This week, Neuron published a review titled Neuroscience-Inspired Artificial Intelligence, authored by Demis Hassabis (the cofounder of DeepMind and creator of AlphaGo) , Dharshan Kumaran (computational neuroscientist) , Christopher Summerfield (cognitive neuroscientist), and Matthew Botvinick (experimental psychologist). The authors called for a wide collaboration and communication between neuroscience community and computer science/engineering, illustrated how AI algorithms nowadays were inspired and validated by neuroscience, and envisioned a future where integrated AI and neuroscience research might yield insights into human cognition and smarter machines. When I was reading this review, I felt a deep excitement: I am so fortunate to witness the interaction of these two fields, not only as an audience, but also as a practitioner.
I started my summer intern as a Data Scientist this week at Tapad, an AdTech company headquartered in New York. Besides the amazing start up environment and cool meeting rooms, enthusiastic and welcoming colleagues, unlimited snacks and coffee, and exciting frontier research on Device Graph, one thing that completely changes my understanding about technology is that: the success of a tech company is not just about technology.
In my previous post, I presented interesting trips patterns of NYC taxis. Here, I focus my analysis of Brooklyn. Brooklyn is a very popular place of interest for both tourists and local people to eat, drink, and have fun. I usually take subway train L or G to visit Brooklyn. However, L train is to be shut down in 2019 for 1.5 years , which may severely affect Brooklyn’s business. Taxi trips, in particular, shared taxi trips, may become the mainstream transportation in Brooklyn.
In the previous post, I used SQL and Python to clean NYC taxi and Uber data in 2015. In this post, I performed data exploration and visualization using Pandas and Tableau, and present some interesting patterns of taxi trips in NYC.
With the ever increasing amount of big data available and the development of dynamic route optimization algorithm, low cost shared ride becomes more and more popular. Uber and Lyft both offer shared ride service, UberPool and Lyft Line, in order to best capture the benefit of shareconomy. Via, an on-demand shared ride start up, offers flat-rate shuttle service in urban areas and has recently expanded its business to Brooklyn.
Here, I explored NYC taxi dataset of year 2015 from Google BigQuery, started with a big picture analysis of NYC taxi services, analyzed the features of NYC taxi trips, and then focused on Brooklyn local trips. I built a simplified model (rather than a sophisticated dynamic TSP algorithm) to assess shared ride efficiency in Brooklyn. I discovered that over 15% trips within Brooklyn are shareable on late weekend night. Shared ride efficiency largely depends on the total number of trips, emphasizing the importance of scale in shareconomy.