In simple terms, that "most important role" is the cycle of observation followed by critical thinking followed by action. It's important to bear in mind that the proper goal of Machine Learning (ML) is not abdication of human responsibility for decision-making. Rather, it's improving our individual and collective ability to make better decisions by leveraging increased speed, accuracy and absence of bias. Our context here is supply chain planning and execution, but there is no reason to limit the scope of Machine Learning. When it comes to designing and creating technology solutions for supply chain analytics and business intelligence, this is not a throw-away idea buried in a long-forgotten PowerPoint presentation.

The rewards of machine learning can be compelling, and it may make you want to get started, now. At the same time, however, you'll want to consider machine learning challenges before you start your own project. This article isn't meant to scare you away; rather, it's meant to ensure you're prepared and that you're carefully thinking about what you'll need to consider before you get started. We spoke with Brian MacDonald, Data Scientist on Oracle's Information Management Platform Team, about the pitfalls he's seen and what companies can do to avoid them. The biggest difficulty, of course, is the skills gap that lies with using machine learning in a big data environment.

Learning image representations to capture fine-grained semantics has been a challenging and important task enabling many applications such as image search and clustering. In this paper, we present Graph-Regularized Image Semantic Embedding (Graph-RISE), a large-scale neural graph learning framework that allows us to train embeddings to discriminate an unprecedented O(40M) ultra-fine-grained semantic labels. Graph-RISE outperforms state-of-the-art image embedding algorithms on several evaluation tasks, including image classification and triplet ranking. We provide case studies to demonstrate that, qualitatively, image retrieval based on Graph-RISE effectively captures semantics and, compared to the state-of-the-art, differentiates nuances at levels that are closer to human-perception.

The late Paul Allen's research vessel, the Petrel, has found another historic warship at the bottom of the ocean. In the wake of an initial discovery in late January, the expedition crew has confirmed that it found the USS Hornet, an aircraft carrier that played a pivotal role in WWII through moments like the Doolittle Raid on Japan and the pivotal Battle of Midway. It was considered lost when it sank at the Battle of Santa Cruz in October 1943, but modern technology spotted it nearly 17,500 feet below the surface of the South Pacific Ocean, near the Solomon Islands. The team initially narrowed down its search area by using data from the era, such as action reports and deck logs from other ships involved in the Santa Cruz fight. From there, tech took over.

Meteorological forecasting provides reliable prediction about the future weather within a given interval of time. Meteorological forecasting can be viewed as a form of hybrid diagnostic reasoning and can be mapped onto an integrated conceptual framework. The automation of the forecasting process would be helpful in a number of contexts, in particular: when the amount of data is too wide to be dealt with manually; to support forecasters education; when forecasting about underpopulated geographic areas is not interesting for everyday life (and then is out from human forecasters' tasks) but is central for tourism sponsorship. We present logic MeteoLOG, a framework that models the main steps of the reasoner the forecaster adopts to provide a bulletin. MeteoLOG rests on several traditions, mainly on fuzzy, temporal and probabilistic logics. On this basis, we also introduce the algorithm Tournament, that transforms a set of MeteoLOG rules into a defeasible theory, that can be implemented into an automatic reasoner. We finally propose an example that models a real world forecasting scenario.

Many applications in preference learning assume that decisions come from the maximization of a stable utility function. Yet a large experimental literature shows that individual choices and judgements can be affected by "irrelevant" aspects of the context in which they are made. An important class of such contexts is the composition of the choice set. In this work, our goal is to discover such choice set effects from raw choice data. We introduce an extension of the Multinomial Logit (MNL) model, called the context dependent random utility model (CDM), which allows for a particular class of choice set effects. We show that the CDM can be thought of as a second-order approximation to a general choice system, can be inferred optimally using maximum likelihood and, importantly, is easily interpretable. We apply the CDM to both real and simulated choice data to perform principled exploratory analyses for the presence of choice set effects.

A model used for velocity control during car following was proposed based on deep reinforcement learning (RL). To fulfil the multi-objectives of car following, a reward function reflecting driving safety, efficiency, and comfort was constructed. With the reward function, the RL agent learns to control vehicle speed in a fashion that maximizes cumulative rewards, through trials and errors in the simulation environment. A total of 1,341 car-following events extracted from the Next Generation Simulation (NGSIM) dataset were used to train the model. Car-following behavior produced by the model were compared with that observed in the empirical NGSIM data, to demonstrate the model's ability to follow a lead vehicle safely, efficiently, and comfortably. Results show that the model demonstrates the capability of safe, efficient, and comfortable velocity control in that it 1) has small percentages (8\%) of dangerous minimum time to collision values (\textless\ 5s) than human drivers in the NGSIM data (35\%); 2) can maintain efficient and safe headways in the range of 1s to 2s; and 3) can follow the lead vehicle comfortably with smooth acceleration. The results indicate that reinforcement learning methods could contribute to the development of autonomous driving systems.

Cross-validation of predictive models is the de-facto standard for model selection and evaluation. In proper use, it provides an unbiased estimate of a model's predictive performance. However, data sets often undergo a preliminary data-dependent transformation, such as feature rescaling or dimensionality reduction, prior to cross-validation. It is widely believed that such a preprocessing stage, if done in an unsupervised manner that does not consider the class labels or response values, has no effect on the validity of cross-validation. In this paper, we show that this belief is not true. Preliminary preprocessing can introduce either a positive or negative bias into the estimates of model performance. Thus, it may lead to sub-optimal choices of model parameters and invalid inference. In light of this, the scientific community should re-examine the use of preliminary preprocessing prior to cross-validation across the various application domains. By default, all data transformations, including unsupervised preprocessing stages, should be learned only from the training samples, and then merely applied to the validation and testing samples.

File photo - An unmanned 11-meter rigid-hull inflatable boat from Naval Surface Warfare Center Carderock operates autonomously during an Office of Naval Research-sponsored demonstration of swarmboat technology on the James River in Newport News, Va.(U.S. Navy photo by John F. Williams/Released) The U.S. Navy will launch a swarm of interconnected small attack drone boats on mock-combat missions to refine command and control technology and prepare its "Ghost Fleet" of autonomous, yet networked surface craft for war. Developed by the Office of Naval Research and Naval Sea Systems Command, "Ghost Fleet" represents a Navy strategy to surveil, counter, overwhelm and attack enemies in a coordinated fashion - all while keeping sailors on host ships at safer distances. The small boats, many of them called Unmanned Surface Vessels, are designed to conduct ISR missions, find and destroy mines and launch a range of attacks including electronic warfare and even mounted guns. The concept is to use advanced computer algorithms bringing new levels of autonomy to surface warfare, enabling ships to coordinate information exchange, operate in tandem without colliding and launch combined assaults. "Ghost Fleet is really helping us in the Command and Control and coms arena. The demonstration will allow us to learn lessons about integrated payloads with USVs," Capt.

Charles River Laboratories International, Inc. (NYSE: CRL) and Atomwise, Inc. today announced the formation of a strategic alliance that offers clients access to Atomwise's artificial intelligence (AI)-powered, structure-based, drug design technology, which allows scientists to predict how well a small molecule will bind to a target protein of interest. By removing sole reliance on empirical screening, AI enables drug researchers to test an extremely large and diverse chemical space in a matter of days and move through the optimization process quickly by focusing only on those compounds predicted to have improved target-binding attributes. This alliance combines two industry-leading drug discovery platforms: Atomwise's AI technology and Charles River's unique portfolio of end-to-end drug discovery and early-stage development capabilities and expertise. Leveraging Atomwise's AI technology and Charles River's integrated drug discovery platform has the potential to significantly streamline the hit discovery, hit-to-lead, and lead optimization process for clients' research efforts. Founded in 2012, the San Francisco Bay Area-based venture has raised over $50 million in venture capital financing.