artificial general intelligence


This is when AI's top researchers think artificial general intelligence will be achieved

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At the heart of the discipline of artificial intelligence is the idea that one day we'll be able to build a machine that's as smart as a human. Such a system is often referred to as an artificial general intelligence, or AGI, which is a name that distinguishes the concept from the broader field of study. It also makes it clear that true AI possesses intelligence that is both broad and adaptable. To date, we've built countless systems that are superhuman at specific tasks, but none that can match a rat when it comes to general brain power. But despite the centrality of this idea to the field of AI, there's little agreement among researchers as to when this feat might actually be achievable.


This is when AI's top researchers think artificial general intelligence will be achieved

#artificialintelligence

At the heart of the discipline of artificial intelligence is the idea that one day we'll be able to build a machine that's as smart as a human. Such a system is often referred to as an artificial general intelligence, or AGI, which is a name that distinguishes the concept from the broader field of study. It also makes it clear that true AI possesses intelligence that is both broad and adaptable. To date, we've built countless systems that are superhuman at specific tasks, but none that can match a rat when it comes to general brain power. But despite the centrality of this idea to the field of AI, there's little agreement among researchers as to when this feat might actually be achievable.


A beginner's guide to AI: Human-level machine intelligence

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This (currently) five part feature should provide you with a very basic understanding of what AI is, what it can do, and how it works. The guide contains articles on (in order published) neural networks, computer vision, natural language processing, algorithms, and artificial general intelligence. There are few technologies that inspire the imagination like artificial intelligence. And, in the field of AI, the Holy Grail is living machines. The quest to imbue machines with the spark of life is an ancient one.


Anticipating the next move in data science – my interview with Thomson Reuters

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Thomson Reuters has a series, AI experts, where they interview thought leaders from different areas - including technology executives, researchers, robotics experts and policymakers - on what we might expect as we move towards AI. As part of that series I recently spoke to Paul Thies of Thomson Reuters, and here are the excerpts from the interview: Anticipating the next move in data science Thomson Reuters: For timely information concerning developments in data science, data mining and business analytics, KDnuggets is widely regarded as a leading outlet in the field. Created in 1993 by founder, editor and president Gregory Piatetsky-Shapiro, it is frequently cited as one of the top sources of data science news and influence by various industry watchers. Thomson Reuters: What are some use cases of data science that you find to be particularly valuable to organizations in this age of Big Data? GREGORY: Where people typically apply data science, probably not surprisingly, are in the areas of customer relationship management (CRM) and consumer analytics.


OpenAI launches reinforcement learning training to prepare for artificial general intelligence

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OpenAI today announced the launch of Spinning Up, a program designed to teach anyone deep reinforcement learning. OpenAI is well known for making funky-looking agents in virtual environments that learn how to walk on their own such as Humanoid v2 or POLO, a collaboration with University of Washington. Reinforcement learning involves providing reward signals to an agent in an environment incentivized to maximize its reward to meet a goal. RL has played a role in major AI breakthroughs such as Google DeepMind's AlphaGo and agents trained in environments like Dota 2. Spinning Up includes a collection of important reinforcement learning research papers, a glossary of terminology necessary to understand RL, and a collection of algorithms for running exercises. The program is being launched not just to help people learn how reinforcement learning works, but to make progress towards OpenAI's general goal of safely creating artificial general intelligence (AGI) by involving more people from fields beyond computer science.


A Very Brief and Critical Discussion on AutoML

arXiv.org Artificial Intelligence

Bin Liu School of Computer Science Nanjing University of Posts and Telecommunications Nanjing, 210023 China Email: bins@ieee.org Abstract This contribution presents a very brief and critical discussion on automated machine learning (AutoML), which is categorized here into two classes, referred to as narrow AutoML and generalized AutoML, respectively. The conclusions yielded from this discussion can be summarized as follows: (1) most existent research on AutoML belongs to the class of narrow AutoML; (2) advances in narrow AutoML are mainly motivated by commercial needs, while any possible benefit obtained is definitely at a cost of increase in computing burdens; (3)the concept of generalized AutoML has a strong tie in spirit with artificial general intelligence (AGI), also called "strong AI", for which obstacles abound for obtaining pivotal progresses. AutoML has recently emerged as a hot research topic in the field of machine learning (ML) and artificial intelligence (AI). As we know, a typical ML pipeline requires a lot of human's participation for e.g., data pre-processing, feature engineering, algorithm selection, model selection and hyperparameter optimization.


The rise of Artificial Intelligence and impending takeover

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It was seven minutes to ten o'clock in the morning, and it was the only good thing that had happened." If you get the feeling that these sentences could have been better structured, it's simply because these seemingly disparate, literary threads have been stitched into a novel by an algorithm. That's also why the human author of this novel, Ross Godwin, calls himself'writer of writers'. He is an artist and creative technologist at Google, and also a former Obama administration ghostwriter. In March 2017, Godwin fitted a Cadillac car with a surveillance camera, global positioning system (GPS) unit, microphone and clock, and connected these devices to a portable artificial intelligence (AI) writing machine that fed on these input data in real-time.


You have no idea what artificial intelligence really does

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WHEN SOPHIA THE ROBOT first switched on, the world couldn't get enough. It had a cheery personality, it joked with late-night hosts, it had facial expressions that echoed our own. Here it was, finally -- a robot plucked straight out of science fiction, the closest thing to true artificial intelligence that we had ever seen. There's no doubt that Sophia is an impressive piece of engineering. It didn't take much to convince people of Sophia's apparent humanity -- many of Futurism's own articles refer to the robot as "her."


A Primer in Adversarial Machine Learning – The Next Advance in AI – Data Science Central

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Summary: What comes next after Deep Learning? How do we get to Artificial General Intelligence? Adversarial Machine Learning is an emerging space that points to that direction and shows that AGI is closer than we think. Deep Learning, Convolutional Neural Nets (CNNs) have given us dramatic improvements in image, speech, and text recognition over the last two years. They suffer from the flaw however that they can be easily fooled by the introduction of even small amounts of noise, random or intentional.


Artificial Intelligence and the Rumsfeld Test - UC Berkeley Sutardja Center

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"But there are also unknown unknowns – the ones we don't know we don't know." An artificial intelligence strategy is the corporate equivalent of your spleen: everyone has one, but not everyone understands quite what it will accomplish. There are bold plans afoot everywhere in the world of AI to be sure, but its reality is still distant from the vision of artificial general intelligence (AGI) – i.e., machines displaying intelligence equivalent to the natural intelligence of humans – of popular imagination. Investors in particular need a sober and realistic view of what's achievable in the field of machine learning-driven AI today, versus what promises nothing more than a waste of time and money. There are many business problems that map to the attributes above.