Named after mathematician and computer pioneer Ada Lovelace, Ai-Da is the world’s first humanoid AI robot artist that can create artistic pieces from sight using her robotic eyes and hands. Ai-Da…

The next few Tuesdays, The Verge's flagship podcast The Vergecast is showcasing a miniseries dedicated to the use of artificial intelligence in industries that are often overlooked, hosted by Verge senior reporter Ashley Carman. This week, the series focuses on AI for the video world. More specifically, we're looking at how AI is being used as a tool to help people streamline the process of creating video content. Yes, this might mean software taking on a bigger role in the very human act of creativity, but what if instead of replacing us, machine learning tools could be used to assist our work? That's what Scott Prevost, VP of Adobe Sensei -- Adobe's machine learning platform -- envisions for Adobe's AI products. "Sensei was founded on this firm belief that we have that AI is going to democratize and amplify human creativity, but not replace it," Prevost says.

We are used to looking only at well-defined and delimited fields, where business thrives and goes on, and where economic resources and technological availability make the road to innovation more straightforward. However, in my opinion, we never stop to analyse what Shakir Mohamed, in his "Decolonial AI", defines as the "peripheries", shifting our "ictu oculi" from the centre towards new paradigms, still unexplored, if not ignored. Therefore, I found this study by Agunbiade Akintunde Ifeanyichukwu, whose name already says it all, since he signs himself Agunbiade A.I., which analyses the relationship between Artificial Intelligence (AI) and the Nigerian legal system, entitled "Artificial Intelligence and Law, a Nigerian Perspective", really interesting. The aim was to explore the ways in which they can influence each other, capturing new and half-known aspects of little-discussed legal systems. This book proposed the development of an indigenous AI system, coupled with ADR mechanisms, that would have the power to reduce the incidence of court congestion, while analysing a comprehensive legal framework of how it would work.

Jeremy Wood is the Head of Product Marketing at Adobe APAC. In today's digital-first economy, there's a perception that artificial intelligence ("AI") and automation can hamper creativity. By introducing automation and software into various processes--whether it's speeding manufacturing or generating effective marketing campaigns--new ideas and innovation can potentially wither. But in reality, nothing could be further from the truth. While we've made great strides in leveraging AI to aid in the creative process, machines ultimately can't think or feel, and need coaching from humans.

Endgame studies have long served as a tool for testing human creativity and intelligence. We find that they can serve as a tool for testing machine ability as well. Two of the leading chess engines, Stockfish and Leela Chess Zero (LCZero), employ significantly different methods during play. We use Plaskett's Puzzle, a famous endgame study from the late 1970s, to compare the two engines. Our experiments show that Stockfish outperforms LCZero on the puzzle. We examine the algorithmic differences between the engines and use our observations as a basis for carefully interpreting the test results. Drawing inspiration from how humans solve chess problems, we ask whether machines can possess a form of imagination. On the theoretical side, we describe how Bellman's equation may be applied to optimize the probability of winning. To conclude, we discuss the implications of our work on artificial intelligence (AI) and artificial general intelligence (AGI), suggesting possible avenues for future research.

A recent debate on so called Foundation models (CRFM) brings a real question of if we can build very large models on any specified domain, similar to current large language models, and replace our ...

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"VICReg could be used to model the dependencies between a video clip and the frame that comes after, therefore learning to predict the future in a video." Humans have an innate capability to identify objects in the wild, even from a blurred glimpse of the thing. We do this efficiently by remembering only high-level features that get the job done (identification) and ignoring the details unless required. In the context of deep learning algorithms that do object detection, contrastive learning explored the premise of representation learning to obtain a large picture instead of doing the heavy lifting by devouring pixel-level details. But, contrastive learning has its own limitations.

The Covid-19 pandemic has accelerated the development of artificial intelligence across the globe. Organizations are using artificial intelligence to increase the productivity of remote workers, enhance the virtual shopping experience, drive the digital transformation process and speed up the development of important drugs to end this on-going pandemic. Real artificial intelligence is creating value by making humans more efficient, not redundant. Specialization: Narrow AI, Specialists, Scientists, Learned Ignoramus, which divides, specializes, and thinks in special categories. Interdisciplinarity is about the interactions between specialised fields and cooperation among special disciplines to solve a specific problem.

Artificial intelligence and machine learning are two popular and often hyped terms these days. And people often use them interchangeably to describe an intelligent software or system. But even though both AI and ML are based on statistics and mathematics, they are not the same thing. In this article, you will learn the differences between AI and ML with some practical examples to help clear up any confusion. Artificial intelligence, or AI, is the ability of a computer or machine to mimic or imitate human intelligent behavior and perform human-like tasks.