Shaip

In every stage of AI development for healthcare use cases, experts face tons of data-related challenges. What are they and how do we fix them? We’ve addressed them here. From doctors’ and healthcare providers’ perspectives, AI is paving the way for robotic arms, sophisticated analysis and diagnostic modules, assistive surgical bots, predictive wings to detect genetic disorders and concerns, and more. Check it out this link.  https://www.shaip.com/blog/4-unique-data-challenges-the-use-of-ai-in-healthcare-causes

We all understand that the performance of an artificial intelligence (AI) module depends entirely on the quality of datasets provided in the training phase. However, they are usually discussed on a superficial level. Most of the resources online specify why quality data acquisition is essential for your AI training data stages, but there is a gap in terms of knowledge that differentiates quality from insufficient data. When you delve deeper into datasets, you will notice tons of intricacies and subtleties that are often overlooked. We’ve decided to shed light on these less-spoken topics. After reading this article, you will have a clear idea of some of the mistakes you’re making during data collection and some ways you could optimize your AI training data quality. Let’s get started. The Anatomy of an AI Project For the uninitiated, an AI or an ML (machine learning) project is very systematic. It is linear and has a solid workflow. To give you an example, here’s how it looks in a generi

 Today a business without Artificial Intelligence (AI) and Machine Learning (ML) is at a significant competitive disadvantage. From supporting and optimizing backend processes and workflows to elevating user experience through recommendation engines, and automation, AI adoption is inevitable and essential to survival in 2021. However, getting to a point where AI delivers seamless and accurate results is challenging. Proper implementation isn’t achieved overnight, it is a long-term process that can continue for months. The longer the AI training period, the more precise the results. With that said, a longer AI training duration demands more volumes of relevant and contextual datasets. From a business perspective, it is near impossible that you will have a perennial source of relevant datasets unless your internal systems are highly efficient. Most businesses must rely on external sources like third-party vendor

Quality data translates to success stories while poor data quality makes for a good case study. Some of the most impactful case studies on AI functionality have stemmed from a lack of quality datasets. While companies are all excited and ambitious about their AI ventures and products, the excitement doesn’t reflect on data collection and training practices. With more focus on output than training, several businesses end up delaying their time to market, losing funding, or even pulling down their shutters for eternity. Read More : -  https://www.computertechreviews.com/data-acquisition-considerations-you-cannot-overlook-in-2021/

Sourcing datasets for artificial intelligence (AI) modules from public/open and free resources are among the most common questions we get asked during our consultation sessions. The entrepreneurs, AI specialists, and techpreneurs have expressed that their budget is a primary concern when deciding where to source their AI training data.    Most entrepreneurs understand the importance of quality and contextual training data for their modules. They realize the difference that relevant data can bring to outcomes and results; however, in many cases, their budget restricts them from acquiring paid, outsourced, or 3rd party training data from reliable vendors and resort to their own efforts in sourcing data. In this blog post, we will explore why you shouldn’t settle for public data resources to save money because of the consequences they will create. Reliable Publicly Available AI Training Data Sources  Before we get into public resources, the first option should be your internal data

The process of developing an artificial intelligence (AI) system is taxing. Even a simple AI module takes months of training to predict, process, or recommend an outcome. Successfully developing AI systems is challenging in terms of labor and time-consuming. Companies working within short timeframes could suffer significant losses if their training period extends past their deadline. Moreover, companies are also likely to feed their systems with bad data. Even if the deadlines are met, using low-quality AI training data will result in the actual cost of full-fledge AI development could end up being exorbitant. To avoid delayed training times and inaccurate results, a sophisticated strategy must be adequately implemented. We are going to cover a different aspect of the expenses involved in developing AI in this post. We’ve previously covered AI training budgeting; today, we will dive deeper and explore other costs involved in AI training data. Let’s get started. The True Costs of AI Tra