What does it take to do well with AI over the long term?
One of the key points to consider is that AI is a tech that empowers data. If you don't have AI in your business soon you won't look too smart. It is like electricity. AI will power modern business. So the smart thing to do is to bring AI in and own the process and the data intelligence it throws off. Outsourcing to AI companies that take your process away is a short cut today that over the long term is just giving your business away.
Where to make a start?
Well one of the basics is to have a deep and a sound understanding of Machine Learning (ML) and Artificial Intelligence (AI) with the capabilities to actually deploy these technologies for your industry case study. You do not need to know how to 'code' AI, you need understand how it benefits your business.
At TonkaBI we understand AI ad ML and we know how to adapt and use the tech with our customers vision in mind. We 'give' the tech to our clients, we build it in to THEIR process such they can do more and bring more process and KNOWLEDGE back in house.
Where clients use to (and still do) outsource to thrid party processors (TPA's), or Call Centers, they can now bring these services back in house and in the process digitise their company.
Eabling our clients to do this bring VALUE back in to the company and increases THEIR productivity. Our tech enablbles this value creation.
How do we do this? We build an AI and or a ML module and then place this on your servers and integrate this in to your process or App or web site etc. We empower you.
As a example of Computer Vision, this is the ability to let a computer process 1000's of pictures per day. This can be for insurance, agriculture, security, education, etc. Many applications.
Our main computer vision AI product is a set of several AI modules, we design very robust AI modules for individual requirement. While at the same time, users/products can do multiple analysis efficiently.
We used Auto-Machine Learning (Auto-ML) approach in our AI product, where Auto-ML decides the best ML Algorithm. We used best ML Algorithm as suggested by Auto-ML which enhances our product capability.
We see other companies develop generic AI products, then focus on insurance domain. However, our products are designed specially for Insurance case studies.
What we do is use a ranges of tools and our own algorithems and code, the core being made up as follows:
TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications
Keras is a high-level neural networks API that works as a layer.
Deep Learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on artificial neural networks. Learning can be supervised, semi-supervised or unsupervised.
Natural Language processing (NLP) is a subfield of computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.
Optical Character Recognition or optical character reader (OCR) is the mechanical or electronic conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene-photo (for example the text on signs and billboards in a landscape photo) or from subtitle text superimposed on an image
Machine Learning (ML) is the scientific study of algorithms and statistical models that computer systems use in order to perform a specific task effectively without using explicit instructions, relying on patterns and inference instead.
Tensor Flow and Keras takes maximum memory and computing time, it is very big challenges for companies to deploy deep learning-based application. We over come this using our embedded application, this takes less memory and less computing time (0.5-1.2 sec per image) and can run on any operating system.
Our AI product allows us to focus on accuracy, for our use cases and successfully identifies body parts, damage, colour with user input images with 80-90 percent accuracy. Furthermore, we developed robust algorithm to calculate damage severity with very good accuracy 75-80 percent. This is growing as more data is applied and as we add features.
Our AI product takes less computing time with highly mechanized deep learning neural network architecture, this enables us to provide faster solutions suitable for insurance. Additionally we deal with error tolerance and to handle outlier/noise. This helps support multi-tasking with error less environments.
This is just one example of the thought and care we bring to our products to support our clients. We understand that AI will grow value, the value is in the process not the AI itself. Our clients should own the process.