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Constraints on the Cloud: why we need machine learning at the Edge


We are entering a new wave of technological innovation driven by artificial intelligence (AI), with machine learning (ML) at the forefront. Even today, ML is an important aspect of any device experience, powering all kinds of tasks, features, and applications. From on-device security, like face unlock, facepay and fingerprint recognition, to smartphone camera and audio functions that allow users to have more intelligent and fun experiences through apps such as Socratic, Snapchat, FaceApp and Shazam, there are a variety of ML-based features used regularly by consumers. However, for ML-based tasks that create massive amounts of data, these are often shifted to the cloud for processing before being sent back to the device with the action. This begs the question: wouldn't it be simpler and quicker for ML processing to happen on the device?

This Free 'Shazam for Nature' App Can Identify Plants and Animals in Photos


Seek is a new app you may want to download if you use an iPhone. It's like a Shazam for nature: the app can help identify the things you photograph using the power of image recognition. The app was created by iNaturalist, an online social network for nature enthusiasts, and its stated goal is to help everyone explore the nature around them. If you ever take a picture and aren't sure of what exactly you just captured, pull out the Seek app and point the Seek Camera at the living thing. Seek will use AI to examine the photo and attempt to match it to one of the roughly 30,000 species it currently recognizes.

5 surefire ways to find the name of that song


There are few things in life more annoying than having a song stuck in your head that you don't know the lyrics to. It's even worse when you don't even know the title of the song or who the hell the artist is behind this banger. How are you supposed to stop singing gibberish to the tune of this track when you don't know a single thing about it? Thankfully, there are now quite a number of apps that help solve this conundrum. Using these tools, you'll be able to identify the next song you come across and never awkwardly belt out nonsensical wrong lyrics ever again.

Shazam reveals 2018's most-searched songs

BBC News

In a world full of smart devices, self-driving cars and voice assistants, Shazam is the closest technology comes to actual magic. The software allows you to hold your phone up to a speaker and answer the age-old question, "what is this song and who's it by?" without the humiliation of having to ask the DJ. And in 2018 the answer, most frequently, was "Solo by Clean Bandit". The song, which features US star Demi Lovato, was tagged 9.1 million times. British artists performed five of Shazam's top 10 songs, with Calvin Harris, Dua Lipa and newcomer Tom Walker all making the chart.

Big Data? No antitrust problem for Apple/Shazam Lexology


Big Data has been a focus for DG Competition for the last few years. In particular, the Commission has been interested in mergers involving the acquisition of a company holding valuable data, even if it has low turnover (see here). Apple's $400 million acquisition of Shazam, approved by the Commission on 6 September 2018, falls squarely within this category. Shazam is a music recognition app. Consumers can use Shazam to record a clip of an unknown song playing in a bar or other public place – Shazam turns this into an audio fingerprint and matches this against its database containing the audio fingerprints for millions of songs in order to identify the song playing.

How Did You Benefit from Machine Learning Today?


In an earlier blog, we talked about how machine learning is used in social media analytics. In this post, we're going to review machine learning (ML) basics and examples, and explore some of the areas you might be unaware of where ML is having a significant impact. "Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data." The goal of ML is pretty simple -- teach computer systems to perform a task. The computer system gains experience by observing patterns from examples rather than being programmed with explicit instructions or rules.

Apple's Shazam takeover investigated by EU competition regulators

The Guardian

The EU has launched a formal investigation into Apple's proposed acquisition of UK music-recognition app Shazam. The European commission announced its in-depth investigation into the deal over concerns that it would harm consumer choice and give Apple an unfair advantage through access to user data, which could aid in poaching customers from rivals. Shazam has been downloaded 1bn times and is used 20m times a day. It is the world's leading music recognition system, able to listen to and identify tracks via a smartphone and then link those tracks to multiple music subscription services, which means it could therefore hold commercially sensitive data on Apple's competitors and their consumers. Noting that Apple Music has become the second-largest music streaming service in Europe, the EC said: "Access to such data could allow Apple to directly target its competitors' customers and encourage them to switch to Apple Music.

Neural Networks Are The New Apps


For a tangible example of how things have changed in the decade since Shazam's smartphone app debuted, think about this: On the Pixel 2, with a feature called Now Playing, Google has shrunk the equivalent of Shazam's countless servers of yore to run entirely on the phone. It can match 70,000 songs, no internet required. And instead of you asking it what song is on, Now Playing listens all the time and tells you before you even ask.

'Shazam for faces' app Blippar now 99.67% accurate

Daily Mail - Science & tech

You will soon be able to instantly identify people using a futuristic facial recognition app on your smartphone. The app scans faces and brings up a profile with information about the person including links to their social media profiles. The augmented reality technology, from London-based firm Blippar, can recognise over 400,000 public figures and has a more than 99 per cent accuracy rate. It can even tell apart identical twins such as American actresses the Olsen twins, British Olympic runners the Brownlee Brothers and Irish pop duo Jedward. Blippar's augmented reality technology (pictured) can recognise over 400,000 public figures and has a more than 99 per cent accuracy rate.

Apple's Acquisition of Shazam Will Benefit Siri


I have previously been critical of Apple's AI effort. Siri lags both Google and Amazon (Alexa) in terms of speed and accuracy. Apple's Shazam acquisition is good for consumers in that it bundles a service people enjoy with Apple Music. More importantly, the acquisition will drive more Siri queries – something Siri needs to get itself into fighting shape to better compete with Google and Amazon.