Canada is a country with a relatively small population of just about 36 million people, but its citizens have still been busy inventing, innovating and entertaining. In the run up to Canada Day, we asked BBC readers to tell us what they thought were some of the biggest contributions the country has made to the world. Here are some of those suggestions. The list of Canadian singers, actors, comedians and entertainers is extensive. Readers flagged artists Shania Twain, Celine Dion, Drake, Leonard Cohen, Joni Mitchell, Neil Young, Arcade Fire, Alanis Morissette and Justin Bieber as just a handful of the musicians who have achieved global fame and swept awards shows for their work.

First, it depends on how noticeable are the signs and how detectable those are with a sensor. Consider if the disease is noticeable only on the skin of the chicken under the feather you will need to design a system that monitors that as well. Also catching life animals with a robotic arm is not going to be that easy - this is not a conveyor belt where the object has a uniform position and density up to me. In this case, the ML is not the challenge it's the execution of the rest of the engineering. You can pretty much do almost anything as long as you put enough effort and good people in it, however, there will also be questions of whether that effort would be less than the commercial value that you will get out of it.

Today we're going to talk about bringing machine learning to your iOS apps. This is a topic that was really big in WWDC 2017, which was a little bit unexpected – I thought there would just be a couple updates, but I'm sure you've been hearing about machine learning already a lot this week. I'm an iOS developer at SoundCloud in Berlin and I have a background in math and CS. I studied a little bit of machine learning in college but nothing too substantial that was practical, so I had to relearn pretty much everything when I got back into ML recently. So what is machine learning? Probably all of you know what it is at this point, but just in case, in a nutshell it's enabling machines to learn like babies. Instead of explicitly telling the machine what you want it to do, the machine should infer this over time based on different ways of modeling. Arthur Samuel, who was an AI pioneer, defined it as the field of study that gives computers the ability to learn without being explicitly programmed. A very simple machine learning problem would be image classification.

Solomonoff induction and its use of the Kolmogorov complexity has fascinated me, to me it's way to overcoming overfitting which happens when we apply more and more complex models to explain the data. I wanted to use it as a way to objectively determine when to switch from a simple model to a more complicated one. An actual implementation of Solomonoff induction is computationally prohibitive. I wanted to try out a reduced version of the induction to see how well it would work. I'm not claiming that I actually implemented Solomonoff's theory, this is an attempt at a practical approximation of it.

In June on /r/MachineLearning we learned of funding to a popular (and essential) Python project, are treated to a collection of machine learning cheat sheets, see how deep learning is done on premium cable television, read about Andre Karpathy's new job, and are introduced to a new machine learning "IDE." This is good news for the project. For the first time ever, NumPy -- a core project for the Python scientific computing stack -- has received grant funding. The proposal, "Improving NumPy for Better Data Science" will receive $645,020 from the Moore Foundation over 2 years, with the funding going to UC Berkeley Institute for Data Science. The principal investigator is Dr. Nathaniel Smith.

We might not always be aware of it, but Artificial Intelligence (AI) is now an established component of our daily experience as consumers. Whether it's choosing a movie based on unique recommendations from Netflix, Amazon making recommendations while you shop or interacting with online customer support via bots. AI is behind the scenes delivering highly personalized, smart interactions for the connected customer. As a business, this experience can be the differentiator between success and failure. Compounding the anytime, anywhere mindsets fostered by mobile and social technologies, AI has generated a base of connected customers that expect personalized and, increasingly, predictive experiences across every touchpoint.

Scientists at the Carnegie Mellon University's Robotics Institute have recently developed a method to which they enabled a computer that reads human body language and their movements on video in real-time. The computer is also able to read the pose of each individual's fingers. Scientists actually developed this method in collaboration with the Panoptic Studio. The Panoptic Studio is a two-story dome consist of 500 video cameras. They analyzed insights from experiments that now make it possible to detect the pose of a group of people using a single camera and a laptop computer. Currently, it is being used to improve body, face and hand detectors by jointly training them.

For most people, machines that can think and act on their own have, until now, been futurist fantasy. Fritz Lang's Metropolis (1927), Stanley Kubrick's 2001 A Space Odyssey (1968), Alex Proyas' I, Robot (2004), and Steven Spielberg's Minority Report (2002) have, along with many other creative works, variously portrayed fictive worlds profoundly altered by Artificial Intelligence and, especially, automata. The roots of these vivid tales reach down to a bedrock of Judeo-Christian folklore and Greek mythology from which, at least since the Middle Ages, have grown parables warning of the danger that comes from taking the place of the Creator.[1] Inhabiting Medieval Jewish folklore is one such, the golem, an automaton-protector made from mud which, in one story, prefiguring Mary Shelley's Frankenstein, runs amok.[2] As technology has evolved stories about the ambitions of its creators that end in tragedy have evolved with it -- lasting well into an age where an unchallenged scientific secularism rules our intellectual and moral worlds. Is this a residue of superstition in an enlightened age or a moral symbiosis? And if the latter, is its lesson that science should split the difference with superstition or that the humanities and religion, along with science, should retain this perspective: that good and evil live in man and not in his machines?

A deep artificial composer, a new algorithm developed by EPFL scientists, can generate melodies that imitates a given style of music. In the long term, the deep artificial composer may one day generate convincing music for multiple instruments – on the fly – with applications in video games or as a tool to assist composers in the creative process.

If you thought human journalists were too biased in their reporting, maybe robots will be better. We rejoiced when automation meant the more efficient production of the Ford Model T. We applauded when artificial intelligence bested humans at games and trivia. We nodded solemnly as machines began to replace fast-food workers and supermarket cashiers. And now, we may not know exactly how to react as computers take over our jobs as news writers. Few jobs these days are truly safe from the rise of AI and the latest industry to be affected is journalism.