One day I was sitting at the table with my sister and we were showing each other songs we like on Spotify. I saw on her phone she was subscribed to one of the same Spotify-created playlists that I subscribe to. I knew the song I wanted to show her was on that playlist, so I asked if I could look for it on her phone and looked through that playlist I was subscribed to. I scrolled through the entire playlist, some 200, songs, and I couldn't find it. I thought, then I went to the search bar and searched the name of the song… No results.

The top opportunities in the artificial intelligence (AI) in drug discovery market segmented by technology will arise in deep learning segment, …

I may be many things, but I'm not a rapper. I discover this when I'm asked to freestyle a few verses on a visit to London's Abbey Road recording studios. Immediately lines from famous rappers flood into my head – some classic Biggie, a few Young Thug yelps, the theme to The Fresh Prince of Bel-Air – but I've got to think up something original. Out of desperation, I decide to rap about my morning routine. Adopting a slow pace and simple rhyme scheme that even the Sugarhill Gang would disdain, I begin: "I wake up at seven and I brush my teeth."

Barry Smith, PhD, SUNY Distinguished Professor in the Department of Philosophy in UB's College of Arts and Sciences, and Jobst Landgrebe, PhD, founder of Cognotekt, a German AI company, have co-authored "Why Machines Will Never Rule the World: Artificial Intelligence without Fear." Their book presents a powerful argument against the possibility of engineering machines that can surpass human intelligence. Machine learning and all other working software applications the proud accomplishments of those involved in AI research are for Smith and Landgrebe far from anything resembling the capacity of humans. Further, they argue that any incremental progress that's unfolding in the field of AI research will in practical terms bring it no closer to the full functioning possibility of the human brain. There cannot be a machine will, they say.

Could machine learning and artificial intelligence be the next big thing for customer relationship management (CRM) software? The experts believe so since these technologies can help businesses get more value from their data. As companies continue to grow, so must the tech stack used to manage them. This is especially true for CRM solutions, which need to keep up with the ever-changing demands of customers and their interactions with businesses. Fortunately, machine learning and AI are enabling CRM to do just that.

Automatic identification of hateful and abusive content is vital in combating the spread of harmful online content and its damaging effects. Most existing works evaluate models by examining the generalization error on train-test splits on hate speech datasets. These datasets often differ in their definitions and labeling criteria, leading to poor model performance when predicting across new domains and datasets. In this work, we propose a new Multi-task Learning (MTL) pipeline that utilizes MTL to train simultaneously across multiple hate speech datasets to construct a more encompassing classification model. We simulate evaluation on new previously unseen datasets by adopting a leave-one-out scheme in which we omit a target dataset from training and jointly train on the other datasets. Our results consistently outperform a large sample of existing work. We show strong results when examining generalization error in train-test splits and substantial improvements when predicting on previously unseen datasets. Furthermore, we assemble a novel dataset, dubbed PubFigs, focusing on the problematic speech of American Public Political Figures. We automatically detect problematic speech in the $305,235$ tweets in PubFigs, and we uncover insights into the posting behaviors of public figures.

Building a Recommendation System is not a trivial task and it comes with its own set of problems and challenges. This article is an effort to provide readers a deeper insight into building recommendation systems. A Recommendation system is an application of machine learning that provides recommendations to users on what they might like based on their historical preferences. It can be further defined as a system that produces individualized recommendations as output or has the effect of guiding the user in a personalized way to interesting objects in a larger space of possible options. Collaborative methods for Recommendation systems are methods that are based solely on the past interactions recorded between users and items in order to produce new recommendations. These interactions are stored in the so-called "user-item interactions matrix".

We are a community dedicated to art produced with the help of artificial neural networks, which are themselves inspired by the human brain. Advances in the machine learning sub field of artificial intelligence brought on by the information age have made it possible for machines to create art that rivals that of what a human being can do. We here at /r/DeepDream mainly focus on applications of deep learning which itself is a sub field of machine learning. As the largest online AI art community, we routinely push the bounds of technology in the pursuit of better-looking artwork. The DeepDream wiki is available here.

In the 1960s, an unprecedented computer program called Eliza attempted to simulate the experience of speaking to a therapist. In one exchange, captured in a research paper at the time, a person revealed that her boyfriend had described her as "depressed much of the time." Eliza's response: "I am sorry to hear you are depressed." Eliza, which is widely characterized as the first chatbot, wasn't as versatile as similar services today. The program, which relied on natural language understanding, reacted to key words and then essentially punted the dialogue back to the user.

Content is always king, if you were to ask any seasoned media professional or publishing company. Whether the content is in the form of a movie, music album or digital news article, it has to be compelling to make the consumer want to spend time on it. At the same time, many companies are also realising that great content has to be distributed as widely as possible, and be personalised to the right audiences to capture their attention in today's competitive, on-demand world. To make their content travel as far as possible and gain as many readers or viewers as possible, these companies could do with a helping hand from artificial intelligence (AI). AsiaOne recently partnered AI company Neural Lab to create an automated personalised article recommender based on smart AI algorithms.