We present a novel method for learning hierarchical abstractions that prioritize competing objectives, leading to improved global expected rewards. Our approach employs a secondary rewarding agent with multiple scalar outputs, each associated with a distinct level of abstraction. The traditional agent then learns to maximize these outputs in a hierarchical manner, conditioning each level on the maximization of the preceding level. We derive an equation that orders these scalar values and the global reward by priority, inducing a hierarchy of needs that informs goal formation. Experimental results on the Pendulum v1 environment demonstrate superior performance compared to a baseline implementation.We achieved state of the art results.

In this paper, we explore the intriguing similarities between the structure of a discrete neural network, such as a spiking network, and the composition of a piano piece. While both involve nodes or notes that are activated sequentially or in parallel, the latter benefits from the rich body of music theory to guide meaningful combinations. We propose a novel approach that leverages musical grammar to regulate activations in a spiking neural network, allowing for the representation of symbols as attractors. By applying rules for chord progressions from music theory, we demonstrate how certain activations naturally follow others, akin to the concept of attraction. Furthermore, we introduce the concept of modulating keys to navigate different basins of attraction within the network. Ultimately, we show that the map of concepts in our model is structured by the musical circle of fifths, highlighting the potential for leveraging music theory principles in deep learning algorithms.

The presence of gender stereotypes in many aspects of society is a well-known phenomenon. In this paper, we focus on studying and quantifying such stereotypes and bias in the Man Bookers Prize winning fiction. We consider 275 books shortlisted for Man Bookers Prize between 1969 and 2017. The gender bias is analyzed by semantic modeling of book descriptions on Goodreads. This reveals the pervasiveness of gender bias and stereotype in the books on different features like occupation, introductions and actions associated to the characters in the book.

A group of Amazon shareholders is asking CEO Jeff Bezos to stop selling and marketing facial recognition technology to governments after civil liberties groups warned of the potential for abuse. Earlier this year, a group of advocacy organisations led by the American Civil Liberties Union (ACLU) published a report detailing how Amazon was marketing its Rekognition tool to American law enforcement agencies. In addition to touting the technology as helping to find suspects, Amazon has said it could be used to preemptively identify "persons of interest" and prevent crimes. A letter signed by 19 shareholders - and provided to The Independent by the ACLU - urges Mr Bezos to halt the tool's expansion until those concerns can be addressed. Amazon supplier investigated over'mistreatment' of workers in China How Alexa recorded a family's conversation then sent it to someone Amazon told to stop selling facial recognition tools to police Amazon supplier investigated over'mistreatment' of workers in China How Alexa recorded a family's conversation then sent it to someone Furnishing police and sheriff's departments with the tool would bolster "government surveillance infrastructure technology" and could drive down Amazon's value, the letter warned. It also echoed concerns about the potential for misuse. "While Rekognition may be intended to enhance some law enforcement activities, we are deeply concerned it may ultimately violate civil and human rights", the letter said.

A Tesla's autopilot function was engaged in the minutes before a fiery crash that killed its driver in California earlier this year, according to a federal inquiry. In the roughly 20 minutes before the vehicle slammed into a barrier near Mountain View and burst into flames, the car's autopilot feature was in "continuous operation", the National Transportation Safety Board (NTSB) found in its initial investigation. During the critical 60 seconds leading up to the crash, the NTSB reported, the car's driver repeatedly placed his hands on the steering wheel. Tesla crashes into parked police car in Autopilot mode Wall Street blasts Elon Musk's'truly bizarre' Tesla earnings call Tesla faces labour investigation after allegation of injury undercount But six seconds before the accident, evidence suggests the driver had removed his hands from the steering wheel. The vehicle also accelerated in the final three seconds.