Robodog 'Spot' designed to sniff out BOMBS goes into 'sit' mode

Daily Mail - Science & tech

A robot dog named'Spot' designed to sniff out bombs went into'sit mode' and refused to move during a live trial by Massachusetts police. Spot was created by Boston Dynamics and was on loan to the bomb squad in 2019 when the failed test happened, according to a report by OneZero. The bomb squad were called to a Walmart in Westboro, Massachusetts after employees spotted a suspicious'old brown briefcase' on a bin in the car park. Officers decided to have Spot examine the briefcase but when they turned him on he went into'sit mode' and wouldn't move - even after multiple reboots. Massachusetts Police were eventually able to get Spot to walk over to the briefcase but the video quality he recorded'wasn't very good' and had to sent a human technician to remove the briefcase - it didn't have a bomb inside.

Machine learning key to growing Australia's cruise industry, says GlobalData


Cruise operators are increasingly offering bite-sized breaks along the coast of Australia for domestic travellers keen to explore the island continent.

Testing and Monitoring Machine Learning Model Deployments


Learn how to test & monitor production machine learning models. You've taken your model from a Jupyter notebook and rewritten it in your production system. Are you sure there weren't any mistakes when you moved from the research environment to the production system? How can you control the risk before your deployment? ML-specific unit, integration and differential tests can help you to minimize the risk.

All about Machine Learning


In the previous article, we studied Artificial Intelligence, its functions, and its python implementations. In this article, we will be studying Machine Learning. One thing that I believe is that if we are able to correlate anything with us or our life, there are greater chances of understanding the concept. So I will try to explain everything by relating it to humans.



This repository aims at summing up in the same place all the important notions that are covered in Stanford's CS 229 Machine Learning course, and include: This material is also available on a dedicated website, so that you can enjoy reading it from any device. Would you like to see these cheatsheets in your native language? You can help us translating them on this dedicated repo!

Regularization in Machine Learning


Now, the coefficients are estimated by minimizing this function. Here, λ is the tuning parameter that decides how much we want to penalize the flexibility of our model. The increase in flexibility of a model is represented by increase in its coefficients, and if we want to minimize the above function, then these coefficients need to be small. This is how the Ridge regression technique prevents coefficients from rising too high. Also, notice that we shrink the estimated association of each variable with the response, except the intercept β0, This intercept is a measure of the mean value of the response when xi1 xi2 … xip 0.