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."

Why has AI become one of the most popular topics among marketers? Because it makes our lives easier. And then there's the next question: How can it help? We're facing an invasive amount of information, and we need to be able to process the huge chunks of data now available to us and make the best use of them. Yet, in most cases, we are at a loss, and the only easy way is to go by intuition alone.

How it works: If you have said the phrase change wallpaper in your command, the program will download a random wallpaper from unsplash.com,

The pursuit of mans' enduring quest to build and discover new technology is astounding. Although automation has its flaws, our endeavors are motivated by making the impossible possible. To make human-centered, empathetic AI. We've come a long way. Technology is our drudge, we the earnest navigator.

Timely delivery is often a critical deciding factor for the ever-impatient customers to choose service A over service B. Hence, operations/logistics team is a key enabler. The attrition rate in large frontline teams is high, close to 75 percent annually. Yet most companies have aggressive growth targets, necessitating recruitment of high volumes of workers constantly. High-growth companies in this domain like Zomato and Swiggy, grew by more than 50-60 percent by the end of 2018, recruited tens of thousands of delivery boys every month. Vahan Inc. has developed an AI-driven virtual assistant that helps logistics companies scale and automate their hiring process by leveraging the common addiction of messaging applications like WhatsApp and FB messenger. In this talk, the speaker will cover in detail how they developed a complete data collection and natural language processing pipeline for Indian languages and built a chatbot over Whatsapp which is currently connecting companies like Dunzo, Zomato, Swiggy & Rapido Express with potential frontline workers and fulfilling the hiring requirements of this industry in a scalable and autonomous fashion.

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 is the most important technology for the business of the future. That's because AI-driven software is already helping companies increase efficiency, improve customer relationships, and boost sales. Researchers estimate that machine learning has the potential to add $2.6 trillion in value to the marketing and sales industry by 2020, as well as another $2 trillion to manufacturing and logistics fields. The International Data Corporation estimates that spending on machine learning will reach $77.6 billion by 2022. This is why companies of all sizes are collaborating with Python development outsourcing firms to source experienced data scientists as needed and develop custom data analytics software.