Collaborating Authors


White-label voice assistants will win the battle for podcast discovery – TechCrunch


Americans are bored, housebound and screened out. This has created a golden opportunity for audio as consumers turn to podcasts, voice assistants and smart speakers – often at the same time. Roughly 128 million Americans use a voice assistant at least once a month. Smartphones account for most voice assistants, but there are also nearly 160 million smart speakers in American homes. One of the hottest forms of audio content is, of course, podcasts.

'Typographic attack': pen and paper fool AI into thinking apple is an iPod

The Guardian

As artificial intelligence systems go, it is pretty smart: show Clip a picture of an apple and it can recognise that it is looking at a fruit. It can even tell you which one, and sometimes go as far as differentiating between varieties. But even cleverest AI can be fooled with the simplest of hacks. If you write out the word "iPod" on a sticky label and paste it over the apple, Clip does something odd: it decides, with near certainty, that it is looking at a mid-00s piece of consumer electronics. In another test, pasting dollar signs over a picture of a dog caused it to be recognised as a piggy bank.

Dating app users swipe left or right 'based on attractiveness and race'

Daily Mail - Science & tech

US researchers found attractiveness and race preferences were the top predictors of whether people would swipe left or right – and nearly twice as important as any other factors. Other individual characteristics – such as personality and hobbies – were poor predictors of which way someone would swipe. On dating apps, a swipe left means you're not interested in the person, while a swipe right means you are interested. The average time for swiping right was just below one second. However, if a swiper didn't like someone, this time got even shorter to about half a second.

Scientists develop AI that can learn which faces you find attractive directly from your brain waves

Daily Mail - Science & tech

An artificial intelligence system has been developed that can delve into your mind and learn which faces and types of visage you find most attractive. Finnish researchers wanted to find out whether a computer could identify facial features we find attractive without any verbal or written input guiding it. The team strapped 30 volunteers to an electroencephalography (EEG) monitor that tracks brain waves, then showed them images of'fake' faces generated from 200,000 real images of celebrities stitched together in different ways. They didn't have to do anything - no swiping right on the ones they like - as the team could determine their'unconscious preference' through their EEG readings. They then fed that data into an AI which learnt the preferences from the brain waves and created whole new images tailored to the individual volunteer.

Python Hacks


Python tips and tricks to make you a better data science or analytics professional. These Python hacks cover a broad range of topics.

Learning Python and AI as a web developer #4


In this post I'll be talking about my experience so far of exploring AI. One of the main reasons for this newsletter is for me to learn more about the Artificial Intelligence world as a whole. By day I'm a web developer, and the aim is to move more towards AI work and projects. I want to get a better appreciation for what's currently out there, and what might be coming next, in order to build knowledge and understanding, whilst working on related practical skills. The hope is that the newsletter might also serve as a useful overview or gateway for other developers that are wanting to do the same.

15 Python Projects: From Beginner To Full-Stack - Comp Sci Central


The best way to learn python is by creating a project. These are 10 of the best python projects for beginner to intermediate Python programmers. These python projects will not only help you learn the fundamentals, but you'll have a lot of fun creating these. With each of these 10 python projects, there is a full video tutorial that walks you step-by-step from start to finish. These projects have been hand-selected and range from beginner to intermediate.

How Machine Learning Is Changing Influencer Marketing


Influencer marketing has grown significantly due to the pervasive use of social media platforms in promoting products and services. In 2019 the practice reached $6.5 billion and is projected to reach $15 billion by 2022. Marketing today is all about algorithms, data and analytics to gain a targeted audience rather than the traditional spray-and-pray approach. The major success factor is figuring out how influencer marketing can become more effective by targeting the right audience to increase customer engagement. Technological advancements such as machine learning (ML), natural processing languages (NLPs) and artificial intelligence (AI) are changing how brands enhance influencer marketing. ML tech is assisting organizations in three areas: Creating relevant copy to reach the intended audience, identifying the right content creators for various marketing segments and recommending impactful workflow processes.

SARDO Is a Smartphone-Sniffing Search and Rescue Drone


For anyone who has ever misplaced their iPhone, Apple's "Find My" app is a game-changer that borders on pure magic. Sign into the app, tap a button to sound an alarm on your MIA device, and, within seconds, it'll emit a loud noise -- even if your phone is set on silent mode -- that allows you to go find the missing handset. Yeah, it's usually stuck behind your sofa cushions or left facedown on a shelf somewhere. You can think of SArdo, a new drone project created by researchers at Germany's NEC Laboratories Europe GmbH, as Apple's "Find My" app on steroids. The difference is that, while finding your iPhone is usually just a matter of convenience, the technology developed by NEC investigators could be a literal lifesaver.

Facebook's new AI teaches itself to see with less human help


Most artificial intelligence is still built on a foundation of human toil. Peer inside an AI algorithm and you'll find something constructed using data that was curated and labeled by an army of human workers. Now, Facebook has shown how some AI algorithms can learn to do useful work with far less human help. The company built an algorithm that learned to recognize objects in images with little help from labels. The Facebook algorithm, called Seer (for SElf-supERvised), fed on more than a billion images scraped from Instagram, deciding for itself which objects look alike. Images with whiskers, fur, and pointy ears, for example, were collected into one pile.