The question of whether AI can reason and possess logic remains a subject of debate. While some argue that AI can reason and exhibit logical thinking, others contend that it is merely a tool programmed by humans.

Proponents of AI reasoning argue that machines can process vast amounts of data and make decisions based on patterns and algorithms. They claim that AI systems like IBM's Watson have demonstrated reasoning abilities by defeating human champions in games like Jeopardy!. Moreover, AI algorithms are designed to learn from their mistakes and improve their performance over time, which indicates a form of reasoning.

On the other hand, skeptics believe that AI lacks true reasoning capabilities as it relies solely on pre-programmed instructions. They argue that while machines can simulate human-like behavior or mimic logical processes, they do not possess genuine understanding or consciousness. Furthermore, they emphasize that AI cannot generate new ideas or think creatively like humans do.

In conclusion, the debate regarding whether AI can reason and possess logic is ongoing. While proponents highlight the ability of machines to process data and make informed decisions based on patterns, skeptics argue that true reasoning requires consciousness and creativity which are lacking in AI systems. As technology continues to advance, further research is needed to explore the boundaries of artificial intelligence's cognitive abilities.

Reference:

Russell, S., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach (3rd ed.). Pearson Education Limited.

@George Benneh Mensah thanks

Bao Pham ,

The same difference between you and your dog exist for AI systems. One cannot make a sweeping argument across the field due to:

1) The spectrum ranges from simple algorithms to complete embodied robotic systems with several dozens of algorithms and functions.

2) The complexity of the problem. Most academic problems are too simple to make a generalization (and get accepted for publication). On the other hand the publication constraints of corporations make it almost impossible to replicate the system and the data (they would loose their competitive advantage).

3) The nature of the algorithm. You can make the distinction between symbolic processing and neural networks.

While recent large language models(LLMs) papers I have seen boast logical processing, they have to be treated carefully. Some experimental setups have been found to have hints embedded in the training data that merely show that the NN learned to recognize the inference pattern and did not reason[1]. The other thing that makes evaluation of LLMs hard is that analysis of terabytes of data by humans to evaluate whether inference is truly made and not just learned on these models is not feasible.

On the other hand, systems such as Watson do carry a range of reasoning routines, but are limited in scope.

As a final note, I have not made emphasis on what logic is AI systems are capable of (first order, second order, alethic, temporal, deontic,probabilistic, etc.) and the data necessary to process the problem. We deal most of the time with incomplete information, but how incomplete is incomplete and what are the constraints to solve the problem?

References

[1] Annotation Artifacts in Natural Language Inference Data

by GururanganF and Swayamdipta

Whether or not AI reasons and has logic is a complex question that is still being debated by experts. Some AI researchers believe that AI can reason and has logic, while others believe that AI is simply a sophisticated pattern recognition machine.

There is evidence to support both sides of the argument. For example, AI systems have been developed that can solve complex problems that require logical reasoning, such as playing chess and proving mathematical theorems. However, AI systems have also been shown to make mistakes, even when presented with simple problems.

One of the challenges in defining AI reasoning is that it is difficult to separate reasoning from other cognitive abilities, such as perception and knowledge. For example, an AI system that can play chess may be able to reason about the different possible moves and their consequences. However, the AI system may also be relying on its knowledge of chess strategies and its ability to perceive the current state of the board.

Another challenge is that AI reasoning is often implemented using different techniques. For example, some AI systems use rule-based reasoning, while others use statistical reasoning. Rule-based reasoning involves using a set of rules to infer new knowledge from existing knowledge. Statistical reasoning involves using statistical methods to make predictions about future events.

Overall, the question of whether or not AI reasons and has logic is still open to debate. However, there is growing evidence to suggest that AI systems are capable of logical reasoning, even if their reasoning abilities are not yet as sophisticated as those of humans.

Here are some examples of AI systems that have been shown to exhibit logical reasoning:

• AlphaGo: AlphaGo is a computer program that can play the game of Go at a superhuman level. AlphaGo uses a combination of machine learning and logical reasoning to make its moves.
• Watson: Watson is a computer program that can answer questions in a comprehensive and informative way, even if they are open ended, challenging, or strange. Watson uses a combination of machine learning and logical reasoning to process and understand information from a variety of sources.
• GPT-3: GPT-3 is a large language model that can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. GPT-3 uses a combination of machine learning and logical reasoning to process and understand information from a variety of sources.