During the evolution of large models, performance evaluation is necessarily performed on the intermediate models to assess their capabilities, and on the well-trained model to ensure safety before practical application. However, current model evaluations mainly rely on specific tasks and datasets, lacking a united framework for assessing the multidimensional intelligence of large models. In this perspective, we advocate for a comprehensive framework of artificial general intelligence (AGI) test, aimed at fulfilling the testing needs of large language models and multi-modal large models with enhanced capabilities. The AGI test framework bridges cognitive science and natural language processing to encompass the full spectrum of intelligence facets, including crystallized intelligence, a reflection of amassed knowledge and experience; fluid intelligence, characterized by problem-solving and adaptive reasoning; social intelligence, signifying comprehension and adaptation within multifaceted social scenarios; and embodied intelligence, denoting the ability to interact with its physical environment. To assess the multidimensional intelligence of large models, the AGI test consists of a battery of well-designed cognitive tests adopted from human intelligence tests, and then naturally encapsulates into an immersive virtual community. We propose that the complexity of AGI testing tasks should increase commensurate with the advancements in large models. We underscore the necessity for the interpretation of test results to avoid false negatives and false positives. We believe that cognitive science-inspired AGI tests will effectively guide the targeted improvement of large models in specific dimensions of intelligence and accelerate the integration of large models into human society.

Counterfactuals and counterfactual reasoning underpin numerous techniques for auditing and understanding artificial intelligence (AI) systems. The traditional paradigm for counterfactual reasoning in this literature is the interventional counterfactual, where hypothetical interventions are imagined and simulated. For this reason, the starting point for causal reasoning about legal protections and demographic data in AI is an imagined intervention on a legally-protected characteristic, such as ethnicity, race, gender, disability, age, etc. We ask, for example, what would have happened had your race been different? An inherent limitation of this paradigm is that some demographic interventions -- like interventions on race -- may not translate into the formalisms of interventional counterfactuals. In this work, we explore a new paradigm based instead on the backtracking counterfactual, where rather than imagine hypothetical interventions on legally-protected characteristics, we imagine alternate initial conditions while holding these characteristics fixed. We ask instead, what would explain a counterfactual outcome for you as you actually are or could be? This alternate framework allows us to address many of the same social concerns, but to do so while asking fundamentally different questions that do not rely on demographic interventions.

The resolution of intelligence tests, in particular numerical sequences, has been of great interest in the evaluation of AI systems. We present a new computational model called KitBit that uses a reduced set of algorithms and their combinations to build a predictive model that finds the underlying pattern in numerical sequences, such as those included in IQ tests and others of much greater complexity. We present the fundamentals of the model and its application in different cases. First, the system is tested on a set of number series used in IQ tests collected from various sources. Next, our model is successfully applied on the sequences used to evaluate the models reported in the literature. In both cases, the system is capable of solving these types of problems in less than a second using standard computing power. Finally, KitBit's algorithms have been applied for the first time to the complete set of entire sequences of the well-known OEIS database. We find a pattern in the form of a list of algorithms and predict the following terms in the largest number of series to date. These results demonstrate the potential of KitBit to solve complex problems that could be represented numerically.

Graph data is ubiquitous in the physical world, and it has always been a challenge to efficiently model graph structures using a unified paradigm for the understanding and reasoning on various graphs. Moreover, in the era of large language models, integrating complex graph information into text sequences has become exceptionally difficult, which hinders the ability to interact with graph data through natural language instructions.The paper presents a new paradigm for understanding and reasoning about graph data by integrating image encoding and multimodal technologies. This approach enables the comprehension of graph data through an instruction-response format, utilizing GPT-4V's advanced capabilities. The study evaluates this paradigm on various graph types, highlighting the model's strengths and weaknesses, particularly in Chinese OCR performance and complex reasoning tasks. The findings suggest new direction for enhancing graph data processing and natural language interaction.

Human intelligence and human consciousness emerge gradually during the process of cognitive development. Understanding this development is an essential aspect of understanding the human mind and may facilitate the construction of artificial minds with similar properties. Importantly, human cognitive development relies on embodied interactions with the physical and social environment, which is perceived via complementary sensory modalities. These interactions allow the developing mind to probe the causal structure of the world. This is in stark contrast to common machine learning approaches, e.g., for large language models, which are merely passively ``digesting'' large amounts of training data, but are not in control of their sensory inputs. However, computational modeling of the kind of self-determined embodied interactions that lead to human intelligence and consciousness is a formidable challenge. Here we present MIMo, an open-source multi-modal infant model for studying early cognitive development through computer simulations. MIMo's body is modeled after an 18-month-old child with detailed five-fingered hands. MIMo perceives its surroundings via binocular vision, a vestibular system, proprioception, and touch perception through a full-body virtual skin, while two different actuation models allow control of his body. We describe the design and interfaces of MIMo and provide examples illustrating its use. All code is available at https://github.com/trieschlab/MIMo .

This paper considers how interactions with AI algorithms can boost human creative thought. We employ a psychological task that demonstrates limits on human creativity, namely semantic feature generation: given a concept name, respondents must list as many of its features as possible. Human participants typically produce only a fraction of the features they know before getting "stuck." In experiments with humans and with a language AI (GPT-4) we contrast behavior in the standard task versus a variant in which participants can ask for algorithmically-generated hints. Algorithm choice is administered by a multi-armed bandit whose reward indicates whether the hint helped generating more features. Humans and the AI show similar benefits from hints, and remarkably, bandits learning from AI responses prefer the same prompting strategy as those learning from human behavior. The results suggest that strategies for boosting human creativity via computer interactions can be learned by bandits run on groups of simulated participants.

Transformer based language models exhibit intelligent behaviors such as understanding natural language, recognizing patterns, acquiring knowledge, reasoning, planning, reflecting and using tools. This paper explores how their underlying mechanics give rise to intelligent behaviors. Towards that end, we propose framing Transformer dynamics as movement through embedding space. Examining Transformers through this perspective reveals key insights, establishing a Theory of Transformers: 1) Intelligent behaviours map to paths in Embedding Space which, the Transformer random-walks through during inferencing. 2) LM training learns a probability distribution over all possible paths. `Intelligence' is learnt by assigning higher probabilities to paths representing intelligent behaviors. No learning can take place in-context; context only narrows the subset of paths sampled during decoding. 5) The Transformer is a self-mapping composition function, folding a context sequence into a context-vector such that it's proximity to a token-vector reflects its co-occurrence and conditioned probability. Thus, the physical arrangement of vectors in Embedding Space determines path probabilities. 6) Context vectors are composed by aggregating features of the sequence's tokens via a process we call the encoding walk. Attention contributes a - potentially redundant - association-bias to this process. 7) This process is comprised of two principal operation types: filtering (data independent) and aggregation (data dependent). This generalization unifies Transformers with other sequence models. Building upon this foundation, we formalize a popular semantic interpretation of embeddings into a ``concept-space theory'' and find some evidence of it's validity.

In this paper, we collect an anthology of 100 visual stories from Visual imagery and language have long since complemented each authors who participated in our systematic creative process of improvised other in visual storytelling. From children's picture books to comics story-building based on image sequences. Following close and news articles, this multimedia nexus forms a complementary reading and thematic analysis of our anthology, we present five interplay between imagery and spoken or written language. While themes that characterize the variations found in this creative visual audiences often experience stories and pictures together, visual storytelling process: (1) Narrating What is in Vision vs. Envisioning; images alone can also operate as starting points--sources of creative (2) Dynamically Characterizing Entities/Objects; (3) Sensing inspiration--for authors to write stories [42]. Researchers have Experiential Information About the Scenery; (4) Modulating the found that visual thinking [5, 6] and drawing [3] can prompt storytelling Mood; (5) Encoding Narrative Biases. In understanding the varied from a multitude of perspectives as long as creativity is not ways that people derive stories from images, we offer considerations disturbed in the process [17]. This affirms how creative writing and for collecting story-driven training data to inform automatic visual imagery are interconnected such that stories can be derived story generation. In correspondence with each theme, we envision from images to culminate in creative visual storytelling.

A growing number of experts have called for these tests to be ditched, saying they boost AI hype and create "the illusion that [AI language models] have greater capabilities than what truly exists." What stood out to me in Will's story is that we know remarkably little about how AI language models work and why they generate the things they do. With these tests, we're trying to measure and glorify their "intelligence" based on their outputs, without fully understanding how they function under the hood. Our tendency to anthropomorphize makes this messy: "People have been giving human intelligence tests--IQ tests and so on--to machines since the very beginning of AI," says Melanie Mitchell, an artificial-intelligence researcher at the Santa Fe Institute in New Mexico. "The issue throughout has been what it means when you test a machine like this. It doesn't mean the same thing that it means for a human."

Dr. Craig Basman discusses new life-saving technology and the variables that can predict sudden cardiac events. Half of those who suffer cardiac arrest experience a telling symptom 24 hours before the incident, according to a study recently published in The Lancet Digital Health journal. This warning symptom was different in men and in women, researchers from Smidt Heart Institute found; the institute is located in the Cedars Sinai Medical Center in Los Angeles. For women, shortness of breath was the symptom that preceded an impending cardiac arrest, while for men, chest pain was the prominent complaint. SKIPPING THE SALT CAN REDUCE HEART DISEASE RISK BY ALMOST 20%, STUDY FINDS: 'KNOW WHAT YOU ARE CONSUMING' Sweating and seizure-like activity occurred in smaller subgroups of both genders, the researchers noted.