We'll have a main talk (30 mins) and 3 excellent lightning talks about the machine learning algorithm that usually achieves the best accuracy on structured/tabular data (e.g. in industry/business applications or in Kaggle competitions): Abstract: With all the hype about deep learning and "AI", it is not well publicized that for structured/tabular data widely encountered in business applications it is actually another machine learning algorithm, the gradient boosting machine (GBM) that most often achieves the highest accuracy in supervised learning tasks. In this talk we'll review some of the main GBM implementations available as R and Python packages such as xgboost, h2o, lightgbm etc, we'll discuss some of their main features and characteristics, and we'll see how tuning GBMs and creating ensembles of the best models can achieve the best prediction accuracy for many business problems. Bio: Szilard studied Physics in the 90s and obtained a PhD by using statistical methods to analyze the risk of financial portfolios. He worked in finance, then more than a decade ago moved to become the Chief Scientist of a tech company in Santa Monica doing everything data (analysis, modeling, data visualization, machine learning, data infrastructure etc). He is the founder/organizer of several meetups in the Los Angeles area (R, data science etc) and the data science community website datascience.la.

Artificial Intelligence has a bad rap from intelligent folk like Stephen Hawking and Elon Musk. But a new Discovery Channel documentary airing today attempts to find out how AI is already helping people. From driverless cars to innovations in health care and education, the two-hour documentary special highlights some of the developments and questions in artificial intelligence. It's a neat introduction to the subject explained by experts in the field and illustrated by examples from the real world. "Most people don't know very much about AI and what they do know comes largely from more alarmist stories," says Charlie Foley, creator and producer of the TV special.

Many datasets can be represented as graphs, being the graph explicitely embedded in data (e.g., the friendship relation of a social network) or built through some suitable similarity measure between data items (e.g., the number of papers coauthored by two researchers). Such graphs often exhibit a complex, multi-scale community structure where each node is invoved in many groups of nodes, so-called communities, of different sizes. One of the most popular graph clustering algorithm is known as Louvain in name of the university of its inventors [Blondel et al., 2008]. It is based on the greedy maximization of the modularity, a classical objective function introduced in [Newman and Girvan, 2004]. The Louvain algorithm is fast, memory-efficient, and provides meaningful clusters in practice. It does not enable an analysis of the graph at different scales, however [Fortunato and Barthelemy, 2007, Lancichinetti and Fortunato, 2011].

Connectionist sequence models (e.g., RNNs) applied to musical sequences suffer from two known problems: First, they have strictly "absolute pitch perception". Therefore, they fail to generalize over musical concepts which are commonly perceived in terms of relative distances between pitches (e.g., melodies, scale types, modes, cadences, or chord types). Second, they fall short of capturing the concepts of repetition and musical form. In this paper we introduce the recurrent gated autoencoder (RGAE), a recurrent neural network which learns and operates on interval representations of musical sequences. The relative pitch modeling increases generalization and reduces sparsity in the input data. Furthermore, it can learn sequences of copy-and-shift operations (i.e. chromatically transposed copies of musical fragments)---a promising capability for learning musical repetition structure. We show that the RGAE improves the state of the art for general connectionist sequence models in learning to predict monophonic melodies, and that ensembles of relative and absolute music processing models improve the results appreciably. Furthermore, we show that the relative pitch processing of the RGAE naturally facilitates the learning and the generation of sequences of copy-and-shift operations, wherefore the RGAE greatly outperforms a common absolute pitch recurrent neural network on this task.

Understand the principles of how human intelligence works, and apply those principles to developing an artificial intelligence which accurately mimics our own. This can be achieved by uniting the scientific community, in an environment free of corporate pressures, to bring about technologies which will improve the human experience rather than compete against it.

C-3PO and R2-D2 are an odd couple in the Star Wars universe. C-3PO is a cowardly droid who obeys pre-defined protocols and routine tasks, while R2-D2 is a curious and adventurous robot who learns from previous problems, uses logical thinking and larger concepts to solve new problems. But together they do things they could not do alone. Similarly, RPA (Robotic Process Automation) and Advanced Analytics are an odd but very complementary combination of new business technologies. Like the diligent but unimaginative C-3PO, RPA follows precise rules to execute repetitive business processes; and like the curious and adaptable R2-D2, Advanced Analytics learns to make complex judgments when faced with new situations.

Is the newest droid in the "Star Wars" universe the future of modern robotics? In the recently released film "Solo: A Star Wars Story," the droid L3-37, also known as L3 or Elthree, showcased a unique set of traits among "Star Wars" robots. The intelligent pilot droid is always changing, improving and repairing itself with found scraps from other bots. L3 is also one of the first bots in the "Star Wars" franchise to bring feminine programming to a major role. L3 is a hodgepodge of various droids and astromechs, which are robots typically used for repairs aboard starships in "Star Wars."

This is my latest short film sharing some thoughts on my #1 speaking topic: humans versus/with machines, artificial intelligence and the future of humanity in a world where machines can hear, see, speak, learn and'think'. On the one hand, artificial intelligence (AI) clearly has the capacity to improve our lives in pretty much every aspect, from energy to medical to smart cities; on the other end it could fundamentally change who we are as humans, and what we think of as'human'. It could be a great destroyer of jobs but it could also free as from unsatisfying routines. But if machines become truly'intelligent' what will be left for us to do... you may ask. I think it will all come down to what I call DIGITAL ETHICS i.e. how will we use technology to our collective human benefit (i.e.

Humans have always been thrilled with the concept of human-like robots and Artificial Intelligence (A.I.). Hollywood movies and science fiction have perhaps inspired several scientists to start working towards this direction. Although the AI bubble has burst many times, significant developments and breakthroughs are now renewing public interest in this field. In 2017, Gartner placed general AI at the stage of early adoption in its hype cycle. It also placed deep learning and machine learning technologies at the peak of this hype cycle. It is important to realize that AI is an umbrella term for several interlinked technologies.

To death and taxes, Benjamin Franklin's binary list of life's certainties, add the expectation that this six-note sequence: Although we ponder ways to avoid or evade Franklin's list of unavoidable events, we generally accept this more benign certainty as immutable. The penultimate note of the tune generates such strong and specific anticipation that you are likely finding it difficult to continue reading without resolving the sequence. That anxious pause is key to composition and music's power. It creates a sense of prophetic certainty that allows musicians to play against expectations by thwarting the expected. The controlled manipulation of certainty and likelihood lurks behind those magical moments in which music has caused a shiver or a tear to fall. By infusing uncertainty or surprise into the mix, musicians literally play on our emotions.