Do you believe the warnings from folks like Prof. Stephen Hawking, Elon Musk and others? Is AI the greatest tool humanity will ever create, or are we "summoning the demon"? To quote the head of AI at Singularity University, Neil Jacobstein, "It's not artificial intelligence I'm worried about, it's human stupidity." In a recent Abundance 360 webinar, I interviewed Bryan Johnson, the founder of a new company called Kernel which he seeded with $100 million. To quote Bryan, "It's not about AI vs. humans. In 2007, he founded Braintree, an online and mobile payments provider. In 2013, PayPal acquired Braintree for $800 million. In 2014, Bryan launched the OS Fund with $100 million of his personal capital to support inventors and scientists who aim to benefit humanity by rewriting the operating systems of life. His investments include endeavors to cure age-related diseases and radically extend healthy human life to 100 (Human Longevity Inc.), replicate the human visual cortex using ...

CAMBRIDGE, MA - 10 Nov 2016: IBM Watson Health (NYSE: IBM) and the Broad Institute of MIT and Harvard today announced a research initiative aimed at discovering the basis of cancer drug resistance. The five year, $50 million project will study thousands of drug resistant tumors and draw on Watson's computational and machine learning methods to help researchers understand how cancers become resistant to therapies. The anonymized data will be made available to the scientific community to catalyze research worldwide. IBM Watson Health and the Broad Institute bring the data prowess of Watson to study cancer drug resistance in a $50M research collaboration. The goal is to identify patterns that could reveal clues into one of the greatest medical mysteries of cancer.

The 7th Internet of @ThingsExpo will take place on June 6-8, 2017, at the Javits Center in New York City, New York. Chris Matthieu is the co-founder and CTO of Octoblu, a revolutionary real-time IoT platform recently acquired by Citrix. Octoblu connects things, systems, people and clouds to a global mesh network allowing users to automate and control design flows, processes and sensor data, and analyze/react to real-time events and messages as well as big data trends and anomalies. Prior to co-founding Octoblu, Chris was the founder of Nodester, an open-source Node.JS PaaS which was acquired by AppFog and the founder of Teleku, a communications-as-a-service cloud platform which was acquired by Voxeo. Earlier Chris served as the Tech Chair of SYS-CON's WebRTC Summit.

Creating aesthetically pleasing pieces of art, including music, has been a long-term goal for artificial intelligence research. Despite recent successes of long-short term memory (LSTM) recurrent neural networks (RNNs) in sequential learning, LSTM neural networks have not, by themselves, been able to generate natural-sounding music conforming to music theory. To transcend this inadequacy, we put forward a novel method for music composition that combines the LSTM with Grammars motivated by music theory. The main tenets of music theory are encoded as grammar argumented (GA) filters on the training data, such that the machine can be trained to generate music inheriting the naturalness of human-composed pieces from the original dataset while adhering to the rules of music theory. Unlike previous approaches, pitches and durations are encoded as one semantic entity, which we refer to as note-level encoding. This allows easy implementation of music theory grammars, as well as closer emulation of the thinking pattern of a musician. Although the GA rules are applied to the training data and never directly to the LSTM music generation, our machine still composes music that possess high incidences of diatonic scale notes, small pitch intervals and chords, in deference to music theory.

Traditionally, optimization of task allocation problems considered only the costs involved in the allocation. However, there has been in recent years more attention to cases where cost should not always be the sole consideration (Campbell et al. 2008). There are circumstances when other criteria need to be taken into account as well during the decision making process. Fairness has been considered as one of the important additional criteria in many application domains (Ogryczak et al. 2005, Gopinathan and Li 2011, Bertsimas et al. 2012). Although there is no common definition for the term, there are two fairness criteria that are often used in the literature: the Nash bargaining criterion and the Rawlsian maximin criterion. The former is based on Nash's four axioms of pareto optimality, independency of irrelevant alternatives, symmetry, and invariance to affine transformations or equivalent utility representations (Nash 1950). The latter is based on Rawls' two principles of justice (Rawls 1971). Rawls' maximin criterion maximizes the welfare level of the worst-off group member and has therefore been used in allocation problems (Jaffe 1981, Kumar and Kleinberg 2000).

Bagging is a device intended for reducing the prediction error of learning algorithms. In its simplest form, bagging draws bootstrap samples from the training sample, applies the learning algorithm to each bootstrap sample, and then averages the resulting prediction rules. We extend the definition of bagging from statistics to statistical functionals and study the von Mises expansion of bagged statistical functionals. We show that the expansion is related to the Efron-Stein ANOVA expansion of the raw (unbagged) functional. The basic observation is that a bagged functional is always smooth in the sense that the von Mises expansion exists and is finite of length 1 + resample size $M$. This holds even if the raw functional is rough or unstable. The resample size $M$ acts as a smoothing parameter, where a smaller $M$ means more smoothing.

We study the online estimation of the optimal policy of a Markov decision process (MDP). We propose a class of Stochastic Primal-Dual (SPD) methods which exploit the inherent minimax duality of Bellman equations. The SPD methods update a few coordinates of the value and policy estimates as a new state transition is observed. These methods use small storage and has low computational complexity per iteration.

This paper examines the problem of locating outlier columns in a large, otherwise low-rank matrix, in settings where {}{the data} are noisy, or where the overall matrix has missing elements. We propose a randomized two-step inference framework, and establish sufficient conditions on the required sample complexities under which these methods succeed (with high probability) in accurately locating the outliers for each task. Comprehensive numerical experimental results are provided to verify the theoretical bounds and demonstrate the computational efficiency of the proposed algorithm.

Group-Lasso (gLasso) identifies important explanatory factors in predicting the response variable by considering the grouping structure over input variables. However, most existing algorithms for gLasso are not scalable to deal with large-scale datasets, which are becoming a norm in many applications. In this paper, we present a divide-and-conquer based parallel algorithm (DC-gLasso) to scale up gLasso in the tasks of regression with grouping structures. DC-gLasso only needs two iterations to collect and aggregate the local estimates on subsets of the data, and is provably correct to recover the true model under certain conditions. We further extend it to deal with overlappings between groups. Empirical results on a wide range of synthetic and real-world datasets show that DC-gLasso can significantly improve the time efficiency without sacrificing regression accuracy.

Neuroimaging modalities such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) provide information about neurological functions in complementary spatiotemporal resolutions; therefore, fusion of these modalities is expected to provide better understanding of brain activity. In this paper, we jointly analyze fMRI and multi-channel EEG signals collected during an auditory oddball task with the goal of capturing brain activity patterns that differ between patients with schizophrenia and healthy controls. Rather than selecting a single electrode or matricizing the third-order tensor that can be naturally used to represent multi-channel EEG signals, we preserve the multi-way structure of EEG data and use a coupled matrix and tensor factorization (CMTF) model to jointly analyze fMRI and EEG signals. Our analysis reveals that (i) joint analysis of EEG and fMRI using a CMTF model can capture meaningful temporal and spatial signatures of patterns that behave differently in patients and controls, and (ii) these differences and the interpretability of the associated components increase by including multiple electrodes from frontal, motor and parietal areas, but not necessarily by including all electrodes in the analysis.