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OpenAI's Artificial Intelligence Strategy

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For several years, there has been a lot of discussion around AI's capabilities. Many believe that AI will outperform humans in solving certain areas. As the technology is in its infancy, researchers are expecting human-like autonomous systems in the next coming years. OpenAI has a leading stance in the artificial intelligence research space. Founded in December 2015, the company's goal is to advance digital intelligence in a way that can benefit humanity as a whole.


The AI 'Intelligence Explosion' Might Happen This Way, Including For AI Self-Driving Cars

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Sometimes you initiate an action and in a domino-like manner it gets going and going, seemingly feeding off itself and rapidly agitating in an almost unstoppable manner. For example, you might be familiar with those popular YouTube videos of a beaker that when filled with a special liquid will spontaneously gush out foam, akin to a type of chain reaction. History indicates that during the initial creation of the atomic bomb, some of the scientists involved were concerned that if the atomic bomb was set off, it might begin a chain reaction due to igniting a fission explosion in the air, and would generate a globally wide conflagration. There is a venue today in which a chain reaction phenomenon is being bandied about by researchers and scientists. Some vehemently assert that we are potentially going to have an AI "intelligence explosion" that will someday occur, and there are various bets that this might happen somewhere between the year 2050 and the year 2100.


GPT3 and AGI: Beyond the dichotomy – part two

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Let's first clarify what AGI should look like When the Arnold Schwarzenegger character comes to earth – he is fully functional. To do so, he must be aware of the context. But GPT3 however has the capacity to respond'AGI-like' to an expanded set of contexts much more than traditional AI systems. Does AGI need to be conscious as we know it or would access consciousness suffice? Finally, let us consider the question of spillover of intelligence.


"The Reasonable Robot" Looks At The Intersection Of Artificial Intelligence (AI) And Law

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I was sent a copy of Ryan Abbott's "The Reasonable Robot" by the publishers. It is an interesting book that discusses a few critical areas of law as they could interact with artificial intelligence (AI). The book is worth reading, even if it is far from perfect. It is an excellent discussion point, a starting place for people to begin to think about artificial intelligence and the law. Software and law has always been an intersection that has interested me.


The Rise of the Transformers

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Rise of the Transformers with Self-Attention Mechanism  The intention of this article is to continue in answering the questions that my friends April Rudin, Tripp Braden, Danielle Guzman and Richard Foster-Fletcher asked about the future of AI. Furthermore Irene Iyakovet interview with me about how


Is AI an Existential Threat?

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When discussing Artificial Intelligence (AI), a common debate is whether AI is an existential threat. The answer requires understanding the technology behind Machine Learning (ML), and recognizing that humans have the tendency to anthropomorphize. We will explore two different types of AI, Artificial Narrow Intelligence (ANI) which is available now and is cause for concern, and the threat which is most commonly associated with apocalyptic renditions of AI which is Artificial General Intelligence (AGI). To understand what ANI is you simply need to understand that every single AI application that is currently available is a form of ANI. These are fields of AI which have a narrow field of specialty, for example autonomous vehicles use AI which is designed with the sole purpose of moving a vehicle from point A to B. Another type of ANI might be a chess program which is optimized to play chess, and even if the chess program continuously improves itself by using reinforcement learning, the chess program will never be able to operate an autonomous vehicle.


Future of AI Part 2

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This part of the series looks at the future of AI with much of the focus in the period after 2025. The leading AI researcher, Geoff Hinton, stated that it is very hard to predict what advances AI will bring beyond five years, noting that exponential progress makes the uncertainty too great. This article will therefore consider both the opportunities as well as the challenges that we will face along the way across different sectors of the economy. It is not intended to be exhaustive. AI deals with the area of developing computing systems which are capable of performing tasks that humans are very good at, for example recognising objects, recognising and making sense of speech, and decision making in a constrained environment. Some of the classical approaches to AI include (non-exhaustive list) Search algorithms such as Breath-First, Depth-First, Iterative Deepening Search, A* algorithm, and the field of Logic including Predicate Calculus and Propositional Calculus. Local Search approaches were also developed for example Simulated Annealing, Hill Climbing (see also Greedy), Beam Search and Genetic Algorithms (see below). Machine Learning is defined as the field of AI that applies statistical methods to enable computer systems to learn from the data towards an end goal. The term was introduced by Arthur Samuel in 1959. A non-exhaustive list of examples of techniques include Linear Regression, Logistic Regression, K-Means, k-Nearest Neighbour (kNN), Naive Bayes, Support Vector Machine (SVM), Decision Trees, Random Forests, XG Boost, Light Gradient Boosting Machine (LightGBM), CatBoost. Deep Learning refers to the field of Neural Networks with several hidden layers. Such a neural network is often referred to as a deep neural network. Neural Networks are biologically inspired networks that extract abstract features from the data in a hierarchical fashion.


GPT-3 vs. AGI

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So what will it take to get to AGI? How will we give computers an understanding of time and space? We humans are great at merging information from multiple senses. A child will use all its senses to learn about blocks. The child learns about time by experiencing it, by interacting with toys and the world. In the same way, AGI will need a robotic body to learn similar things, at least at the outset.


A beginner's guide to AI: Separating the hype from the reality

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An advanced artificial intelligence created by OpenAI, a company founded by genius billionaire Elon Musk, recently penned an op-ed for The Guardian that was so convincingly human many readers were astounded and frightened. Just writing that sentence made me feel like a terrible journalist. That's a really crappy way to start an article about artificial intelligence. The statement contains only trace amounts of truth and is intended to shock you into thinking that what follows will be filled with amazing revelations about a new era of technological wonder. Here's what the lede sentence of an article about the GPT-3 op-ed should look like, as Neural writer Thomas Macaulay handled it earlier this week: The Guardian today published an article purportedly written "entirely" by GPT-3, OpenAI's vaunted language generator.


Artificial Intelligence (AI) is Over Mature to do Underage Things

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Artificial Intelligence (AI) is the future. But can we call the future dumb? Some possibilities could drive us to such a situation. AI is designed to make human jobs easy and the technology is trying its best to restrain the position. However, what it fails to comply with are the basic human activities that we find very easy. Everyone has seen AI beating the world champion in the board game Go, the quiz game Jeopardy, the card game Poker and the video game Dota 2. AI has come a long to what it is today.