adoption


AI at the Edge Still Mostly Consumer, not Enterprise, Market

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Data-driven experiences are rich, immersive and immediate. Think pizza delivery by drone, video cameras that can record traffic accidents at an intersection, freight trucks that can identify a potential system failure. These kinds of fast-acting activities need lots of data -- quickly. So they can't sustain latency as data travels to and from the cloud. That to-and-fro takes too long.


TensorFlow deepens its advantages in the AI modeling wars

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TensorFlow remains the dominant AI modeling framework. Most AI (artificial intelligence) developers continue to use it as their primary open source tool or alongside PyTorch, in which they develop most of their ML (machine learning), deep learning, and NLP (natural language processing) models. In the most recent O'Reilly survey on AI adoption in the enterprise, more than half of the responding data scientists cited TensorFlow as their primary tool. This finding is making me rethink my speculation, published just last month, that TensorFlow's dominance among working data scientists may be waning. Neverthless, PyTorch remains a strong second choice, having expanded its usage in the O'Reilly study to more than 36 percent of respondents, up from 29 percent in the previous year's survey.


Enterprise AI Goes Mainstream, but Maturity Must Wait - InformationWeek

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Artificial intelligence's emergence into the mainstream of enterprise computing raises significant issues -- strategic, cultural, and operational -- for businesses everywhere. What's clear is that enterprises have crossed a tipping point in their adoption of AI. A recent O'Reilly survey shows that AI is well on the road to ubiquity in businesses throughout the world. The key finding from the study was that there are now more AI-using enterprises -- in other words, those that have AI in production, revenue-generating apps -- than organizations that are simply evaluating AI. Taken together, organizations that have AI in production or in evaluation constitute 85% of companies surveyed.


DSC Webinar Series: Accelerating AI Adoption with Machine Learning Operations (MLOps)

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Massive investments in data science teams and machine learning platforms have yet to yield results for most companies. The last mile for AI project success is the deployment and management of models in production requiring new technology and practices. This new area is called Machine Learning Operations or MLOps.


ModelOps Is The Key To Enterprise AI

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In the last two years, large enterprise organizations have been scaling up their artificial intelligence and machine learning efforts. To apply models to hundreds of use-cases, organizations need to operationalize their machine learning models across the organization. At the center of this scaling up effort is ModelOp, the company that builds solutions to scale the processes that take models from the data science lab into production. Even before their recent $6 million Series A funding led by Valley Capital Partners with participation from Silicon Valley Data Capital, they are already the leader providing ModelOps solutions to Fortune 1000 companies. ModelOps is a capability that focuses on getting models into 24/7 production.


AI-Supported Sales Reps: How To Make It Work

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Most organizations have begun to invest in AI to guide their sales representatives as it helps organizations stay adaptable to changing customer needs and evolving markets. AI guided selling usually takes the form of machine learning generated advice offered to reps on their CRM or other software. It's primarily designed to help salespeople stay organized, prioritize leads, choose the customer most likely to buy for their next sales call, and so on. When its impact is fully realized, it gives salespeople more time to sell and information that they leverage to sell more effectively. I've seen solutions like this work, even quite well.


AI for business: What's going wrong, and how to get it right ZDNet

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Despite years of hype (and plenty of worries) about the all-conquering power of Artificial Intelligence (AI), there still remains a significant gap between the promise of AI and its reality for business. Tech firms have pitched AI's capabilities for years, but for most organisations, the benefits of AI remain elusive. It's hard to gauge the proportion of businesses that are effectively using artificial intelligence today, and to what extent. Adoption rates shown in recent reports fall anywhere between 20% and 30%, with adoption typically loosely defined as "implementing AI in some form". A survey led by KPMG among 30 of the Global 500 companies found that although 30% of respondents reported using AI for a selective range of functions, only 17% of the companies were deploying the technology "at scale" within the enterprise.


Key Challenges That Healthcare AI Needs to Overcome in 2020 - Dataconomy

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The promise of artificial intelligence (AI) is finally being realized across a wide variety of industries. AI is now viewed as a crucial technology to adopt for enterprises to thrive in today's business environment. Healthcare, in particular, has been one of the industries that AI advocates expect to be revolutionized by AI. Potential use cases paint a clear picture of how healthcare stakeholders stand to benefit from AI in the months ahead. Patient care standards are projected to improve, diagnostic capabilities are expected to expand, and facilities should become far more efficient.


Automotive DevOps: Rules of the Road Ahead

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The Indian automotive industry is on the edge of disruption due to increasing automation, new business models and digitization. This disruption is also through innovation and transformational change as industry players are adapting to shifting preferences on car ownership and new technological developments such as Autonomous Vehicles (AVs), IoT, cloud and proliferation electric and connected vehicles. Apart from electric and connected vehicles, the auto industry is also adopting technologies like cloud and IoT to improve the driving experience. From design and operation to servicing, cloud technology will be increasingly used at every stage to reduce costs and eliminate any scope for wastage. Cloud computing enables better vehicle engineering and thanks to advanced analytic capabilities, design teams can deliver exactly what customers want.


AI Stats News: 34% Of Employees Expect Their Jobs To Be Automated In 3 Years

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Recent surveys, studies, forecasts and other quantitative assessments of the progress and impact of AI highlight the precarious nature of the future of work (long after the coronavirus pandemic ends), the continuing mixed attitudes of consumers about data privacy, and the possible resilience of this year's investments in AI. The IT department's need for AI talent has tripled between 2015 and 2019, but the number of AI jobs posted by IT is still less than half of that stemming from other business units; departments recruiting AI talent in high volumes include marketing, sales, customer service, finance, and research and development. By 2025, at least two of the top 10 global retailers will establish robot resource organizations to manage nonhuman workers; 77% of retailers plan to deploy AI by 2021, with the deployment of robotics for warehouse picking as the No. 1 use case [Gartner] By 2024, AI, virtual personal assistants, and chatbots will replace almost 69% of the manager's workload [Gartner] "Supervised machine learning doesn't live up to the hype. It isn't actual artificial intelligence akin to C-3PO, it's a sophisticated pattern-matching tool… Rather than seeing exponential improvements in the quality of AI performance (a la Moore's Law), we're instead seeing exponential increases in the cost to improve AI systems"--Stefan Seltz-Axmacher, founder, Starsky Robotics "…why are we holding our hands behind our back trying to build AI without mechanisms that infants have?"--Gary "We haven't really gone to great depth with deep learning yet. We've had a limited amount of training data so far. We've had limited structures with limited compute power. But the key point is that deep learning learns the concept, it learns the features. "…such capabilities [as "deepfake" transformation of the human face] were called image processing 15 years ago, but are routinely termed AI today.