Collaborating Authors

IBM Watson and Autodesk reinvent customer service


Every year, companies spend 1.3 trillion dollars on 265 billion customer service calls. On average, the cost to find and hire a call center agent costs $4000 (not including salary), with an additional $4,800 for training -- and with frustrated agents tending to drop like flies in the face of an often brutally stressful job, these costs mount up.

Morphological Computation and Learning to Learn In Natural Intelligent Systems And AI Artificial Intelligence

At present, artificial intelligence in the form of machine learning is making impressive progress, especially the field of deep learning (DL) [1]. Deep learning algorithms have been inspired from the beginning by nature, specifically by the human brain, in spite of our incomplete knowledge about its brain function. Learning from nature is a two-way process as discussed in [2][3][4], computing is learning from neuroscience, while neuroscience is quickly adopting information processing models. The question is, what can the inspiration from computational nature at this stage of the development contribute to deep learning and how much models and experiments in machine learning can motivate, justify and lead research in neuroscience and cognitive science and to practical applications of artificial intelligence.

A Multi-Agent Learning Approach to Online Distributed Resource Allocation

AAAI Conferences

Resource allocation in computing clusters is traditionally centralized, which limits the cluster scale. Effective resource allocation in a network of computing clusters may enable building larger computing infrastructures. We consider this problem as a novel application for multiagent learning (MAL). We propose a MAL algorithm and apply it for optimizing online resource allocation in cluster networks. The learning is distributed to each cluster, using local information only and without access to the global system reward. Experimental results are encouraging: our multiagent learning approach performs reasonably well, compared to an optimal solution, and better than a centralized myopic allocation approach in some cases.

Google expands its Cloud AI portfolio


Google on Tuesday rolled out several new products and capabilities within its Cloud AI portfolio, including new products and features in Contact Center AI and new versions of Document AI. It also announced improvements to the AI Platform for machine learning operations (MLOps) practitioners. Google considers its AI expertise as a key selling point for Google Cloud. "We are steadily transferring advancements from Google AI research into cloud solutions that help you create better experiences for your customers," Andrew Moore, head of Google Cloud AI & Industry Solutions, wrote in a blog post Tuesday. Google's Contact Center AI (CCAI) software, which became generally available last November, enables businesses to deploy virtual agents for basic customer interactions.

Google Cloud launches pre-packaged AI services around contact centre and talent acquisition


The importance of artificial intelligence (AI) and machine learning to both the biggest cloud providers and their customers continues to rise – and Google Cloud aims to get a step up on its rivals by offering pre-packaged AI services. At Google Next back in July, Google Cloud AI chief scientist Fei-Fei Li noted that AI was'no longer a niche for the tech world' but'the differentiator for businesses in every industry.' It's not difficult to see why. Take the various companies who cite AI and machine learning capability as key when they make the switch regardless of who they shop with – from Bloomberg with Google, to Formula 1 with AWS. Google's pre-packaged AI offerings are based around improving the enterprise contact centre and talent acquisition respectively.