Data anonymization is the process of mitigating direct and indirect privacy risks within data, such that there is a measurable way to ensure records cannot be attributed to a specific individual or entity. With an estimated 2.5 quintillion bytes of data being generated every day and an increasing reliance on data to power new applications, machine learning models and AI technologies, the importance of implementing effective anonymization techniques and removing any bottlenecks is crucial to accelerating future developments and innovations. This post is a general introduction to anonymization, and the tools and techniques for providing sufficient privacy protections, so that personally identifiable information (PII) is safe from exposure and exploitation. Data anonymization should be considered a continuous process; one that can require rapid iteration of applying various privacy engineering techniques and then measuring those privacy outcomes until a desired end state is reached. In the following sections, we'll dive deeper into our core tenets of the data anonymization process, and then walkthrough how you might apply them to a notional dataset.
Sleep staging using nocturnal sounds recorded from common mobile devices may allow daily at-home sleep tracking. The objective of this study is to introduce an end-to-end (sound-to-sleep stages) deep learning model for sound-based sleep staging designed to work with audio from microphone chips, which are essential in mobile devices such as modern smartphones. Patients and Methods: Two different audio datasets were used: audio data routinely recorded by a solitary microphone chip during polysomnography (PSG dataset, N 1154) and audio data recorded by a smartphone (smartphone dataset, N 327). The audio was converted into Mel spectrogram to detect latent temporal frequency patterns of breathing and body movement from ambient noise. The proposed neural network model learns to first extract features from each 30-second epoch and then analyze inter-epoch relationships of extracted features to finally classify the epochs into sleep stages. Results: Our model achieved 70% epoch-by-epoch agreement for 4-class (wake, light, deep, REM) sleep stage classification and robust performance across various signal-to-noise conditions. The model performance was not considerably affected by sleep apnea or periodic limb movement. Conclusion: The proposed end-to-end deep learning model shows potential of low-quality sounds recorded from microphone chips to be utilized for sleep staging. Future study using nocturnal sounds recorded from mobile devices at home environment may further confirm the use of mobile device recording as an at-home sleep tracker. Sound-based sleep staging can be a potential candidate for non-contact home sleep trackers. However, existing works were limited to audio measured with a contact manner (ie, tracheal sounds), with a limited distance (ie, 25 cm), or by a professional microphone. For convenience, a more practical way is to utilize easily obtainable audio, such as sounds recorded from commercial mobile devices.
Koomey's law This law posits that the energy efficiency of computation doubles roughly every one-and-a-half years (see Figure 1–7). In other words, the energy necessary for the same amount of computation halves in that time span. To visualize the exponential impact this has, consider the face that a fully charged MacBook Air, when applying the energy efficiency of computation of 1992, would completely drain its battery in a mere 1.5 seconds. According to Koomey's law, the energy requirements for computation in embedded devices is shrinking to the point that harvesting the required energy from ambient sources like solar power and thermal energy should suffice to power the computation necessary in many applications. Metcalfe's law This law has nothing to do with chips, but all to do with connectivity. Formulated by Robert Metcalfe as he invented Ethernet, the law essentially states that the value of a network increases exponentially with regard to the number of its nodes (see Figure 1–8).
The graph represents a network of 1,368 Twitter users whose tweets in the requested range contained "iot machinelearning", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 22 June 2022 at 12:26 UTC. The requested start date was Wednesday, 22 June 2022 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 1-day, 19-hour, 59-minute period from Monday, 20 June 2022 at 04:01 UTC to Wednesday, 22 June 2022 at 00:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.
Some people see artificial intelligence as a danger to democracy; others see it as a huge opportunity. Researchers and experts explain how algorithms and big data are deployed in Switzerland – and how they aren't. Voting in Switzerland takes place every three months. Fierce debates take place before the referendums, and the tone can be particularly aggressive online. Insults, pure hate and even murder threats are not unusual.
Sean Moriarity, the author of Genetic Algorithms in Elixir, lays out Machine Learning in the Elixir space. We talk about where it is today and where it's going in the future. Sean talks more about his book, how that led to working with José Valim which then led to the creation of Nx. He fills us in on recent ML events with Google and Facebook and shows us how Elixir fits into the bigger picture. It's a fast developing area and Sean helps us follow the important points even if we aren't doing ML ourselves… because our teams may still need it.
From their DeepMind project beating champions of Alpha Go at their own game, to recent announcements Magneta and Springboard, not to mention driverless cars, its clear that AI and Machine Learning are central to Google's strategy across its vast portfolio. In a recent interview with Hollywood Reporter, Alphabet chairman Eric Schmidt played down the fears that surround advancements in AI: 'To be clear, we're not talking about consciousness, we're not talking about souls, we're not talking about independent creativity." However, being acutely aware of the concerns around intelligent technology, the company's AI research division Google Brain recently published an AI Precision Safety whitepaper. Powerful Infrastructure Underpinning all of these projects, as well as the company's flagship Search, Translate and Youtube products is Google Cloud Platform, providing developers with the tools to build a range of programs from simple websites to complex, intelligent applications. As part of our AI in Business Festival, we spoke to Miles Ward, Global Head of Solutions at Google Cloud Platform, to find out more about the machine learning tools they offer to developers. From their DeepMind project beating champions of Alpha Go at their own game, to recent announcements Magneta and Springboard, not to mention driverless cars, its clear that AI and Machine Learning are central to Google's strategy across its vast portfolio. In a recent interview with Hollywood Reporter, Alphabet chairman Eric Schmidt played down the fears that surround advancements in AI: 'To be clear, we're not talking about consciousness, we're not talking about souls, we're not talking about independent creativity."