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Artificial Intelligence Classification Matrix

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All the problems discussed in the previous posts can create two major cross-sectional problems: the likely event to run out of money before hitting relevant milestones toward the next investment, as well as whether pursuing specific business applications to break even instead of focusing on product development. In terms instead of classifying different companies operating in the space, there might be several different ways to think around machine intelligence startups (e.g., the classification proposed by Bloomberg Beta investor Shivon Zilis in 2015 is very accurate and useful for this purpose). The solutions usually provided might either integrate with the clients' stack (through APIs or building specifically on top of customers' platform) or otherwise full-stacks solutions. Virtual agents and chatbots cover the low-cost side of the group, while physical world systems (e.g., self-driving cars, sensors, etc.), drones, and actual robots are the capital and talent-intensive side of the coin. The results of this categorization can be summarized into the following matrix, plotting the groups with respect to short-term monetization (STM) and business defensibility.


SoftCOM AI: Hard on the competition with zero faults - Huawei Publications

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Huawei's SoftCOM AI solution introduces AI to All Cloud Networks. Designed to create self-driving network architecture, SoftCOM AI is a transformative solution that can help operators compete with OTT companies by using predictive AI to minimize network faults. Telecom services are divided into three tiers: device, network and IT infrastructure, and upper-layer applications. However, in today's telecom's landscape, cross-sector competition is threatening telco revenue models. Thanks to dramatic increases in network speeds, IT and Internet companies are offering cloud services in traditional telco territory: backbone networks, some MANs, IT infrastructure, and IT applications.


Tech Mahindra Launches GAiA 2.0 to Expedite Adoption of Artificial Intelligence & Machine Learning by Enterprises

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Dallas, New Delhi – August 26th, 2019: Tech Mahindra Ltd. a leading provider of digital transformation, consulting and business re-engineering services and solutions, today announced the release of GAiA 2.0, the latest version of its Enterprise Artificial Intelligence (AI) & Machine Learning (ML) lifecycle management platform GAiA, powered by Acumos. GAiA 2.0 will enable comprehensive AI and ML driven platform capabilities and services to be deployed across mainstream, optimizing enterprise operations in real time across industry verticals. It offers an enriched marketplace of models and numerous features to empower enterprises across industry verticals to build, manage, share and rapidly deploy AI and ML driven services and applications addressing critical business problems. Manish Vyas, President, Communications, Media & Entertainment Business, and CEO, Network Services, Tech Mahindra, said, "GAiA 2.0 is a reinvention based on insights and feedback received for the initial version. We have now incorporated features such as Jupyter Notebook Integration, AutoML support, Model Validation, Security and Governance that add extremely high value to business transformation journey of an enterprise, by unlocking and delivering superior connected experiences. Tech Mahindra with proven expertise in leveraging digital technologies will help foster collaborative innovation by democratizing Artificial Intelligence and Machine Learning."


Can't-Miss Keynotes at Deep Learning World – June 3-7 in Vegas

@machinelearnbot

Don't miss the opportunity to witness keynote sessions by industry heavyweights at the upcoming inaugural Deep Learning World conference in Las Vegas. Deep Learning World is the premier conference covering the commercial deployment of deep learning. The event's mission is to foster breakthroughs in the value-driven operationalization of established deep learning methods. Check out these Can't-Miss Keynotes this June in Las Vegas: Applied Deep Learning: Self-Driving Cars and Fake News Detection Michael Tamir, Uber Applied deep learning has fast become a standard tool for many industry machine learning applications. New advances in neural network techniques have opened the doors to solving problems at scale that were out of reach until recently.


Google aims to make artificial intelligence easy to use for companies

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For companies that are trying to figure how to take advantage of newer technologies like artificial intelligence (AI), but do not have adequate technical know-how or enough financial muscle to do so, Google Inc. believes its newly-launched automated tool, Cloud AutoML, might just provide an answer.