This project looks into the application of Machine Learning (ML) techniques in the prediction of Immunogenicity (Categorical; Positive or Negative) based on a peptide and its associated amino acid properties. This study uses peptide data from the Immune Epitode Database (IEDB). The R package "Peptides" has been used to compute the amino acid properties and mashup with peptide data to enable the use of ML algorithms for immunogenicity analysis, particularly, the algorithms that are more efficient with numeric and categorical data instead of string sequence. Tensorflow is an open source software library ML that provides linear regression and classification algorithms (open sourced by Google in Nov 2015) for multi-dimensional arrays (aka "Tensors"). K-fold cross-validation as well as hold-out of test data was used to train and test the generated models.

Artificial Lawyer caught up with Canadian legal consultant and futurist Jordan Furlong of Law Twenty One and asked him about his perspective on what opportunities and challenges AI faced in the legal sector. Do you see a strategic advantage for the law firms that embrace AI? If yes, how would that advantage manifest itself? We should probably begin by creating a working definition of'AI', which is a term applied so broadly in the legal sphere that, as Ryan McClead has pointed out, it might as well just be written as'magic'. Michael Mills of Neota Logic has suggested instead the term'cognitive technologies', which encompasses a wide range of tech applications including machine learning, natural language processing, and expert systems.

This A.I. Chatbot Is Scaring Lawyers and Fighting Homelessness

Mphasis,a leading IT services and solutions provider, today launched Digital Acceleration Program – Sparks;. Sparks accelerates digital transformation for organizations without them having to invest in future proof technology or research labs by constantly bringing in industry disruptors revolutionizing the global banking and insurance sectors. These partnerships are a catalyst in verticalyzing cutting-edge digital technology to help Mphasis; customers pragmatically deliver end-to-end solutions. Sparks currently introduces two vital elements Customer Experience; and Data, Information Intelligence; aspects of digital. Sparks evolution roadmap consists of bringing in three other elements: Commerce, Operations, and Architecture Technology Transformation.

Mphasis, a leading IT services and solutions provider, today launched'Digital Acceleration Program - Sparks'. Sparks accelerates digital transformation for organizations without them having to invest in future proof technology or research labs by constantly bringing in industry disruptors revolutionizing the global banking and insurance sectors. These partnerships are a catalyst in verticalyzing cutting-edge digital technology to help Mphasis' customers pragmatically deliver end-to-end solutions. Sparks currently introduces two vital elements'Customer Experience' and'Data, Information & Intelligence' aspects of digital. Sparks evolution roadmap consists of bringing in three other elements: Commerce, Operations, and Architecture & Technology Transformation.

Ford announced a due date for its self-driving future. Stepping on the gas could soon be a thing of the past -- as well as hitting the brakes, or getting behind the wheel altogether. Ford has announced its plan to develop fully autonomous cars by 2021. The company sees fully autonomous cars as having "as big of an impact on society" as its moving assembly lines did a century ago, Ford President and CEO Mark Fields said in a press conference yesterday. "A driver is not going to be required."

The race for who will come to the market with an auto-piloted car is heating up again. Ford has announced that it will have a fleet of autonomous cars on the road by 2021. The driverless vehicles will be available for ride sharing. The company said it will more than double its team devoted to developing autonomous driving, and invest even more money in its Silicon Valley campus. Ford's CEO Mark Fields has said that the company will triple its investment in the technology which includes currently available help with parking and avoiding traffic jams.

We consider a problem of learning kernels for use in SVM classification in the multi-task and lifelong scenarios and provide generalization bounds on the error of a large margin classifier. Our results show that, under mild conditions on the family of kernels used for learning, solving several related tasks simultaneously is beneficial over single task learning. In particular, as the number of observed tasks grows, assuming that in the considered family of kernels there exists one that yields low approximation error on all tasks, the overhead associated with learning such a kernel vanishes and the complexity converges to that of learning when this good kernel is given to the learner.

Probabilistic techniques are central to data analysis, but different approaches can be difficult to apply, combine, and compare. This paper introduces composable generative population models (CGPMs), a computational abstraction that extends directed graphical models and can be used to describe and compose a broad class of probabilistic data analysis techniques. Examples include hierarchical Bayesian models, multivariate kernel methods, discriminative machine learning, clustering algorithms, dimensionality reduction, and arbitrary probabilistic programs. We also demonstrate the integration of CGPMs into BayesDB, a probabilistic programming platform that can express data analysis tasks using a modeling language and a structured query language. The practical value is illustrated in two ways. First, CGPMs are used in an analysis that identifies satellite data records which probably violate Kepler's Third Law, by composing causal probabilistic programs with non-parametric Bayes in under 50 lines of probabilistic code. Second, for several representative data analysis tasks, we report on lines of code and accuracy measurements of various CGPMs, plus comparisons with standard baseline solutions from Python and MATLAB libraries.

The likelihood function of a finite mixture model is a non-convex function with multiple local maxima and commonly used iterative algorithms such as EM will converge to different solutions depending on initial conditions. In this paper we ask: is it possible to assess how far we are from the global maximum of the likelihood? Since the likelihood of a finite mixture model can grow unboundedly by centering a Gaussian on a single datapoint and shrinking the covariance, we constrain the problem by assuming that the parameters of the individual models are members of a large discrete set (e.g. estimating a mixture of two Gaussians where the means and variances of both Gaussians are members of a set of a million possible means and variances). For this setting we show that a simple upper bound on the likelihood can be computed using convex optimization and we analyze conditions under which the bound is guaranteed to be tight. This bound can then be used to assess the quality of solutions found by EM (where the final result is projected on the discrete set) or any other mixture estimation algorithm. For any dataset our method allows us to find a finite mixture model together with a dataset-specific bound on how far the likelihood of this mixture is from the global optimum of the likelihood