Software will come and go but there's one thing that has always stayed the same since the start of the personal computing era - the spreadsheet. Love it or hate it, the spreadsheet is the backbone of many jobs and tasks, from organizing your day to managing the finances of a country. And yet, the core functionality has always stayed the same. Yes, there's been some nice bells and whistles added over the years, like images and graphs, but don't you wish your spreadsheet could do more? By integrating Cohere's language AI into your Google documents, you can now perform advanced text analysis such as categorization, sentiment analysis, summarization, and more.

In this series we examine some of the most popular doomsday scenarios prognosticated by modern AI experts. Previous articles include Misaligned Objectives, Artificial Stupidity, Wall-E Syndrome, Humanity Joins the Hivemind, and Killer Robots. We've covered a lot of ground in this series (see above), but nothing comes close to our next topic. The "democratization of expertise" might sound like a good thing -- democracy, expertise, what's not to like? But it's our intent to convince you that it's the single greatest AI-related threat our species faces by the time you finish reading this article.

As a field, AI centers around creating flexible automated systems.

PRNewswire/ — The "Europe Artificial Intelligence In Fintech Market Size, Share & Industry Trends Analysis Report By Component (Solutions and …

Polarization and echo chambers are often studied in the context of explicitly political events such as elections, and little scholarship has examined the mixing of political groups in non-political contexts. A major obstacle to studying political polarization in non-political contexts is that political leaning (i.e., left vs right orientation) is often unknown. Nonetheless, political leaning is known to correlate (sometimes quite strongly) with many lifestyle choices leading to stereotypes such as the "latte-drinking liberal." We develop a machine learning classifier to infer political leaning from non-political text and, optionally, the accounts a user follows on social media. We use Voter Advice Application results shared on Twitter as our groundtruth and train and test our classifier on a Twitter dataset comprising the 3,200 most recent tweets of each user after removing any tweets with political text. We correctly classify the political leaning of most users (F1 scores range from 0.70 to 0.85 depending on coverage). We find no relationship between the level of political activity and our classification results. We apply our classifier to a case study of news sharing in the UK and discover that, in general, the sharing of political news exhibits a distinctive left-right divide while sports news does not.

In recent years there has been substantial growth in the capabilities of systems designed to generate text that mimics the fluency and coherence of human language. From this, there has been considerable research aimed at examining the potential uses of these natural language generators (NLG) towards a wide number of tasks. The increasing capabilities of powerful text generators to mimic human writing convincingly raises the potential for deception and other forms of dangerous misuse. As these systems improve, and it becomes ever harder to distinguish between human-written and machine-generated text, malicious actors could leverage these powerful NLG systems to a wide variety of ends, including the creation of fake news and misinformation, the generation of fake online product reviews, or via chatbots as means of convincing users to divulge private information. In this paper, we provide an overview of the NLG field via the identification and examination of 119 survey-like papers focused on NLG research. From these identified papers, we outline a proposed high-level taxonomy of the central concepts that constitute NLG, including the methods used to develop generalised NLG systems, the means by which these systems are evaluated, and the popular NLG tasks and subtasks that exist. In turn, we provide an overview and discussion of each of these items with respect to current research and offer an examination of the potential roles of NLG in deception and detection systems to counteract these threats. Moreover, we discuss the broader challenges of NLG, including the risks of bias that are often exhibited by existing text generation systems. This work offers a broad overview of the field of NLG with respect to its potential for misuse, aiming to provide a high-level understanding of this rapidly developing area of research.

Cutscenes form an integral part of many video games, but their creation is costly, time-consuming, and requires skills that many game developers lack. While AI has been leveraged to semi-automate cutscene production, the results typically lack the internal consistency and uniformity in style that is characteristic of professional human directors. We overcome this shortcoming with Cine-AI, an open-source procedural cinematography toolset capable of generating in-game cutscenes in the style of eminent human directors. Implemented in the popular game engine Unity, Cine-AI features a novel timeline and storyboard interface for design-time manipulation, combined with runtime cinematography automation. Via two user studies, each employing quantitative and qualitative measures, we demonstrate that Cine-AI generates cutscenes that people correctly associate with a target director, while providing above-average usability. Our director imitation dataset is publicly available, and can be extended by users and film enthusiasts.

ADI is the DSAI arm of the Aboitiz Group, whose task is to unlock the power of data science and artificial intelligence by testing and …

Market dynamics are forces that impact the prices and behaviors of the Global Artificial Intelligence (AI) in Drug Discovery Market stakeholders.

Cupixel, an experience start-up that brings, announces that the company secured $5M in seed funding led by JOANN.