An investigative journalist receives a video from an anonymous whistleblower. It shows a candidate for president admitting to illegal activity. But is this video real? If so, it would be huge news – the scoop of a lifetime – and could completely turn around the upcoming elections. But the journalist runs the video through a specialized tool, which tells her that the video isn't what it seems.

A few weeks ago, I was interviewed by Tim Hughes, of DLA Ignite, about the five biggest mistakes that people make about AI and its impact on the workplace. This article is based on the full interview, which you can find here. It is the use of machinery to replicate a unique human activity, from punch cards that operated sophisticated weaving looms during the industrial revolution, to mid-twentieth century business computers that calculated bills and operated the payroll. Very often these activities are repetitive, error-prone, and in some cases life-threatening. The principle of automation is nearly always the same.

Hackers have tweaked machine learning technology in such a way as to clone an individual's voice, usually by utilizing spyware and devices that …

The course is designed specifically for aspiring machine learning and data science professionals and takes individuals with zero industry experience, …

An investigative journalist receives a video from an anonymous whistleblower. It shows a candidate for president admitting to illegal activity. But is this video real? If so, it would be huge news – the scoop of a lifetime – and could completely turn around the upcoming elections. But the journalist runs the video through a specialized tool, which tells her that the video isn't what it seems.

AI can already create photorealistic faces, objects, and landscapes. We can already recreate any voice. GPT-3 can already write dialogue and movie plots almost indistinguishable from ones written by humans. Even generated music is making fast progress. It's startling to realize that Hollywood movies that cost $300M to produce today might be generated for a few cents within our lifetimes.

We demonstrate the complementary natures of neural knowledge graph embedding, fine-grain entity type prediction, and neural language modeling. We show that a language model-inspired knowledge graph embedding approach yields both improved knowledge graph embeddings and fine-grain entity type representations. Our work also shows that jointly modeling both structured knowledge tuples and language improves both.

Detection of semantic similarity plays a vital role in sentence matching. It requires to learn discriminative representations of natural language. Recently, owing to more and more sophisticated model architecture, impressive progress has been made, along with a time-consuming training process and not-interpretable inference. To alleviate this problem, we explore a metric learning approach, named FILM (Fast, Interpretable, and Low-rank Metric learning) to efficiently find a high discriminative projection of the high-dimensional data. We construct this metric learning problem as a manifold optimization problem and solve it with the Cayley transformation method with the Barzilai-Borwein step size. In experiments, we apply FILM with triplet loss minimization objective to the Quora Challenge and Semantic Textual Similarity (STS) Task. The results demonstrate that the FILM method achieves superior performance as well as the fastest computation speed, which is consistent with our theoretical analysis of time complexity.

This intelligent study provides historical data forecast from 2020 to 2026.

Amica's new app uses artificial intelligence (AI) to help divorce couples fairly and separate their assets.