IBM Research Editorial Staff

Think 2019 Kicks Off with Live Debate Between Man and Machine

Today, an artificial intelligence (AI) system engaged in a live, public debate with a human debate champion at Think 2019 in San Francisco.

Continue reading

We Have Winners! … Of The IBM Q Teach Me Quantum Challenge

We’re happy to announce the winners of the fourth IBM Q Award: the IBM Q Teach Me Quantum Challenge.

Continue reading

IBM Researchers Remove the “Mem” from Memcache

Data Store for Memcache replaces DRAM with NVM storage for caching using the same memcache API. In a benchmark it proved be 33 percent faster.

Continue reading

AI Can Help Retailers Understand the Consumer

AI can help retailers understand the consumer; retailers now need to look at much finer market segments.

Continue reading

Fingernail Sensors and AI Can Help Clinicians to Monitor Health and Disease Progression

Grip strength is a useful metric in a surprisingly broad set of health issues. It has been associated with the effectiveness of medication in individuals with Parkinson’s disease, the degree of cognitive function in schizophrenics, the state of an individual’s cardiovascular health, and all-cause mortality in geriatrics. At IBM Research, one of our ongoing challenges […]

Continue reading

NeuNetS: Automating Neural Network Model Synthesis for Broader Adoption of AI

On December 14, 2018, IBM released NeuNetS, a fundamentally new capability that addresses the skills gap for the development of latest AI models for a wide range of business domains. NeuNetS uses AI to automatically synthesize deep neural network models faster and easier than ever before, scaling up the adoption of AI by companies and […]

Continue reading

TAPAS: Frugally Predicting the Accuracy of a Neural Network Prior to Training

Constructing a neural network model for each new dataset is the ultimate nightmare for every data scientist. What if you could forecast the accuracy of the neural network earlier thanks to accumulated experience and approximation? This was the goal of a recent project at IBM Research and the result is TAPAS or Train-less Accuracy Predictor […]

Continue reading

AI Year in Review: Highlights of Papers and Predictions from IBM Research AI

For more than seventy years, IBM Research has been inventing, exploring, and imagining the future. We have been pioneering the field of artificial intelligence (AI) since its inception. We were there when the field was launched at the famous 1956 Dartmouth workshop. Just three years later, an IBMer and early computer pioneer, Arthur Samuel, coined […]

Continue reading

Could AI Help People Change Their Behaviour?

Throughout life, many of us develop unhealthy habits that may feel nearly impossible to change. To quit smoking, reduce alcohol consumption, eat a healthier diet, or become more physically active requires effort and the right state of mind. A team of behavioural scientists at University College London (UCL)  and researchers at IBM Research-Ireland are looking […]

Continue reading

Efficient Deep Learning Training on the Cloud with Small Files

Here I describe an approach to efficiently train deep learning models on machine learning cloud platforms (e.g., IBM Watson Machine Learning) when the training dataset consists of a large number of small files (e.g., JPEG format) and is stored in an object store like IBM Cloud Object Storage (COS). As an example, I train a […]

Continue reading

Interpretability and Performance: Can the Same Model Achieve Both?

Interpretability and performance of a system are usually at odds with each other, as many of the best-performing models (viz. deep neural networks) are black box in nature. In our work, improving simple models, we try to bridge this gap by proposing a method to transfer information from a high-performing neural network to another model […]

Continue reading

Algebraic Gradient-Based Solver (AGS): A Novel Solver for Approximate Marginal MAP Inference

There is a deep connection between planning and inference, and over the last decade, multiple researchers have introduced explicit reductions showing how stochastic planning can be solved using probabilistic inference with applications in robotics, scheduling, and environmental problems. However, heuristic methods and search are still the best-performing approaches for planning in large combinatorial state and […]

Continue reading