We review the historical development and current trends of artificially intelligent agents (agentic AI) in the social and behavioral sciences: from the first programmable computers, and social simulations soon thereafter, to today's experiments with large language models. This overview emphasizes the role of AI in the scientific process and the changes brought about, both through technological advancements and the broader evolution of science from around 1950 to the present. Some of the specific points we cover include: the challenges of presenting the first social simulation studies to a world unaware of computers, the rise of social systems science, intelligent game theoretic agents, the age of big data and the epistemic upheaval in its wake, and the current enthusiasm around applications of generative AI, and many other topics. A pervasive theme is how deeply entwined we are with the technologies we use to understand ourselves.

The rise of'deep learning' has caused a lot of excitement around the revolutionary capabilities of these artificially intelligent agents. But it's also raised fear and suspicion about what exactly is going on inside each algorithm. One way for us to gain some understanding of our silicon-based friends (or foes?) is for them to disclose their framework of decision-making in a way that we humans can understand – by using the concept of personality. My research explores how some of these deep learning agents can be better understood through their'personalities' – like whether they are'greedy', 'selfish' or'prudent'. We are now at the dawn of a new era in AI technology – a so-called fourth industrial revolution that will reshape every industry.

--Angry Birds is a popular video game in which the player is provided with a sequence of birds to shoot from a slingshot. The task of the game is to destroy all green pigs with maximum possible score. Angry Birds appears to be a difficult task to solve for artificially intelligent agents due to the sequential decision-making, non-deterministic game environment, enormous state and action spaces and requirement to differentiate between multiple birds, their abilities and optimum tapping times. We describe the application of Deep Reinforcement learning by implementing Double Dueling Deep Q-network to play Angry Birds game. One of our main goals was to build an agent that is able to compete with previous participants and humans on the first 21 levels. In order to do so, we have collected a dataset of game frames that we used to train our agent on. We evaluate our agent using results of the previous participants of AIBirds competition, results of volunteer human players and present the results of AIBirds 2018 competition. I NTRODUCTION Angry Birds has been one of the most popular video games for a period of several years. The main goal of the game is to kill all green pigs on the level together with applying as much damage as possible to the surrounding structures.

As usual, I was inspired during my drive home when listening to a podcast. This particular show was with the Dr. Ben Goertzel, CEO of SingularityNet. When he started talking, I was amazed and awe struck by their mission found on their website. Let's unpack these three sentences a bit. First, Singularity's idea is to create a world where there are loads upon loads of artificially intelligent beings that are busy solving problems.

Gary Marcus has recently published a detailed, rather extensive critique of Deep Learning. While many of Dr. Marcus's points are well-known among those deeply familiar with the field and have been somewhat well-publicized for years, these discussions haven't yet reached many who are newly involved in decision-making in this space. Overall, the discussion the critique has generated seems clarifying and useful. I have decided to write up my thoughts because, while I think Dr. Marcus' critique is thoughtful, necessary and often justified, I disagree with some of the conclusions. To start, Dr. Marcus' assessment that Deep Learning, as originally defined, is merely a statistical technique for classifying patterns is spot on in my opinion.

Editor's note: This is the first entry in a new video series, HardWIRED: Welcome to the Robotic Future, in which we explore the many fascinating machines that are transforming society. And we can't do that without first defining what a robot even is. When you hear the word "robot," the first thing that probably comes to mind is a silvery humanoid, à la The Day the Earth Stood Still or C-3PO (more golden, I guess, but still metallic). A robot can be a lot of things these days―and this is just the beginning of their proliferation. With so many different kinds of robots, how do you define what one is?

Long before Skynet released the Terminator on our unsuspecting world, the idea of artificially intelligent robots taking over the world was a common recurrence in pop culture. Despite us being many years away from that grim probability, current artificial intelligence (AI) developments have many practical uses in today's world, especially when it comes to customer engagement.