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  • Project

SkyScan

Automatic collection of labeled datasets
  • Data, Edge
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  • Data, Edge

SkyScan

Automatic collection of labeled datasets
Quickly and efficiently building and labeling image datasets for machine learning applications can be a prohibitively time-consuming and expensive activity. SkyScan demonstrates a low-cost system that can capture images of aircraft in flight and automatically label the image with captured metadata.
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  • Project

Synthesizing Robustness

Improving synthetic data with generative deep learning networks
  • Data
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  • Data

Synthesizing Robustness

Improving synthetic data with generative deep learning networks
It is generally thought that a good AI model needs a lot of good data. But what about when it is unfeasible or unreasonable to collect such a large dataset? How best to leverage small datasets for machine learning tasks is an active area of exploration. Synthetic data has the potential to alleviate object rarity and long-tail distributions, provided the synthetic data introduces more signal than noise into the system.
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  • Project

AI Sonobuoy

Designing a $100 AI-Enabled Sonobuoy
  • Edge
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  • Edge

AI Sonobuoy

Designing a $100 AI-Enabled Sonobuoy
You can now do a lot more with a lot less when it comes to deploying AI systems. Low-power edge inference techniques are fundamentally changing the design of fieldable smart sensors. This project is looking at the evolution of design in AI enabled, acoustic sensors that live at the edge by designing and fielding a smart hydrophone.
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  • Project

Daedulus

DIY Open Source cell towers for research
  • Edge
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  • Edge

Daedulus

DIY Open Source cell towers for research
This project spun up a fully operational 5G mobile phone infrastructure based on Open Source code and low cost commercially available Software Defined Radios and Computers.
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  • Project

Birdseye on RF

Using low-cost sensors to locate the source of an RF signal
  • Edge
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  • Edge

Birdseye on RF

Using low-cost sensors to locate the source of an RF signal
These days there are all kinds of invisible signals in the environment around us. If you see a problematic consumer drone in your area, how do you find its operator? In the Birdseye project, we simulated the use of mobile, low-cost sensors to geolocate a moving RF-signal. More specifically, we simulated the collection of RF signals coming off a commercial drone flying overhead to see if we could use it to locate its controller.
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  • Project

AI Assurance Auditing

Identifying risks before AI is deployed
  • Trust
Quick Look
  • Trust

AI Assurance Auditing

Identifying risks before AI is deployed
IQT Labs is developing a pragmatic, multi-disciplinary approach to auditing AI & ML tools. Our goal is to help people understand the limitations of AI/ML and identify risks before these tools are deployed in high stakes situations. We believe auditing can help diverse stakeholders build trust in emerging technologies...when that trust is warranted. For more info, check out this report which describes our auditing approach and what we found when we audited FakeFinder an Open Source deepfake detection tool.
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  • Project

FakeFinder

Batch processing of deepfake detection within videos
  • Trust
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  • Trust

FakeFinder

Batch processing of deepfake detection within videos
Increasingly fake videos are popping up around us because DeepFake generation models are getting better at producing realistic output at an alarming scale. FakeFinder was a hands-on project aimed at better understanding Open Source models that can be effective for debunking such videos at a similar rate.
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  • Project

Where in the World

Verifying the geographic location of outdoor images
  • Data
Quick Look
  • Data

Where in the World

Verifying the geographic location of outdoor images
Suppose that a photograph has surfaced under dubious circumstances, raising the question of where it was really taken. One potential solution, cross-view image geolocalization (CVIG), is the process of geolocating an outdoor photograph by comparing it to satellite imagery of possible locations. This project touched on each major component of CVIG deep learning.
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