Petuum, the creator of the world's first composable platform for MLOps, and the Inception Institute for Artificial Intelligence (IIAI), have agreed to partner on the development of revolutionary AI applications. Petuum has recently announced a limited release of the composable platform, which includes the AI OS, Universal Pipelines, Deployment Manager, and Experiment Manager, for select private beta partners. Through the partnership with Petuum, IIAI's enterprise AI/ML teams will operationalize and scale their applications into production. Founded in 2018, IIAI's mission is to build full-stack AI solutions and operating systems for enterprise businesses and developers. Besides being the research arm for G42, IIAI is also empowering stakeholders with AI applications and incubating new technology at the cutting edge of ML innovation.

Petuum is an artificial intelligence and deep learning R&D platform dedicated to helping enterprises solve bottlenecks and other difficulties encountered in the deployment of artificial intelligence and machine learning. Petuum is building a next-generation all-in-one AI/ML platform for deep learning, predictive analytics, knowledge extraction, content summarization, and ensemble models, which can be used for a large number of applications, such as natural language processing, image and video understanding, and data transfer. Petuum supports different hardware platforms and can also be programmed in many popular languages such as Python, R, Java, C and Julia. In November last year, Petuum received a $15 million Series A venture capital round led by Advantech Capital, with investors including Tencent, Northern Light Venture Capital and Oriza Ventures.

Petuum is very excited to announce the launch of our newest open source offering, AdaptDL, a resource-adaptive deep learning (DL) training and scheduling framework. The goal of AdaptDL is to make distributed DL easy and efficient in dynamic-resource environments such as shared clusters and the cloud. During our benchmark studies when using AdaptDL with Amazon Web Services (AWS), we recorded a reduction in cost by up to 80% when AdaptDL was set to automatically provision spot instances on AWS when available. AdaptDL can automatically determine the optimal number of resources given a job's need. It will efficiently add or remove resources dynamically to ensure the highest-level performance.

Victor Thu is the General Manager of Neurobots at Petuum, partnering closely with Bin Zhao, to build easy-to-use cutting-edge AI/ML solutions that integrate seamlessly into RPA as IPA (intelligent process automation). Before Petuum, Victor led marketing and product marketing for Digitate, a startup that focuses on solving IT operational challenges using AI and automation. As an industry technologist, Victor held different roles leading go-to-market strategies for innovative technologies, including the converged desktop, mobility, and identity solutions at VMware. Victor was an industry subject matter expert on business and workforce mobility with topics such as BYOD (Bring Your Own Devices). Victor also spent over three years in the Asia Pacific region, leading end-user computing product marketing for VMware and Citrix across the region.

Petuum, an artificial intelligence platform company, announced Petuum Neurobots, a series of intelligent process automation (IPA) tools that provide cutting-edge AI capabilities to robotic process automation (RPA). This enables enterprise customers to quickly and cost-effectively deploy powerful RPA bots with the most advanced AI and machine learning (ML) technology. "We believe AI technology must be accessible to all companies of all sizes across different industries to achieve better business results. Neurobots are a critical part of Petuum's larger strategy to provide state-of-the-art AI that will transform RPA bots into intelligent agents that are cognitive, able to learn, think, and collaborate (with human and other bots), and therefore realize fully-operationalized business automation," said Dr. Eric Xing, founder and CEO of Petuum. The first Neurobots introduced include Kaibot for interactive conversational applications such as chatbots, Chimebot for automatic speech recognition, Chicbot for fashion applications and Pixbot for image understanding and enhancements.

The past thirty years of machine learning breakthroughs are intimately entwined with a big idea in computing: parallel distributed processing, where a parts of a program run simultaneously on multiple processors to speed computation. Eric Xing, a Carnegie Mellon professor of machine learning, three years ago founded Petuum, based in Pittsburg, which has received $108 million in funding from Japanese conglomerate SoftBank, along with Advantech Capital, Chinese computing giant Tencent, Northern Light Venture Capital, and Oriza Ventures. The company plans to ship the first version of its AI platform software next summer, an offering Xing hopes will "industrialize" machine learning, thereby making it more reliable and more broadly available. Petuum founder and CEO Eric Xing came up with the idea for the AI software while on sabbatical from Carnegie Mellon at Facebook in 2010. Much of the challenge of AI is a systems engineering challenge, and at the heart of that is a problem of parallelizing the running of algorithms across all kinds of configurations of machines.

I recently had the opportunity to participate in a fireside chat session at Forrester's New Tech & Innovation 2018 forum with J.P. Gownder, a vice president and principal analyst at the firm. It was a timely and much-needed discussion on some of the biggest questions today in artificial intelligence (AI) and I hope that the audience walked away with a better understanding of this pivotal and complex technology. For those that were unable to attend the event, this blog post will provide an overview of several of the questions and answers that we looked at in the session. At Petuum, we often talk about the "industrialization" of AI, but this term is likely unfamiliar to most people. I started the session by exploring what we mean when we talk about AI being industrialized in the same way as textiles, electronics, and other products.

At first blush, Scot Barton might not seem like an AI pioneer. He isn't building self-driving cars or teaching computers to thrash humans at computer games. But within his role at Farmers Insurance, he is blazing a trail for the technology. Barton leads a team that analyzes data to answer questions about customer behavior and the design of different policies. His group is now using all sorts of cutting-edge machine-learning techniques, from deep neural networks to decision trees.

At first blush, Scot Barton might not seem like an AI pioneer. He isn't building self-driving cars or teaching computers to thrash humans at computer games. But within his role at Farmers Insurance, he is blazing a trail for the technology. Barton leads a team that analyzes data to answer questions about customer behavior and the design of different policies. His group is now using all sorts of cutting-edge machine-learning techniques, from deep neural networks to decision trees.

At first blush, Scot Barton might not seem like an AI pioneer. He isn't building self-driving cars or teaching computers to thrash humans at computer games. But within his role at Farmers Insurance, he is blazing a trail for the technology. Barton leads a team that analyzes data to answer questions about customer behavior and the design of different policies. His group is now using all sorts of cutting-edge machine-learning techniques, from deep neural networks to decision trees.