Just as artificial intelligence (AI) and ML are being deployed in a wide variety of novel applications, threat actors can not only abuse the technology to …

Deep learning is a subset of machine learning. Deep learning takes it a step further.

The Swiss Federal Institute of Technology in Zurich (ETH) has opened a new research center for Artificial Intelligence.

"We are laser-focused on continuous improvements to customers' experience across our stores. By leveraging Everseen's Visual AI and machine-learning technology, we're not only able to remove friction for the customer, but we can also remove controllable costs from the business and redirect those resources to improving the customer experience even more." This post was inspired by a recent Kroger article announcing the deployment of visual artificial intelligence (AI) in 2500 stores and new IHL Group edge computing research. Multiple technological trends have been converging for some time and their combination is leading to transformative store operations improving solutions. By 2021, one billion video cameras will be deployed around the world.

An intervention that delivered machine learning mortality predictions with behavioral nudges to oncology clinicians significantly increased the rate of …

Long-term temporal credit assignment is an important challenge in deep reinforcement learning (RL). It refers to the ability of the agent to attribute actions to consequences that may occur after a long time interval. Existing policy-gradient and Q-learning algorithms typically rely on dense environmental rewards that provide rich short-term supervision and help with credit assignment. However, they struggle to solve tasks with delays between an action and the corresponding rewarding feedback. To make credit assignment easier, recent works have proposed algorithms to learn dense "guidance" rewards that could be used in place of the sparse or delayed environmental rewards. This paper is in the same vein -- starting with a surrogate RL objective that involves smoothing in the trajectory-space, we arrive at a new algorithm for learning guidance rewards. We show that the guidance rewards have an intuitive interpretation, and can be obtained without training any additional neural networks. Due to the ease of integration, we use the guidance rewards in a few popular algorithms (Q-learning, Actor-Critic, Distributional-RL) and present results in single-agent and multi-agent tasks that elucidate the benefit of our approach when the environmental rewards are sparse or delayed.

Obtaining training data for Multi-hop Question Answering (QA) is extremely time-consuming and resource-intensive. To address this, we propose the problem of \textit{unsupervised} multi-hop QA, assuming that no human-labeled multi-hop question-answer pairs are available. We propose MQA-QG, an unsupervised question answering framework that can generate human-like multi-hop training pairs from both homogeneous and heterogeneous data sources. Our model generates questions by first selecting or generating relevant information from each data source and then integrating the multiple information to form a multi-hop question. We find that we can train a competent multi-hop QA model with only generated data. The F1 gap between the unsupervised and fully-supervised models is less than 20 in both the HotpotQA and the HybridQA dataset. Further experiments reveal that an unsupervised pretraining with the QA data generated by our model would greatly reduce the demand for human-annotated training data for multi-hop QA.

Data analytics and machine learning phData grew its revenue by 160.29% from … In our space, there are "go wide" providers and "go deep" providers.

The Machine Learning Artificial intelligence Market reports gives a far reaching review of the worldwide market size and global trends with values.

Roles already growing in demand include data analysts and scientists, AI and machine learning specialists and robotics engineers.