Democratization of artificial intelligence means making AI available for all. In other words, open-source datasets and tools developed by companies like Microsoft and Google - which demand less knowledge of AI from the user - are made available so that anyone can build innovative AI software. This has led to the rise of ‘citizen data scientists’.
This article will explore the four chapters to know in the democratization of AI: why democratization is needed, what components of AI should be democratized, the steps involved in the democratization of these components, and the democratization framework itself.
All users have to do is upload sufficient images of every class, mention the class names, and click on a button to train the model. GCP will find the most effective artificial intelligence algorithm to classify images.
Colab Pro users enjoy higher random access memory (RAM) and lower model training time. Above all, the model trained can then be integrated with users’ applications. For example, suppose users want an image classifier to classify types of blood cells, they can train a convolutional neural network (CNN) model on Colab, save it, and integrate it with the Flask backend of their local systems.
AI democratization reduces entry barriers to both individuals or organizations - essentially, budding data scientists. Since they can use open-source datasets to train AI models on the cloud, learning AI doesn’t demand a lot of financial investment. Plus, it can be picked up by anyone from any corner of the globe. They can also extend their learning by participating in contests and datathons.
Democratizing AI cuts down the overall expenditure needed to build AI solutions. Companies use open-source data, algorithms, and models on the cloud to build powerful and useful AI systems for a wide variety of applications.
Tools like transformers, TensorFlow, PyTorch, and ImageNet help build highly accurate models quickly, thus reducing time in talent development. Any natural language processing (NLP) model, such as Google’s Bidirectional Encoder Representations from Transformers (BERT), can be picked from the transformers’ library and trained with a custom dataset for a custom application. BERT even performs better intent recognition than traditional techniques.
Such helpful artificial intelligence tools are easily and quickly adopted. For example, chatbots are common in most websites to resolve frequently asked customer queries. Another common application of NLP involves sentiment analysis. This lets business leaders know the types of products/services customers care about. In text classification, sentiment analysis analyzes whether the sentiment of messages are positive, negative, or neutral.
AI is used in hate speech detection on social media to identify cyberbullying and protect potential victims. As AI evolves, it has become better at understanding the semantics of language and is able to detect subtle undertones.
When an AI product is released, it’s essential to decide which parts must be democratized. Data storage, data computation, algorithms, model development, and marketplace are the five elements that can be democratized, in order of their increasing sophistication.
Data refers to the large volumes of records that are carefully analyzed to derive knowledge and insights for important business decisions. It can be structured as tables with rows and columns or be semi-structured or unstructured like images, videos, audio, and text with emoticons.
The Kaggle datasets and those publicly shared on GitHub like Prajna Bhandary's mask detection dataset are all examples of democratization of data. Data visualization tools are also democratized to help users visualize open-source datasets.
Storage and computing involve building and/or deploying models on clouds such as Amazon Web Services (AWS), Microsoft Azure, and GCP. They work on a pay-as-you-go policy. The resources provided include central processing units (CPUs) and GPUs, databases, space to upload datasets, etc. However, certifications are essential to using the services efficiently.
AI algorithms such as BERT, CNN, recurrent neural networks (RNNs), long short-term memory (LSTM) and even machine learning algorithms like decision trees, support vector machines, etc. are democratized. This enables anyone to choose the most suitable algorithm for their application from the enclosed list. However, it’s important to note that users need to have some knowledge of computer science, mathematics, and statistics to use them.
Researchers upload new AI algorithms that they develop on GitHub repositories. The algorithms developed for a use case can either be developed on a local system or on the cloud. Advantages of the latter include the abundance of GPUs and the provision to share a live link of the application after it’s deployed on the cloud. Anyone can use the system.
Training a suitable model is a vital step toward building AI products. AutoML is an excellent example of democratization of model development. It runs a set of algorithms for a dataset and helps decide which shows the best performance. However, developers who use AutoML must be trained properly to ensure that a reliable model is built. They need to be able to explain the deterministic outputs given by the model.
For example, an AI image classifier model sometimes classifies healthy samples as diseased. Why is this? What happens if a scan of some other disease is inputted? How will the system respond to an input whose output doesn't fall into any of the specified categories? The best example is a facial recognition system. How will it classify a stranger or a person it is not trained to classify? The developer must be able to address such queries confidently.
The model developed should not suffer from any bias as well. It so happens that humans are biased which makes us create biased datasets unconsciously. For example, including more men than women or including a larger number of one complexion in a dataset can cause bias. Another example is including more surgical masks and very few colored ones in a mask detection dataset. When an AI model is trained with such biased datasets, it too becomes biased. This must be avoided.
The last end of the spectrum refers to an artificial intelligence or data science marketplace for data, algorithms, and models. Kaggle is the best example, where contests are held to determine and reward the best model with interesting cash prizes. However, the limitation of these marketplaces is the misinterpretation and, therefore, the wrong application of the data, algorithms, or models provided.
Democratizing AI comprises four key steps as discussed below.
The first step is to ensure affordable access to data, algorithms, storage, model development, and the marketplace. For example, sharing datasets on open-source platforms like Kaggle so that users can use them free of cost. Another is AI algorithms on GitHub repositories.
Although these resources needn't necessarily be free, they should be provided at an affordable cost. Users should not be compelled to pay thousands of dollars for a dataset or an algorithm! If there is no affordable access, the democratization of AI will be pointless.
The second step is to ensure abstraction as not all users have knowledge of SQL queries or running advanced commands on a terminal to access the data. Getting the data should not demand much programming. Since democratization declares that AI is accessible to all, it’s vital to get confirmation of the accessibility of AI components from the company democratizing the same.
The third step is the ability to control different elements of the stack. Users must have control over what they execute, when they run it, and how to use the results of their experiment.
Google Colab is an online Jupyter notebook to train a model without the need to install packages as they are pre-built. It provides impressive hardware support such as GPUs and RAM to train AI, especially neural network models, with complex architecture. It doesn’t restrict users from capturing screenshots of the classification reports of the model or downloading the model as an h5 file for integration with other applications. If such an option to control is not allowed, then the democratization of artificial intelligence will become less useful.
The fourth step is to inspect the ownership. The following questions must be answered:
Who are the people/organizations owning the data?
Is the data owned by those who generated it or by those who draw valuable insights from it?
Is there a single owner or can ownership be divided among certain parties?
The four steps discussed above should be considered by both vendors democratizing artificial intelligence as well as the users. For example, if a dataset in Source-A costs $250 whereas a dataset in Source-B costs just $10 - or is free - for the same problem statement, end-users will naturally prefer Source-B.
A popular business model is to initially provide free access to datasets and begin to charge once demand goes up. Stating this policy explicitly and providing safeguards on the use of data will help win the hearts of customers.
In recent years, there has been a significant complaint of increasing misuse of artificial intelligence models, such as applying an algorithm in the wrong context or misinterpreting the mathematical results displayed. This has substantiated the need to train casual users on the context in which data was procured and disclosed.
Further, the necessary mathematics required to understand the results of the models must be explained to power users. While providing free access to data, models and algorithms, a clear-cut user manual should also be shared. This will train users on how the democratized AI components should be used.
AI systems should be developed, tested, and maintained by experienced professionals with an in-depth understanding of key AI components as well as a commitment toward responsible AI. The actions to be taken by AI leaders to avoid misuse, abuse, bias, and other problems are training, governance, intellectual property (IP) rights, and open-sourcing.
Training users with the appropriate foundations of data science is essential for the safe use of AI. For example, a given dataset must be split into train, test, and validation with some ratios.
Consider the figure below. It shows how an image dataset has been split into train, test, and validation sets with the popular 80-20% split ratio. First, 80% of the main dataset is used as a training set and the remaining 20% is kept aside as a test dataset. Second, 80% of this training dataset is further split into 80% for training and 20% for validation.
The validation dataset gives an idea of how the fitted model will perform on unseen inputs. The model will first be trained with the training dataset. The fitted or trained model is then tested with the validation dataset and, finally, with the test dataset. This approach is called the validation set approach.
A simpler alternative is to split the dataset into two - 80% for training and 20% for testing. The train-test split is done in one step. This is a common approach for splitting a dataset. If a user skips this step, the fitted model will show inappropriate results.
Ownership, control, and how rights relate to insights drawn from data must be specified clearly. AI created using data that is not governed by teams within an organization tasked with data integrity is termed shadow AI. This is a concern. Thus, it’s important to build AI/ML models using data that is monitored, secure, and understood because, often, data created to build a certain AI model may later become open-source.
Governance is essential so that models are developed successfully with good validation metrics, such as accuracy and explainable results, and to identify and ignore biased models before they are developed and deployed on the cloud. Models whose results are difficult to understand or cannot be explained deterministically should also be kept away from development and deployment.
A democratization framework should specify who owns the IP rights of AI elements. Some companies refuse to use the cloud for image classification or audio processing on the off chance that confidential datasets may be processed in secret. It’s a common misconception that tools and platforms like cloud services increase the power of democratization. However, it is data ownership that drives this.
Companies that allow for democratization must provide users the right to use, study, change, and distribute software including its source code to anyone, regardless of the purpose. In other words, if an AI component is being democratized, it must be open-source in a manner that does not infringe on privacy, confidentiality, and competitive dynamics.
The democratization of AI will enable everyone to experiment and learn artificial intelligence programming. It will simultaneously minimize development costs for GPU support by providing the necessary resources. However, as AI components are made available for free, models run the risk of being misinterpreted and applied in the wrong context. The best way to overcome this challenge is to adhere to a democratization framework.
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