The Internet of Things (IoT) is a prime example that highlights why the quality and quantity of data are both critical for successful machine learning (ML) and artificial intelligence (AI) initiatives.
When implementing any strategic initiative, it’s important for organizations to build a considered plan upfront taking in a number of variables. This principle certainly holds true in the fast emerging areas of artificial intelligence (AI) and machine learning (ML).
With all the hype around AI, how can organizations determine where best to apply the technology?
The unstoppable march of Artificial Intelligence (AI) and machine learning is already touching our lives in so many ways. But its effects have only just begun to take hold.
To build a successful solution, you need the right data – and a lot of it.
Artificial Intelligence (AI) is increasingly being adopted by industries around the world as organizations seek to uncover new efficiencies and capabilities. Adopters of AI are improving business processes and boosting their profitability while creating better experience for their customers and their employees through intelligent automation. AI systems are not just changing how we interact with our devices and digital virtual …
Low online sales conversion rates. Inefficient customer service. These are the problems Flamingo is solving with its game-changing machine learning technology focused on the field of conversational commerce. Using Appen and Amazon Web Services to power its virtual-assistant platforms, the machine learning company now creates better online experiences and enables conversational commerce for financial …
When it comes to your AI strategy, have you considered the amount and type of data you’ll need to effectively train your machine learning models? This white paper aims to help business executives embarking on—or looking to improve—their machine learning initiatives, and covers why machine learning requires a high volume of data, the importance of high-quality data, and what data sources to consider.
Watch this webinar for key insights on how to collect data for machine learning, including pros and cons and trade offs that come with different approaches. When it comes to annotating data for academic purposes, there are specific industry standards that are commonly used. However, when it comes to the commercial sector, building a solution that relies on machine learning …
When it comes to building its AI platform, the LinkedIn team’s goal has been to make end-to-end machine learning easy, fast, robust and automatic.