IBL News | New York
Artificial Intelligence (AI) initiatives continue to accelerate as more organizations embrace the digital transformation of their core operations, research firm Gartner said in its last report, “Hype Cycle for Artificial Intelligence, 2021,” and as shown in the graphic below.
As a result, business firms will further explore emerging technologies to secure competitive advantages, such as nonfungible tokens (NTF), sovereign cloud, data fabric, generative AI, and composable networks.
“Technology innovation is a key enabler of competitive differentiation and is the catalyst for transforming many industries. Breakthrough technologies are continually appearing, challenging even the most innovative organizations to keep up,” said Brian Burke, Research Vice President at Gartner.
There are four trends that are driving AI innovation in the near term, stated Gartner.
- Responsible AI. “Stakeholders are demanding increased trust, transparency, fairness, and auditability of AI technologies,” according to Svetlana Sicular, Research Vice President at Gartner.
- Small and wide data. “By 2025, 70 percent of organizations will be compelled to shift their focus from big to small and wide data, providing more context for analytics and making AI less data-hungry,” Gartner predicts.
- Operationalization of AI platforms. “Only half of AI projects make it from pilot into production, and those that do take an average of nine months to do so,” said Sicular.
- Efficient use of resources. “Given the complexity and scale of the data, models and compute resources involved in AI deployments, AI innovation requires such resources to be used at maximum efficiency,” Gartner explains.
On the other hand, O’Reilly announced a free virtual event that will cover the latest developments, tools, best practices, and critical issues for data and AI. It will take place from 10:00 am to 1:30 pm ET on Thursday, October 14.
Topics of discussion will include prototyping and pipelines to deployment, DevOps, and responsible and ethical AI. In addition, Tim O’Reilly, O’Reilly’s Founder, and CEO will deliver the closing address, “The Future of Data and AI.”
- AI in Healthcare
- Speaker: Jeremy Howard, Founding Researcher, fast.ai
- How to Keep Up with ML
- Speaker: Aurélien Géron, Author of Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
- Prototype to Pipeline: Evolving from Data Exploration to Automated Data Processing
- Speaker: Sev Leonard, Senior Software Engineer, Fletch
- Watch Me Learn: Querying Data the Right Way
- Speaker: Vinoo Ganesh, Head of Business Engineering at Ashler Capital, Citadel
- Improve Data Quality with a Focus on Data Reliability and Observability
- Speaker: Barr Moses, Cofounder and CEO, Monte Carlo
- Train and Predict with Amazon Redshift ML Using SQL
- Speakers: Chris Fregly, Developer Advocate for AI and Machine Learning, Amazon Web Services, and Antje Barth, Senior Developer Advocate for AI and Machine Learning, Amazon Web Services
- What’s Still Missing from the Responsible AI Movement
- Speaker: Aileen Nielsen, Fellow in Law and Technology, ETH Zurich, and Author of Practical Time Series Analysis and Practical Fairness
- MLOps from Zero to One
- Speaker: Noah Gift, Lecturer at UC Davis and Northwestern and Author of Practical MLOps: Operationalizing ML Models
- NeuralQA: A Usable Library for Question Answering on Large Datasets Using BERT-Based Models
- Speaker: Victor Dibia, Research Engineer in Machine Learning, Cloudera Fast Forward Labs
- Demystifying Scalable Machine Learning with the Spark Ecosystem
- Speaker: Adi Polak, Senior Software Engineer and Developer, Microsoft, and Author of the upcoming book Machine Learning with Apache Spark