The Promise of Deep Learning for Rule-Based Tasks

The promise of AI is extraordinary, but anyone with technical chops knows there’s a long way to go. Deep learning and neural networks have gotten pretty good at things like image recognition, statistics and language analysis.

However, there’s a big gap when it comes to logic-based tasks such as supply and demand management, retail space allocation, anomaly detection and business process optimization. When we need to establish a set of rules for the learning machines to solve rather than just having them write a mathematical algorithm, we haven’t been able to rely on unsupervised learning.

For either kind of task, whether the machine learning is led by logic or led by math, there’s also a big problem with return on investment (ROI). Hiring top AI experts can cost millions of dollars, making it hard for companies to access the technology. In addition, companies that have shelled out the big bucks for AI projects often end up with not much to show for it.

I’m an entrepreneur first and an AI expert second, and I am a firm believer that this quandary can be solved. And it must be solved.

Have you run into the problem of logic versus mathematics in deep learning and neural networks? Have you faced ROI challenges because of it? We’d love to hear more. Let us know in the comments.

What is the RLM and how does it relate to ROI?

The Ryskamp Learning Machine, offered by my company useAIble, creates a new paradigm for machine learning. The system combines mathematical and logical functions to create a framework that can solve many real-world business problems exponentially faster than its competitors. Its output is a rules framework — its own dynamic system — rather than just an answer generator.

The RLM is also capable of solving more types of problems, is completely transparent about the decisions it makes, and adds many other new dimensions to the field of machine learning.

The RLM does not have a “black box” problem like traditional machine learning approaches, and this is huge for business. The Total RecallTM capability stores all results and key learning events from the RLM, providing full transparency.

The Learning Visualizer allows users to review and refer to key learning events and understand exactly why decisions were made, and the whole system is easy to use. A person with a basic computer science degree can configure and deploy our system quickly, and these changes are just the tip of the iceberg.

We’re thrilled that our focus, an unconventional logic-based approach to ML, is gaining traction and visibility in the industry. Since my company’s passion is for applied technology, I am happy to offer the complete code to students, researchers and small organizations. Contact me at Rix.Ryskamp@useAIble.com  to learn more and be part of this exciting opportunity.

useAIble joined RE•WORK at the Deep Learning Summit in San Francisco earlier this year, and you can register here to watch video presentations, interviews and highlights from the summit.