In a recent column, New York Times technology correspondent Kevin Roose revealed a conversation he had shared with Bing's Chatbot that's equal parts fascinating and unsettling. The artificial intelligence service in question is a sibling of the popular ChatGPT, produced by the American artificial intelligence company OpenAI. But Roose wasn't just chatting with the OpenAI Codex, the company's most recent model, he was speaking with its chat mode persona, Sydney, a name given to it by Microsoft in its early stages of development. Though Roose and Sydney's conversation is, at first glance, alarming, the AI's responses to Roose's questions are far from unexpected. Its erratic use of emojis and seemingly unfiltered, emotional way of speaking feels human because, in some ways, it is – just not in the way our cultural anxieties over artificial intelligence might lead us to believe (Olson, 2023).

Despite recent breakthroughs in the field of artificial intelligence (AI) - or more specifically machine learning (ML) algorithms for object recognition and natural language processing - it seems to be the majority view that current AI approaches are still no real match for natural intelligence (NI). More importantly, philosophers have collected a long catalogue of features which imply that NI works differently from current AI not only in a gradual sense, but in a more substantial way: NI is closely related to consciousness, intentionality and experiential features like qualia (the subjective contents of mental states) and allows for understanding (e.g., taking insight into causal relationships instead of 'blindly' relying on correlations), as well as aesthetical and ethical judgement beyond what we can put into (explicit or data-induced implicit) rules to program machines with. Additionally, Psychologists find NI to range from unconscious psychological processes to focused information processing, and from embodied and implicit cognition to 'true' agency and creativity. NI thus seems to transcend any neurobiological functionalism by operating on 'bits of meaning' instead of information in the sense of data, quite unlike both the 'good old fashioned', symbolic AI of the past, as well as the current wave of deep neural network based, 'sub-symbolic' AI, which both share the idea of thinking as (only) information processing. In the following I propose an alternative view of NI as information processing plus 'bundle pushing', discuss an example which illustrates how bundle pushing can cut information processing short, and suggest first ideas for scientific experiments in neuro-biology and information theory as further investigations.

I've decided to test how GPT-3 would solve a classic "A bat and a ball" puzzle. It is usually used to showcase the difference between System 1 and System 2 of thinking. Will it make the same mistake as humans do when using System 1, and will it be able to solve it eventually? "System 1 and System 2" is a popular model that describes two modes of human decision-making and reasoning. It was proposed by psychologists Keith Stanovich and Richard West in 2000.

Although robots are more than capable today of carrying out all kinds of business tasks efficiently and accurately, the concept of building machines that can think like humans has always been a dream for tech companies and smart city developers. However, the actual way in which the human mind works and processes information is up for debate, with several parties having conflicting opinions regarding the same. Once enough data is generated, simulation models can be created to build software that can think along the same rational or emotional lines as humans. Human thinking is generally influenced by a variety of factors--cognitive, behavioral, geometric, kinematic and physical. Using cognitive modeling, such factors can be considered while attempting to create robots that think and behave like humans. The concept of human thinking is still too vague to be accurately replicated in robots.

I recently started a new newsletter focus on AI education. TheSequence is a no-BS( meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. I was recently having a debate about strong vs. weak AI with one of my favorite new thinkers in this market and it reminded me of something that I wrote over a year ago. So I decided to dust it off and restructure those thoughts in a new article.

Any business looking to streamline its processes and move to more efficient models will encounter automation, machine learning, and artificial intelligence along the way. Although in 2020 we're a far cry from sentient machinery taking over, these buzzwords are currently hot property across every industry, from manufacturing to services. So it's essential to understand these terms by definition and the way they interact. Traditionally, there has been a pyramid model for technology with artificial intelligence (AI) sitting at the top. Below are the technological building blocks required as the platforms required for AI to function.

Any business looking to streamline its processes and move to more efficient models will encounter automation, machine learning, and artificial intelligence along the way. Although in 2020 we're a far cry from sentient machinery taking over, these buzzwords are currently hot property across every industry, from manufacturing to services. So it's essential to understand these terms by definition and the way they interact. Traditionally, there has been a pyramid model for technology with artificial intelligence (AI) sitting at the top. Below are the technological building blocks required as the platforms required for AI to function.

I recently started a new newsletter focus on AI education. TheSequence is a no-BS( meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. I was recently having a debate about strong vs. weak AI with one of my favorite new thinkers in this market and it reminded me of something that I wrote over a year ago. So I decided to dust it off and restructure those thoughts in a new article.

Artificial Intelligence (AI) is the sub-domain of computing. The goal of research for Artificial Intelligence is to develop programs (software), which will enable computers to behave in a way that is characterized as intelligent. The first researches relate to the very roots of computing. The idea of creating machines that will be able to perform various tasks intelligently was the central preoccupation of computer science researchers who ventured to research artificial intelligence throughout the second half of the 20th century. Today, research in artificial intelligence is focused on expert systems, translation systems in limited domains, the recognition of human speech and written text, automatic proofers of the theorem, as well as the constant interest in creating generally intelligent, autonomous agents.

Artificial Intelligence is a paragliding term for technologies such as RPA and describes the ability of a computer to imitate human thinking. RPA is a rule-based, non-intelligence program that automates repetitive tasks. Artificial Intelligence, the buzzword that has spread in the tech world, has given rise to hundreds of discussions about the developments that surround it, and how it is reaching the industries. All the hype around AI and its technologies -Robotic Process Automation, Machine Learning (ML), and Natural Learning Process (NLP)- has created a lot of confusion. One of the many myths is that they are synonymous with Artificial Intelligence and Robotic Process Automation (RPA).