Selecting the best architecture for deep learning architectures is typically a time-consuming process that requires expert input, but using AI can streamline this process. I am developing an evolutionary algorithm for architecture selection that is up to 50,000 times faster than other methods, with only a small increase in error rate. Deep learning models are […]
The ability to infer abstract high-level concepts from raw sensory inputs is a key part of human intelligence. Developing models that recapitulate this ability is an important goal in AI research. A fundamental challenge in this respect is disentangling the underlying factors of variation that give rise to the observed data. For example, factors of […]
Today, with contributions made by IBM scientists, IBM introduces Deep Learning as a Service within Watson Studio, a rich set of cloud-based tools for developers and data scientists to help remove the barriers of training deep learning models in the enterprise. Deep learning and machine learning require expensive hardware and software resources as well as […]
IBM researchers developed a novel compression algorithm that could significantly improve training times for deep learning models in large-scale AI systems.
In an upcoming presentation at the 2018 AAAI Conference, our team of deep learning experts at IBM Research India propose a new and exploratory technique that automatically ingests and infers deep learning algorithms in published research papers and recreates them in source code for inclusion in libraries for multiple deep learning frameworks (Tensorflow, Keras, Caffe). With […]
Recently, impressive progress has been made in neural network question answering (QA) systems which can analyze a passage to answer a question. These systems work by matching a representation of the question to the text to find the relevant answer phrase. But what if the text is potentially all of Wikipedia? And what if the […]
My IBM Research AI team and I recently completed the first formal theoretical study of the convergence rate and communications complexity associated with a decentralized distributed approach in a deep learning training setting. The empirical evidence proves that in specific configurations, a decentralized approach can result in a 10x performance boost over a centralized approach […]
Automated knowledge base construction is a long-standing challenge in AI. The goal is to abstract concise representations from various sources of knowledge, such as unstructured documents, web data and knowledge bases. The outcome is a knowledge graph that can be used to enhance downstream applications like search engines and business analytics. Highly accurate and extensive […]
Among the various fields of exploration in artificial intelligence, deep learning is an exciting and increasingly important area of research that holds great potential for helping computers understand and extract meaning from data, e.g. deciphering images and sounds. To help further the creation and adoption of interoperable deep learning models, IBM joined the Open Neural […]
Applications of AI are quickly becoming ubiquitous, powered by algorithms that learn from large amounts of data. Humans, on the other hand, learn very differently: they are able to reason based on a small number of assumptions and a set of logical rules. Our IBM Research team designed a method capable of combining these two […]
IBM Research publishes close to ideal scaling with new distributed deep learning software which achieved record communication overhead.