Recent advances in large-scale pre-training such as GPT-3 allow seemingly high quality text to be generated from a given prompt. However, such generation systems often suffer from problems of hallucinated facts, and are not inherently designed to incorporate useful external information. Grounded generation models appear to offer remedies, but their training typically relies on rarely-available parallel data where corresponding information-relevant documents are provided for context. We propose a framework that alleviates this data constraint by jointly training a grounded generator and document retriever on the language model signal. The model learns to reward retrieval of the documents with the highest utility in generation, and attentively combines them using a Mixture-of-Experts (MoE) ensemble to generate follow-on text. We demonstrate that both generator and retriever can take advantage of this joint training and work synergistically to produce more informative and relevant text in both prose and dialogue generation.

Wondering who that hot new collaborator is, on your favorite artist's new album? It might just be artificial intelligence. Progress in AI music is accelerating rapidly, thanks to researchers and musicians at major tech conferences and universities who want to integrate widespread AI into the music world. Many artists feel we're about to enter a "golden age" of creativity, powered by artificial intelligence, that can push music in new directions. Let's look at some of the newest ways artificial intelligence is transforming the music industry from top to bottom. For 30 years, musician and composer David Cope has been working on Experiments in Musical Intelligence (EMI).

HALIFAX, NS, June 1, 2021 /CNW/ - Global Spatial Technology Solutions ("GSTS" or "the Company") an Artificial Intelligence (AI) and Maritime Analytics company, announced today that it has been selected by Defence Research and Development Canada (DRDC) to provide advanced Maritime Risk Detection and Assessment capabilities in support of maritime border security and surveillance. The solution will identify ships in an area of interest and using the cutting-edge techniques of artificial intelligence and machine learning, consolidate a ship's identity, movement history, and risk status with information collected from multiple sensors. Fusing the intelligence into a single operating picture, GSTS's solution enables users to improve Maritime Domain Awareness. The total contract is funded under the Canadian Safety and Security Program. This powerful solution will leverage OCIANA, an AI-based platform developed by GSTS that rapidly processes data from multiple sensor sources to provide intelligence in near real-time.

The report, titled Data Science and Machine-Learning Platforms Market, is one of the most comprehensive and essential additions to the Reports …

In today's music industry, the separation between digital and analogue is almost impossible to determine. At the most basic level, the majority of today's music is crafted using highly intelligent software. However, at the cutting edge of AI and the music industry, innovators are continuously pushing the boundaries of human/machine collaboration in musical creation as well as business. One such innovator is Vernica Serjilus, professionally as Verphoria, an American singer, record producer, songwriter, entrepreneur, and the Founder and CEO of Hierarchy Music. Hierarchy Music is a global music company that connects musicians globally with Grammy Award-winning, multi-platinum music services. At the crux of Hierarchy Music's operations is data AI and back-end exposure which allow us to bring exposure to new artists, or existing artists and their brands, utilizing both Hierarchy Music and Hierarchy Media's back-end network.

Many facets of society, economics, and even everyday life have been significantly transformed by technological advancements in recent years, rendering them unrecognisable from only a decade ago. This is especially true in the communications profession, where Public Relations is one of the fields that has seen major business disruption-related developments in recent years. Artificial intelligence (AI), is a new technology, which is already making headlines in the tech and mainstream media, despite the fact that it is still in its early phases. AI has a lot of promise for B2B companies, but it's not without its detractors. Let's start with what is PR and the use of Artificial intelligence in PR.

For many people, it remains unclear what this technology is all about, so this is a great place to begin the conversation. AI is a branch in computer engineering that addresses the smart behaviour of machines. It's an ingeniously mimicked ability of a system to mimic human behaviour and our normal reaction patterns. This can be made possible with particular algorithms which produce the AI work in a specified range of actions (based on what the algorithm codes for). This implies that using AI, a number of our everyday tasks can now be performed efficiently by programmed machine technologies.

When this regulation comes into effect, it will change the way that we conduct AI research and development.

Annie Brown is the founder of Lips, an AI enabled social commerce platform for women and LGBTQ creators.

In adversarial data collection (ADC), a human workforce interacts with a model in real time, attempting to produce examples that elicit incorrect predictions. Researchers hope that models trained on these more challenging datasets will rely less on superficial patterns, and thus be less brittle. However, despite ADC's intuitive appeal, it remains unclear when training on adversarial datasets produces more robust models. In this paper, we conduct a large-scale controlled study focused on question answering, assigning workers at random to compose questions either (i) adversarially (with a model in the loop); or (ii) in the standard fashion (without a model). Across a variety of models and datasets, we find that models trained on adversarial data usually perform better on other adversarial datasets but worse on a diverse collection of out-of-domain evaluation sets. Finally, we provide a qualitative analysis of adversarial (vs standard) data, identifying key differences and offering guidance for future research.