Andrea GaglianoContributor
2020 has made every industry reimagine how to move forward in light of COVID-19: civil rights movements, an election year and countless other big news moments. On a human level, weve had to adjust to a new way of living. Weve started to accept these changes and figure out how to live our lives under these new pandemic rules. While humans settle in, AI is struggling to keep up.
The issue with AI training in 2020 is that, all of a sudden, weve changed our social and cultural norms. The truths that we have taught these algorithms are often no longer actually true. With visual AI specifically, were asking it to immediately interpret the new way we live with updated context that it doesnt have yet.
Algorithms are still adjusting to new visual queues and trying to understand how to accurately identify them. As visual AI catches up, we also need a renewed importance on routine updates in the AI training process so inaccurate training datasets and preexisting open-source models can be corrected.
Computer vision models are struggling to appropriately tag depictions of the new scenes or situations we find ourselves in during the COVID-19 era. Categories have shifted. For example, say theres an image of a father working at home while his son is playing. AI is still categorizing it as leisure or relaxation. It is not identifying this as work or office, despite the fact that working with your kids next to you is the very common reality for many families during this time.
Image Credits: Westend61/Getty Images
On a more technical level, we physically have different pixel depictions of our world. At Getty Images, weve been training AI to see. This means algorithms can identify images and categorize them based on the pixel makeup of that image and decide what it includes. Rapidly changing how we go about our daily lives means that were also shifting what a category or tag (such as cleaning) entails.
Think of it this way cleaning may now include wiping down surfaces that already visually appear clean. Algorithms have been previously taught that to depict cleaning, there needs to be a mess. Now, this looks very different. Our systems have to be retrained to account for these redefined category parameters.
This relates on a smaller scale as well. Someone could be grabbing a door knob with a small wipe or cleaning their steering wheel while sitting in their car. What was once a trivial detail now holds importance as people try to stay safe. We need to catch these small nuances so its tagged appropriately. Then AI can start to understand our world in 2020 and produce accurate outputs.
Image Credits: Chee Gin Tan/Getty Images
Another issue for AI right now is that machine learning algorithms are still trying to understand how to identify and categorize faces with masks. Faces are being detected as solely the top half of the face, or as two faces one with the mask and a second of only the eyes. This creates inconsistencies and inhibits accurate usage of face detection models.
One path forward is to retrain algorithms to perform better when given solely the top portion of the face (above the mask). The mask problem is similar to classic face detection challenges such as someone wearing sunglasses or detecting the face of someone in profile. Now masks are commonplace as well.
Image Credits: Rodger Shija/EyeEm/Getty Images
What this shows us is that computer vision models still have a long way to go before truly being able to see in our ever-evolving social landscape. The way to counter this is to build robust datasets. Then, we can train computer vision models to account for the myriad different ways a face may be obstructed or covered.
At this point, were expanding the parameters of what the algorithm sees as a face be it a person wearing a mask at a grocery store, a nurse wearing a mask as part of their day-to-day job or a person covering their face for religious reasons.
As we create the content needed to build these robust datasets, we should be aware of potentially increased unintentional bias. While some bias will always exist within AI, we now see imbalanced datasets depicting our new normal. For example, we are seeing more images of white people wearing masks than other ethnicities.
This may be the result of strict stay-at-home orders where photographers have limited access to communities other than their own and are unable to diversify their subjects. It may be due to the ethnicity of the photographers choosing to shoot this subject matter. Or, due to the level of impact COVID-19 has had on different regions. Regardless of the reason, having this imbalance will lead to algorithms being able to more accurately detect a white person wearing a mask than any other race or ethnicity.
Data scientists and those who build products with models have an increased responsibility to check for the accuracy of models in light of shifts in social norms. Routine checks and updates to training data and models are key to ensuring quality and robustness of models now more than ever. If outputs are inaccurate, data scientists can quickly identify them and course correct.
Its also worth mentioning that our current way of living is here to stay for the foreseeable future. Because of this, we must be cautious about the open-source datasets were leveraging for training purposes. Datasets that can be altered, should. Open-source models that cannot be altered need to have a disclaimer so its clear what projects might be negatively impacted from the outdated training data.
Identifying the new context were asking the system to understand is the first step toward moving visual AI forward. Then we need more content. More depictions of the world around us and the diverse perspectives of it. As were amassing this new content, take stock of new potential biases and ways to retrain existing open-source datasets. We all have to monitor for inconsistencies and inaccuracies. Persistence and dedication to retraining computer vision models is how well bring AI into 2020.
See the original post here:
AI is struggling to adjust to 2020 - TechCrunch
- Classic reasoning systems like Loom and PowerLoom vs. more modern systems based on probalistic networks - November 8th, 2009 [November 8th, 2009]
- Using Amazon's cloud service for computationally expensive calculations - November 8th, 2009 [November 8th, 2009]
- Software environments for working on AI projects - November 8th, 2009 [November 8th, 2009]
- New version of my NLP toolkit - November 8th, 2009 [November 8th, 2009]
- Semantic Web: through the back door with HTML and CSS - November 8th, 2009 [November 8th, 2009]
- Java FastTag part of speech tagger is now released under the LGPL - November 8th, 2009 [November 8th, 2009]
- Defining AI and Knowledge Engineering - November 8th, 2009 [November 8th, 2009]
- Great Overview of Knowledge Representation - November 8th, 2009 [November 8th, 2009]
- Something like Google page rank for semantic web URIs - November 8th, 2009 [November 8th, 2009]
- My experiences writing AI software for vehicle control in games and virtual reality systems - November 8th, 2009 [November 8th, 2009]
- The URL for this blog has changed - November 8th, 2009 [November 8th, 2009]
- I have a new page on Knowledge Management - November 8th, 2009 [November 8th, 2009]
- N-GRAM analysis using Ruby - November 8th, 2009 [November 8th, 2009]
- Good video: Knowledge Representation and the Semantic Web - November 8th, 2009 [November 8th, 2009]
- Using the PowerLoom reasoning system with JRuby - November 8th, 2009 [November 8th, 2009]
- Machines Like Us - November 8th, 2009 [November 8th, 2009]
- RapidMiner machine learning, data mining, and visualization tool - November 8th, 2009 [November 8th, 2009]
- texai.org - November 8th, 2009 [November 8th, 2009]
- NLTK: The Natural Language Toolkit - November 8th, 2009 [November 8th, 2009]
- My OpenCalais Ruby client library - November 8th, 2009 [November 8th, 2009]
- Ruby API for accessing Freebase/Metaweb structured data - November 8th, 2009 [November 8th, 2009]
- Protégé OWL Ontology Editor - November 8th, 2009 [November 8th, 2009]
- New version of Numenta software is available - November 8th, 2009 [November 8th, 2009]
- Very nice: Elsevier IJCAI AI Journal articles now available for free as PDFs - November 8th, 2009 [November 8th, 2009]
- Verison 2.0 of OpenCyc is available - November 8th, 2009 [November 8th, 2009]
- What’s Your Biggest Question about Artificial Intelligence? [Article] - November 8th, 2009 [November 8th, 2009]
- Minimax Search [Knowledge] - November 8th, 2009 [November 8th, 2009]
- Decision Tree [Knowledge] - November 8th, 2009 [November 8th, 2009]
- More AI Content & Format Preference Poll [Article] - November 8th, 2009 [November 8th, 2009]
- New Planners Solve Rescue Missions [News] - November 8th, 2009 [November 8th, 2009]
- Neural Network Learns to Bluff at Poker [News] - November 8th, 2009 [November 8th, 2009]
- Pushing the Limits of Game AI Technology [News] - November 8th, 2009 [November 8th, 2009]
- Mining Data for the Netflix Prize [News] - November 8th, 2009 [November 8th, 2009]
- Interview with Peter Denning on the Principles of Computing [News] - November 8th, 2009 [November 8th, 2009]
- Decision Making for Medical Support [News] - November 8th, 2009 [November 8th, 2009]
- Neural Network Creates Music CD [News] - November 8th, 2009 [November 8th, 2009]
- jKilavuz - a guide in the polygon soup [News] - November 8th, 2009 [November 8th, 2009]
- Artificial General Intelligence: Now Is the Time [News] - November 8th, 2009 [November 8th, 2009]
- Apply AI 2007 Roundtable Report [News] - November 8th, 2009 [November 8th, 2009]
- What Would You do With 80 Cores? [News] - November 8th, 2009 [November 8th, 2009]
- Software Finds Learning Language Child's Play [News] - November 8th, 2009 [November 8th, 2009]
- Artificial Intelligence in Games [Article] - November 8th, 2009 [November 8th, 2009]
- Artificial Intelligence Resources - November 8th, 2009 [November 8th, 2009]
- Alan Turing: Mathematical Biologist? - April 25th, 2012 [April 25th, 2012]
- BBC Horizon: The Hunt for AI ( Artificial Intelligence ) - Video - April 30th, 2012 [April 30th, 2012]
- Can computers have true artificial intelligence" Masonic handshake" 3rd-April-2012 - Video - April 30th, 2012 [April 30th, 2012]
- Kevin B. Korb - Interview - Artificial Intelligence and the Singularity p3 - Video - April 30th, 2012 [April 30th, 2012]
- Artificial Intelligence - 6 Month Anniversary - Video - April 30th, 2012 [April 30th, 2012]
- Science Breakthroughs - April 30th, 2012 [April 30th, 2012]
- Hitman: Blood Money - Part 49 - Stupid Artificial Intelligence! - Video - April 30th, 2012 [April 30th, 2012]
- Research Members Turned Off By HAARP Artificial Intelligence - Video - April 30th, 2012 [April 30th, 2012]
- Artificial Intelligence Lecture No. 5 - Video - April 30th, 2012 [April 30th, 2012]
- The Artificial Intelligence Laboratory, 2012 - Video - April 30th, 2012 [April 30th, 2012]
- Charlie Rose - Artificial Intelligence - Video - April 30th, 2012 [April 30th, 2012]
- Expert on artificial intelligence to speak at EPIIC Nights dinner - May 4th, 2012 [May 4th, 2012]
- Filipino software engineers complete and best thousands on Stanford’s Artificial Intelligence Course - May 4th, 2012 [May 4th, 2012]
- Vodafone xone™ Hackathon Challenges Developers and Entrepreneurs to Build a New Generation of Artificial Intelligence ... - May 4th, 2012 [May 4th, 2012]
- Rocket Fuel Packages Up CPG Booster - May 4th, 2012 [May 4th, 2012]
- 2 Filipinos finishes among top in Stanford’s Artificial Intelligence course - May 5th, 2012 [May 5th, 2012]
- Why Your Brain Isn't A Computer - May 5th, 2012 [May 5th, 2012]
- 2 Pinoy software engineers complete Stanford's AI course - May 7th, 2012 [May 7th, 2012]
- Percipio Media, LLC Proudly Accepts Partnership With MIT's Prestigious Computer Science And Artificial Intelligence ... - May 10th, 2012 [May 10th, 2012]
- Google Driverless Car Ok'd by Nevada - May 10th, 2012 [May 10th, 2012]
- Moving Beyond the Marketing Funnel: Rocket Fuel and Forrester Research Announce Free Webinar - May 10th, 2012 [May 10th, 2012]
- Rocket Fuel Wins 2012 San Francisco Business Times Tech & Innovation Award - May 13th, 2012 [May 13th, 2012]
- Internet Week 2012: Rocket Fuel to Speak at OMMA RTB - May 16th, 2012 [May 16th, 2012]
- How to Get the Most Out of Your Facebook Ads -- Rocket Fuel's VP of Products, Eshwar Belani, to Lead MarketingProfs ... - May 16th, 2012 [May 16th, 2012]
- The Digital Disruptor To Banking Has Just Gone International - May 16th, 2012 [May 16th, 2012]
- Moving Beyond the Marketing Funnel: Rocket Fuel Announce Free Webinar Featuring an Independent Research Firm - May 23rd, 2012 [May 23rd, 2012]
- MASA Showcases Latest Version of MASA SWORD for Homeland Security Markets - May 23rd, 2012 [May 23rd, 2012]
- Bluesky Launches Drones for Aerial Surveying - May 23rd, 2012 [May 23rd, 2012]
- Artificial Intelligence: What happened to the hunt for thinking machines? - May 25th, 2012 [May 25th, 2012]
- Bubble Robots Move Using Lasers [VIDEO] - May 25th, 2012 [May 25th, 2012]
- UHV assistant professors receive $10,000 summer research grants - May 27th, 2012 [May 27th, 2012]
- Artificial intelligence: science fiction or simply science? - May 28th, 2012 [May 28th, 2012]
- Exetel taps artificial intelligence - May 29th, 2012 [May 29th, 2012]
- Software offers brain on the rain - May 29th, 2012 [May 29th, 2012]
- New Dean of Science has high hopes for his faculty - May 30th, 2012 [May 30th, 2012]
- Cognitive Code Announces "Silvia For Android" App - May 31st, 2012 [May 31st, 2012]
- A Rat is Smarter Than Google - June 5th, 2012 [June 5th, 2012]