New computational algorithms make it possible to build neural networks with many input nodes and many layers, and distinguish "deep learning" of these networks from previous work on artificial neural nets.
A study by German scientists from Jena and Hamburg, published today in the journal Nature, shows that artificial intelligence (AI) can substantially improve our understanding of the climate and the Earth system. Especially the potential of deep learning has only partially been exhausted so far. In particular, complex dynamic processes such as hurricanes, fire propagation, and vegetation dynamics can be better described with the help of AI. As a result, climate and Earth system models will be improved, with new models combining artificial intelligence and physical modeling. In the past decades mainly static attributes have been investigated using machine learning approaches, such as the distribution of soil properties from the local to the global scale.
When it comes to deep learning frameworks, TensorFlow is one of the most preferred toolkits. However, one framework that is fast becoming the favorite of developers and data scientists is PyTorch. PyTorch is an open source project from Facebook which is used extensively within the company. For a long time, Facebook developers used another homegrown framework called Caffe2, which was adopted by academia and researchers. Last year, Facebook announced that it is merging the efforts of developing Caffe2 and PyTorch to focus on creating a unified framework that is accessible to the community.
With plenty of machine learning tools currently available, why would you ever choose an artificial neural network over all the rest? This clip and the next could open your eyes to their awesome capabilities! You'll get a closer look at neural nets without any of the math or code - just what they are and how they work. Soon you'll understand why they are such a powerful tool! Deep Learning is primarily about neural networks, where a network is an interconnected web of nodes and edges.
"Sparsity, that's the direction where deep learning should expand," says Gopi Prashanth, who is vice president of engineering at AI-startup Landing AI, run by former Google AI luminary Andrew Ng. In an interview with ZDNet, Prashanth reflected on the challenge of taking something built for really big data, the machine learning approach called deep learning, and re-engineering it for very little data, perhaps just one single sample at a time. It is not an academic concern. The mandate of Ng and his team is to put AI to work for business. That requires using techniques such as machine learning in some settings where there my be very few good examples of a problem to use to train the machine.
Big Data and artificial intelligence (AI) have brought many advantages to businesses in recent years. But with these advances comes a raft of new terminology that we all have to get to grips with. As a result, some business users are left unsure of the difference between terms, or use terms with different meanings interchangeably. 'Neural networks' and'deep learning' are two such terms that I've noticed people using interchangeably, even though there's a difference between the two. Therefore, in this article, I define both neural networks and deep learning, and look at how they differ.
AI research quickly accelerated, with Kunihiko Fukushima developing the first true, multilayered neural network in 1975. The original goal of the neural network approach was to create a computational system that could solve problems like a human brain. However, over time, researchers shifted their focus to using neural networks to match specific tasks, leading to deviations from a strictly biological approach. Since then, neural networks have supported diverse tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games, and medical diagnosis. As structured and unstructured data sizes increased to big data levels, people developed deep learning systems, which are essentially neural networks with many layers.
Click to learn more about author Rosaria Silipo. Automatic machine translation has been a popular subject for machine learning algorithms. After all, if machines can detect topics and understand texts, translation should be just the next step. Machine translation can be seen as a variation of natural language generation. In a previous project, we worked on the automatic generation of fairy tales (see "Once upon a Time … by LSTM Network").
Amgen's drug discovery group is a few blocks beyond that. Until recently, Barzilay, one of the world's leading researchers in artificial intelligence, hadn't given much thought to these nearby buildings full of chemists and biologists. But as AI and machine learning began to perform ever more impressive feats in image recognition and language comprehension, she began to wonder: could it also transform the task of finding new drugs? The problem is that human researchers can explore only a tiny slice of what is possible. It's estimated that there are as many as 1060 potentially drug-like molecules--more than the number of atoms in the solar system. But traversing seemingly unlimited possibilities is what machine learning is good at. Trained on large databases of existing molecules and their properties, the programs can explore all possible related molecules.
This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. In September 2012, Alex Krizhevsky and Ilya Sutskever, two AI researchers from the University of Toronto, made history at ImageNet, a popular competition in which participants develop software that can recognize objects in a large database of digital images. Krizhevsky and Sutskever, and their mentor, AI pioneer Geoffrey Hinton, submitted an algorithm that was based on deep learning and neural networks, an artificial intelligence technique that the AI community viewed with skepticism because of its past shortcomings. AlexNet, the deep learning algorithm developed by the U of T researchers, was able to win the competition with an error rate of 15.3 percent, a whopping 10.8 percent better than the runner up. By some accounts, the event triggered the deep learning revolution, creating interest in the field by many academic and commercial organizations.
Has Deep Learning become synonymous with Artificial Intelligence? Read a discussion on the topic fuelled by the opinions of 7 participating experts, and gain some additional insight into the future of research and technology. Deep learning has achieved some very impressive accomplishments of late. I won't review them here, but chances are you already know about them anyhow. Given these high-profile successes, one could forgive the uninitiated (be they laymen or tech-savvy individuals) for the casual confounding of terms such as "artificial intelligence" and "deep learning," among others.