Situationally-aware artificial agents operating with competence in natural environments face several challenges: spatial awareness, object affordance detection, dynamic changes and unpredictability. A critical challenge is the agent's ability to identify and monitor environmental elements pertinent to its objectives. Our research introduces a neurosymbolic modular architecture for reactive robotics. Our system combines a neural component performing object recognition over the environment and image processing techniques such as optical flow, with symbolic representation and reasoning. The reasoning system is grounded in the embodied cognition paradigm, via integrating image schematic knowledge in an ontological structure. The ontology is operatively used to create queries for the perception system, decide on actions, and infer entities' capabilities derived from perceptual data. The combination of reasoning and image processing allows the agent to focus its perception for normal operation as well as discover new concepts for parts of objects involved in particular interactions. The discovered concepts allow the robot to autonomously acquire training data and adjust its subsymbolic perception to recognize the parts, as well as making planning for more complex tasks feasible by focusing search on those relevant object parts. We demonstrate our approach in a simulated world, in which an agent learns to recognize parts of objects involved in support relations. While the agent has no concept of handle initially, by observing examples of supported objects hanging from a hook it learns to recognize the parts involved in establishing support and becomes able to plan the establishment/destruction of the support relation. This underscores the agent's capability to expand its knowledge through observation in a systematic way, and illustrates the potential of combining deep reasoning [...].

Intelligence is understood as the ability to create novel skills that allow to achieve goals under previously unknown conditions. To this end, intelligence utilises reasoning methods such as deduction, induction and abduction as well as other methods such as abstraction and classification to develop a world model. The methods are applied to indirect and incomplete representations of the world, which are obtained through perception, for example, and which do not depict the world but only correspond to it. Due to these limitations and the uncertain and contingent nature of reasoning, the world model is constructivist. Its value is functionally determined by its viability, i.e., its potential to achieve the desired goals. In consequence, meaning is assigned to representations by attributing them a function that makes it possible to achieve a goal. This representational and functional conception of intelligence enables a naturalistic interpretation that does not presuppose mental features, such as intentionality and consciousness, which are regarded as independent of intelligence. Based on a phenomenological analysis, it is shown that AGI can gain a more fundamental access to the world than humans, although it is limited by the No Free Lunch theorems, which require assumptions to be made.

From early days, a key and controversial question inside the artificial intelligence community was whether Artificial General Intelligence (AGI) is achievable. AGI is the ability of machines and computer programs to achieve human-level intelligence and do all tasks that a human being can. While there exist a number of systems in the literature claiming they realize AGI, several other researchers argue that it is impossible to achieve it. In this paper, we take a different view to the problem. First, we discuss that in order to realize AGI, along with building intelligent machines and programs, an intelligent world should also be constructed which is on the one hand, an accurate approximation of our world and on the other hand, a significant part of reasoning of intelligent machines is already embedded in this world. Then we discuss that AGI is not a product or algorithm, rather it is a continuous process which will become more and more mature over time (like human civilization and wisdom). Then, we argue that pre-trained embeddings play a key role in building this intelligent world and as a result, realizing AGI. We discuss how pre-trained embeddings facilitate achieving several characteristics of human-level intelligence, such as embodiment, common sense knowledge, unconscious knowledge and continuality of learning, by machines.

The "brain in a jar" is a thought experiment of a disembodied human brain living in a jar of sustenance. The thought experiment explores human conceptions of reality, mind, and consciousness. This article will explore a metaphysical argument against artificial intelligence on the grounds that a disembodied artificial intelligence, or a "brain" without a body, is incompatible with the nature of intelligence.[1] The brain in a jar is a different inquiry than traditional questions about artificial intelligence. The brain in a jar asks whether thinking requires a thinker. The possibility of artificial intelligence primarily revolves around what is necessary to make a computer (or a computer program) intelligent.

GPT-3 is the latest attempt by OpenAI, a tech research lab in San Francisco, to unlock artificial intelligence with an anvil rather than a hairpin. As brute force strategies go, the results are impressive. The language-generating model performs well across a striking range of contexts. Given only simple prompts, GPT-3 writes not just interesting short stories and clever songs, but also executable code such as web graphics. GPT-3's ability to dazzle with prose and poetry that appears entirely natural, even erudite or lyrical, is less surprising. It's a parlor trick that its predecessor performed a year earlier, though its then-massive 1.5 billion parameters are swamped by GPT-3's power, which uses 175 billion parameters to enhance its stylistic abstractions and semantic associations. Just like their great-grandmother, Joseph Weizenbaum's ELIZA, a natural language processing program developed in the 1960s, these systems benefit considerably from human reliance on familiar heuristics for speakers' cognitive abilities.

"Perhaps expectations are too high, and… this will eventually result in disaster. Suppose that five years from now, funding collapses miserably as autonomous vehicles fail to roll. And there's a big backlash so that you can't get money for anything connected with AI. Everybody hurriedly changes the names of their research projects to something else. This condition is called the AI Winter," said AI expert Drew McDermott in 1984.

A fun pastime for armchair philosophers of technology is whether the entire field of artificial intelligence, so riven with hype at the moment, will any day now experience a crushing fall-off in enthusiasm and a consequent collapse in funding. It's the notion of an "AI winter," and it has hit the field a couple of times in the past forty years, right after big breakthroughs. The answer is No, there won't be an AI winter. It's different for a simple reason: artificial intelligence, in its latest incarnation, called deep learning, has become "industrialized." For the first time ever, AI is part of how companies work.

In 1965, Herbert Simon predicted that "within twenty years, machines would be capable of doing any work a man can do." Five years later, Marvin Minsky forecasted that "in from three to eight years we would have a machine with the general intelligence of an average human being." Bringing up these predictions is not necessarily meant to emphasize that roughly 50 years later they're still a long way from turning real – in spite of the obvious breakthroughs. It's more about acknowledging the fact that the early founders of AI were overly optimistic about the future of AI. This optimism fueled not only the hype, but also their own dismissal of any criticism – particularly coming from philosophers.

The recent practical successes [26] of Artificial Intelligence (AI) programs of the Reinforcement Learning and Deep Learning varieties in game playing, natural language processing and image classification, are now calling attention to the envisioned pitfalls of their hypothetical extension to wider domains of human behavior. Several voices from the industry and academia are now routinely raising concerns over the advances [49] of often heavily media-covered representatives of this new generation of programs such as Deep Blue, Watson, Google Translate, AlphaGo and AlphaZero. Most of these cutting-edge algorithms generally fall under the class of supervised learning, a branch of the still evolving taxonomy of Machine Learning techniques in AI research. In most cases the implementation choice is artificial neural networks software, the workhorse of the Connectionism school of thought in both AI and Cognitive Psychology. Confronting the current wave of connectionist architectures, critics usually raise issues of interpretability (Can the remarkable predictive capabilities be 1 trusted in real-life tasks? Are these capabilities transferable to unfamiliar situations or to different tasks altogether? How informative are the results about the real world; about human cognition?

Artificial intelligence helps farmers, doctors and rescue workers make a positive impact on society. Artificial intelligence (AI) powers many gadgets, like smartphones, smart thermostats and voice-activated virtual assistants that bring modern conveniences to daily life. Increasingly, AI is also being used to tackle critical social challenges. AI is a branch of computer science where machines can sense, learn, reason, act and adapt to the real world, amplifying human capabilities and automating tedious or dangerous tasks. Some experts believe AI has the potential to spark a serious social revolution.