Self-driving Cars to Replace NYC Taxi Fleet
Myth or reality of tomorrow? Algorology Newsletter #3 🚕
New York City’s entire taxi fleet — nearly 13,250 vehicles — could be replaced by just 3,000 ridesharing cars if these services were optimized, according to a new study from the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology (MIT).
These things sound scary and I’m sure no one will argue this is a reality of the next 5 years. But what’s really interesting is the fact that we are seeing more and more coverage coming from mainstream media warming up society for this change.
“To our knowledge, this is the first time that scientists have been able to experimentally quantify the trade-off between fleet size, capacity, waiting time, travel delay, and operational costs for a range of vehicles, from taxis to vans and shuttles,” Rus said in a press release. “What’s more, the system is particularly suited to autonomous cars, since it can continuously reroute vehicles based on real-time requests.”
🤓 The First Toothbrush With Artificial Intelligence
2016 was definitely a year of AI “everywhere” but seems it doesn’t go away.. And to be honest, I’m actually excited about this one, since there was so little innovation recently in the tooth care related things.
This new toothbrush comes with proprietary AI technology in the toothbrush in addition to 3-D motion sensors, accelerometer, gyroscope and magnetometer.
“Patented deep learning algorithms are embedded directly inside the toothbrush on a low-power processor. Raw data from the sensors runs through the processor, enabling the system to learn your habits and refine accuracy the more it’s used”
🕴Automation Comes for Japanese White-Collar Jobs
Starting from January 2017, Tokyo-based Fukoku Life Insurance Mutual Company is handing over the roles of 34 human insurance agents to IBM Watson Explorer, which is a cognitive search and content analysis platform that uses machine learning and language processing to analyze data for trends and patterns.
It’s interesting that most of the stuff that those agents were doing is a typical machine learning task called classification (like la, which makes it obvious why those people are doomed to lose their jobs. Current advances in deep learning make it impossible to compete with a machine that is able to analyze millions of micro parameters and spot things that human brain can’t spot on a scale.
Fukoku Life is using IBM Watson Explorer to classify and categorize diseases, injuries and surgical procedures. When insurance policy holders call the company’s hotline to make an insurance claim, the IBM Watson supercomputer is able to analyze the customer’s voice and detect keywords.
🎨 Designed by Machines?
I love this one, the piece is totally inline with the upcoming trend of Human-AI assistance in the workplaces where requirements are not descriptive and there are no clear processes or data that can lead to an automation.
If we talk about creative collaboration, when designers work “in pair” with algorithms to solve product tasks, we see a lot of good examples and clear potential. It’s especially interesting how algorithms can improve our day-to-day work on websites and mobile apps.
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