Expert Advice: Page

Practical guidance from industry experts on AI

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    Inside the MLOps lifecycle stages

    Developers tasked to train machine learning models are turning to the MLOps lifecycle. The different stages are meant to increase operational speed and efficiency.

    John Burke • Feb. 27, 2023
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    How industries use AI to ensure sustainability

    How can e-commerce companies optimize shipping routes to reduce emissions? How can data centers lower energy use? The answer is a sustainability strategy driven by AI technologies.

    Kashyap Kompella • Feb. 10, 2023
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    Recent developments show us the future of chatbots

    Experts in conversational AI are optimistic about what recent advancements in chatbot technology mean for the future. Despite challenges, these advancements can point the way forward.

    Kashyap Kompella • Dec. 16, 2022
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    Unlocking the potential of white box machine learning algorithms

    Transparent, explainable machine learning algorithms have demonstrated benefits and use cases. Although white box AI is nascent and largely unknown, it's worth exploring further.

    John Burke • Dec. 7, 2022
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    How significant is AI’s role in Industry 4.0?

    Many examples exist to demonstrate the effectiveness of AI in Industry 4.0. It entails the newest revolution in manufacturing, so naturally advanced tech like AI will play a crucial role.

    John Burke • Dec. 6, 2022
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    How financial institutions can streamline compliance with AI

    AI systems help make compliance processes more efficient and effective for financial institutions. Automation can reduce problems like human error and regulatory breaches.

    Yuktesh Kashyap • Oct. 25, 2022
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    Real-world hyperautomation examples show AI’s business value

    Hyperautomation examples in the real world help businesses automate as many of their processes as possible and achieve their strategic goals. AI is instrumental in these efforts.

    John Burke • June 24, 2022
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    Weighing quantum AI’s business potential

    Quantum AI has the potential to revolutionize business computing, but logistic complexities create sizeable obstacles for near-term adoption and success.

    Jerald Murphy • June 15, 2022
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    Use an AI governance framework to surmount challenges

    As AI governance adapts to the rapidly expanding field of AI, businesses need a holistic framework to surmount challenges with clearly defined roles and responsibilities.

    Kashyap Kompella • April 20, 2022
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    How AI ethics is the cornerstone of governance

    The concept of AI ethics ensures that AI systems provide accuracy and reliability. Businesses will benefit from adopting AI ethics strategies of their own.

    Kashyap Kompella • April 1, 2022
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    How companies can achieve AI ROI

    Companies realize AI for security is crucial to mitigate today's threats and think ROI from such investments is achievable. The investment community is also bullish on the future of AI ROI.

    Jerald Murphy • March 17, 2022
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    AI’s growing cybersecurity role

    Artificial intelligence capabilities are increasingly used to detect cybersecurity threats. As threats proliferate, AI cybersecurity capabilities will likely be the norm.

    John Burke • Jan. 27, 2022
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    Securing AI during the development process

    AI systems can have their data corrupted or 'poisoned' by bad actors. Luckily, there are protective measures developers can take to ensure their systems remain secure.

    John Burke • Jan. 25, 2022
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    11 data science skills for machine learning and AI

    As companies realize the power of data, they're tasked with finding data science practitioners with AI and ML skill sets to help them use the data to make better business decisions.

    Ron Schmelzer • May 7, 2021
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    Data science’s ongoing battle to quell bias in machine learning

    Machine learning expert Ben Cox of H2O.ai discusses the problem of bias in predictive models that confronts data scientists daily and his techniques to identify and neutralize it.

    Ron Karjian • Aug. 11, 2020
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    How to create NLP metrics to improve your enterprise model

    As standardized NLP framework evaluations become popular, experts urge users to focus on individualized metrics for enterprise success.

    George Lawton • Feb. 28, 2020
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    How to create a data set for machine learning with limited data

    A shortage of data for machine learning training sets can halt a company's AI development in its tracks. Turning to external sources and hidden data can solve the problem.

    Kassidy Kelley • Oct. 30, 2019
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    How to improve candidate matching with AI in HR recruitment

    AI in HR recruitment can source talent externally but can also be used to improve internal projects. AI platforms can match qualified existing employees to potential open projects.

    Kassidy Kelley • June 17, 2019
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    How to overcome the data scientist shortage

    The industrywide data scientist shortage is leading companies like Schneider Electric to look internally for employees with a technical background and invest in training them.

    Kassidy Kelley • May 31, 2019
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    How will the GDPR and AI clash affect enterprise applications?

    Compliance rules for GDPR and AI implementation may not seamlessly work together. Experts say AI's automated decisions on customer data are risky and need to be explainable.

    Kassidy Kelley • March 29, 2019
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    AI in the construction industry refurbishes trade procedures

    From design to reducing workplace injury, AI in the construction industry is changing manual labor jobs. Deploying cobots and AI systems is creating visible business value.

    Ron Schmelzer • Feb. 28, 2019
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    Perfect AI-defined infrastructure by analyzing your data center

    Before implementing AI, evaluate your IT team and data storage center. Experts explain the fundamental elements of data storage required to tailor an AI-defined infrastructure.

    Kassidy Kelley • Feb. 28, 2019
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    Data scientist vs. business analyst: What’s the difference?

    Data science and business analyst roles differ in that data scientists must deep dive into data and come up with unique business solutions -- but the distinctions don't end there.

    Matt Mueller and Ed Burns • April 25, 2018
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    Artificial intelligence data storage planning best practices

    AI storage planning is similar to the storage planning you're used to: Consider capacity, IOPS and reliability requirements for source data and the application's database.

    Brien Posey • Feb. 5, 2018
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    How to use machine learning to build a predictive algorithm

    Machine learning is an invaluable tool for solving business problems, but don't jump into it for predictive analytics without understanding these important factors.

    Bridget Botelho • Jan. 17, 2018