The Future of Artificial Neural Networks: Emerging Trends and Developments
Imagine you could install a chip into your brain that makes it several times more efficient. While that technology may not yet exist (at least publicly), an artificial neural network processor is similar in ways. While these algorithm-driven brains are already useful for a variety of tasks, they will improve even more in the future thanks to neural network accelerator chips.
Here are some current and possible leaps in neural network processors that are taking deep learning applications to the next level…
Self-learning capabilities for security
To a certain extent, many existing neural network processors are limited by computing power and available data. A neural network chip can improve on this by labeling data on its own, rather than being presented labeled data for training purposes.
This technique is also called “unsupervised learning,” meaning it has no output data to compare its conclusions to. It can also use unlabelled data similarities to classify the data, using it to recognize patterns. It can then make associations between datasets without being told specifically how.
In this way, a neural network chip can identify potential cyber threats it has never encountered before based on data outside of existing classifications. As cyber attacks have increased to about 2,000 daily, companies will be able to stay a step ahead of hackers using deep learning.
Better image recognition for healthcare
Current neural network processors can apply Natural Language Processing (NLP), which can understand abstract text based on its training model. However, even with advanced NLP, some of the data the neural network uses for its outputs could be inconsistent with text alone.
A continuing trend is the neural network’s ability to draw data from non-text sources. While these networks can already identify objects and faces, the application is gaining more practical abilities that can be used in business.
As examples, a neural network can be used in healthcare to monitor cardiovascular conditions (and provide possible treatment options). It can also be used to detect safety hazards within a work site to avoid human injuries.
Increased marketing power for brands
Neural network processors are already useful in sales/marketing, sorting through huge swaths of data that would take humans days or weeks. Advancing neural network chips will be able to better target advertising audiences, using the data to customize the messaging.
In this instance, human teams or AI produce multiple ad campaigns that target different customer demographics. Neural network accelerator chips will be able to craft marketing for one specific customer – which could boost sales success, as about 70% of customers prefer customized ads.
Advanced neural network chips are already here
Imagine you could install a chip to supercharge your brain. While that’s not yet available (at least to the public), you can beef up your artificial network with a neural network accelerator chip that greatly increases its processing power.
These accelerators are designed to better handle the workload of neural networks, through improving memory use and making better predictions (with simpler inputs.) The newer neural network chips also require less computing power, which will to reduce your costs – and your corporate footprint.
Learn more about the benefits of the latest inference neural network accelerator chip.