If there is one industry in which advanced technology has made a significant impact, it's the finance sector. For years, the finance sector has been dominated by traditional, 'legacy' banks. Who've built a reputation for long, slow and tedious processes. But with the introduction of digital banking and fintech, there has been a shift and many banks have embarked upon their digital transformation journeys. One of the main reasons for this shift is artificial intelligence.

It's a rather simple concept -- writing tests for features that haven't been written yet. Although we won't cover TDD entirely -- if you're interested in learning the basics I'd recommend reading up on Agile Alliance's article here before reading on. The C2 Group has had years of experience working with browser-based solutions, conquering web projects and requests of all sizes, but this was the first opportunity C2 had to develop and test an Alexa skill. Based on research and the experiences of other developers, we knew an Alexa skill would be a great chance to try out TDD since writing tests for a product with a repeatable function has a more practical application than writing tests for websites. Alexa skills introduce an interesting development process.

A woman is typing at her desk in an office. Companies should ensure that their AI applications do not have an adverse impact on any group of people. "Did you mean Stephen Williams?" In 2016, a Seattle Times article found that searching for a female contact on LinkedIn -- in this case, Stephanie Williams -- yielded a prompt that asked if you meant to search for a similar-sounding man's name, Stephen Williams. In 2018, Amazon had to shut down an AI recruiting tool designed to screen applicant resumes because it was discriminating against women.

Amazon will be the last of three major tech companies to alter its policy on listening to customers' voice recordings amid mounting public pressure. On Friday, Amazon began allowing users of its Echo smart speaker -- equipped with the voice-assistant Alexa -- to opt out of program that gathered some users' voice recordings for human review. After being collected, recordings were sent to third-party contractors who then listened to the clips and documented its content and whether or not the device was able to complete the command. Amazon will let users choose whether or not their voice recordings can be used in a program that listens to audio snippets for Echo smart-speaker improvements. As previously reported, the program accidentally scraped up private information from users by accident, including arguments, official business talk, and audio of people having sex.

Welcome to your 2019 kitchen. There's a smart oven, which lets you adjust the temperature and cook time from your phone. There's a smart refrigerator, which can tell you when your produce is about to go bad. There's a smart scale, which (hopefully) makes you a better baker. If you're wondering why you'd ever need a smart sink--and how it's different than a regular sink--we broke down exactly what the Kohler Sensate smart faucet is (and isn't) below.

Not all voice assistants can handle the same requests. We put Siri, Alexa and Google to the test. Apple is suspending a program that allows third-party contractors to listen to the questions you ask Siri. This comes after a whistleblower said that workers listen to the recordings to help Siri improve and determine if the request was handled correctly, a process called grading, reported The Guardian. Amazon and Google came under fire earlier this year for similar reasons when it was discovered workers were listening to the recordings the Echo and Google Assistant collected.

A German privacy watchdog has ordered Google to cease manual reviews of audio snippets generated by its voice AI. This follows a leak last month of scores of audio snippets from the Google Assistant service. A contractor working as a Dutch language reviewer handed more than 1,000 recordings to the Belgian news site VRT which was then able to identify some of the people in the clips. It reported being able to hear people's addresses, discussion of medical conditions, and recordings of a woman in distress. The Hamburg data protection authority told Google of its intention to use Article 66 powers of the General Data Protection Regulation (GDPR) to begin an "urgency procedure" under Article 66 of GDPR last month.

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."