For children with speech and language disorders, early-childhood intervention can make a great difference in their later academic and social success. But many such children--one study estimates 60 percent--go undiagnosed until kindergarten or even later. Researchers at the Computer Science and Artificial Intelligence Laboratory at MIT and Massachusetts General Hospital's Institute of Health Professions hope to change that, with a computer system that can automatically screen young children for speech and language disorders and, potentially, even provide specific diagnoses. This week, at the Interspeech conference on speech processing, the researchers reported on an initial set of experiments with their system, which yielded promising results. "We're nowhere near finished with this work," says John Guttag, the Dugald C. Jackson Professor in Electrical Engineering and senior author on the new paper.

Sure, dolphins use sonar, whiz through the ocean at incredible speeds, and battle sharks. Last week, a study published in Russia's St. Petersburg Polytechnical University Journal: Physics and Mathematics claimed to have recorded two dolphins doing just that. Two Black Sea bottlenose dolphins, named Yasha and Yana, exchanged a series of vocal pulses that resembled "a conversation between two people," wrote the study's author, Vyacheslav Ryabov, a senior researcher at the T. I. Vyazemsky Karadag Scientific Station. What's more, Yana and Yasha were exceedingly polite, listening to one another at turns without interrupting. "As this language exhibits all the design features present in the human spoken language, this indicates a high level of intelligence and consciousness in dolphins, and their language can be ostensibly considered a highly developed spoken language, akin to the human language," Ryabov wrote.

Artificial intelligence and big data are shaping up to have the biggest impact on the healthcare industry, and most of that money will be coming from venture capital funds, according to a new survey of digital health executives and investors. Silicon Valley Bank surveyed 122 founders, executives and investors in health technology, asking about the biggest opportunities and threats for the industry in the next year. The survey was conducted during Silicon Valley Bank's HealthTech NYC event and was attended by such companies and investment firms as Celmatix, Aledad and Andreesen Horowitz. Almost half of respondents say big data, followed by artificial intelligence (35 percent) are the most promising technologies in terms of impact on investment. "Big data has been integral to our work at Celmatix. It has empowered physicians to be able to counsel women about their chances of having a baby, based on their relevant personal metrics, and not just their age," Dr. Piraye Yurttas Beim, Chief Executive Officer of Celmatix said in a statement.

The live-action adaptation of "Ghost in the Shell" starring Scarlett Johansson as the cyberpunk-fighting cyborg just dropped its first bit of footage. No doubt manga, anime and genre fans everywhere will have something to say about this brief glimpse of the new dystopia. The first footage from the feature film dropped during the finale of "Mr. Robot," because if a show about hackers taking down evil corporations entertains you, wait until you breathe in the cyberpunk horror that is "Ghost in the Shell." The movie takes place in a fictional, futuristic Japanese city and follows "The Major" (Johansson) and the members of a covert task force within the Japanese National Public Safety Commission made up of former detectives and military operatives.

In this interview, we chat with Wizeline's head of growth, Matt Pasienski. As a PhD-educated data scientist and chatbot enthusiast, Matt has lots of interesting opinions on the emerging role of artificial intelligence in the worlds of business and social media. So if you're curious about any of these topics, don't be afraid to hit him up on Twitter. To learn more about Matt and our team's work on chatbots, visit Wizeline's solutions page. Adam: Everyone, welcome to the program, I'm Adam, and today we're being joined by Matt Pasienski who runs the services team at Wizeline.

A team of researchers has found a new way to detect dangerous strains of bacteria, potentially preventing outbreaks of food poisoning. The team developed a method that utilizes machine learning and tested it with isolates of Escherichia coli strains. The details are in a paper that was just published in the journal Proceedings of the National Academy of Sciences. Most strains of Escherichia coli are harmless and naturally found in the human body. There are pathogenic strains, however, and they are a rising health concern.

Fragmentation methods such as the many-body expansion (MBE) are a common strategy to model large systems by partitioning energies into a hierarchy of decreasingly significant contributions. The number of fragments required for chemical accuracy is still prohibitively expensive for ab-initio MBE to compete with force field approximations for applications beyond single-point energies. Alongside the MBE, empirical models of ab-initio potential energy surfaces have improved, especially non-linear models based on neural networks (NN) which can reproduce ab-initio potential energy surfaces rapidly and accurately. Although they are fast, NNs suffer from their own curse of dimensionality; they must be trained on a representative sample of chemical space. In this paper we examine the synergy of the MBE and NN's, and explore their complementarity. The MBE offers a systematic way to treat systems of arbitrary size and intelligently sample chemical space. NN's reduce, by a factor in excess of $10^6$ the computational overhead of the MBE and reproduce the accuracy of ab-initio calculations without specialized force fields. We show they are remarkably general, providing comparable accuracy with drastically different chemical embeddings. To assess this we test a new chemical embedding which can be inverted to predict molecules with desired properties.

We propose an extension to Hawkes processes by treating the levels of self-excitation as a stochastic differential equation. Our new point process allows better approximation in application domains where events and intensities accelerate each other with correlated levels of contagion. We generalize a recent algorithm for simulating draws from Hawkes processes whose levels of excitation are stochastic processes, and propose a hybrid Markov chain Monte Carlo approach for model fitting. Our sampling procedure scales linearly with the number of required events and does not require stationarity of the point process. A modular inference procedure consisting of a combination between Gibbs and Metropolis Hastings steps is put forward. We recover expectation maximization as a special case. Our general approach is illustrated for contagion following geometric Brownian motion and exponential Langevin dynamics.

Independent component analysis (ICA) is a method for recovering statistically independent signals from observations of unknown linear combinations of the sources. Some of the most accurate ICA decomposition methods require searching for the inverse transformation which minimizes different approximations of the Mutual Information, a measure of statistical independence of random vectors. Two such approximations are the Kernel Generalized Variance or the Kernel Canonical Correlation which has been shown to reach the highest performance of ICA methods. However, the computational effort necessary just for computing these measures is cubic in the sample size. Hence, optimizing them becomes even more computationally demanding, in terms of both space and time. Here, we propose a couple of alternative novel measures based on randomized features of the samples - the Randomized Generalized Variance and the Randomized Canonical Correlation. The computational complexity of calculating the proposed alternatives is linear in the sample size and provide a controllable approximation of their Kernel-based non-random versions. We also show that optimization of the proposed statistical properties yields a comparable separation error at an order of magnitude faster compared to Kernel-based measures.

Bibliographic analysis considers author's research areas, the citation network and paper content among other things. In this paper, we combine these three in a topic model that produces a bibliographic model of authors, topics and documents using a non-parametric extension of a combination of the Poisson mixed-topic link model and the author-topic model. We propose a novel and efficient inference algorithm for the model to explore subsets of research publications from CiteSeerX. Our model demonstrates improved performance in both model fitting and a clustering task compared to several baselines.