As technology continues to scale at a rapid pace, a sensible prediction is to forecast the future of tech innovation. With new data in hand and the unlikelihood of a tech regression, it's easy to imagine what the next year could hold in store.
From the potential of machine learning algorithms to new principles that aggregate training data to navigate buyer and seller behaviors, numerous tech disciplines will soon see greater leaps than before.
IPv4 to IPv6 Conversion
Currently, if internet numbers are the proper dataset, IPv4 is still holding on strong. Most IP addresses rely on the IPv4 address model. However, since IPv4 address blocks are running out due to their scarcity, a new contender needs to take over the IPv4 address space.
It's likely that, in the next year, the IP model will scale from IPv4 to IPv6.
While it's still possible to buy IPv4 addresses from trusted sellers, as a buyer, you need to be conscious of the need for new IP addresses. It appears that many brokers and sellers will likely start offering IPv4 address conversion components to transfer established IPv4 blocks over to their modern counterparts. Only time will tell how smooth the class transfer process will be.
Greater Machine Learning
If you've read the Gartner MQ Master Data Management report, you may have come across the term 'machine learning'. While machine learning isn't a new concept, it hasn't hit its apex quite yet. So, what is machine learning? Machine learning uses algorithms and statistics to parse big data and garner insights into specific data points.
Machine learning and big data comprise a large swath of factors. A data set or data point can include numbers, objects, inputs, and clicks. Machine learning is able to determine the class of a data set during data mining and use that to find specific tasks and patterns.
It's the best way to handle a large amount of data or even a small amount of labeled data in a more efficient way.
Stemming from machine learning is the concept of deep learning. Where machine learning can gain insight into a great amount of input data and form accurate predictions based on patterns, deep learning takes this a step further.
Deep learning can find incredibly small patterns, even in unlabeled data. This is also referred to as a deep neural network in the machine learning model.
It's called a deep neural network because it has many different layers and clusters. There are also concepts such as unsupervised learning, reinforcement learning, and supervised learning that impact these computer systems and predictive analytics models.
Artificial Intelligence
Like neural networks and deep learning algorithms, artificial intelligence algorithms have been around for quite some time. However, a smart prediction is that AI will continue to grow in usage and application.
The deployment of an AI database, like a chatbot, can greatly impact the amount of data a business can process and can overcome several process weaknesses. AI is even being applied to determine a medical diagnosis in recent years.
AI can navigate complex tasks, navigate biases, and even provide reinforcement for computer vision protocols such as natural language speech recognition. Depending on how it's used by an applicant, AI can even assist data scientists with decision trees and other general labeled data tasks.
The technology is ripe for further application and is incredibly appealing to potential buyers. Each branch of artificial intelligence has the potential to make key differences in various markets.
When you combine these different technologies, there's no telling how they may be applied. From IPv4 brokers converting to IPv6 addresses and recurrent neural networks within the deep learning model, the potential for tech innovation to continue its upward trend of industry disruption is immense.