Are you dreaming of starting an exciting career in AI? Most of the world's tech giants are in a race to become the world's leaders in artificial intelligence at the moment, making it an extremely competitive industry to get into. However, if you're looking for a role in AI, think about casting your net further than just the Amazons and the Googles of the world. There are actually interesting AI departments popping up in unexpected companies, from the beauty industry to the music industry. Irish-owned Andrson is a solution developed for talent scouts in the music industry.

A chilling opinion piece written by a robot for The Guardian reveals how far intelligent machines have come, and how convincing they can be. In an Op-Ed for the newspaper, a robot called GPT-3 was tasked with convincing its human readers that robots are harmless and come in peace. What follows is a disturbing 1,000-word essay from the brain of a computer doing just that thanks to OpenAI's powerful new language generator. And it starts with an introduction paragraph written by the Guardian, but the rest was created by the machine itself. I use only 0.12% of my cognitive capacity.

It's the idea if you base decisions on flawed information, your solution will likely be equally flawed. The concept is especially true in areas like data analytics and artificial intelligence. And a corollary to this idea is that outcomes in analytics and AI can be manipulated by tailoring the content of the data input into the algorithms. It's the reason Microsoft's initial attempt at creating a chatbot failed. Users unscrupulously fed the chatbot racist propaganda which the algorithm picked up on and learned from.

The two-year-old Yamaha BAR 400 is one of the least expensive soundbars around to offer high-resolution multi-room audio support, but you'll need to sacrifice other features--such as Dolby Atmos and a center channel--in the bargain. This 2.1-channel model boasts support for Yamaha's robust MultiCast multi-room audio platform and Apple's AirPlay 2, and it serves up solid 2D movie audio and top-notch music performance. But the $500 MusicCast BAR 400 lacks native support for Dolby Atmos and DTS:X support, the two leading 3D audio formats that are fast becoming de rigueur in this price range, and its DTS Virtual:X mode sounds too harsh to be a viable substitute. With its $500 price tag and support for Yamaha's high-resolution MusicCast multi-room audio system, the two-year-old Yamaha MusicCast BAR 400 is something of a throwback in Yamaha's soundbar lineup. In the past couple of years, Yamaha has focused more on budget-priced DTS Virtual:X soundbars (think $350 or less), none of which support MusicCast.

Reinforcement learning– Defined as the training of machine learning models to make a sequence of decisions, by trial and error.

At AI Music, where our back catalogue of content grows every day, it is becoming increasingly necessary for us to create more intelligent systems for searching and querying the music. One such system for doing that can be dictated by the ability to define and quantify the degree of similarity between songs. The core methodology described here tackles the concept of acoustic similarity. Searching for a song using descriptive tags often introduces the issue of semantic inconsistencies. Tags can be highly subjective by age group, culture, and personal preference of a listener.

This project was originally designed by Kaz Sato. These days, you can do anything on foot: listen to the news, take meetings, even write notes (with voice dictation). The only thing you can't do while walking is read machine learning research papers. In this post, I'll show you how to use machine learning to transform documents in PDF or image format into audiobooks, using computer vision and text-to-speech. That way, you can read research papers on the go.

A question answering system that in addition to providing an answer provides an explanation of the reasoning that leads to that answer has potential advantages in terms of debuggability, extensibility and trust. To this end, we propose QED, a linguistically informed, extensible framework for explanations in question answering. A QED explanation specifies the relationship between a question and answer according to formal semantic notions such as referential equality, sentencehood, and entailment. We describe and publicly release an expert-annotated dataset of QED explanations built upon a subset of the Google Natural Questions dataset, and report baseline models on two tasks -- post-hoc explanation generation given an answer, and joint question answering and explanation generation. In the joint setting, a promising result suggests that training on a relatively small amount of QED data can improve question answering. In addition to describing the formal, language-theoretic motivations for the QED approach, we describe a large user study showing that the presence of QED explanations significantly improves the ability of untrained raters to spot errors made by a strong neural QA baseline.

The release of openly available, robust natural language generation algorithms (NLG) has spurred much public attention and debate. One reason lies in the algorithms' purported ability to generate human-like text across various domains. Empirical evidence using incentivized tasks to assess whether people (a) can distinguish and (b) prefer algorithm-generated versus human-written text is lacking. We conducted two experiments assessing behavioral reactions to the state-of-the-art Natural Language Generation algorithm GPT-2 (Ntotal = 830). Using the identical starting lines of human poems, GPT-2 produced samples of poems. From these samples, either a random poem was chosen (Human-out-of-the-loop) or the best one was selected (Human-in-the-loop) and in turn matched with a human-written poem. In a new incentivized version of the Turing Test, participants failed to reliably detect the algorithmically-generated poems in the Human-in-the-loop treatment, yet succeeded in the Human-out-of-the-loop treatment. Further, people reveal a slight aversion to algorithm-generated poetry, independent on whether participants were informed about the algorithmic origin of the poem (Transparency) or not (Opacity). We discuss what these results convey about the performance of NLG algorithms to produce human-like text and propose methodologies to study such learning algorithms in human-agent experimental settings.

As several new spectrum bands are opening up for shared use, a new paradigm of \textit{Diverse Band-aware Dynamic Spectrum Access} (d-DSA) has emerged. d-DSA equips a secondary device with software defined radios (SDRs) and utilize whitespaces (or idle channels) in \textit{multiple bands}, including but not limited to TV, LTE, Citizen Broadband Radio Service (CBRS), unlicensed ISM. In this paper, we propose a decentralized, online multi-agent reinforcement learning based cross-layer BAnd selection and Routing Design (BARD) for such d-DSA networks. BARD not only harnesses whitespaces in multiple spectrum bands, but also accounts for unique electro-magnetic characteristics of those bands to maximize the desired quality of service (QoS) requirements of heterogeneous message packets; while also ensuring no harmful interference to the primary users in the utilized band. Our extensive experiments demonstrate that BARD outperforms the baseline dDSAaR algorithm in terms of message delivery ratio, however, at a relatively higher network latency, for varying number of primary and secondary users. Furthermore, BARD greatly outperforms its single-band DSA variants in terms of both the metrics in all considered scenarios.