Ride-hail apps like Uber and Lyft generated 65 percent more rides than taxis did in New York in 2017
- By December 2017, drivers using ride-hail apps — Uber, Lyft, Gett, Via and Juno — performed 65 percent more rides per month than taxi drivers did in New York City.
- The rise of Uber, Lyft and their cohorts isn’t exactly surprising, but even with Uber’s slowing growth, ride-hail companies very quickly performed about five million more monthly pickups that taxi drivers did.
- By December 2017, Uber surpassed 10 million rides a month in New York City.
- It also appears ride-hail apps are filling some of the gaps in the outer boroughs of New York City such as Queens and Brooklyn — a value proposition both Uber and Lyft have often pitched.
- Uber performed a little more than five million rides a month in the outer boroughs as of December 2017 and Lyft performed approximately 1.5 million rides a month, while green and yellow taxis each fell far below the million-rides-a-month mark.
Investors share their predictions for AI and machine learning in 2018
- Generally, we see the core machine learning tools and building block services maturing, and now we are most interested in companies that are “moving up” the stack toward vertical applications, “moving down” the stack toward purpose-built hardware, and “moving out” of the data center toward intelligence at the edge.
- For example, training “at the core” in a data center will likely be the predominant mode of training models, so the cloud providers will have a strong incentive to build special-purpose hardware in order to improve performance, reduce their reliance on suppliers, and have a higher level of control on margins.
- Two of the primary approaches we have seen in startups tackling the problem of improving understanding and user experience is to either narrow down the potential universe of requests and responses to a particular use case or to include humans in the loop to augment the machine learning system.
Julius Dein used to perform magic tricks at weddings — here’s how he amassed 20 million online followers and a six-figure income in just two years
- Two years ago, Julius Dein was a college student performing magic tricks at weddings and bar mitzvahs to make some extra cash.
- Julius Dein is the impresario of the eponymous Instagram, YouTube, Snapchat, and Facebook accounts, where he performs comedic pranks and magic tricks to crowds of incredulous onlookers.
- Head over to Dein's Instagram page and you'll find a comprehensive video collection of Dein performing his mischievous brand of magic tricks.
- Julius Dein Making magic tricks palatable to an internet-savvy audience isn't easy, according to Dein.
- Julius Dein Like any internet star with a sizable online following, Dein spends a great deal of his time thinking about what people would or wouldn't click on.
Comparison of machine learning techniques in email spam detection
- This report compares the performance of three machine learning techniques for spam detection including Random Forest (RF), k-Nearest Neighbours (kNN) and Support Vector Machines (SVM).
- The idea of automatically classifying spam and non-spam emails by applying machine learning methods has been popular in academia and has been a topic of interest for many researchers.
- This comparison is a real-time process, and therefore the main drawback of this approach is that the kNN algorithm must compute the distance and sort all the training data for each prediction, which can be slow if given a large training dataset (James, Witten, Hastie, & Tibshirani, 2013, pp.
- We determine from the results that k-Nearest Neighbours (kNN) and Support Vector Machine (SVM) perform similar weak regarding accuracy and Random Forest (RF) outperforms both.
- Therefore due to its design Random Forest performs relatively well "out-of-the-box" compared to k-Nearest Neighbours and Support Vector Machine.
Azure Functions – Significant Improvements in HTTP Trigger Scaling
- Again this test highlights what a significant improvement has been made in how Azure Functions responds to demand – the new model is able to deal with the sudden influx of users immediately, whereas in January it took nearly the full execution of the test for the system to catch up with the demand.
- The minimalist nature of this test (return a string) very much highlights the changes made to the Azure Functions hosting model and we can see that not only is there barely any lag in growing to meet the 400 user demand but that response time has been utterly transformed.
- In the real world representative tests there is still a significant response time gap for HTTP triggered compute between Azure Functions and AWS Lambda however it is not clear from these tests alone if this is related to Functions or other Azure components.
How fasting boosts exercise's effects on endurance
- As expected, the results showed that the mice that exercised daily (the EX and EXADF groups) performed better in endurance tests than the two groups that did not exercise at all (CTRL and ADF).
- However, the ADF mice that exercised daily (the EXADF group) had better endurance — that is, they could run farther and last longer — than the daily exercise mice that were allowed to eat what they wanted (the EX group).
- The results showed that the effect of ADF was to "shift fuel preference" in muscles toward fatty acids and away from carbohydrates, and it also "enhanced endurance" in the ADF mice that exercised (EXADF).
- Instead, the increased endurance in the exercising ADF mice (EXADF group) compared with the unrestricted eating exercising mice (EX group) came from a reduction in their respiratory exchange ratio, or the ratio of CO2 produced to O2 consumed.