"Man," here, stands for the collective human race. But, why not use the latin word for "Wise woman" or "Wise person?" There was a movement in the tech world a few years ago to use female pronouns more often. Here's another question -- why do we call a list of bad things a "blacklist?" And, why is the opposite a "whitelist?" Why does white represent good and black represent bad?

An engineer in Poland has taken the age-old potato clock experiment to the next level. Marek Baczynski has revealed what he says is the world's first self-driving potato, and it runs on its own electricity. On YouTube, the inventor explains how an energy harvester and a capacitor can be used to store the'minuscule' energy generated by the potato, eventually powering it enough to drive – and, with a simple script, he granted it'the gift of freedom.' The potato takes about 15 minutes to charge, which will carry it just a few inches at a time. According to Baczynski, it can travel about seven and a half meters (24.6 feet) over the course of an entire day Potatoes are able to conduct electricity thanks to the presence of electrolytes.

In recent years, Artificial Intelligence (AI) researchers have finally cracked problems that they've worked on for decades, from Go to human-level speech recognition. A key piece was the ability to gather and learn on mountains of data, which pulled error rates past the success line. In short, big data has transformed AI, to an almost unreasonable level. Blockchain technology could transform AI too, in its own particular ways. Some applications of blockchains to AI are mundane, like audit trails on AI models. Some appear almost unreasonable, like AI that can own itself -- AI DAOs. All of them are opportunities. This article will explore these applications. Before we discuss applications, let's first review what's different about blockchains compared to traditional big-data distributed databases like MongoDB. We can think of blockchains as "blue ocean" databases: they escape the "bloody red ocean" of sharks competing in an existing market, opting instead to be in a blue ocean of uncontested market space. Famous blue ocean examples are Wii for video game consoles (compromise raw performance, but have new mode of interaction), or Yellow Tail for wines (ignore the pretentious specs for wine lovers; make wine more accessible to beer lovers). By traditional database standards, traditional blockchains like Bitcoin are terrible: low throughput, low capacity, high latency, poor query support, and so on. But in blue-ocean thinking, that's ok, because blockchains introduced three new characteristics: decentralized / shared control, immutable / audit trails, and native assets / exchanges.

Over the past few years we've developed artificially intelligent machines that can do many things that used to require human minds: understanding speech, diagnosing disease, checking the terms of a contract, designing a mechanical part from scratch, even coming up with new scientific hypotheses that are supported by subsequent research. As this new software is embedded in hardware we'll get self-driving cars, trucks, and combines; delivery and inspection drones; and robots of many kinds. These technologies are improving more quickly than even their creators would have predicted at the start of the decade, and the fact that the world's best players of both the Asian strategy game go and no limit heads up Texas hold-em poker are now AI systems indicates just how deeply they're encroaching into human territory. So shouldn't we be preparing ourselves for massive AI-induced technological unemployment? A widely cited 2015 analysis by Carl Frey and Michael Osborne of Oxford University found that 47% of current jobs in the US were susceptible to computerization.

This paper considers the use of Machine Learning (ML) in medicine by focusing on the main problem that this computational approach has been aimed at solving or at least minimizing: uncertainty. To this aim, we point out how uncertainty is so ingrained in medicine that it biases also the representation of clinical phenomena, that is the very input of ML models, thus undermining the clinical significance of their output. Recognizing this can motivate both medical doctors, in taking more responsibility in the development and use of these decision aids, and the researchers, in pursuing different ways to assess the value of these systems. In so doing, both designers and users could take this intrinsic characteristic of medicine more seriously and consider alternative approaches that do not "sweep uncertainty under the rug" within an objectivist fiction, which everyone can come up by believing as true.

We are increasingly surrounded by artificially intelligent technology that takes decisions and executes actions on our behalf. This creates a pressing need for general means to communicate with, instruct and guide artificial agents, with human language the most compelling means for such communication. To achieve this in a scalable fashion, agents must be able to relate language to the world and to actions; that is, their understanding of language must be grounded and embodied. However, learning grounded language is a notoriously challenging problem in artificial intelligence research. Here we present an agent that learns to interpret language in a simulated 3D environment where it is rewarded for the successful execution of written instructions. Trained via a combination of reinforcement and unsupervised learning, and beginning with minimal prior knowledge, the agent learns to relate linguistic symbols to emergent perceptual representations of its physical surroundings and to pertinent sequences of actions. The agent's comprehension of language extends beyond its prior experience, enabling it to apply familiar language to unfamiliar situations and to interpret entirely novel instructions. Moreover, the speed with which this agent learns new words increases as its semantic knowledge grows. This facility for generalising and bootstrapping semantic knowledge indicates the potential of the present approach for reconciling ambiguous natural language with the complexity of the physical world.

Despite the fundamental nature of the inhomogeneous Poisson process in the theory and application of stochastic processes, and its attractive generalizations (e.g. Cox process), few tractable nonparametric modeling approaches of intensity functions exist, especially when observed points lie in a high-dimensional space. In this paper we develop a new, computationally tractable Reproducing Kernel Hilbert Space (RKHS) formulation for the inhomogeneous Poisson process. We model the square root of the intensity as an RKHS function. Whereas RKHS models used in supervised learning rely on the so-called representer theorem, the form of the inhomogeneous Poisson process likelihood means that the representer theorem does not apply. However, we prove that the representer theorem does hold in an appropriately transformed RKHS, guaranteeing that the optimization of the penalized likelihood can be cast as a tractable finite-dimensional problem. The resulting approach is simple to implement, and readily scales to high dimensions and large-scale datasets.

Abstract--This paper introduces a family of local feature aggregation functions and a novel method to estimate their parameters, such that they generate optimal representations for classification (or any task that can be expressed as a cost function minimization problem). T o achieve that, we compose the local feature aggregation function with the classifier cost function and we backpropagate the gradient of this cost function in order to update the local feature aggregation function parameters. Experiments on synthetic datasets indicate that our method discovers parameters that model the class-relevant information in addition to the local feature space. Further experiments on a variety of motion and visual descriptors, both on image and video datasets, show that our method outperforms other state-of- the-art local feature aggregation functions, such as Bag of Words, Fisher V ectors and VLAD, by a large margin. A typical image or video classification pipeline, which uses handcrafted features, consists of the following components: local feature extraction (e.g.

Current grammar-based NeuroEvolution approaches have several shortcomings. On the one hand, they do not allow the generation of Artificial Neural Networks (ANNs) composed of more than one hidden-layer. On the other, there is no way to evolve networks with more than one output neuron. To properly evolve ANNs with more than one hidden-layer and multiple output nodes there is the need to know the number of neurons available in previous layers. In this paper we introduce Dynamic Structured Grammatical Evolution (DSGE): a new genotypic representation that overcomes the aforementioned limitations. By enabling the creation of dynamic rules that specify the connection possibilities of each neuron, the methodology enables the evolution of multi-layered ANNs with more than one output neuron. Results in different classification problems show that DSGE evolves effective single and multi-layered ANNs, with a varying number of output neurons.

The rise of Artificial Intelligence (AI), and an increasingly connected society has already, according to the UK's MI5 made it "much harder for spies to hide in the shadows", but now, if Robert Cardillo has his way, so called robo-automation tools will perform 75 percent of the tasks currently done by the new front line of American intelligence spies – the analysts who collect, analyse, and interpret images beamed from drones, satellites, and other feeds around the globe. Cardillo, the director of the National Geospatial-Intelligence Agency, (NGA), announced his push toward "automation" and "AI" at a conference this week in San Antonio. The annual conference, hosted by the United States Geospatial Intelligence Foundation, brings together technologists, soldiers, and intelligence professionals to discuss national security threats, changes in technology, and data collection and processing. AI is on the rise, and last year former President Barack Obama's White House created a Defcon Scale for Cyberattacks, and released a white paper on its potential future impacts in the final months of the administration, and police forces around the world are increasingly using preliminary "pre-crime" technologies to predict when, where and by whom crimes will likely be committed. And all of that is in addition to the likes of companies like Amazon and Netflix who are using machine learning to calculate what movie you will want to watch or which book you may buy.