Yesterday, we talked about how Facebook has decided to monitor political speech. Today, we want to tell you about an area where Facebook is boldly going forward - dating. Facebook recently launched this new feature in the U.S. after testing it overseas. We wanted to know how people should feel about trusting Facebook in this particularly sensitive area since the company has long been under scrutiny for the way it handles users' data, so we've called Lisa Bonos. She writes about dating and relationships for the Washington Post.

In this paper, we study problems at the interface of two important fields: \emph{submodular optimization} and \emph{online learning}. Submodular functions play a vital role in modelling cost functions that naturally arise in many areas of discrete optimization. These functions have been studied under various models of computation. Independently, submodularity has been considered in continuous domains. In fact, many problems arising in machine learning and statistics have been modelled using continuous DR-submodular functions. In this work, we are study the problem of maximizing \textit{non-monotone} continuous DR-submodular functions within the framework of online learning. We provide three main results. First, we present an online algorithm (in full-information setting) that achieves an approximation guarantee (depending on the search space) for the problem of maximizing non-monotone continuous DR-submodular functions over a \emph{general} convex domain. To best of our knowledge, no prior approximation algorithm in full-information setting was known for the non-monotone continuous DR submodular functions even for the \emph{down-closed} convex domain. Second, we show that the online stochastic mirror ascent algorithm (in full information setting) achieves an improved approximation ratio of $(1/4)$ for maximizing the non-monotone continuous DR-submodular functions over a \emph{down-closed} convex domain. At last, we extend our second result to the bandit setting where we present the first approximation guarantee of $(1/4)$. To best of our knowledge, no approximation algorithm for non-monotone submodular maximization was known in the bandit setting.

The invasive and expensive diagnosis process of bladder cancer, which is one of the most common and aggressive cancers in the United States, may be soon helped by a novel non-invasive diagnostic method thanks to advances in machine learning research at the San Diego Supercomputer Center (SDSC), Moores Cancer Center, and CureMatch Incorporated. Research scientists Igor Tsigelny and Valentina Kouznetsova have been working on the development of a machine-learning (ML) model that looks at a patient's metabolites and their chemical descriptors. The model accurately classifies the stages of bladder cancer in a patient, according to the researchers. Tsigelny is the lead author on a recently published study in the Metabolomics journal called'Recognition of Early and Late Stages of Bladder Cancer using Metabolites and Machine Learning'. When a patient experiences early symptoms of bladder cancer (e.g., blood in urine, pain during urination, etc.), the current method of diagnosis is often a painful, invasive series of tests.

Today's leading minds talk AI with host Byron Reese Listen to this episode or read the full transcript at www.VoicesinAI.com Byron Reese: This is Voices in AI, brought to you by Gigaom and I am Byron Reese. Today, our guest is Hilary Mason. She is the GM of Machine Learning at Cloudera, and the founder and CEO of Fast Forward Labs, and the Data Scientist in residence at Accel Partners, and a member of the Board of Directors at the Anita Borg Institute for Women in Technology, and the co-founder of hackNY.org. That's as far down as it would let me read in her LinkedIn profile, but I've a feeling if I'd clicked that'More' button, there would be a lot more.

Today's leading minds talk AI with host Byron Reese Listen to this episode or read the full transcript at www.VoicesinAI.com Byron Reese: This is Voices in AI, brought to you by Gigaom and I am Byron Reese. Today, our guest is Hilary Mason. She is the GM of Machine Learning at Cloudera, and the founder and CEO of Fast Forward Labs, and the Data Scientist in residence at Accel Partners, and a member of the Board of Directors at the Anita Borg Institute for Women in Technology, and the co-founder of hackNY.org. That's as far down as it would let me read in her LinkedIn profile, but I've a feeling if I'd clicked that'More' button, there would be a lot more.

Construction and the methods we use are crucial to our success in modern architecture. We build houses and massive structures using our computers and we harness the that processing power to create new solutions. But artificial intelligence in construction takes things to a whole new level! It's a tool that can help us push the boundaries further and it can do a lot to the industry as a whole. So then why haven't we seen more innovation?

Having first appeared on the scene of machine learning in 1986 and artificial neural networks in 2000, the study of deep learning continues to explode with new research, advanced techniques, higher benchmarks, and broader applications. Keeping pace in such an active field with an average of 30 new deep learning papers uploaded to arXiv per day over the previous month is daunting, to say the least. While there are many key deep learning scientists and engineers active today, the following list of 12 researchers and innovators in the field are among the most important – and they so happen to actively share on social media, making their progress and insights much easier to keep up with. So, start paying attention to these 12 top deep learning individuals, and be prepared to expand your understanding and awareness of the incredible advancements deep learning is bringing to science, industry, and society. While it in no way correlates to everyone's contribution to the field, the list is sorted by the number of Twitter followers so you can see who appears to have the most reach today.

TOKYO, Sep 20, 2019 - (JCN Newswire) - Honda Foundation, the public interest incorporated foundation established by Soichiro Honda and his younger brother Benjiro and currently led by President Hiroto Ishida, is pleased to announce that the Honda Prize 2019 will be awarded to Dr. Geoffrey Hinton, Professor Emeritus of the University of Toronto and Chief Scientific Adviser of the Vector Institute for his pioneering research in the field of deep learning(1) in artificial intelligence (AI) and his contribution to practical application of the technology. The Honda Prize, established in 1980 and awarded once each year, is an international award that recognizes the work of individuals or groups generating new knowledge to drive the next generation, from the standpoint of eco-technology(2). Dr. Hinton has created a number of technologies that have enabled the broader application of AI, including the backpropagation algorithm(3) that forms the basis of the deep learning approach to AI. AI is expected to play an important role not only in the advancement of science and technology but also in resolving many different global issues that humankind must address in the areas of energy and climate change. The Prize will be awarded to Dr. Hinton for his outstanding achievements worthy of the highest recognition. This year marks the 40th award of the prize.

Over the past several years, the retail industry has undergone a tremendous transformation. With the rapid growth of eCommerce, spearheaded by tech giants like Amazon, retail has become as much as an online experience as a physical one. It's been estimated that by the end of 2019, 1.92 billion people in the world will be shopping online. With such a large growing audience, shifting consumer preferences, and innovative new technology, it's no wonder that the retail industry is in a constant state of transformation. In order to shed some light on how much the industry has evolved, I had a conversation with Ajoy Krishnamurti, Chief Business Officer for Retail and eCommerce at Crayon Data.

Data presents an invaluable opportunity for firms to innovate, but only if they know what to do with it. In her latest research, Wharton professor of operations, information and decisions Lynn Wu looks at how different organizational structures influence the use of data analytics to spur innovation. Her paper, "Data Analytics Supports Decentralized Innovation," is forthcoming in the journal Management Science and was co-authored by Wharton operations, information and decisions professor Lorin Hitt and Wharton doctoral candidate Bowen Lou. Wu spoke with Knowledge@Wharton about the research. An edited transcript of the conversation follows.