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The possibility of general AI

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We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. One of the challenges in following the news about developments in the field of artificial intelligence is that the term "AI" is often used indiscriminately to mean two unrelated things. The first use of the term AI is something more precisely called narrow AI. It is powerful technology, but it is also pretty simple and straightforward: You take a bunch of data about the past, use a computer to analyze it and find patterns, and then use that analysis to make predictions about the future. This type of AI touches all our lives many times a day, as it filters spam out of our email and routes us through traffic.


The History of Artificial Intelligence - Science in the News

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It began with the "heartless" Tin man from the Wizard of Oz and continued with the humanoid robot that impersonated Maria in Metropolis. By the 1950s, we had a generation of scientists, mathematicians, and philosophers with the concept of artificial intelligence (or AI) culturally assimilated in their minds. One such person was Alan Turing, a young British polymath who explored the mathematical possibility of artificial intelligence. Turing suggested that humans use available information as well as reason in order to solve problems and make decisions, so why can't machines do the same thing? This was the logical framework of his 1950 paper, Computing Machinery and Intelligence in which he discussed how to build intelligent machines and how to test their intelligence.


How do Kernel Regularizers work with neural networks?

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Regularization is the process of fine-tuning neural network models by inducing a penalty term in the error parameter to obtain an optimal and reliable model which converges better with minimal loss during testing and performs better for unseen data. Regularization helps us get a more generic and reliable model which functions well with respect to changes in patterns of data and any possible uncertainties. So in this article let us see how kernel regularizers work with neural networks and place at what layers of the neural networks are useful to obtain optimal neural networks. Regularization is the process of adding penalty factors to the network layers to alter the weight propagation through the layers which facilitate the model to converge optimally. There are mainly two types of penalties that can be enforced on the network layers which are named as L1 regularization considers the weight of the layers as it is while the L2 regularization considers the squares of weights.



Viewpoint: AI Can Enhance Human Intelligence Instead Of Replacing It - AI Summary

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Some have feared that artificial intelligence will replace human workers in the future, but it can be used in a more synergistic way to enhance human intelligence, according to a March 18 column published in Harvard Business Review.


Are babies the key to the next generation of artificial intelligence?

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Babies can help unlock the next generation of artificial intelligence (AI), according to Trinity neuroscientists and colleagues who have just published new guiding principles for improving AI. The research, published today in the journal Nature Machine Intelligence, examines the neuroscience and psychology of infant learning and distills three principles to guide the next generation of AI, which will help overcome the most pressing limitations of machine learning. Dr. Lorijn Zaadnoordijk, Marie Sklodowska-Curie Research Fellow at Trinity College explained: "Artificial intelligence (AI) has made tremendous progress in the last decade, giving us smart speakers, autopilots in cars, ever-smarter apps, and enhanced medical diagnosis. These exciting developments in AI have been achieved thanks to machine learning which uses enormous datasets to train artificial neural network models. "However, progress is stalling in many areas because the datasets that machines learn from must be painstakingly curated by humans.


Neural Network Identifies Bird Calls, Even On Your Pi

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Recently, we've stumbled upon the extensive effort that is the BirdNET research platform. BirdNET uses a neural network to identify birds by the sounds they make, and is a joint project between the Cornell Lab of Ornithology and the Chemnitz University of Technology. What strikes us is – this project is impressively featureful and accessible for a variety of applications. No doubt, BirdNET is aiming to become a one-stop shop for identifying birds as they sing. There's plenty of ways BirdNET can help you.


Know this before starting a career in ML/AI….

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The human mind is one of the most uncanny objects to exist in the entire universe. It can perceive the environment by the simple process of visualization. This visualization enables the human mind to recollect events and uses it as an inspiration to define future events. Without this ability, we are doomed as living beings as our ability to decide the next move based on past events ceases to exist. Similarly, we are surrounded by facts and statistics, basic mathematics which is generally used for setting a reference or for analysis purposes.


What is Artificial Intelligence? How does AI work, Types, Trends and Future of it?

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Let's take a detailed look. This is the most common form of AI that you'd find in the market now. These Artificial Intelligence systems are designed to solve one single problem and would be able to execute a single task really well. By definition, they have narrow capabilities, like recommending a product for an e-commerce user or predicting the weather. This is the only kind of Artificial Intelligence that exists today. They're able to come close to human functioning in very specific contexts, and even surpass them in many instances, but only excelling in very controlled environments with a limited set of parameters. AGI is still a theoretical concept. It's defined as AI which has a human-level of cognitive function, across a wide variety of domains such as language processing, image processing, computational functioning and reasoning and so on.


Debate over AI sentience marks a watershed moment

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We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. The AI field is at a significant turning point. On the one hand, engineers, ethicists, and philosophers are publicly debating whether new AI systems such as LaMDA – Google's artificially intelligent chatbot generator – have demonstrated sentience, and (if so) whether they should be afforded human rights. At the same time, much of the advance in AI in recent years, is based on deep learning neural networks, yet there is a growing argument from AI luminaries such as Gary Marcus and Yann LeCun that these networks cannot lead to systems capable of sentience or consciousness. Just the fact that the industry is having this debate is a watershed moment.