In the field of machine learning, domain-specific annotated data is an invaluable resource for training effective models. However, in the medical domain, this data often includes Personal Health Information (PHI), raising significant privacy concerns. The stringent regulations surrounding PHI limit the availability and sharing of medical datasets, which poses a substantial challenge for researchers and practitioners aiming to develop advanced machine learning models. In this paper, we introduce a novel method to "clone" datasets containing PHI. Our approach ensures that the cloned datasets retain the essential characteristics and utility of the original data without compromising patient privacy. By leveraging differential-privacy techniques and a novel fine-tuning task, our method produces datasets that are free from identifiable information while preserving the statistical properties necessary for model training. We conduct utility testing to evaluate the performance of machine learning models trained on the cloned datasets. The results demonstrate that our cloned datasets not only uphold privacy standards but also enhance model performance compared to those trained on traditional anonymized datasets. This work offers a viable solution for the ethical and effective utilization of sensitive medical data in machine learning, facilitating progress in medical research and the development of robust predictive models.

Any enterprise can unlock AI -- but only if leaders know how to actually leverage their data, define problems and iterate. In this VB On-Demand event, join industry experts as they dig into how enterprises can turn data into company-wide AI and machine learning solutions. Companies are foundering in the quest to realize AI objectives, not to mention a return on their investment in AI, and it comes down to the right data and the right expertise, says Paula Martinez, CEO and co-founder of Marvik, a machine learning consultancy. First, there's the expense and effort of uncovering good quality data from the mountain that's always growing, and labeling it properly to actually put analytics and machine learning developments into production. And then, going from proof of concept to a production-ready solution with quality standards that can be launched at scale is another enormous obstacle -- and a large part of that is finding a team with the right skills to carry out the task successfully.

The surge in digital transformation initiatives across businesses and the heightened need for real-time insights has led to an explosion in data creation. But few organisations have a proper understanding of where all their data exists in the first place. Every company has different siloed data sets running on-premises and across multiple public and private clouds and various servers. A recent global survey commissioned by IBM with Morning Consult found 9 out of 10 IT professionals in India reporting that their company draws from 20 or more different data sources to inform its AI, BI, and analytics systems. "This has led to data silos and complexity and as a result most data remains unanalysed, inaccessible or untrusted," says Siddhesh Naik, Data, AI & Automation sales leader, IBM Technology Sales, IBM India/South Asia.

Are you prepared for AI implementation? Do you know what your accompanying data strategy should be? If not, it is likely you aren't alone. According to research by Secondmind, 82 per cent of supply chain managers are frustrated by AI systems and tools during the coronavirus pandemic. In its survey of 500-plus supply chain planners and managers across Europe and the United States, 37 per cent cited a lack of reliable data to feed into AI systems as a concern, at a time when accuracy and speed of decision-making were of the essence.

While buzzwords such as predictive maintenance, artificial intelligence, digital twin and augmented reality have promised to enable the fabled digital transformation of manufacturing, when it comes to Industry 4.0, most practical applications start and end with machine connectivity. And when it comes to driving value, look no further than answering these questions: "What's happening?" Simply put, most manufacturers are unable to see what's actually happening on the shop floor in real time because their machines are not connected to any sort of data collection or data visualization system. This inability to both see and use data to drive continuous improvement leads to massive inefficiencies that affect every component of a company's operations, from the shop floor all the way to the C-Suite. As the excitement around the opportunity presented by AI continues to grow, Lou Zhang, chief data scientist at MachineMetrics, offers his perspective on where AI lands within the Analytics Journey and its relationship to technologies such as machine monitoring and data collection.

While buzzwords such as predictive maintenance, artificial intelligence, digital twin and augmented reality have promised to enable the fabled digital transformation of manufacturing, when it comes to Industry 4.0, most practical applications start and end with machine connectivity. And when it comes to driving value, look no further than answering these questions: "What's happening?" Simply put, most manufacturers are unable to see what's actually happening on the shop floor in real time because their machines are not connected to any sort of data collection or data visualization system. This inability to both see and use data to drive continuous improvement leads to massive inefficiencies that affect every component of a company's operations, from the shop floor all the way to the C-Suite. As the excitement around the opportunity presented by AI continues to grow, Lou Zhang, chief data scientist at MachineMetrics, offers his perspective on where AI lands within the Analytics Journey and its relationship to technologies such as machine monitoring and data collection.

While buzzwords such as predictive maintenance, artificial intelligence, digital twin and augmented reality have promised to enable the fabled digital transformation of manufacturing, when it comes to Industry 4.0, most practical applications start and end with machine connectivity. And when it comes to driving value, look no further than answering these questions; "What's happening?" Simply put, most manufacturers are unable to see what's actually happening on the shop floor in real time because their machines are not connected to any sort of data collection or data visualization system. This inability to both see and use data to drive continuous improvement leads to massive inefficiencies that affect every component of a company's operations, from the shop floor all the way to the C-Suite. That said, as the excitement around the opportunity presented by AI continues to grow, we conducted an interview with our very own Lou Zhang, Chief Data Scientist at MachineMetrics, so he could give us his perspective on where AI lands within the Analytics Journey and its relationship to technologies such as machine monitoring and data collection. How far along is the manufacturing industry as a whole when it comes to taking advantage of AI, for machine monitoring and/or other applications?

"In the past, large volumes of data made us sweat". So said Pari Bajpay, vice president of Next Generation Enablement at CenturyLink, during a presentation titled "Can AI deliver its promise of a cost-effective, improved experience in telecom?" at the TM Forum's recent Digital Transformation World event in Nice. "We didn't have the networking, compute and storage capacity to cope. A lot of the data would be turned off and you would only work on the critical aspects of the data because what you had on the other end of it was humans that could not process such large volumes," noted Bajpay. However, as big data technology has matured, Bajpay and his team at CenturyLink have grappled with the issue and are now leveraging AI to extract more value from their data.