Among the many definitions of Artificial Intelligence (AI) there is one trait that is never compromised. That the “intelligence” should always grow and never be static. In other words, the decision-making ability of any AI platform should keep learning and become more sophisticated.
As the AI platform encounters more data, it keeps refining its decision algorithm. This process, usually called training the AI, is one of the trickier and exciting part of putting together an AI solution. The more data the AI model encounters, the more the AI platform is trained; which in turn makes the decisions more relevant and real world like.
The best solutions that incorporate AI absorb all the data they are exposed to, sifting through them to pick and choose those that are relevant and can add value to maximizing the probability of fulfilling their reason to exist, be it surfacing the next best TV show to watch or sending an emergency signal to a maintenance control room for preventive case of the machine part.
Data From All Over to Train The AI
Data is crucial to train the AI models. Data Integration provides the necessary technologies to access the data that is required to successfully maintain and grow Artificial Intelligence solutions.
Even at first glance, the volume and variety of data is mind boggling. Just the initial challenge of how to get a handle on the different types and sources of data becomes a challenge of scale and complexity. There is data that is being produced by machines (log data), there is data being produced by humans and healthcare devices. Then there is data that is being generated by business systems. There are video files, audio formats, JSON files, structured and unstructured and semi structured data. The list goes on and on.
Data Integration helps with accessing data from all sources onto the platform of choice where the AI “brain” sits, refining and making decisions.
Data Latency and Deep Learning
Without getting too technical, there are two other important considerations to make AI more powerful.
The first consideration is how recent and up-to-date is the information the AI uses to make decisions. Real-time data streaming capabilities fulfil this need. The second consideration is the ability to mine, transform and iteratively move and sift through large data sets. This comes from classic data migration and transformation capabilities. Both these capabilities together combine to stream, feed, and extract insights from data for the AI models.
For more information on how to ensure your AI powered platforms and devices have the best access to data, read more about Data Integration Platform Cloud here.