Neural Information Processing Systems (NeurIPS) is a multi-track machine learning and computational neuroscience conference that includes invited talks, demonstrations, symposia and oral and poster presentations of refereed papers. Following the conference, there are workshops which provide a less formal setting.

Influencer Marketing is the newest breakthrough to have shaken up the Digital Marketing landscape. Once an experimental channel, it has grown exponentially over the past few years – up to $6.5 billion this year and projected to rise to up to $10 billion by 2020. Now a mainstay of Marketing organizations, the key success factor is figuring out how to get the most from it. Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) are revolutionizing the way brands conduct Influencer Marketing. AI-powered Influencer Marketing tech is helping brands in three key areas: identifying the right creators, suggesting impactful workflow actions, and creating more relevant content.

In this blog, I'll discuss how I worked collaboratively with various domain experts, using reinforcement learning to develop innovative solutions in rocket engine development. In doing so, I'll demonstrate the application of ML techniques to the manufacturing industry and the role of the Machine Learning Product Manager. Machine learning (ML) has had an incredible impact across industries with numerous applications such as personalized TV recommendations and dynamic price models in your rideshare app. Because it is such a core component to the success of companies in the tech industry, advances in ML research and applications are developing at an astonishing rate. For industries outside of tech, ML can be utilized to personalize a user's experience, automate laborious tasks and optimize subjective decision making.

Soon as I heard this reply from Siri, I knew I found a perfect partner to savour my hours of solitude. From stupid questions to some pretty serious advice, Siri has been always there for me. How amazing it is to tell someone everything and anything and not being judged at all. A top class feeling it is and that's what the beauty of a chatbot is. Stay updated with the latest technology trends while you're on the move - Join DataFlair's Telegram Channel A chatbot is an intelligent piece of software that is capable of communicating and performing actions similar to a human.

In today's competitive and crowded news market, finding and retaining loyal readers is an essential component of sustainability for news organisations. After all, with so many competing ways to access news, reader loyalty is hard to come by. The data team at the South China Morning Post (SCMP) recently set out to understand how readers develop loyalty to specific news outlets and how to nurture that loyalty. In January 2019, the team began to build an algorithm using machine learning to predict reader loyalty. We've named our predictive engine Bluefin because: While the ability to predict reader loyalty has many applications, we were interested in using the prediction to optimise our marketing campaigns.

In today's competitive and crowded news market, finding and retaining loyal readers is an essential component of sustainability for news organisations. After all, with so many competing ways to access news, reader loyalty is hard to come by. The data team at the South China Morning Post (SCMP) recently set out to understand how readers develop loyalty to specific news outlets and how to nurture that loyalty. In January 2019, the team began to build an algorithm using machine learning to predict reader loyalty. We've named our predictive engine Bluefin because: While the ability to predict reader loyalty has many applications, we were interested in using the prediction to optimise our marketing campaigns.

Probability and statistics are the basis of Data Science. Statistics is, in simple terms, the use of mathematics to perform technical analysis of data. With the help of statistical methods, we make estimates for further analysis. Statistical methods themselves are dependent on the theory of probability, which allows us to make predictions. Both statistics and probability are separate and complicated fields of mathematics.

This is the seventh post of my column Deep Learning for Life Sciences where I give concrete examples of how Deep Learning can already now be applied in Computational Biology, Genetics and Bioinformatics. In the previous posts, I demonstrated how to use Deep Learning for Ancient DNA, Single Cell Biology, OMICs Data Integration, Clinical Diagnostics and Microscopy Imaging. Today we are going to dive into the exciting History of Human Evolution and learn that it is straightforward to borrow methodology from the Natural Language Processing (NLP) and apply it to Human Population Genetics in order to infer regions of Neanderthal introgression in modern human genomes. When ancestors of Modern Humans migrated out of Africa 50 000 - 70 000 years ago, they encountered Neanderthals and Denisovans, two groups of ancient hominins that populated Europe and Asia at that time. We know that Modern Humans interbred with both Neanderthals and Denisovans since there is evidence of the presence of their DNA in genomes of Modern Humans of non-African origin.

Earlier this week, Gil Press of Forbes published a piece that explored the AI-related predictions from 120 professionals from various industries. I've taken the liberty of exploring the ten opinions that I feel are most relevant, interesting, and valuable to TDS and Medium readers. I've linked to Gil's piece above, so feel free to read all the other great foretellings from the soothsayers of today's AI world. But first, let's take a look at the future of AI from the eyes of some of the world's CEO's, VP's, Marketers, and Engineers. "With AI actually baked into the chips themselves, a whole new era of computing at the source is being empowered -- and we are only at the beginning. AI chips are already improving vehicles' abilities to process visual data more efficiently, paving the way for autonomous vehicles of the future. For smart cities, AI chips will assist with crucial tasks such as real-time traffic monitoring, locating missing persons, and finding stolen vehicles. For smart homes, chips will ensure more privacy and reliability by processing data at the source. Demand for these new technologies will set the stage for a variety of new applications and use cases, fueling the activity of next-generation products and refinement of product needs. A new age of AI chips means a new age of technology".