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Artificial Intelligence in practice

CIO

The world has known the term artificial intelligence for decades. Developing AI When most people think about artificial intelligence, they likely imagine a coder hunched over their workstation developing AI models. Today, integrating AI into your workflow isn’t hypothetical, it’s MANDATORY.

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How the world can tackle the power demands of artificial intelligence

CIO

The world must reshape its technology infrastructure to ensure artificial intelligence makes good on its potential as a transformative moment in digital innovation. New technologies, such as generative AI, need huge amounts of processing power that will put electricity grids under tremendous stress and raise sustainability questions.

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LLM benchmarking: How to find the right AI model

CIO

But how do companies decide which large language model (LLM) is right for them? LLM benchmarks could be the answer. They provide a yardstick that helps user companies better evaluate and classify the major language models. LLM benchmarks are the measuring instrument of the AI world.

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China banks on open source in AI cold war with US

CIO

After more than two years of domination by US companies in the arena of artificial intelligence,the time has come for a Chinese attackpreceded by many months of preparations coordinated by Beijing. Its approach couldchange the balance of power in the development of artificial intelligence.

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The Role of Artificial Intelligence in Pandemic Response: Lessons Learned From COVID-19

In March 2020, the world was hit with an unprecedented crisis when the COVID-19 pandemic struck. As the disease tragically took more and more lives, policymakers were confronted with widely divergent predictions of how many more lives might be lost and the best ways to protect people.

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Have we reached the end of ‘too expensive’ for enterprise software?

CIO

Generative artificial intelligence ( genAI ) and in particular large language models ( LLMs ) are changing the way companies develop and deliver software. These autoregressive models can ultimately process anything that can be easily broken down into tokens: image, video, sound and even proteins.

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AI in action: How enterprises are scaling AI for real business impact

CIO

To capitalize on the enormous potential of artificial intelligence (AI) enterprises need systems purpose-built for industry-specific workflows. The Insurance LLM is trained on 12 years worth of casualty insurance claims and medical records and is powered by EXLs domain expertise.

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How to Leverage AI for Actionable Insights in BI, Data, and Analytics

In the rapidly-evolving world of embedded analytics and business intelligence, one important question has emerged at the forefront: How can you leverage artificial intelligence (AI) to enhance your application’s analytics capabilities?

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MLOps 101: The Foundation for Your AI Strategy

Many organizations are dipping their toes into machine learning and artificial intelligence (AI). Download this comprehensive guide to learn: What is MLOps? How can MLOps tools deliver trusted, scalable, and secure infrastructure for machine learning projects? Why do AI-driven organizations need it?

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How to Choose an AI Vendor

You know you want to invest in artificial intelligence (AI) and machine learning to take full advantage of the wealth of available data at your fingertips. But rapid change, vendor churn, hype and jargon make it increasingly difficult to choose an AI vendor.

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5 Things a Data Scientist Can Do to Stay Current

With the number of available data science roles increasing by a staggering 650% since 2012, organizations are clearly looking for professionals who have the right combination of computer science, modeling, mathematics, and business skills. Fostering collaboration between DevOps and machine learning operations (MLOps) teams.

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Trusted AI 102: A Guide to Building Fair and Unbiased AI Systems

The risk of bias in artificial intelligence (AI) has been the source of much concern and debate. How to choose the appropriate fairness and bias metrics to prioritize for your machine learning models. How to successfully navigate the bias versus accuracy trade-off for final model selection and much more.

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Data Science Fails: Building AI You Can Trust

The game-changing potential of artificial intelligence (AI) and machine learning is well-documented. Any organization that is considering adopting AI at their organization must first be willing to trust in AI technology.

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The Recruiting Crossword Puzzle

On top of ever-increasing advancements on the technology front (hello, artificial intelligence), try adding record-low unemployment and candidates’ virtual omnipresence and you’ve got yourself a pretty passive, well-informed, and crowded recruiting landscape.

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Democratizing AI for All: Transforming Your Operating Model to Support AI Adoption

Democratization puts AI into the hands of non-data scientists and makes artificial intelligence accessible to every area of an organization.