Remove Artificial Inteligence Remove Artificial Intelligence Remove Machine Learning
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Can Artificial Intelligence Replace Human Intelligence?

The Crazy Programmer

Artificial Intelligence is a science of making intelligent and smarter human-like machines that have sparked a debate on Human Intelligence Vs Artificial Intelligence. Will Human Intelligence face an existential crisis? Impacts of Artificial Intelligence on Future Jobs and Economy.

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Taktile makes it easier to leverage machine learning in the financial industry

TechCrunch

Meet Taktile , a new startup that is working on a machine learning platform for financial services companies. This isn’t the first company that wants to leverage machine learning for financial products. They could use that data to train new models and roll out machine learning applications.

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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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Slack’s former head of machine learning wants to put AI in reach of every company

TechCrunch

Adam Oliner, co-founder and CEO of Graft used to run machine learning at Slack, where he helped build the company’s internal artificial intelligence infrastructure. With a small team, he could only build what he called a “miniature” solution in comparison to the web scale counterparts.

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Embedding BI: Architectural Considerations and Technical Requirements

While data platforms, artificial intelligence (AI), machine learning (ML), and programming platforms have evolved to leverage big data and streaming data, the front-end user experience has not kept up. Traditional Business Intelligence (BI) aren’t built for modern data platforms and don’t work on modern architectures.

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CatalyzeX grabs $1.64M seed to help developers find right machine learning model

TechCrunch

Machine learning is exploding, and so are the number of models out there for developers to choose from. While Google can help, it’s not really designed as a model search engine. working on machine learning projects, where they saw the kinds of research challenges they are attempting to fix with CatalyzeX.

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Social services provider uses artificial intelligence to provide genuine help

CIO

In addition, the incapacity to properly utilize advanced analytics, artificial intelligence (AI), and machine learning (ML) shut out users hoping for statistical analysis, visualization, and general data-science features.

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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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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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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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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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Realizing the Benefits of Automated Machine Learning

While everyone is talking about machine learning and artificial intelligence (AI), how are organizations actually using this technology to derive business value? This white paper covers: What’s new in machine learning and AI.

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The New Tech Experience: Innovation, Optimization, and Collaboration

Speaker: Paul Weald, Contact Center Innovator

Learn how to streamline productivity and efficiency across your organization with machine learning and artificial intelligence! How you can leverage innovations in technology and machine learning to improve your customer experience and bottom line.

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How AI and ML Can Accelerate and Optimize Software Development and Testing

Speaker: Eran Kinsbruner, Best-Selling Author, TechBeacon Top 30 Test Automation Leader & the Chief Evangelist and Senior Director at Perforce Software

In this session, Eran Kinsbruner will cover recommended areas where artificial intelligence and machine learning can be leveraged. With new AI and ML algorithms spanning development, code reviews, unit testing, test authoring, and AIOps, teams can boost their productivity and deliver better software faster.