The Internet of Things might have less Internet than we thought?

“Privacy was never dead, it just went away for a while…”

Talking on the keynote stage on the first day of QCon London 2020. (📷: Danilo Teodoro)
The “Bracelet of Silence.” (📷: University of Chicago)
Screenshot of the “Yeelight” iOS app. (📷: Yeelight)
No cats were harmed in the making of this talk. (📷: Tran Mau Tri Tam)

“Big data isn’t big, its the interaction of small data with big systems”—Alistair Croll, Solve for Interesting

The Coral Dev Board, from Google. (📷: Alasdair Allan)
MobileNet SSD model running on the Edge TPU (left) at 75fps, same model running on the CPU of the Dev Board (right). (📹: Alasdair Allan)
A Retro Rotary Phone powered by AIY Projects and the Raspberry Pi. (📹: Alasdair Allan)
TensorFlow for speech recognition on the Raspberry Pi. (📹: Alasdair Allan)
Some of the machine learning accelerator hardware on my desk. (📷: Alasdair Allan)
Test image 🍌🍎 (left) with TensorFlow (middle) and AI2GO (right) object detection bounding boxes shown. (📷: Alasdair Allan)
Inferencing time in milli-seconds for the Raspberry Pi 3 (blue, left) and 4 (green, right). (📊: Alasdair Allan)
Inferencing time in milli-seconds for TensorFlow (blue, right) and TensorFlow Lite (green, left) on the Raspberry Pi 4 for MobileNet v1 0.75 depth and MobileNet v2. (📊: Alasdair Allan)
Final benchmarking results in milli-seconds for MobileNet v1 SSD 0.75 depth model and the MobileNet v2 SSD model, both trained using the Common Objects in Context (COCO) dataset with an input size of 300×300, alongside the Xnor AI2GO platform and their proprietary binary weight model. (📈: Alasdair Allan)
Inferencing time in milli-seconds for the for MobileNet v1 SSD 0.75 depth model (left hand bars) and the MobileNet v2 SSD model (right hand bars), trained using the Common Objects in Context (COCO) dataset with an input size of 300×300. Stand alone platforms are shown in green, while the (single) bars for the Xnor AI2GO platform are timings for their proprietary binary weight model and are shown in blue. All other measurements using accelerator hardware attached to the Raspberry Pi 3, Model B+, are in yellow, while measurements on the Raspberry Pi 4, Model B, in red. (📊: Alasdair Allan)
The Raspberry Pi 4 with the Coral USB Accelerator from Google. (📷: Alasdair Allan)
Offline and real-time speech-to-text running on Raspberry Pi Zero. (📹: Picovoice)
The SparkFun Edge. (📷: Alasdair Allan)
The “Yes” Voice Demo. (📹: Alasdair Allan)
Adding stickers to a stop sign in an adversarial attack. (📷: Eykholt et al., 2018)
The Michigan Micro Mote. (📷: University of Michigan)
Mark Zuckerberg at Facebook’s F8 conference in 2019. (📷: Facebook)

Scientist, Author, Hacker, Maker, and Journalist.

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