In many applications of recommendation systems, we have data in the form of an incomplete matrix, where one dimension is growing and the other dimension is fixed. For instance, in recommendation systems, there is a fixed set of potential products (rows of a matrix) to offer customers that arrive over time (columns of a matrix). Three other applications are choosing machine learning models (rows) for each new customer's dataset (columns) [FSE18], choosing which survey questions (rows) to ask to respondents (columns) that arrive sequentially [ZTCS19], or choosing which lab tests (rows) to order for each new patient (columns) [HL14]. In these cases, there is an inherent asymmetry with respect to the dimensions in the budget: we have a budget over each column, not over each row. We could choose any machine learning model and recommend it for each dataset, or choose any survey question and give it to every user, but it is very hard to run every machine learning pipeline on an arbitrary dataset, or to give every survey question to an arbitrary respondent (indeed, in [ZTCS19], users omitting too many answers was the precise motivation for their problem).

Artificial intelligence in financial payments will be explored fully in March by the AI In Financial Services event and the AI In Finance Summit event in March. The AI In Financial Services event is scheduled for March 17th at the America Square Conference Centre in London. The AI in Finance Summit is slated for March 31 to April 1 at the etc Venues, also in London. The Technova AI In Financial Services event will focus on business transformation to robotic automation, customer innovation to ethical transparency, equipping attendees with the skills and expertise to capitalize on the artificial intelligence revolution, notes RAM Research and PYRPTS. Among noted speakers is Kathy Liu, Head In Innovation, HSBC Global Operations.

One in every four adults in America now owns a voice-activated smart speaker. While we love the convenience of talking to a gadget to play music, make calls, and such, most of us get a little creeped out when they "wake up" when they're not supposed to. I often trigger Siri when saying "seriously," or "Suli" – the name of my parents' dog. My friend Bill Keeshan says his Alexa connected device "gets triggered by my daughter saying'actually.'" All too familiar anecdotes aside, researchers at Northeastern University and the Imperial College of London spent the last six months streaming 125 hours of popular Netflix TV shows to a handful of voice-activated smart speakers.

You might be wondering what exactly Braze.com is and why it grabs their geographic, associations and more. I found out Friday that after I composed a message on the PayPal-owned digital payments app Venmo to pay my personal trainer Jarek, Venmo passed on my geographic locations and associations (including Jarek) to Braze, which calls itself a "customer engagement" company. Just last month, the Norwegian Consumer Council issued a blistering report showing what happened to users of the dating sites OKCupid and Grindr in the background, after people revealed all about their interests. OkCupid "shared highly personal data about sexuality, drug use, political views, and more," with Braze, according to the report. Grindr, a popular dating and social app used by gay and bisexual men, sent data to Braze about the "relationship type" men were seeking on the app, per the report.

We investigate the problem of fair recommendation in the context of two-sided online platforms, comprising customers on one side and producers on the other. Traditionally, recommendation services in these platforms have focused on maximizing customer satisfaction by tailoring the results according to the personalized preferences of individual customers. However, our investigation reveals that such customer-centric design may lead to unfair distribution of exposure among the producers, which may adversely impact their well-being. On the other hand, a producer-centric design might become unfair to the customers. Thus, we consider fairness issues that span both customers and producers. Our approach involves a novel mapping of the fair recommendation problem to a constrained version of the problem of fairly allocating indivisible goods. Our proposed FairRec algorithm guarantees at least Maximin Share (MMS) of exposure for most of the producers and Envy-Free up to One item (EF1) fairness for every customer. Extensive evaluations over multiple real-world datasets show the effectiveness of FairRec in ensuring two-sided fairness while incurring a marginal loss in the overall recommendation quality.

AI appears neutral, but it's made by humans, which means it internalizes all the same bias as we have - including gender bias. AI is a mirror of ourselves. According to Harvard Business Review, there have been many incidences of AI adopting gender bias from humans. Harvard Business Review cites an example of natural language processing (NLP) that is present in Amazon's Alexa and Apple's Siri. Harvard Business Review also cites word-embeddings as a bias aspect of AI.

There has been a lot of discussion as of late that advances in AI (artificial intelligence) will translate into better voice assistants. There was even a documentary that went as far to say how AI technology is influencing our lives and how the data behind it is driving even bigger trends in digital transformation. We've all heard and seen massive growth number predictions for AI to soar well beyond $400 billion by 2025. One key factors driving growth in this market is advances in voice recognition, which is also going to help drive the market for voice assistants. All month long I have been reporting how Apple Siri, Amazon Alexa, Google Assistant, and Cortana virtual assistants have been navigating their way into our world in an effort to make our lives more convenient.

Artificial Intelligence Marketing (AIM) provides superior solutions to bridge the gap between analytics and execution. It is the process of going through massive piles of data to originate positive results. As per the courtesy of Forbes, retailers invested around 5.9 billion US dollars on AIM. North America, Europe, and Asia-Pacific are mainly using this type of digital marketing and advertising. Likewise, remote health monitoring, wearable AR, IoT kitchen appliances, and brain-sensing gadgets lie under the game-changing innovations.

We were very excited to chat recently with Trip O'Dell who, among his other impressive roles, has been a product design lead at Amazon where he worked on the future vision for their virtual assistant, Alexa. Our discussion with Trip was full of juicy insights about Voice UI and the ethical considerations behind Alexa's design, and how we as designers need to be leaders in making those ethical choices. Below is an article we wrote and edited in collaboration with Trip using speech-to-text tools and a linear audio editor. All the words are his, we just helped put them on your screen. When it comes to Voice UI there's a lot of conversation around voices, but very little around personality--whether or not we want to describe it as a personality--that we're creating. How trustworthy should that personality be? Where should there be a natural boundary?

No one likes it when a stranger butts into their conversation. Especially when they interrupt with some astonishing non-sequitur. Create a free Amazon Business account and get a 30-day free trial to Business Prime. You're watching TV and chatting about the painfully demanding couple on House Hunters International when a distant voice pipes up: "The circumference of the Earth is 24,901 miles." Or whatever you call your Google Home person.