The world is ending and it's all because of artificial intelligence (AI). At least, if we're to believe the endless stream of stories from futurists predicting the collapse of the global economy because AI stole our media relations strategy jobs. These fears are not unfounded. Rapid leaps in AI are putting entire professions and industries on the chopping block. Travel agents are all but extinct thanks to flight-tracking algorithms; IBM's Watson is gunning to displace accountants.

We're so excited to announce our first guest: Ellen Broad. Ellen is a renowned expert in the field of data ethics and AI. She has a wealth of knowledge and experience, having worked around the world advising governments and corporates on data sharing, open data, strategy and licensing. Ellen's writing has appeared in the New Scientist, the Guardian and a range of civil service and tech publications. She has spoken about AI and data to ABC Radio National's'Big Ideas' and'Future Tense' programmes, and at SXSW in the US Ellen's recently published first book Made by Humans: The AI Condition, analyses our role and responsibilities in automation and how machine learning is affecting our decisions.

Artificial Intelligence in Drug Discovery Market provides pin point analysis of changing competition dynamics and keeps you ahead of competitors.

Many works have been proposed in the literature to capture the dynamics of diffusion in networks. While some of them define graphical markovian models to extract temporal relationships between node infections in networks, others consider diffusion episodes as sequences of infections via recurrent neural models. In this paper we propose a model at the crossroads of these two extremes, which embeds the history of diffusion in infected nodes as hidden continuous states. Depending on the trajectory followed by the content before reaching a given node, the distribution of influence probabilities may vary. However, content trajectories are usually hidden in the data, which induces challenging learning problems. We propose a topological recurrent neural model which exhibits good experimental performances for diffusion modelling and prediction.

We contribute a pop-song automation framework for lead melody generation and accompaniment arrangement. The framework reflects the major procedures of human music composition, generating both lead melody and piano accompaniment by a unified strategy. Specifically, we take chord progression as an input and propose three models to generate a structured melody with piano accompaniment textures. First, the harmony alternation model transforms a raw input chord progression to an altered one to better fit the specified music style. Second, the melody generation model generates the lead melody and other voices (melody lines) of the accompaniment using seasonal ARMA (Autoregressive Moving Average) processes. Third, the melody integration model integrates melody lines (voices) together as the final piano accompaniment. We evaluate the proposed framework using subjective listening tests. Experimental results show that the generated melodies are rated significantly higher than the ones generated by bi-directional LSTM, and our accompaniment arrangement result is comparable with a state-of-the-art commercial software, Band in a Box.

We live in a world flooded by information. It's harder and harder for us to keep track of it or to manually curate it for others; luckily, modern data science can sort through the vast amounts of information and surface those items that are relevant to us. Machine-learning algorithms rely on user knowledge and patterns observed in the data to make inferences and suggestions about what we may like or be interested in. With machine-learning technologies becoming more and more accessible to developers, there's a push for companies to take advantage of these algorithms to improve their products and their users' experience. Unfortunately, these algorithms are usually not transparent to the end users. People are not sure which of their actions are taken into account by these algorithms, and their outputs are not always easy to make sense of.

Researchers at Carnegie Mellon University have devised a way to automatically transform the content of one video into the style of another, making it possible to transfer the facial expressions of comedian John Oliver to those of a cartoon character, or to make a daffodil bloom in much the same way a hibiscus would. Because the data-driven method does not require human intervention, it can rapidly transform large amounts of video, making it a boon to movie production. It can also be used to convert black-and-white films to color and to create content for virtual reality experiences. "I think there are a lot of stories to be told," said Aayush Bansal, a Ph.D. student in CMU's Robotics Institute. Film production was his primary motivation in helping devise the method, he explained, enabling movies to be produced more quickly and cheaply.

Being with all of you, if only virtually, is always a happy place to be. One of the albums I can't stop listening to this year is Both Directions at Once: The Lost Album. Recorded in 1963 by sublime saxophonist John Coltrane during his "classic quartet" period, the original master tapes were lost or destroyed by the Impulse! This year, the 55-year-old project was unearthed from his family's surviving reference copy. Both Directions at Once occasionally veers into the superlative--it provides a glimpse into the tension between rehearsal process and commercial artifact that informed Coltrane's music in the aftermath of his 1961 juggernaut My Favorite Things--but it's hardly the jazz musician's most transcendent work.

This is a sequel to the post I wrote over a year ago about some basic concepts of machine learning. Sometimes sequels are better like Terminator 2 or Wrath of Kahn, but you can't enjoy them unless you've see the first movie. Go read that post and come back. There are many concepts in machine learning that are important to understand in order to be in the know. More importantly, if you're going to implement AI, sell AI, integrate AI, or write about AI, you might want to brush up on these core, yet advanced, concepts to have a good, strong foundation with which to start from.