Consider TPOT your Data Science Assistant. TPOT is a Python tool that automatically creates and optimizes machine learning pipelines using genetic programming. TPOT will automate the most tedious part of machine learning by intelligently exploring thousands of possible pipelines to find the best one for your data. Once TPOT is finished searching (or you get tired of waiting), it provides you with the Python code for the best pipeline it found so you can tinker with the pipeline from there. TPOT is built on top of scikit-learn, so all of the code it generates should look familiar... if you're familiar with scikit-learn, anyway.

Take a look around and you'll see evidence of the widespread adoption of wearable sensors for health and fitness, such as the Fitbit, Garmin or other devices. What many people may not know is that we are also using sensors to monitor the structural integrity of bridges and buildings, as well as tracking the movements of insects and other animals. With the rapid growth of the Internet of Things (IoT), tens of billions of sensor devices are projected to connect in the next decade. These connected sensor devices will automate processes across a broad range of economic sectors, from industrial plants to healthcare management, delivering productivity gains and hopefully quality-of-life improvements. The core of these sensor devices that will be deployed across this broad range of applications is largely the same, featuring a microprocessor, memory and a wired or wireless communication interface to the internet, along with a battery or other energy source.

A Learning System Based on Genetic Adaptive Algorithms.

This paper addresses classification tasks on a particular target domain in which labeled training data are only available from source domains different from (but related to) the target. Two closely related frameworks, domain adaptation and domain generalization, are concerned with such tasks, where the only difference between those frameworks is the availability of the unlabeled target data: domain adaptation can leverage unlabeled target information, while domain generalization cannot. We propose Scatter Component Analyis (SCA), a fast representation learning algorithm that can be applied to both domain adaptation and domain generalization. SCA is based on a simple geometrical measure, i.e., scatter, which operates on reproducing kernel Hilbert space. SCA finds a representation that trades between maximizing the separability of classes, minimizing the mismatch between domains, and maximizing the separability of data; each of which is quantified through scatter. The optimization problem of SCA can be reduced to a generalized eigenvalue problem, which results in a fast and exact solution. Comprehensive experiments on benchmark cross-domain object recognition datasets verify that SCA performs much faster than several state-of-the-art algorithms and also provides state-of-the-art classification accuracy in both domain adaptation and domain generalization. We also show that scatter can be used to establish a theoretical generalization bound in the case of domain adaptation.

Artificial intelligence (AI) is intelligence exhibited by machines. In computer science, an ideal "intelligent" machine is a flexible rational agent that perceives its environment and takes actions that maximize its chance of success at some goal.[1] Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving".[2] As machines become increasingly capable, facilities once thought to require intelligence are removed from the definition. For example, optical character recognition is no longer perceived as an exemplar of "artificial intelligence" having become a routine technology.[3] Capabilities still classified as AI include advanced Chess and Go systems and self-driving cars. AI research is divided into subfields[4] that focus on specific problems or on specific approaches or on the use of a particular tool or towards satisfying particular applications. The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects.[5] General intelligence is among the field's long-term goals.[6] Approaches include statistical methods, computational intelligence, soft computing (e.g. machine learning), and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics. The AI field draws upon computer science, mathematics, psychology, linguistics, philosophy, neuroscience and artificial psychology. The field was founded on the claim that human intelligence "can be so precisely described that a machine can be made to simulate it."[7] This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence, issues which have been explored by myth, fiction and philosophy since antiquity.[8] Attempts to create artificial intelligence has experienced many setbacks, including the ALPAC report of 1966, the abandonment of perceptrons in 1970, the Lighthill Report of 1973 and the collapse of the Lisp machine market in 1987. In the twenty-first century AI techniques became an essential part of the technology industry, helping to solve many challenging problems in computer science.[9]

Southern California recently experienced a 55-hour closure of the 91 Freeway, resulting in a 6-mile backup that intersected State Route 71 and Interstate 15. The closure was called the "Coronageddon" (it ran through the heart of Corona). Just a few years ago a big closure of Highway 405, dubbed "Carmageddon," resulted in a traffic jam that reached immense proportions and made national news. These are extreme instances of massive traffic congestion, but more commonly we all deal with daily traffic jams as people get to work and school, the lunch rush hour, and the all-too-familiar and stressful evening commute. Traffic patterns are studied by cities, but most use a low tech approach.

A swarm intelligence similar to the one that predicted Oscar winners and Kentucky Derby finishers has come to nearly unanimous conclusions about the presidential potential of Hilary Clinton and Donald Trump. From social issues to trustworthiness and ethics, the swarm spoke loud and clear, expressing practically the same sentiment for both candidates -- extreme pessimism. The swarm consisted of 85 Democratic, Republican, or independent American voters who were asked to answer identical questions on Clinton and Trump through the swarm intelligence platform UNU. The speed at which they came to a conclusion helps calculate the percentage of "brainpower" for a particular swarm. Anywhere between 70 and 85 people participated in each round.

A core methodology at Stitch Fix is blending recommendations from machines with judgments of expert humans. Our machines produce recommendations via algorithms operating over structured data, while our human stylists curate and modify these recommendations on the basis of unstructured data and knowledge that isn't yet reflected in our dataset (e.g., new fashion trends). This helps us choose the best 5 items to offer each client in each fix. The success of this strategy within our styling organization prompts consideration of how machines and humans might be brought together in the realm of fashion design. In this post we describe one implementation of such a system.

Causal modeling has long been an attractive topic for many researchers and in recent decades there has seen a surge in theoretical development and discovery algorithms. Generally discovery algorithms can be divided into two approaches: constraint-based and score-based. The constraint-based approach is able to detect common causes of the observed variables but the use of independence tests makes it less reliable. The score-based approach produces a result that is easier to interpret as it also measures the reliability of the inferred causal relationships, but it is unable to detect common confounders of the observed variables. A drawback of both score-based and constrained-based approaches is the inherent instability in structure estimation. With finite samples small changes in the data can lead to completely different optimal structures. The present work introduces a new hypothesis-free score-based causal discovery algorithm, called stable specification search, that is robust for finite samples based on recent advances in stability selection using subsampling and selection algorithms. Structure search is performed over Structural Equation Models. Our approach uses exploratory search but allows incorporation of prior background knowledge. We validated our approach on one simulated data set, which we compare to the known ground truth, and two real-world data sets for Chronic Fatigue Syndrome and Attention Deficit Hyperactivity Disorder, which we compare to earlier medical studies. The results on the simulated data set show significant improvement over alternative approaches and the results on the real-word data sets show consistency with the hypothesis driven models constructed by medical experts.

This article is the first in a series of three. See also: Part 1, Part 2, Part 3. Is it possible for a computer program to write its own programs? Could human software developers be replaced one day by the very computers that they master? Just like the farmer, the assembly line worker, and the telephone operator, could software developers be next? While this kind of idea seems far-fetched, it may actually be closer than we think. This article describes an experiment to produce an AI program, capable of developing its own programs, using a genetic algorithm implementation with self-modifying and self-improving code.