Human-like agents are an increasingly important topic in games and beyond. Believable non-player characters enhance the gaming experience by improving immersion and providing entertainment. They also offer players the opportunity to engage with AI entities that can function as opponents, teachers, or cooperating partners. Additionally, in games where bots are prohibited -- and even more so in non-game environments -- there is a need for methods capable of identifying whether digital interactions occur with bots or humans. This leads to two fundamental research questions: (1) how to model and implement human-like AI, and (2) how to measure its degree of human likeness. This article offers two contributions. The first one is a survey of the most significant challenges in implementing human-like AI in games (or any virtual environment featuring simulated agents, although this article specifically focuses on games). Thirteen such challenges, both conceptual and technical, are discussed in detail. The second is an empirical study performed in a tactical video game that addresses the research question: "Is it possible to distinguish human players from bots (AI agents) based on empirical data?" A machine-learning approach using a custom deep recurrent convolutional neural network is presented. We hypothesize that the more challenging it is to create human-like AI for a given game, the easier it becomes to develop a method for distinguishing humans from AI-driven players.

Artificial Intelligence has come a long way since its inception. Today, AI is no longer limited to just crunching numbers and following pre-programmed rules. With advances in machine learning and natural language processing, AI can now simulate human-like qualities, such as empathy and creativity. However, the question arises: should we create AI that is too human-like? Let's explore the advantages and disadvantages of creating human-like AI and weigh the potential benefits against the risks.

After making many permutations to the thought stream lookback range, model temperature, and few-shot examples, the messages produced seem to be qualitatively worse when using a long thought stream than when using an arbitrarily short one, though this requires more experimentation, and a good benchmark. Intuitively this makes sense because a GPT trained on the internet wouldn't have many training examples of what a human was thinking (at least in a direct access format like this) before they said or wrote something. I'll need to rethink the way thoughts are incorporated or if they can be removed entirely. Perhaps thinking is an emergent property of intelligence and does not need to be explicitly included.

Of course, deep learning has made progress, but on those foundational questions, not so much; on natural language, compositionality and reasoning, which differ from the kinds of pattern recognition on which deep learning excels, these systems remain massively unreliable, exactly as you would expect from systems that rely on statistical correlations, rather than an algebra of abstraction. Minerva, the latest, greatest AI system as of this writing, with billions of "tokens" in its training, still struggles with multiplying 4-digit numbers.

In short, much of our understanding of the world is given by nature, with learning as a matter of fleshing out the details. There is an alternate, empiricist view which inverts this: symbolic manipulation is a rarity in nature, primarily arising as a learned capacity for communicating acquired gradually by our hominin ancestors over the last two million years. On this view, the primary cognitive capacities are non-symbolic learning abilities bound up with improving survival, such as rapidly recognizing prey, predicting their likely actions, and developing skillful responses. This assumes that the vast majority of complex cognitive abilities are acquired through a general, self-supervised learning capacity, one that acquires an intuitive world-model capable of the central features of common sense through experience. It also assumes that most of our complex cognitive capacities do not turn on symbolic manipulation; they make do, instead, with simulating various scenarios and predicting the best outcomes.

We've already begun the humanisation process by giving AI human-like physical features, mannerisms and names. Companies will likely take these steps in humanising AI to increase public acceptance of these technologies. The movement to humanise AI, though, involves more than human likeness alone. "The goal of human-like AI is to replicate the positive capabilities of human beings, not the weaknesses," according to Mike Myer, CEO of conversational AI platform Quiq. He explains that more human-like AI in these circumstances would involve making the AI more personable so that the interaction feels authentic, as though coming from a human being.

Artificial Intelligence (AI) has emerged as the most popular buzzword of the technology industry. Since its inception, AI has managed to generate enough curiosity around itself. However, it is yet to advance enough to match up with the expectations. The notion that machines will replace humans is still far-fetched. With researchers, scientist and corporates all over the globe trying to integrate this technology more into our daily lives, it has become an imperative to consider ways to make this technology more human.

One of the biggest issues with current "AI" is that it is very narrow. It's a programme to interpret data, or to drive a car, or to play chess, or to act as a carer, or to draw a picture. But almost any human can make a stab at doing all of those, and with a bit of training or learning can get better at them all. If we want to get closer to the SF ideal of AI, and also to make it a lot easier to use AI in the world around us, then what we really need is a "general purpose AI" -- or what is commonly called Artificial General Intelligence (AGI). There is a lot of research going into AGI at the moment in academic institutions and elsewhere (for examples/comment see KORTELING2021, Stanford AI100 Report), but it is really early days.

Machine learning is the science of teaching a computer to solve problems by example rather than writing sequential algorithms which instructions run one by one. Data preparation for machine learning is the prior step towards training a model, and usually involves two substeps: creating a dataset and transforming the data. In this post I'll be focusing on the former in the context of building a human-like AI to play chess in PHP. Because contrary to popular belief, Python is not the only programming language for data science in this world. I am preparing the data on this GitHub repo with MySQL, PHP and Rubix ML, a machine learning and deep learning library for the PHP language.

Magic Leap showed off a demo of Mica, a humanlike artificial intelligence that can be viewed in the company's augmented reality glasses, the Magic Leap One Creator Edition. I saw a demo of Mica, a short-haired woman who doesn't speak but still communicates in warm ways with the viewer. I put the AR glasses on my head and looked through prescription inserts to see the virtual overlays on the real world. I thought it was the best thing Magic Leap showed off. I walked into a physical room and sat in a chair.