The artificial intelligence systems used by image recognition tools, such as those that certain connected cars use to identify street signs, can be tricked to make an incorrect identification by a low-cost but effective attack using a camera, a projector and a PC, according to Purdue University researchers. A research paper describes an Optical Adversarial Attack, or OPAD, which uses a projector to project calculated patterns that alter the appearance of the 3D objects to AI-based image recognition systems. The paper will be presented in October at an ICCV 2021 Workshop. In an experiment, a pattern was projected onto a stop sign, causing the image recognition to read the sign as a speed limit sign instead. The researchers say this attack method could also work with image recognition tools in applications ranging from military drones to facial recognition systems, potentially undermining their reliability.

Apple has acquired Xnor.ai, a Seattle startup specializing in low-power, edge-based artificial intelligence tools, sources with knowledge of the deal told GeekWire. Speaking on condition of anonymity, sources said Apple paid an amount similar to what was paid for Turi, in the range of $200 million. Xnor.ai didn't immediately respond to our inquiries, while Apple emailed us its standard response on questions about acquisitions: "Apple buys smaller technology companies from time to time and we generally do not discuss our purpose or plans." When we visited Xnor.ai's office in Seattle's Fremont neighborhood this morning, a move was clearly in progress -- presumably to Apple's Seattle offices. The arrangement suggests that Xnor's AI-enabled image recognition tools could well become standard features in future iPhones and webcams.

This blog post discusses how to turn your images into text describing what is in them so you can later perform analysis on their contents and topics, all right out of a Jupyter Notebook. An example of when this would be useful is if you are given thousands of tweets, and want to know if the image media has any effect on engagement. Lucky for us, instead of writing our own image recognition tool, the engineers at Amazon, Google, and Microsoft completed this task and made their APIs accessible. Here we'll be using Rekognition, Amazon's deep learning-based image and video analysis tool. This blog serves as an example for how to extract information using different Rekognition operations and is not a replacement for reading the documentation.