Smart replies, dumb people
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This document discusses smart replies and how they are being used by communication platforms and apps to reduce typing. It notes that 79% of smartphone users use mobile to read email and 80% are expected to use messaging apps by 2018. Smart replies provided by machine learning can reduce typing by suggesting responses. The document discusses challenges like personalization across personal style, relationships, culture and language. It also discusses privacy challenges and how communication may be affected by increased use of smart replies.
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Embedded machine learning-based road conditions and driving behavior monitoring
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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Smart replies, dumb people
- 1. Smart Replies, Dumb People Shira Weinberg
- 4. Communication is mobile We use mobile for most of our communication 79% of smartphone users use mobile to read email 80% of smartphone users are expected to be on messaging apps by 2018
- 5. We are busy and multitasking
- 6. We are lazy
- 7. New needs New features
- 8. Smart replies Reduce typing by providing machine learning based smart replies and suggestions
- 10. Google Inbox 10% of the emails sent using Inbox are using smart replies! (Google, March 2016)
- 11. Smart keyboards
- 12. Facebook M-powered suggested responses
- 13. Cortana in Skype Cortana can assist you within your Skype conversations
- 14. Cortana in Skype
- 15. The challenges (well, a few of them)
- 16. Personalization is key Personal style Relationship ` Culture Language
- 17. The privacy challenge • Anonymization • Scrubbing • Data retention • Handling raw data vs. “inferences”
- 18. Choosing where to invest next
- 20. How will our communication be affected?
- 21. Creating bias
- 22. Personalization vs. Strengthening biases
- 23. How will that affect our communication?
- 24. How will that affect our communication? A few possible scenarios: Unified communication
- 25. How will that affect our communication? A few possible scenarios: Unified communication Low usage, minor effect
- 26. How will that affect our communication? A few possible scenarios: Unified communication Low usage, minor effect Simple use cases
- 27. Conversations driven by clicks only