Democratizing AI: When do we elect to make AI like SaaS?

I’ll start with historical context before explaining my view on how AI should get ready to become democratized for implementation and use by any business professional. Kind of like SaaS.

Deep Learning has become synonymous with Artificial Intelligence (AI). Deep Neural Networks (DNNs) combined with powerful training and optimization techniques have led to advances in the field of AI, once Machine Learning became a common phrase. However, the concept of Neural Networks (NNs) is not new. Neural Networks date back to 1940–1950, and the idea of using back-propagation in NNs was first proposed in the 1970s. Geoferry Hinton and Yan LeCun showcased the first implementation of leveraging back-propagation, demonstrating the potential of powering AI systems with Neural Networks. However, at the time, the techniques did not scale to more practical scenarios, and even a decade ago could not outperform other classical Machine Learning techniques such as Support Vector Machines (SVMs).

Then, in 2012, “AlexNet” took the Machine Learning research community by storm. It was a showcase of a dramatic improvement in image classification tasks using deep Convolutional Neural Networks (CNNs). The authors showcased for the first time, that the deeper a Neural Network model is, the higher its performance: in other words, one can push the boundary of performance with more sophisticated models and more data. Similar trends appeared across a variety of tasks such as Speech Recognition, Text Classification, and Text Generation. As a result of this monumental achievement, the prestigious Turing Award was presented to the famed Deep Learning trio: Geoffrey Hinton, Yan LeCun and Yoshua Bengio. Other pioneers who have directly or indirectly contributed to the field are too numerous to name but include: Fei Fei Li, Zoubin Ghahramani, Jitendra Malik, Andrew Ng and Juergen Schmidhuber.

Applied Research

Research also led to a surge in startups and applied research groups leveraging state of the art DNN techniques. Some of the top startups and applied research groups which were among the first few or have had the most impact in AI through Deep Learning:

  1. Deepmind: Founded in 2010, Deepmind built and showcased a system in 2013 which outperformed humans on a variety of games, which immediately got attention from Google, thereby leading to an acquisition in 2014. Deepmind has delivered some of the scientific breakthroughs, one of the latest being AlphaFold: a grand challenge in biology of predicting protein 3D folding structure based only on the amino acid sequence .

Despite multiple applied research start-ups in the field of AI, the field has not been democratized easily. Most of the above-mentioned startups were either acquired or have partnered with large firms such as Microsoft (OpenAI). A key moment in NLP (and now beyond NLP) arrived when Transformer based DNNs were popularized by the work described in the seminal paper “Attention is all you need” by Vaswani et. al. This paper heralded the “ImageNet moment for NLP” for many experts in the field and led to large scale, pre-trained language models.

Open Source

The beginning of making AI easily accessible to the masses arrived, as it usually does in many fields, with open source:

  1. Tensorflow and Keras: Google released some of the most popular Deep Learning toolkits which has laid the foundation for research and development of AI in the community.

In addition, application specific open source AI companies like Rasa for conversational AI applications are also gaining visibility and popularity in the developer community.

But, and this is a big BUT, despite open source availability it’s been impractical to leverage open source AI without prior knowledge or expertise in Deep Learning, since the toolkits are primarily designed for developers and not for application users. There are some frameworks such as Dataiku DSS, which let developers configure Machine Learning and Deep Learning pipelines through a “No-code” interface, however, they still require some expertise and lack application interfaces. Moreover, they do not have capabilities for auto-improving with time or provide ease of self-management. Hence, Deep Learning remains the purview of big technology companies with total expertise in the field, and maybe just a few start-ups.

Democratization of AI

In the last decade terabytes and terabytes of data have been generated every day through digital channels across various applications. Unsupervised and Self-Supervised techniques can be leveraged to build AI models and applications which can extract information from historical data. The true democratization of AI would involve providing access to all application users with models that have the capability to “Self Train” and “Self Manage”, by discovering patterns from data automatically. Then it’s not about data pipelines, and ML toolkits. The AI models deal with those themselves. It just makes Deep Learning AI so much more accessible. This was the kind of revolution that SaaS brought about in traditional software applications, and made SaaS accessible to every business user.

Peter Relan, Founder YouWeb Incubator

Chandra Khatri, Chief Scientist & Head of Conversational AI at Got It AI

Note: My view is quite informed by a company I’ve incubated and I currently run, Got It AI , that is addressing key limitations of NLP and democratizing the field of Conversational AI.



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Peter Relan

Peter Relan


Parallel Entrepreneur. Co-Founder The Knowledge Project. Early investor in Discord. Founder of YouWeb Incubator & Mentor for 25+ start-ups. Now running Got It.