He is best known for being CEO of one of the world's largest companies. But before Tim Cook took the reins at Apple, he started his career in a very surprising place. Speaking on the Table Manners podcast, Mr Cook revealed that he started working when he was just 11 years old. He says: 'A lot of [his upbringing] was centred on work and the belief that hard work was essential for everybody, regardless of your age. 'And so I started working when I was probably 11 or 12 on the paper route.'

In this paper, we study the robustness of "data-centric" approaches to finding neural network architectures (known as neural architecture search) to data distribution shifts. To audit this robustness, we present a data poisoning attack, when injected to the training data used for architecture search that can prevent the victim algorithm from finding an architecture with optimal accuracy. We first define the attack objective for crafting poisoning samples that can induce the victim to generate sub-optimal architectures. To this end, we weaponize existing search algorithms to generate adversarial architectures that serve as our objectives. We also present techniques that the attacker can use to significantly reduce the computational costs of crafting poisoning samples. In an extensive evaluation of our poisoning attack on a representative architecture search algorithm, we show its surprising robustness. Because our attack employs clean-label poisoning, we also evaluate its robustness against label noise. We find that random label-flipping is more effective in generating sub-optimal architectures than our clean-label attack. Our results suggests that care must be taken for the data this emerging approach uses, and future work is needed to develop robust algorithms.

In a car outside a convenience store in Flint, Michigan, in late 2016, Kemo Parks handed his cousin Dequavion Harris a gun. Things happened quickly after that: Witnesses saw Harris "with his arm up and extended" toward a red truck. The wounded driver sped off but crashed into a tree. EMTs rushed him to the hospital. He was dead on arrival.

At this point, when you are reading this, I might be already updated this project with more features, but if you want only to run a quick test on your webcam, replace the main.py

The thriving industry of Data Science is continuously evolving with the technological advancements in Machine Learning and Artificial intelligence. This has opened up whole new avenues for Data Scientists worldwide. Professionals who can handle Big Data and have the necessary knowledge required for understanding, analysing and processing data are in high demand in the job market. However, there is one important thing that also needs to be addressed is the raging problem caused by the gender gap in this sector. As per the statistical report from the Boston Consulting Group, only 15 to 22 per cent of the Data Science-related professional roles are occupied by women.

You must be wondering how to become a Data Scientist? What skills are required to become a Data Scientist? I know so many questions will be there in your mind. With this article, you will find all the answers. But first, let's talk about the scope of Data Scientist in today's world.

Crypto industry peaks deserve more than two hands to handle; more traders are looking into AI as an advantage. Quick analysis and making the right calls at the right time can be a game-changer. Will AI be the new frontier in the crypto markets? Admittedly, precise calls in the crypto space or any other industry are scarce, with complex graphs and other determinants to contemplate. Decent patterns at the right pace, coupled with a thirsty investor, can always get the job done.

Trusting Artificial Intelligence Crypto industry peaks deserve more than two hands to handle; more traders are looking into AI as an advanta.

This article is reposted from the Parity Substack. Follow us there for updates. Welcome back to Parity, now coming from the brand new team. We took over for Dr. Rumman Chowdhury while she's off solving some of the world's most pressing (and challenging) algorithmic issues at Twitter. Rumman's still with us as our lead investor and board member, helping us immensely as we grow.

I started my journey on Kaggle a year ago, straight after a brief acquaintance with the basics of Python and a couple of books on Machine Learning and Deep Learning. I'm still a beginner, though my Kaggle profile turned out to be the most valuable part of my portfolio which landed me on my first job in Data Science just 5 months later. Here I want to share with you a couple of things I've learned from the awesome Kaggle community during this very first year full of hard work. I know there's a bunch of great notebooks claiming to teach you from an absolute beginner, but it's still best to first build some solid foundation of theory and tech behind data science before jumping straight into the competition. There's no need to read the Deep Learning Book from cover to cover, just find some sources Kaggle makes you able to jump straight in the top 30% of literally any competition by just making a copy of the most scoring public work.