Apple has programmed its Siri voice assistant to avoid politically charged subjects, and deflect or duck questions that require its AI to take a stand on issues, it emerged this week. From a tranche of documents leaked by a former contract worker who evaluated Siri responses to user questions for accuracy, The Guardian obtained a set of guidelines drawn up last year to ensure Siri's responses to "sensitive" topics comes across as neutral. In keeping with these guidelines, Siri's responses were revised to endorse "equality" while avoiding the word "feminism," even if asked directly. Where once Siri responded to the question, "Are you a feminist?" The leaked guidelines reportedly state, "Siri should be guarded when dealing with potentially controversial content."

An internal project to rewrite how Apple's Siri voice assistant handles "sensitive topics" such as feminism and the #MeToo movement advised developers to respond in one of three ways: "don't engage", "deflect" and finally "inform". The project saw Siri's responses explicitly rewritten to ensure that the service would say it was in favour of "equality", but never say the word feminism – even when asked direct questions about the topic. Last updated in June 2018, the guidelines are part of a large tranche of internal documents leaked to the Guardian by a former Siri "grader", one of thousands of contracted workers who were employed to check the voice assistant's responses for accuracy until Apple ended the programme last month in response to privacy concerns raised by the Guardian. In explaining why the service should deflect questions about feminism, Apple's guidelines explain that "Siri should be guarded when dealing with potentially controversial content". When questions are directed at Siri, "they can be deflected … however, care must be taken here to be neutral".

Machine Learning Use Cases – Google says that use cases mean, the specific situation in which a product or service could potentially be used. So, be it banking, energy, fin-tech, healthcare, insurance, marketing and public sector to name a few, everywhere machine learning is used. With the advent of technology and digitalization, it is used and needed everywhere. Discussing machine learning use cases, all its usage and the industry in which it is used will need a book or a thesis. So, here we can talk about its daily usage in business and later you can tell me in the comment section about the sector you would like to get in-depth knowledge and we can discuss that too.

The telecommunications industry will thrive, based on the capability of its service providers to innovate as they move ahead with implementing advancing technologies. Artificial intelligence (AI) and machine learning (ML) are 2 digital forces already impacting how work is performed, whether it's your favorite beverage being prepared by a robot barista, or virtual assistants handling increasingly larger volumes of requests flooding customer interaction centers. To date, the role of AI within the telecom industry has been limited to chatbots which automate the routine customer enquiry, extracting the intent to ensure a customer is routed quickly to the proper channel. Telecom providers, however, are increasingly moving towards using AI to not only lower operating costs and improve network efficiency, but to also improve the customer-engagement experience. For example, by using exploratory data analysis that looks for specific patterns, AI can also detect anomalies in the network or even predict the possibility of a dire event happening.

I have been thinking of interactive ways of getting my postgraduate thesis on Racial Bias, Gender Bias, AI new ways to approach Human Computer Interaction out to everyone. Life has been super busy so I have decided to add snippets of the thesis for now. For this research paper, the researcher has identified a number of areas of concern in regards to systems powered by AI being deployed in situations that affect the lives of humans. These examples will be used to further highlight this area of concern. Suggestions have made that decision-support systems powered by AI can be used to augment human judgement and reduce both conscious and unconscious biases (Anderson & Anderson, 2007).

It was written as an advanced text to serve as a basis for a graduate course, and/or as a reference to the researcher diving into this fascinating world at the intersection of optimization and machine learning. Such a course was given at the Technion in the years 2010-2014 with slight variations from year to year, and later at Princeton University in the years 2015-2016. The core material in these courses is fully covered in this book, along with exercises that allow the students to complete parts of proofs, or that were found illuminating and thought-provoking. Most of the material is given with examples of applications, which are interlaced throughout different topics. These include prediction from expert advice, portfolio selection, matrix completion and recommendation systems, SVM training and more.

This paper studies the problem of finding the exact ranking from noisy comparisons. A comparison over a set of $m$ items produces a noisy outcome about the most preferred item, and reveals some information about the ranking. By repeatedly and adaptively choosing items to compare, we want to fully rank the items with a certain confidence, and use as few comparisons as possible. Different from most previous works, in this paper, we have three main novelties: (i) compared to prior works, our upper bounds (algorithms) and lower bounds on the sample complexity (aka number of comparisons) require the minimal assumptions on the instances, and are not restricted to specific models; (ii) we give lower bounds and upper bounds on instances with unequal noise levels; and (iii) this paper aims at the exact ranking without knowledge on the instances, while most of the previous works either focus on approximate rankings or study exact ranking but require prior knowledge. We first derive lower bounds for pairwise ranking (i.e., compare two items each time), and then propose (nearly) optimal pairwise ranking algorithms. We further make extensions to listwise ranking (i.e., comparing multiple items each time). Numerical results also show our improvements against the state of the art.

Steve Jobs once said, "A lot of times, people don't know what they want until you show it to them'. This makes sense, especially in this era of constant choice overload. Consumers today have access to a plethora of products just at the click of their mouse. These innumerable choices can sometimes turn out to be confusing and hampering and do more harm than good. For instance, a company may offer millions of products on its website, but how does a consumer find a new and appealing product from amongst those?

This January, Tim Cook made the astonishing assertion that Apple's greatest contribution as a company would be in healthcare. Now, nine months later, we might have a bit more of an idea what the heck he was talking about. A recent report from The Guardian uncovered pages of internal Apple documents containing Siri functionality instructions and transcripts. The investigation focused on the way Apple made changes to Siri's answers to questions about feminism, gender equality, and the #MeToo movement. But it also contained some information about what Apple has in the works for its oft-struggling voice assistant overall.

Fox News Flash top headlines for Sept. 6 are here. Check out what's clicking on Foxnews.com An Apple project to rewrite how the Siri voice assistant handles sensitive topics like feminism and the #MeToo movement told developers to either not engage, deflect or inform. According to leaked documents obtained by The Guardian, the project saw Siri's responses rewritten to never explicitly say the word "feminism" -- although it was OK for the AI-powered assistant to say it was in favor of equality. Apple's guidelines explain that "Siri should be guarded when dealing with potentially controversial content," and that when questions are directed at the voice assistant, they can be "deflected. However, care must be taken here to be neutral."