In the Google paper, the authors enumerate many risk factors, design patterns, and anti-patterns to needs to be taken into consideration in an architecture. These include design patterns such as: boundary erosion, entanglement, hidden feedback loops, undeclared consumers, data dependencies and changes in the external world. By contrast, Deep Learning systems (applies equally to machine learning), code is created from training data. A recent paper from the folks at Berkeley are exploring the requirements for building these new kinds of systems (see: "Real-Time Machine Learning: The Missing Pieces").

As chief data scientist and senior vice president at American International Group Inc., Dalal is now applying technologies like computer vision, natural language processing and sensors to probabilistic risk analysis. The discipline of risk analysis that underlies insurance underwriting has historically relied on humans asking the right questions, but machine learning excels at letting the data determine what questions to ask. Machines can, and that's why Dalal sees so much promise in algorithms that find patterns humans can't. In the same way that a more complete correlation of temperature to o-ring failure might have prevented the Challenger disaster, machine learning works best when "we're not looking for the obvious risks.

The team's long-term vision for AI is ambitious: reproduce what's going on in an actual human conversation. There's a much bigger gap to bridge between AI and the human brain, and that's cognition: "the mental faculty of knowing, which includes perceiving, recognizing, conceiving, judging, reasoning, and imagining" In all fairness, today's AI does very little of that. For example, neural networks do not perceive, judge, or recognize a face. And whatever makes us happy is the product of some arbitrary personal criteria -- an expression of our personalities that little has to do with achieving the best possible outcome.

One of the more interesting goals in neuroscience is to reconstruct perceived images by analyzing brain scans. So an important goal is to find better ways to crunch the data from fMRI scans and so produce more accurate brain-image reconstructions. The result is a much better way to reconstruct the way a brain perceives images. "Extensive experimental comparisons demonstrate that our approach can reconstruct visual images from fMRI measurements more accurately," say Changde and co. That's interesting work with significant implications.

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, decision trees, ensembles, correlation, outliers, regression, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, time series, cross-validation, model fitting, dataviz, AI and many more. To keep receiving these articles, sign up on DSC. TensorFlow is an open source software library for machine learning across a range of tasks, and developed by Google to meet their needs for systems capable of building and training neural networks to detect and decipher patterns and correlations, analogous to the learning and reasoning which humans use (Source: Wikipedia.)

The fact that I use a rather than a dot symbol to represents the points, helps: some points are so close to each other that if you represent points with dots, you won't visually see the double points (in our example, double points could correspond to double star systems - and these very small-scale point interactions are part of what makes the distribution non-random in two of our charts). True randomness is the realization of a Poisson stochastic process, and we need to use metrics that uniquely characterizes a Poisson process to check whether a point distribution is truly random or not. But we can use Monte-Carlo simulations to address this issue: simulate a random process, compute the distribution of distances (with confidence intervals) based on thousands of simulations, and compare with distances computed on your data. So you need to pick up 10,000 points randomly, compute distances, and compare with equivalent computations based on simulated data.

Even as an IT generalist, it pays to at least get comfortable with the matrix of machine learning outcomes, expressed with quadrants for the counts of true positives, true negatives, false positives (items falsely identified as positive) and false negatives (positives that were missed). For example, overall accuracy is usually defined as the number of instances that were truly labeled (true positives plus true negatives) divided by the total instances. If you want to know how many of the actual positive instances you are identifying, sensitivity (or recall) is the number of true positives found divided by the total number of actual positives (true positives plus false negatives). And often precision is important too, which is the number of true positives divided by all items labeled positive (true positives plus false positives).

Even as an IT generalist, it pays to at least get comfortable with the matrix of machine learning outcomes, expressed with quadrants for the counts of true positives, true negatives, false positives (items falsely identified as positive) and false negatives (positives that were missed). For example, overall accuracy is usually defined as the number of instances that were truly labeled (true positives plus true negatives) divided by the total instances. If you want to know how many of the actual positive instances you are identifying, sensitivity (or recall) is the number of true positives found divided by the total number of actual positives (true positives plus false negatives). And often precision is important too, which is the number of true positives divided by all items labeled positive (true positives plus false positives).

Since linear regression gives output as continuous values, so in such case we use mean squared error metric to evaluate the model performance. Suppose that you have a dataset D1 and you design a linear regression model of degree 3 polynomial and you found that the training and testing error is "0" or in another terms it perfectly fits the data. A) There are high chances that degree 4 polynomial will over fit the data B) There are high chances that degree 4 polynomial will under fit the data C) Can't say D) None of these Since is more degree 4 will be more complex(overfit the data) than the degree 3 model so it will again perfectly fit the data. A) It is high chances that degree 2 polynomial will over fit the data B) It is high chances that degree 2 polynomial will under fit the data C) Can't say D) None of these If a degree 3 polynomial fits the data perfectly, it's highly likely that a simpler model(degree 2 polynomial) might under fit the data.

This is a known pain point with traditional anomaly detection systems, and Moogsoft promises to deliver highly actionable "situations" (clusters of related alerts). Netuitive calls itself a "full-stack monitoring solution built on a machine learning platform, designed with DevOps teams and modern infrastructure application environments in mind." In addition to standard monitoring features (for example, metrics, dashboards, and alerts), Netuitive offers an anomaly detection system based on advanced machine learning algorithms. Anodot defines itself as a "real-time analytics and automated anomaly detection system that discovers outliers in vast amounts of time series data and turns them into valuable business insights" -- in other words, machine learning-based anomaly detection for business intelligence.