Machine Learning & AI Main Developments in 2018 and Key Trends for 2019 - Dec 11, 2018.
As we bid farewell to one year and look to ring in another, KDnuggets has solicited opinions from numerous Machine Learning and AI experts as to the most important developments of 2018 and their 2019 key trend predictions.
2019 Predictions, AI, Ajit Jaokar, Andriy Burkov, Anima Anandkumar, Brandon Rohrer, Daniel Tunkelang, Machine Learning, Pedro Domingos, Rachel Thomas, Zachary Lipton
- Are Deep Neural Networks Creative? - May 12, 2016.
Deep neural networks routinely generate images and synthesize text. But does this amount to creativity? Can we reasonably claim that deep learning produces art?
Artificial Intelligence, Deep Learning, Generative Adversarial Network, Generative Models, Recurrent Neural Networks, Reinforcement Learning, Zachary Lipton
- The ICLR Experiment: Deep Learning Pioneers Take on Scientific Publishing - Feb 15, 2016.
Deep learning pioneers Yann LeCun and Yoshua Bengio have undertaken a grand experiment in academic publishing. Embracing a radical level of transparency and unprecedented public participation, they've created an opportunity not only to find and vet the best papers, but also to gather data about the publication process itself.
Academics, arXiv, Deep Learning, ICLR, Neural Networks, Yann LeCun, Yoshua Bengio, Zachary Lipton
- Deep Learning Transcends the Bag of Words - Dec 7, 2015.
Generative RNNs are now widely popular, many modeling text at the character level and typically using unsupervised approach. Here we show how to generate contextually relevant sentences and explain recent work that does it successfully.
Beer, Deep Learning, Generative Models, Recurrent Neural Networks, Zachary Lipton
- MetaMind Mastermind Richard Socher: Uncut Interview - Oct 20, 2015.
In a wide-ranging interview, Richard Socher opens up about MetaMind, deep learning, the nature of corporate research, and the future of machine learning.
Convolutional Neural Networks, Deep Learning, Image Recognition, MetaMind, Recurrent Neural Networks, Richard Socher, Zachary Lipton
- Does Deep Learning Come from the Devil? - Oct 9, 2015.
Deep learning has revolutionized computer vision and natural language processing. Yet the mathematics explaining its success remains elusive. At the Yandex conference on machine learning prospects and applications, Vladimir Vapnik offered a critical perspective.
Berlin, Deep Learning, Machine Learning, Support Vector Machines, SVM, Vladimir Vapnik, Yandex, Zachary Lipton
- Recycling Deep Learning Models with Transfer Learning - Aug 14, 2015.
Deep learning exploits gigantic datasets to produce powerful models. But what can we do when our datasets are comparatively small? Transfer learning by fine-tuning deep nets offers a way to leverage existing datasets to perform well on new tasks.
Deep Learning, Image Recognition, ImageNet, Machine Learning, Neural Networks, Transfer Learning, Zachary Lipton
- Deep Learning and the Triumph of Empiricism - Jul 7, 2015.
Theoretical guarantees are clearly desirable. And yet many of today's best-performing supervised learning algorithms offer none. What explains the gap between theoretical soundness and empirical success?
Big Data, Data Science, Deep Learning, Mathematics, Statistics, Zachary Lipton
- The Myth of Model Interpretability - Apr 27, 2015.
Deep networks are widely regarded as black boxes. But are they truly uninterpretable in any way that logistic regression is not?
Deep Learning, Deep Neural Network, Interpretability, Support Vector Machines, Zachary Lipton
- Do We Need More Training Data or More Complex Models? - Mar 23, 2015.
Do we need more training data? Which models will suffer from performance saturation as data grows large? Do we need larger models or more complicated models, and what is the difference?
Big Data, convnet, Generalized Linear Models, K-nearest neighbors, Training Data, Zachary Lipton
- Data Science’s Most Used, Confused, and Abused Jargon - Feb 10, 2015.
As data science has spread through the mainstream, so too has a dense vocabulary of ill-defined jargon. In a split-personality post, we offer several perspectives on many of data science's most confused terms.
Big Data Privacy, Data Science, Deep Learning, Zachary Lipton
- (Deep Learning’s Deep Flaws)’s Deep Flaws - Jan 26, 2015.
Recent press has challenged the hype surrounding deep learning, trumpeting several findings which expose shortcomings of current algorithms. However, many of deep learning's reported flaws are universal, affecting nearly all machine learning algorithms.
convnet, Deep Learning, Ian Goodfellow, Machine Learning, Neural Networks, Yoshua Bengio, Zachary Lipton
- The High Cost of Maintaining Machine Learning Systems - Jan 21, 2015.
Google researchers warn of the massive ongoing costs for maintaining machine learning systems. We examine how to minimize the technical debt.
Google, Machine Learning, Software Engineering, Technical Debt, Zachary Lipton
- MetaMind Competes with IBM Watson Analytics and Microsoft Azure Machine Learning - Jan 14, 2015.
While Microsoft and IBM rush to bring data science and visualization to the masses, MetaMind follows another path, offering deep learning as a service.
Azure ML, Deep Learning, IBM Watson, MetaMind, Richard Socher, Zachary Lipton
- Differential Privacy: How to make Privacy and Data Mining Compatible - Jan 9, 2015.
Can privacy coexist with machine learning and data mining? Differential privacy allows the learning of general characteristics of populations while guaranteeing the privacy of individual records.
arXiv, Big Data, Cynthia Dwork, Data Mining, Differential Privacy, Zachary Lipton
- IBM Watson Analytics vs. Microsoft Azure Machine Learning (Part 1) - Dec 16, 2014.
IBM Watson Analytics prototype seeks to abstract away data science, taking ordinary natural language queries and answering them based on the content of uploaded datasets. Microsoft Azure Machine Learning goes the opposite route, streamlining existing data mining methodology for fast results and integration with MS's other cloud services.
Azure ML, Cloud Analytics, Data Mining Software, IBM Watson, Zachary Lipton
- Geoff Hinton AMA: Neural Networks, the Brain, and Machine Learning - Dec 9, 2014.
In a wide-ranging Q&A, Geoff Hinton addresses the future of deep learning, its biological inspirations, and his research philosophy.
Backpropagation, Deep Learning, Geoff Hinton, Michael Jordan, Neural Networks, Neuroscience, Zachary Lipton
