

Final Thoughts

The inclusion of AI in the travel industry in the post COVID world is imminent. Agreeably, it will be a couple of years before the airports start bustling with the rush of people packed closed to each other and waiting for departures. A significant level of quid pro quo needs to be enacted for this distant, pun intended, reality to take a rebirth. Artificial Intelligence will take the mainstage in being the underpinning technology for all things automation.

Inclusion of AI in the travel industry will attract business-interest not limited to the airports, but branching well into the hospitality sector be it hotels, restaurants, or mobile food vans. With arguably the most talented technocrats under one roof, Appinventiv can be your technological partner.

Prateek Saxena
Prateek Saxena
Co-founder of Appinventiv
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8 Examples of Artificial Intelligence in our Everyday Lives

At the time, McCarthy only created the term to distinguish the AI field from cybernetics. However, AI is more popular than ever today due to: Increased …

The applications of artificial intelligence have grown over the past decade. Here are examples of artificial intelligence that we use in our everyday lives.

Main Examples of Artificial Intelligence Takeaways:

  • Artificial intelligence is an expansive branch of computer science that focuses on building smart machines.
  • American computer scientist John McCarthy coined the term artificial intelligence back in 1956.
  • Artificial intelligence and robotics are two entirely separate fields.
  • The four artificial intelligence types are reactive machines, limited memory, Theory of Mind, and self-aware.
  • Other subsets of AI include big data, machine learning, and natural language processing.
  • Artificial intelligenceexamples include Face ID, the search algorithm, and recommendation algorithm, among others.

The words artificial intelligence may seem like a far-off concept that has nothing to do with us. But the truth is that we encounter several examplesofartificial intelligence in our daily lives.

From Netflix‘s movie recommendation to Amazon‘s Alexa, we now rely on various AImodels without knowing it. In this post, we’ll consider eight examples of how we’re already using artificial intelligence.

What is Artificial Intelligence

Artificial intelligence is an expansive branch of computer science that focuses on building smartmachines. Thanks to AI, these machines can learn from experience, adjust to new inputs, and perform human-like tasks. For example, chess-playing computers and self-driving cars rely heavily on natural language processing and deep learning to function.

American computer scientist John McCarthy coined the term artificial intelligence back in 1956. At the time, McCarthy only created the term to distinguish the AI field from cybernetics.

However, AI is more popular than ever today due to:

  • Increased data volumes
  • Advancements in computing and storage
  • Advanced algorithms

Hollywood movies tend to depict artificial intelligence as a villainous technology that is destined to take over the world.

One example is the artificial superintelligence system, Skynet, from the film franchise Terminator. There’s also VIKI, an AI supercomputer from the movie I, Robot, who deemed that humans can’t be trusted with their own survival.

Holywood has also depicted AI as superintelligent robots, like in movies I Am Mother and Ex Machina.

However, the current AItechnologies are not as sinister — or quite as advanced. With that said, these depictions raise an essential question:

Are Robot Examples of Artificial Intelligence?


Examples of artificial intelligence image shows a white robot with big eyes
Alex Knight / Unsplash.com

No, not exactly. Artificial intelligence and robotics are two entirely separate fields. Robotics is a technology branch that deals with physical robots — programmable machines designed to perform a series of tasks. On the other hand, AI involves developing programs to complete tasks that would otherwise require humanintelligence. However, the two fields can overlap to create artificially intelligent robots.

Most robots are not artificially intelligent. For example, industrialrobots are usually programmed to perform the same repetitive tasks. As a result, they typically have limited functionality.

However, introducing an AIalgorithm to an industrial robot can enable it to perform more complex tasks. For instance, it can use a path-finding algorithm to navigate around a warehouse autonomously.

To understand how that’s possible, we must address another question:

What are the Four Types of AI?

The four artificial intelligencetypes are reactive machines, limited memory, Theory of Mind, and self-aware. These AI types exist as a type of hierarchy, where the simplest level requires basic functioning, and the most advanced level is — well, all-knowing. Other subsets of AI include big data,machine learning, and natural language processing.

1. Reactive Machines

The simplest types of AIsystems are reactive. They can neither learn from experiences nor form memories. Instead, reactive machines react to some inputs with some output.

Examplesofartificial intelligence machines in this category include GooglesAlphaGo and IBM‘s chess-playing supercomputer, Deep Blue.


Showdown between Google DeepMind's AlphaGo AI and Lee Sedol.
Showdown between Google DeepMind’s AlphaGo AI and South Korea’s 9-dan professional Go player Lee Sedol. | Still Shot from AlphaGo – The Movie | Full Documentary

Deep Blue can identify chess pieces and knows how each of them moves. While the machine can choose the most optimal move from several possibilities, it can’t predict the opponent’s moves.

A reactive machine doesn’t rely on an internal concept of the world. Instead, it perceives the world directly and acts on what it sees.

2. Limited Memory

Limited memory refers to an AI‘s ability to store previous data and use it to make better predictions. In other words, these types of artificial intelligence can look at the recent past to make immediate decisions.

Note that limited memory is required to create every machine learning model. However, the model can get deployed as a reactive machine type.

The three significant examplesofartificial intelligence in this category are:

  1. Reinforcement Learning: Models that learn to make better predictions after several cycles of trial and error.
  2. Long Short-Term Memory (LSTMs): Models for predicting the next element in a sequence.
  3. Evolutionary Generative Adversarial Networks (E-GAN): The model produces a kind of growing thing that evolves.

A small, yellow self-driving bus deployed in Berlin, Germany last 2019.
In 2019, small, yellow self-driving buses were deployed in Berlin, making Germany’s capital city the first in the country to use autonomous vehicles on the roads. | falco/Pixabay.com

Self-driving cars are limited memory AI that makes immediate decisions using data from the recent past.

For example, self-driving cars use sensors to identify steep roads, traffic signals, and civilians crossing the streets. The vehicles can then use this information to make better driving decisions and avoid accidents.

3. Theory of Mind

In Psychology, “theory of mind” refers to the ability to attribute mental state — beliefs, intent, desires, emotion, knowledge — to oneself and others. It’s the fundamental reason we can have social interactions.

Unfortunately, we’re yet to reach the Theory of Mindartificial intelligence type. Although voice assistants exhibit such capabilities, it’s still a one-way relationship.

For example, you could yell angrily at Google Maps to take you in another direction. However, it’ll neither show concern for your distress nor offer emotional support. Instead, the map application will return the same traffic report and ETA.

An AIsystem with Theory of Mind would understand that humans have thoughts, feelings, and expectations for how to be treated. That way, it can adjust its response accordingly.

4. Self-Awareness

The final step of AI development is to build self-aware machines — that can form representations of themselves. It’s an extension and advancement of the Theory of MindAI.

A self-aware machine has human-level consciousness, with the ability to think, desire, and understand its feelings. At the moment, these types of artificial intelligence only exist in movies and comic book pages. Self-aware machines do not exist.

Although self-aware machines are still decades away, several artificial intelligence examples already exist in our everyday lives.

What is Artificial Intelligence Used for Today?

Several examplesof artificial intelligence impact our lives today. These include FaceID on iPhones, the search algorithm on Google, and the recommendationalgorithm on Netflix. You’ll also find other examples of how AI is in use today on social media, digital assistants like Alexa, and ride-hailing apps such as Uber.

1. Face Detection and Recognition Technology

Virtual filters on Snapchat and the FaceID unlock on iPhones are two examplesofAI applications today. While the former uses face detection technology to identify any face, the latter relies on face recognition.

So, how does it work?

The TrueDepth camera on the Apple devices projects over 30,000 invisible dots to create a depth map of your face. It also captures an infrared image of the user’s face.


Apple FaceID technology reveal during the iPhone X launch in 2017.
Apple’s FaceID technology helps protect the information users store in their iPhone and iPad Pro. Face ID uses the TrueDepth camera and machine learning for a secure authentication solution. | Still shot from the iPhone X launch in 2017. | Apple

After that, a machine learning algorithm compares the scan of your face with what a previously enrolled facial data. That way, it can determine whether to unlock the device or not.

According to Apple, FaceID automatically adapts to changes in the user’s appearance. These include wearing cosmetic makeup, growing facial hair, or wearing hats, glasses, or contact lens.

The Cupertino-based tech giant also stated that the chance of fooling FaceID is one in a million.

2. Text Editor

Several text editors today rely on artificial intelligence to provide the best writing experience.

For example, document editors use an NLP algorithm to identify incorrect grammar usage and suggest corrections. Besides auto-correction, some writing tools also provide readability and plagiarism grades.

INK showing how its artificial intelligence system optimizes content for search.
INK is powered by a natural language processing technology that allows it to make intelligent SEO recommendations, helping writers and marketer make their content more relevant to their target audience. | INK | inkforall.com

However, editors such as INK took AI usage a bit further to provide specialized functions. It uses artificial intelligence to offer smart web content optimization recommendations.

Just recently, INK has released a study showing how its AI-powered writing platform can improve content relevance and help drive traffic to sites. You can read their full study here.

3. Social Media

Social media platforms such as Facebook, Twitter, and Instagram rely heavily on artificial intelligence for various tasks.

Currently, these social media platforms use AI to personalize what you see on your feeds. The model identifies users’ interests and recommends similar content to keep them engaged.


An image of a social media feed resembling Facebook.
Social media networks use artificial intelligence algorithms to personalize user feeds and filter unnecessary information like hate speech and posts inciting violence and discrimination. | 200degrees/Pixabay.com

Also, researchers trained AImodels to recognize hate keywords, phrases, and symbols in different languages. That way, the algorithm can swiftly take down social media posts that contain hate speech.

Other examplesof artificial intelligence in social media include:

  • Emoji as part of predictive text
  • Facial recognition to automatically tag friends in photos
  • Smart filter to identify and remove spam messages
  • Smart replies for quickly responding to messages

Plans for social media platform involve using artificial intelligence to identify mental health problems. For example, an algorithm could analyze content posted and consumed to detect suicidal tendencies.

4. Chatbots

Getting queries directly from a customer representative can be very time-consuming. That’s where artificial intelligence comes in.

Computer scientists train chat robots or chatbots to impersonate the conversational styles of customer representatives using natural language processing.


An image showing Whatsapp layout design.
Chatbots are currently being used by many businesses to assist potential customers with their queries. | 200degrees/Pixabay.com

Chatbots can now answer questions that require a detailed response in place of a specific yes or no answer. What’s more, the bots can learn from previous bad ratings to ensure maximum customer satisfaction.

As a result, machines now perform basic tasks such as answering FAQs or taking and tracking orders.

5. Recommendation Algorithm

Media streaming platforms such as Netflix, YouTube, and Spotify rely on a smart recommendation system that’s powered by AI.

First, the system collects data on users’ interests and behavior using various online activities. After that, machine learning and deep learning algorithms analyze the data to predict preferences.

That’s why you’ll always find movies that you’re likely to watch on Netflix’s recommendation. And you won’t have to search any further.

6. Search Algorithm

Search algorithms ensure that the top results on the search engine result page (SERP) have the answers to our queries. But how does this happen?

Search companies usually include some type of quality control algorithm to recognize high-quality content. It then provides a list of search results that best answer the query and offers the best user experience.


Google search
Search engines like Google is powered by multiple algorithms that help it match people’s queries with the best answers available online. | Google

Since search engines are made entirely of codes, they rely on natural language processing (NLP) technology to understand queries.

Last year, Google announced Bidirectional Encoder Representations from Transformers(BERT), an NLP pre-training technique. Now, the technology powers almost all English-based query on Google Search.

7. Digital Assistants

In October 2011, Apple’s Siri became the first digital assistant to be standard on a smartphone. However, voice assistants have come a long way since then.

Today, Google Assistant incorporates advanced NLP and ML to become well-versed in human language. Not only does it understand complex commands, but it also provides satisfactory outputs.


OK Google voice command hovering over an iPhone device.
Google Assistant is one of the most popular digital assistants available today. | Kaufdex/Pixabay.com

Also, digital assistants now have adaptive capabilities for analyzing user preferences, habits, and schedules. That way, they can organize and plan actions such as reminders, prompts, and schedules.

8. Smart Home Devices

Various smart home devices now use AIapplications to conserve energy.

For example, smart thermostats such as Nest use our daily habits and heating/cooling preferences to adjust home temperatures. Likewise, smart refrigerators can create shopping lists based on what’s absent on the fridge’s shelves.

The way we use artificial intelligence at home is still evolving. More AIsolutions now analyze human behavior and function accordingly.

Wrapping Up: Other Examples of Artificial Intelligence

We encounter AI daily, whether you’re surfing the internet or listening to music on Spotify.

Other examplesofartificial intelligence are visible in smart email apps, e-commerce, smart keyboard apps, as well as banking and finance. Artificial intelligence now plays a significant role in our decisions and lifestyle.

The media may have portrayed AI as a competition to human workers or a concept that’ll eventually take over the world. But that’s not the case.

Instead, artificial intelligence is helping humans become more productive and helping us live a better life.

Read More: The Best Artificial Intelligence Books you Need to Read Today

AI can be used in all retirement plans if focused properly – WPS panelists

“For this moment, we use alternative data as an input into our decision-making process and not yet to replace” human judgment, APG’s Ms. Halme said.

All retirement plans — regardless of size — can apply artificial intelligence to their portfolios in some way, but robust governance and operational capabilities must be in place.

Speaking Wednesday at the Pensions & Investments WorldPensionSummit on a panel addressing how the integration of data will revolutionize investing, panelists said the size of a retirement plan does not matter when it comes to making use of data and technology.

“It’s not about small schemes or big schemes — it depends on what you’re trying to achieve with AI,” said Charles Wu, deputy CIO and general manager, defined contribution investments at the A$44 billion ($31.1 billion) State Super, Sydney. “Probably where it’s going to save (smaller plans) a fair bit of cost will be on the administration side and operational side. The hurdle to apply this on investment, it is a little bit higher.”

Terhi Halme, senior sustainability specialist at APG, said the manager, along with other participating managers and asset owners, enlisted the help of an external technology firm as it worked on its sustainable development investments asset owner platform. The platform helps institutional firms invest alongside the United Nation’s sustainable development goals using data and standardization. “So you can also look elsewhere for the resources, and it doesn’t necessarily require that you build in all of the capabilities and the data internally,” Ms. Halme said.

Over the long run, the hope is that small plans “will get even more benefit, because they don’t have the resources, they don’t have the analysts and internal head count,” said Ashby Monk, executive director and research director at Stanford University’s Global Projects Center and co-founder and chairman of Long Game.

However, plans must ensure they have robust governance structures in place to embed AI and use alternative data in their portfolios, panelists said.

Plan executives must also think about where they want to embed a new data-driven capability.

The process will be similar to that about a decade ago, when plan executives began considering the insourcing of investment management, Mr. Monk said.

“We need to really think about what are the safe spaces to onboard AI. Recognize that our industry is not very good at failing, and yet there is no innovation without failure. … That’s hard in our world where we have prudent person rules and fiduciary duties. We’re not designed for that,” Mr. Monk said.

Plans should find safe areas to experiment with AI, he said. “The first place to apply AI is not to try to generate alpha. It would be, let’s think about our operational footprint, see if we can unleash AI to take a bunch of unstructured data and make it structured … to do better passive investing.” Once there is comfort around the role AI can take in an investment process, then look into how it can be integrated “in attempts to beat the market,” Mr. Monk said.

Panelists added that the use of AI will bring about a number of advantages for plan executives, including the ability to better hold investment managers to account and to help inform decisions.

But panelists were split as to whether investment managers should be worried about machines taking their jobs.

“For this moment, we use alternative data as an input into our decision-making process and not yet to replace” human judgment, APG’s Ms. Halme said.

State Super’s Mr. Wu agreed. “I think we’re far from it,” with human judgment needed throughout the investment process.

“I’m actually really positive with the development, because it actually unlocks the time of doing some mundane work” and you can do something of higher cognitive value, he said. “It allows you time to think.”

Mr. Monk, however, said investment executives should polish their resumes.

“It’s not just AI — tech is going to transform our industry. We really haven’t been beaten by software the way other industries have. … We kind of hide our (intellectual property) in boxes unlike other open-source industries, so we haven’t had that tech revolution yet to really transform our operations. And it’s coming.”

That trend has been exacerbated by the coronavirus pandemic, he said.

“There’s gonna be a lot of freedom time, which is code for certain people may not be required,” Mr. Monk said.

Gaining the enhanced “inferential insights” that come with using AI “will really play out to empower long-term investors,” he added.

But human beings “will always have a comparative advantage in human relationships — so as long as this is a business where we need to have conversations, exchange ideas, discover things, we will always need to be in the loop, even if the robots 50 years from now are far better than us at capital allocation and risk management,” Mr. Monk said.

Using Algorithms to Collaborate Virtually and Dismantle Barriers

… in Penn Engineering and the department of neuroscience at PSOM, had already started brainstorming ways of reinventing the traditional conference …

Using Algorithms to Collaborate Virtually and Dismantle Barriers

When the COVID-19 pandemic began taking hold in the U.S., one of the first “superspreader” events was an academic conference. It quickly became clear that the traditional format for these events would need to radically change.

Konrad Kording, a Penn Integrates Knowledge Professor with appointments in the departments of bioengineering and computer and information science in Penn Engineering and the department of neuroscience at PSOM, had already started brainstorming ways of reinventing the traditional conference format with the issues of prohibitive costs and environmental impact of travel in mind when the pandemic made it a necessity.

The resulting event, Neuromatch, involved algorithmically analyzing participants’ work in order to connect researchers who might not otherwise meet. Building on the success of that “unconference,” Dr. Kording and his colleagues launched the Neuromatch Academy (NMA), a free-ranging online summer school organized around the same principles.

Ashley Juavinett, writing for The Simons Collaboration on the Global Brain, recently wrote about Neuromatch:

“Kording already had experience quickly pulling together online events. Early in the pandemic, together with Dan Goodman, Titipat Achakulvisut and Brad Wyble, he developed an online ‘unconference,’ which featured both lectures and a virtual networking component designed to mimic the in-person interactions that make conferences so valuable. Soon after, they decided to spin that success into a full-fledged summer school offering live lectures with top computational neuroscientists, guided coding exercises to teach mathematical approaches to neural modeling and analysis, and community support from mentors and teaching assistants (TAs).

“The result was a summer school with well-designed content, a diverse student body, including participants from U.S.-sanctioned Iran, and a determined group of organizers who managed to pull off the most inclusive computational neuroscience school yet. NMA now has its eye on a future with even broader representation across countries, languages, and skill levels. This year has been incredibly difficult for many, but NMA has provided an important precedent for how to collaborate across, and even dismantle, all sorts of barriers.”

Read more at https://www.simonsfoundation.org/2020/09/17/the-self-organized-movement-to-create-an-inclusive-computational-neuroscience-school/)

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