Including Deep Learning into your Applications: How IBM Watson Studio's Services can Help

作者/来源:  Mark van Rijmenam / Datafloq    责任编辑: 闫文美 时间: 2018年11月06日

Using AI to automate processes means substantial efficiency improvements in nearly any business function. Now, that same solution is being deployed to create better, faster and smarter AI technologies. Think of IBM Watson Studio as an AI that trains other AIs. It helps automate the Deep Learning process, so you can train your system to handle independent tasks much more rapidly. After all, some of the biggest barriers to entry with AI technology are the availability of skilled programmers, standardized formats for easy deployment and the sheer complexity involved in training any AI. With IBM Watson Studio services now available, those barriers are now awfully thin.

The Importance of Deep Learning

Deep learning is essentially what turns a static if/then based machine model into a self-improving model that learns from experiences and the feedback it gets. Before deep learning and the neural networks that allow it, all AI was really a complex set of if/then statements. If a user inputs A, then the appropriate response is B. Deep learning allows computers to think outside the box. Instead of having only two options, they may have a long list of possible responses to a single query.

The challenge lies in teaching the machine how to recognise when to deploy a particular solution. Complex equations apply weight to one solution or another based on additional criteria. For example, a perfect cup of coffee is different for different people. An AI pouring that cup might look at historical purchasing data to help decide how much cream to add and what type of sweetener the customer might prefer. Building in that flexibility is where Watson Studio's APIs come in.

Why is Deep Learning Important to App Development?

Deep learning is the method by which you train AI to accomplish a task. It requires the use of a ton of training data and a lot of computational power. For apps, AI can incorporate everything from in-app customer service to better product selections to automated loan offerings. Insurance companies might use in-app AI systems to instantly handle claims, while lending institutions might use it to approve or deny loans. The smarter the app, the less need there is for a human on the other end of the technology to handle the finalisation of an action.

What Advantages does Deep Learning Offer?

Deep learning teaches your systems how to handle specific tasks. The more complex the task, the longer it takes to train an AI, however, once trained, an AI can accomplish manual and repetitive tasks much more rapidly than a human being. Going back to the insurance example, an AI with the right training can use pictures of a vehicle to determine the likely damage. Then, an adjuster can handle the person-to-person communications necessary to keep customers happy. When AI is handling the grunt work, the adjuster can take on a lot more cases each day, dramatically improving their efficiency.

Deep learning offers the same benefits to AI--it improves efficiency. Training AI without the benefit of deep learning modules means a lot of manual computations and adjustments. Within a deep learning framework, drag and drop interfaces like the one used in IBM Watson Studio, make the process of training substantially more streamlined and more accessible to those without expert knowledge of the underlying science.

Self-Build or APIs: What Does IBM Watson Studio APIs Have to Offer?

When you develop an AI using an API, it is ready for nearly instant deployment. IBM Watson Studio helps you create intelligent AIs using a functional API framework that has a lot of the work done for you. Not only do you get a starter with pre-defined services, but you also get access to a lot of data, both yours and information stored in the Watson Knowledge Catalog. Working with the IBM Watson Studio API means you can unlock your own data more quickly, putting it to work.

Getting Started with APIs

Watson offers three development services already in place: Discovery, Assistant, and Visual Recognition. Watson Discovery works with raw data to track trends and find hidden patterns. Watson Assistant is what you would use to develop chatbots that handle functions like basic customer service or booking a reservation. Watson Visual Recognition is what you would use to train your AI for use with images. Software that uses AI to recommend clothing or creating a virtual changing room might use this platform. Determine what you need AI to do before you start working with Watson Studio. Then, getting started is easy using an API platform that helps with deployment.

Benefits of IBM Watson Studio APIs for Developers

IBM Watson Studio APIs make AI development a much smaller job for developers. Building smart apps from scratch requires a huge capital investment in equipment, time and expertise. These APIs allow developers to jump right into the building and training an AI, without the need for equipment. All of the development happens in the cloud, reducing the need for large upfront spending. Instead, developers can start running simulations and uploading data almost immediately. Since training an AI, even with all of the tools available through Watson Studio, can take weeks or months, anything that streamlines the process is valuable for businesses that need roll out as soon as possible.



Article Author: 

Founder

Mark van Rijmenam is the founder of Datafloq. He is an expert on AI, Blockchain and Big Data, a highly sought-after speaker and author of the book Think Bigger and co-author of the book 'Blockchain: Transforming Your Business and Our World'. He is named a global top 10 Big Data influencer and one of the most influential Blockchain people. He is pursuing a PhD at the University of Technology, Sydney on how organizations should deal with Big Data, Blockchain and AI and he is a faculty member of the Blockchain Research Institute.