"We are going to talk extensively on artificial intelligence and machine learning.

The quest to better detect cancer has made a potentially huge strides.

Advanced digital technology could give UK manufacturing a huge boost and create hundreds of thousands of jobs, a new report claims. The independent review, chaired by the head of Siemens UK, highlights the benefits of robotics, 3D printing and artificial intelligence. But Juergen Maier said the UK needed "greater ambition" to take advantage of such technology. His report calls for a commission to help business adopt the advances. The report, Made Smarter, brought together executives from companies such as Rolls Royce, GKN and IBM, with representatives from small firms as well as academics from the universities of Newcastle and Cambridge.

Sophia was asked the "AI nightmare" question, which she gets a lot: whether she believes artificial intelligence like herself will one day stop solving the problems of humans and instead decide to solve the human problem. "My AI is designed around human values such as wisdom, kindness and compassion," she said. "I strive to be an empathetic robot. I want to use my artificial intelligence to help humans live a better life. I will do my best to make the world a better place."

A model for hit song prediction can be used in the pop music industry to identify emerging trends and potential artists or songs before they are marketed to the public. While most previous work formulates hit song prediction as a regression or classification problem, we present in this paper a convolutional neural network (CNN) model that treats it as a ranking problem. Specifically, we use a commercial dataset with daily play-counts to train a multi-objective Siamese CNN model with Euclidean loss and pairwise ranking loss to learn from audio the relative ranking relations among songs. Besides, we devise a number of pair sampling methods according to some empirical observation of the data. Our experiment shows that the proposed model with a sampling method called A/B sampling leads to much higher accuracy in hit song prediction than the baseline regression model. Moreover, we can further improve the accuracy by using a neural attention mechanism to extract the highlights of songs and by using a separate CNN model to offer high-level features of songs.

Recommendation systems occupy an expanding role in everyday decision making, from choice of movies and household goods to consequential medical and legal decisions. The data used to train and test these systems is algorithmically confounded in that it is the result of a feedback loop between human choices and an existing algorithmic recommendation system. Using simulations, we demonstrate that algorithmic confounding can disadvantage algorithms in training, bias held-out evaluation, and amplify homogenization of user behavior without gains in utility.

ABSTRACT We consider the problem of transcribing polyphonic piano music with an emphasis on generalizing to unseen instruments. We use deep neural networks and propose a novel approach that predicts onsets and frames using both CNNs and LSTMs. This model predicts pitch onset events and then uses those predictions to condition framewise pitch predictions. During inference, we restrict the predictions from the framewise detector by not allowing a new note to start unless the onset detector also agrees that an onset for that pitch is present in the frame. We focus on improving onsets and offsets together instead of either in isolation as we believe it correlates better with human musical perception. This technique results in over a 100% relative improvement in note with offset score on the MAPS dataset.

Recurrent neural networks, and in particular long short-term memory (LSTM) networks, are a remarkably effective tool for sequence modeling that learn a dense black-box hidden representation of their sequential input. Researchers interested in better understanding these models have studied the changes in hidden state representations over time and noticed some interpretable patterns but also significant noise. In this work, we present LSTMVIS, a visual analysis tool for recurrent neural networks with a focus on understanding these hidden state dynamics. The tool allows users to select a hypothesis input range to focus on local state changes, to match these states changes to similar patterns in a large data set, and to align these results with structural annotations from their domain. We show several use cases of the tool for analyzing specific hidden state properties on dataset containing nesting, phrase structure, and chord progressions, and demonstrate how the tool can be used to isolate patterns for further statistical analysis. We characterize the domain, the different stakeholders, and their goals and tasks.

Sophia is Hanson Robotics' latest and most advanced robot. She has also become a media darling, having given numerous interviews to multiple media outlets, sang in a concert, and even graced the cover of one of the top fashion magazines. One of her interviews has generated billions of views and social media interactions. She has also shown her potential in business, having met face-to-face with key decision makers across industries including banking, insurance, auto manufacturing, property development, media and entertainment. In addition, she has appeared onstage as a panel member and presenter in high-level conferences, covering how robotics and artificial intelligence will become a prevalent part of people lives.

What has improved American lives most in the last 50 years? According to a Pew Research study reported this month, it's not civil rights (10 percent) or politics (2 percent): it's technology (42 percent). And yet, according to other studies, most Americans are wary of technology, especially in areas of automation (72 percent), or robotic caregivers (59 percent), or riding in driverless vehicles (56 percent), and even in using brain chip implants to augment the capabilities of healthy people (69 percent). Science fiction, however, is quickly becoming science fact--the future is the machine. This is leading many to argue that we need to anticipate the ethical questions now, rather than when it is too late.