Democratising AI: It’s nothing political
What is AI democratisation?
Democracy is “a government representing all people”; this happens to be the most popular definition of the word. However, even things/resources can be democratised, which means they are readily available for all.
The idea is the same in digital parlance as well. AI democratisation is a design that makes the benefits of artificial intelligence accessible to all, even to those who do not have any knowledge of it.
The 20th century saw a boom of techies. This century, however, is all about data and intelligence and using them to recognise trends, make decisions, forecast, learn, and improve, and above all, make them accessible to everyone in the organisation.
But the catch is that AI development demands a lot - resources, expertise, computing power, and money. In short, it is a technology that is limited in its reach for the rich and/or the tech-savvy population.
Others find themselves lagging - a situation that firms up the need for democratising AI. The three main benefits of doing so are:
- Reduced entry barriers for individuals and organisations. With little investment, they can both enter the world of AI and experiment with building AI models by accessing publicly available datasets.
- Lessened overall cost of building AI solutions since data and algorithms are made available for free so that developers can start extending them to build more powerful solutions.
- Accelerated adoption in the fields of business and academics. Use of NLP (Natural Language Processing) in businesses and ML (Machine Learning) and deep learning to augment human decision-making is becoming commonplace. This enables businesses to be more agile and hyper-productive.*
Levels of democratisation
Which part(s) of the AI product adds the most value for the end user? This question must be answered by technology vendors before they release their product in the market. The gamut of AI democratisation includes data, algorithms, model development and marketplace.
Data:It is the raw input for ML from which insights are generated, decisions made and outcomes predicted. This level is easy to democratise and enables basic analyses of datasets.
Algorithms:At this level, data gets engaged. Democratising algorithms, though more complex than data democratisation, is still easy. But it needs a basic understanding of computer science, statistics and mathematics to make sense of the algorithm. Github boasts of hundreds of AI code repositories, and the numbers are growing by the day.
Data storage and computing:Democratisation has just started getting complex. With the big AI/ML providers such as Amazon Web Services (AWS), Google Compute Platform and Microsoft Azure, moving to cloud platforms, access to central processing units and graphic processing units have become easy. However, specific training needs are a necessity at this level since cloud storage and computing platforms require vendor-specific certification.
Model development:Models are targeted to solve specific problems such as sales prediction, facial recognition, speech detection and so on. Democratising model development means the ability to process a variety of data formats (with varied levels of structuring), run a number of algorithms on the data and select the best ensemble to make the model development process more accessible. Nevertheless, this level too requires users to be trained to avoid building bias into the model, to be able to explain its results and to make the right decisions.
Marketplace:Democratising this level is at a nascent stage; but it also means there are opportunities waiting to be tapped. Kaggle, a subsidiary of Google LLC, is a community of ML practitioners and one of the most popular AI marketplaces. For example, Kaggle throws challenges such as Netflix movie recommendation, Titanic ML competition and NLP that attracts skills from all over the world.
As technology advances, one of two things happens: it remains as costly as it started out and risks perishing, or it gets democratised and helps further technological advancement.
Printing press democratised information and the internet did the same to knowledge. AI, the present and future of technology, is on this path now. The full power of AI is yet to be realised and democratising it feels just the right way forward to achieving more out of it. As Kay Firth-Butterfield, the head of AI and ML of World Economic Forum’s wrote,“The long-term success in the development of AI will depend on the agility of the collaboration, the diversity and integrity of the data, and the accuracy of the risk assessments.”
*For organizations on the digital transformation journey, agility is key in responding to a rapidly changing technology and business landscape. Now more than ever, it is crucial to deliver and exceed on organizational expectations with a robust digital mindset backed by innovation. Enabling businesses to sense, learn, respond, and evolve like a living organism, will be imperative for business excellence going forward. A comprehensive, yet modular suite of services is doing exactly that. Equipping organizations with intuitive decision-making automatically at scale, actionable insights based on real-time solutions, anytime/anywhere experience, and in-depth data visibility across functions leading to hyper-productivity, Live Enterprise is building connected organizations that are innovating collaboratively for the future.