This paper aims to question the suitability of the Turing Test, for testing machine intelligence, in the light of advances made in the last 60 years in science, medicine, and philosophy of mind. While the main concept of the test may seem sound and valid, a detailed analysis of what is required to pass the test highlights a significant flow. Once the analysis of the test is presented, a systematic approach is followed in analysing what is needed to devise a test or tests for intelligent machines. The paper presents a plausible generic framework based on categories of factors implied by subjective perception of intelligence. An evaluative discussion concludes the paper highlighting some of the unaddressed issues within this generic framework.

Gradient boosted models are a fundamental machine learning technique. Robustness to small perturbations of the input is an important quality measure for machine learning models, but the literature lacks a method to prove the robustness of gradient boosted models. This work introduces VERIGB, a tool for quantifying the robustness of gradient boosted models. VERIGB encodes the model and the robustness property as an SMT formula, which enables state of the art verification tools to prove the model's robustness. We extensively evaluate VERIGB on publicly available datasets and demonstrate Figure 1: Example of the lack of robustness in a gradient a capability for verifying large models. Finally, we show boosted model trained over a traffic signs dataset. In the that some model configurations tend to be inherently more first row, an "80 km/h speed limit" sign is misclassified as robust than others.

Interpretability of machine learning (ML) models becomes more relevant with their increasing adoption. In this work, we address the interpretability of ML based question answering (QA) models on a combination of knowledge bases (KB) and text documents. We adapt post hoc explanation methods such as LIME and input perturbation (IP) and compare them with the self-explanatory attention mechanism of the model. For this purpose, we propose an automatic evaluation paradigm for explanation methods in the context of QA. We also conduct a study with human annotators to evaluate whether explanations help them identify better QA models. Our results suggest that IP provides better explanations than LIME or attention, according to both automatic and human evaluation. We obtain the same ranking of methods in both experiments, which supports the validity of our automatic evaluation paradigm.

As reinforcement learning (RL) scales to solve increasingly complex tasks, interest continues to grow in the fields of AI safety and machine ethics. As a contribution to these fields, this paper introduces an extension to Deep Q-Networks (DQNs), called Empathic DQN, that is loosely inspired both by empathy and the golden rule ("Do unto others as you would have them do unto you"). Empathic DQN aims to help mitigate negative side effects to other agents resulting from myopic goal-directed behavior. We assume a setting where a learning agent coexists with other independent agents (who receive unknown rewards), where some types of reward (e.g. negative rewards from physical harm) may generalize across agents. Empathic DQN combines the typical (self-centered) value with the estimated value of other agents, by imagining (by its own standards) the value of it being in the other's situation (by considering constructed states where both agents are swapped). Proof-of-concept results in two gridworld environments highlight the approach's potential to decrease collateral harms. While extending Empathic DQN to complex environments is non-trivial, we believe that this first step highlights the potential of bridge-work between machine ethics and RL to contribute useful priors for norm-abiding RL agents.

Does this star have a planet? A new algorithm could help astronomers predict, on the basis of a star's chemical fingerprint, whether that star will host a giant gaseous exoplanet. "It's like Netflix," Natalie Hinkel, a planetary astrophysicist at the Southwest Research Institute in San Antonio, Texas, told Eos. Netflix "sees that you like goofy comedy, science fiction, and kung fu movies--a variety of different patterns" to predict whether you'll like a new movie. Likewise, her team's machine learning algorithm "will learn which elements are influential in deciding whether or not a star has a planet."

Whatever term you choose, they refer to a roughly related set of pre-modeling data activities in the machine learning, data mining, and data science communities. Data cleansing may be performed interactively with data wrangling tools, or as batch processing through scripting. This may include further munging, data visualization, data aggregation, training a statistical model, as well as many other potential uses. Data munging as a process typically follows a set of general steps which begin with extracting the data in a raw form from the data source, "munging" the raw data using algorithms (e.g. I would say that it is "identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data" in the context of "mapping data from one'raw' form into another..." all the way up to "training a statistical model" which I like to think of data preparation as encompassing, or "everything from data sourcing right up to, but not including, model building."

Our Bionic Factory is a centre of expertise focused on AI, data engineering and intelligent automation. This centre will help you tap into additional forms of capital (Behavioral and Cognitive) as you move towards being a Bionic Organization and outperform others that rely solely on more traditional types of capital (Financial, Natural Resources, Human). SCALE.AI is a super cluster dedicated to building the next-generation supply chain and boosting industry performance through AI technologies. Close to 120 partners, including PwC Canada, have joined forces to create this Canadian innovation consortium.

Researchers from the Max Planck Institute for Intelligent Systems, a member of NVIDIA's NVAIL program, developed an end-to-end deep learning algorithm that can take any speech signal as input – and realistically animate it in a wide range of adult faces. "There is an extensive literature on estimating 3D face shape, facial expressions, and facial motion from images and videos. Less attention has been paid to estimating 3D properties of faces from sound," the researchers stated in their paper. "Understanding the correlation between speech and facial motion thus provides additional valuable information for analyzing humans, particularly if visual data are noisy, missing, or ambiguous." The team first collected a new dataset of 4D face scans together with speech.

Newswire) VSBLTY Groupe Technologies Corp. (CSE: VSBY) (5VS.F) (VSBGF), a leading retail software and technology company, is teaming with Energetika, an international provider of "intelligent lighting" solutions, to install safety lighting and integrated security to Mexico City, and other Latin American cities designated as a "Smart City." Accessibility, habitability, sustainability, air quality, noise levels, energy, health and economic vitality are among the elements necessary to be selected as a "Smart City." Energetika is a leading provider of smart lighting solutions for economically efficient applications that incorporate security. Energetika chose VSBLTY to provide security technology that includes crowd analytics and facial recognition for residential, commercial and governmental applications. VSBLTY technology provides enhanced customer engagement and audience measurement using machine learning and computer vision.

Retailers are getting smarter about artificial intelligence (AI), and the latest example of that innovative effort comes from Walmart. According to a new report, the retail chain, hoping to reduce checkout theft, is turning to cameras powered by AI, with deployments underway in some 1,000 stores. "The retailer began investing in the surveillance program, dubbed Missed Scan Detection, several years ago in an effort to combat shrinkage -- loss due to several causes including theft, scanning errors, waste and fraud," the report stated. "The AI-powered cameras were rolled out to more than 1,000 stores about two years ago and the retail giant has seen positive results since then, according to Jenkins, who said shrinkage has reduced in stores where the cameras have been added." Artificial intelligence is moving from theory to reality, and that holds true for the world of retail as well.