WASHINGTON – A series of privacy missteps in recent months has raised fresh concerns over the future of voice-activated online digital assistants, a growing market seen by some as the next frontier in computing. Recent incidents involving Google, Apple and Amazon devices underscore that despite strong growth in the market for smart speakers and devices, more work is needed to reassure consumers that their data is protected when they use the internet-connected technology. Apple said this past week that it was suspending its "Siri grading" program, in which staffers listen to snippets of conversations to improve its voice-recognition ability, after the Guardian newspaper in Britain reported that the contractors were hearing confidential medical information, criminal dealings and even sexual encounters. "We are committed to delivering a great Siri experience while protecting user privacy," Apple said in a statement, adding it would allow consumers to opt into this feature in a future software update. Google meanwhile said it would put a hold on listening to and transcribing conversations in the European Union gleaned from its Google Assistant, in the wake of a privacy investigation in Germany.

We consider the task of tensor estimation, i.e. estimating a low-rank 3-order $n \times n \times n$ tensor from noisy observations of randomly chosen entries in the sparse regime. In the context of matrix (2-order tensor) estimation, a variety of algorithms have been proposed and analyzed in the literature including the popular collaborative filtering algorithm that is extremely well utilized in practice. However, in the context of tensor estimation, there is limited progress. No natural extensions of collaborative filtering are known beyond ``flattening'' the tensor into a matrix and applying standard collaborative filtering. As the main contribution of this work, we introduce a generalization of the collaborative filtering algorithm for the setting of tensor estimation and argue that it achieves sample complexity that (nearly) matches the conjectured lower bound on the sample complexity. Interestingly, our generalization uses the matrix obtained from the ``flattened'' tensor to compute similarity as in the classical collaborative filtering but by defining a novel ``graph'' using it. The algorithm recovers the tensor with mean-squared-error (MSE) decaying to $0$ as long as each entry is observed independently with probability $p = \Omega(n^{-3/2 + \epsilon})$ for any arbitrarily small $\epsilon > 0$. It turns out that $p = \Omega(n^{-3/2})$ is the conjectured lower bound as well as ``connectivity threshold'' of graph considered to compute similarity in our algorithm.

Apple workers have stopped listening to Siri queries worldwide, the company said this week. Apple plans to bring back human reviews of Siri voice recordings at some unspecified date, but the company said it will only review them when customers specifically opt in to the practice. Separately, Google today confirmed that it recently "paused" human reviews of Google Assistant queries worldwide. Apple's decision to stop having humans listen to Siri queries follows a report last week that contractors who review the recordings for accuracy heard private discussions and even sexual encounters. Apple calls the human reviews of Siri recordings "grading."

Like Google and Amazon before it, Apple has been caught sending voice assistant recordings to contractors, who listen to snippets of your requests and conversations, without telling anyone. In response to the privacy concerns that raises, Apple says it will eventually give users control over whether their Siri data gets sent to third-party eavesdroppers, but it's unclear whether that consent will be opt-in or opt-out. Letting people opt out of data collection is better than not giving them any choice at all. But for decades, that's been the extent of the conversation. It gives too many giant tech companies plausible deniability for the rampant hoovering of your personal information, and allows them to implicitly blame the victim when they overreach: Don't get angry at us, you could have opted out this whole time.

Apple has suspended its practice of having human contractors listen to users' Siri recordings to "grade" them, following a Guardian report revealing the practice. The company said it would not restart the programme until it had conducted a thorough review of the practice. It has also committed to adding the ability for users to opt out of the quality assurance scheme altogether in a future software update. Apple said: "We are committed to delivering a great Siri experience while protecting user privacy. While we conduct a thorough review, we are suspending Siri grading globally. Additionally, as part of a future software update, users will have the ability to choose to participate in grading."

My family and I continue to have more and more conversations with Alexa, Siri and Google Assistant lately. Having three AI based sources within speaking range of each other, we have a tendency to fact check them against one another - especially when someone doesn't quite trust or agree with the answer they get. For example, is Australia considered a continent or is it Oceania? Is a hot dog a sandwich? Who is the best NBA player of all time ever?

Are they relevant, or just a fad like the Nintendo Wii? There seem to be personal assistants being added to everything, from smartphones, to watches, to speakers. And now, a Personal Assistant is coming to Microsoft Azure. Microsoft is reviving Clippy to be the Cloud Assistant you need for helping work with and maintaining your Microsoft Azure cloud resources. Personal Assistants are great at helping with very specific tasks like setting reminders and using voice commands to get stuff done.

Technology changes so fast that our lives are radically different from even a decade ago, yet slowly enough that sometimes we don't even notice the changes. We live in the future, in other words, and sometimes it takes a moment to realise what an odd, and perhaps unsettling, future it is. So I'm going to try laying it out for you in plain English. Not one, but two, controversial billionaires have created projects aimed at reading minds. Elon Musk, the South African co-founder of PayPal, unveiled the latest step in his plan to build mind-reading implants two weeks ago.

--Collaborative filtering is one of the most popular techniques in designing recommendation systems, and its most representative model, matrix factorization, has been wildly used by researchers and the industry. However, this model suffers from the lack of interpretability and the item cold-start problem, which limit its reliability and practicability. In this paper, we propose an interpretable recommendation model called Multi-Matrix F actorization (MMF), which addresses these two limitations and achieves the state-of-the-art prediction accuracy by exploiting common attributes that are present in different items. In the model, predicted item ratings are regarded as weighted aggregations of attribute ratings generated by the inner product of the user latent vectors and the attribute latent vectors. MMF provides more fine grained analyses than matrix factorization in the following ways: attribute ratings with weights allow the understanding of how much each attribute contributes to the recommendation and hence provide interpretability; the common attributes can act as a link between existing and new items, which solves the item cold-start problem when no rating exists on an item. We evaluate the interpretability of MMF comprehensively, and conduct extensive experiments on real datasets to show that MMF outperforms state-of-the-art baselines in terms of accuracy. I NTRODUCTION In recent years, recommendation systems gain increasing interest by both researchers and the industry [1], [2]. The most popular recommendation systems are based on collaborative filtering (CF) technique, which provides recommendations based on other similar users' choice [3]. Matrix factorization (MF) is one of the most common collaborative filtering models, whose main idea is to learn user latent vectors and item latent vectors, so that the inner product of the two vectors can approximate the original matrix with the minimal approximation error. MF has advantages of simplicity and performing well in many domains, such as recommendation systems, computer vision and document clustering [4]-[7]. However, it suffers from two limitations.

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