Deep learning (DL) could be defined as a form of machine learning based on artificial neural networks which harness multiple processing layers in order to extract progressively better and more high-level insights from data. In essence it is simply a more sophisticated application of artificial intelligence (AI) platforms and machine learning (ML).
Here are some of the top trends in deep learning:
Model Scale Up
A lot of the excitement in deep learning right now is centered around scaling up large, relatively general models (now being called foundation models). They are exhibiting surprising capabilities such as generating novel text, images from text, and video from text. Anything that scales up AI models adds yet more capabilities to deep learning. This is showing up in algorithms that go beyond simplistic responses to multi-faceted answers and actions that dig deeper into data, preferences, and potential actions.
Scale Up Limitations
However, not everyone is convinced that the scaling up of neural networks is going to continue to bear fruit. Roadblocks may lie ahead.
“There is some debate about how far we can get in terms of aspects of intelligence with scaling alone,” said Peter Stone, PhD, Executive Director, Sony AI America.
“Current models are limited in several ways, and some of the community is rushing to point those out. It will be interesting to see what capabilities can be achieved with neural networks alone, and what novel methods will be uncovered for combining neural networks with other AI paradigms.”
AI and Model Training
AI isn’t something you plug in and, presto, instant insights. It takes time for the deep learning platform to analyze data sets, spot patterns, and begin to derive conclusions that have broad applicability in the real world. The good news is that AI platforms are rapidly evolving to keep up with model training demands.
Instead of weeks to learn enough to begin to function, AI platforms are undergoing fundamental innovation, and are rapidly reaching the same maturity level as data analytics. As datasets become larger, deep learning models become more resource-intensive, requiring a lot of processing power to predict, validate, and recalibrate millions of times. Graphics Processing Units (GPUs) are advancing to handle this computing and AI platforms are evolving to keep up with model training demands.
“Organizations can enhance their AI platforms by combining open-source projects and commercial technologies,” said Bin Fan, VP Open Source and Founding Engineer at Alluxio.
“It is essential to consider skills, speed of deployment, the variety of algorithms supported, and the flexibility of the system while making decisions.”
Deep learning workloads are increasingly containerized, further supporting autonomous operations, said Fan. Container technologies enable organizations to have isolation, portability, unlimited scalability, and dynamic behavior in MLOps. Thus, AI infrastructure management would become more automated, easier, and more business-friendly than before.
“Containerization being the key, Kubernetes will aid cloud-native MLOps in integrating with more mature technologies,” said Fan.
“To keep up with this trend, organizations can find their AI workloads running on more flexible cloud environments in conjunction with Kubernetes.”
Prescriptive Modeling over Predictive Modeling
Modeling has gone through many phases over the last many years. Initial attempts tried to predict trends from historical data. This had some value, but didn’t take into account factors such as context, sudden traffic spikes, and shifts in market forces. In particular, real-time data played no real part in early efforts at predictive modeling.
As unstructured data became more important, organizations wanted to mine it to glean insight. Coupled with the rise in processing power, suddenly real time analysis rose to prominence. And the immense amounts of data generated by social media has only added to the need to address real time information.
How does this relate to AI, deep learning, and automation?
“Many of the current and previous industry implementations of AI have relied on the AI to inform a human of some anticipated event, who then has the expert knowledge to know what action to take,” said Frans Cronje, CEO and Co-founder of DataProphet.
“Increasingly, providers are moving to AI that can anticipate a future event and take the correspondent action.”
This opens the door to far more effective deep learning networks. With real time data being constantly used by multi-layered neural networks, AI can be utilized to take more and more of the workload away from humans. Instead of referring the decision to a human expert, deep learning can be used to prescribe predicted decisions based on historical, real-time, and analytical data.