The Definitive Handbook on Making AI Accessible to the Masses

The goal of democratising Artificial Intelligence is to make it attainable for all, regardless of their background or prior knowledge. This can be accomplished by providing open-source datasets and tools, developed by major companies such as Microsoft and Google, to the public. Through these accessible resources, anyone can now become a “citizen data scientist” and create innovative AI applications without having specialised skills in the field.

This essay will provide an in-depth exploration of the importance of democratisation of artificial intelligence (AI), the various aspects of AI that should be democratised, the process of democratising them, and the democratisation framework that should be adopted. Moreover, it will delve into the implications of democratising AI and the potential benefits of doing so. It is imperative that we understand the importance of democratising AI in order to ensure that its potential is realised in an equitable manner.

A few instances of AI being more accessible to the general public

  • Kaggle is home to an extensive library of datasets that are accessible to the public. For those looking to train their models, they can quickly and easily download a new dataset from the internet and begin utilising it. Likewise, users can utilise Google Cloud Platform (GCP) to construct and train an image classifier without needing to write any code.

    In order to utilise Google Cloud Platform (GCP) to train the model, users must supply a sufficient number of examples from each class, appropriately labelled, and then initiate the process by pressing the appropriate button. GCP will then utilise the most efficient artificial intelligence algorithm available to classify the images.
  • When it comes to developing sophisticated neural network models in the cloud, Google Colab is a viable open-source solution that utilises powerful graphics processing units (GPUs). Although Nvidia GPUs and other high-performance hardware are essential for training deep learning models, not all local systems have the capacity to access such resources. In order to address this issue, Google has provided Colab, granting free access to their hardware to anyone with a Gmail account, thereby enabling individuals to generate AI models.

    Customers of Colab Pro benefit from increased RAM and reduced training time for their models. What’s more, the trained model can be incorporated into custom software developed by users. For example, Colab users can train a convolutional neural network (CNN) model, store it, and then integrate it with the Flask backend of their local systems to use it as a blood cell classification picture classifier.

The advantages of making AI accessible to everyone

Eliminating Obstacles to Entry

The proliferation of Artificial Intelligence (AI) technology has made it more accessible to people and companies. By utilising cloud computing and open-source datasets, data science education has become less expensive and is accessible to individuals from all over the world. To further their knowledge in the field, participants can take part in data hacking competitions and other similar events.

Keeping expenses to a minimum

The democratisation of Artificial Intelligence (AI) technologies has the potential to reduce the expense of developing AI solutions. To create flexible and reliable AI solutions that can be applied to a variety of use cases, companies are increasingly utilising open-source data, algorithms, and models that are hosted on cloud-based platforms. This approach allows organisations to access the latest AI technologies without the need for expensive hardware investments.

Producing Reliable Models

The utilisation of resources like transformers, TensorFlow, PyTorch, and ImageNet allows for faster and more accurate model construction, thereby saving precious time that would otherwise be spent on training new personnel. For example, the transformers’ library enables a natural language processing (NLP) model to be chosen and trained utilising a custom dataset specifically designed for a particular application. Google’s Bidirectional Encoder Representations from Transformers (BERT) can be utilised to train a custom model and has been found to outperform conventional methods in its ability to identify intentions.

Analysis of Emotions

The use of Artificial Intelligence technologies has been rapidly and widely accepted due to their advantageous properties. Many websites now utilise chatbots in order to respond to common inquiries from visitors. Additionally, sentiment analysis, a form of Natural Language Processing, has become a pervasive practice. Companies can utilise this method to gain insight into what goods and services their customers value most. Sentiment analysis is an approach used in text categorization to determine if the tone of a text is positive, negative, or neutral.

Find Discriminatory Language

In order to protect people from the risks of cyberbullying, Artificial Intelligence (AI) is being used to detect hate speech on social media platforms. AI has reached a level of sophistication where it can understand the meaning of language and distinguish between subtle variations in language. This helps to identify potential instances of cyberbullying, allowing measures to be taken to protect prospective victims.

What parts don’t belong in a democratic society?

It is of paramount importance to identify which components of an AI product should be made accessible to the public prior to its launch. In order of complexity, these five areas are: Market, Model Creation, Algorithms, Data Processing and Data Storage. Each of these elements requires careful consideration and planning to ensure the successful public deployment of the AI product.

Data

Data is an umbrella term used to refer to large volumes of information used to inform and guide critical business decisions. This data can take the form of structured data, such as rows and columns of numerical information in a table-like format, or it can also be semi-structured or unstructured in nature, comprising of images, videos, audio files, text, and emoticons.

Data democratisation is the process of making data available to everyone, regardless of their technical ability or financial resources. Examples of this include the Kaggle datasets and those released on GitHub, such as Prajna Bhandary’s mask detection dataset. This has enabled users to access high-quality data visualisation tools without needing to invest in expensive software or services. With the help of these tools, users can now analyse and visualise their data in a more comprehensive way, allowing them to draw better insights from their data.

Data-Handling and Processing

In order to effectively store and process data, many organisations are turning to cloud-based services such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). These services offer a “pay as you go” payment model, providing users with access to a wide range of resources, such as CPUs and GPUs, databases, and storage for uploaded datasets. It is important to ensure that users have the necessary accreditations in order to make full use of these services.

Algorithms

The use of machine learning methods, such as decision trees, support vector machines, and artificial intelligence algorithms like BERT, CNN, RNNs and LSTMs, is now accessible to anyone. This provides users with a range of options to choose from, depending on what works best for their needs. It is important to recognise, however, that in order to use these technologies effectively, a basic understanding of computer science, mathematics, and statistics is necessary.

Researchers have recently made available the latest Artificial Intelligence (AI) algorithms they have developed on the open source platform, GitHub. It is possible to create customised algorithms for specific use-cases in either a local environment or in the cloud. Hosting the algorithms in the cloud has several advantages, such as access to a large number of graphics processing units (GPUs) and the ability for users to easily collaborate by sharing a direct connection to the installed program. Furthermore, this system is available to anyone who wishes to take advantage of it.

Creation of a Model

For successful development of AI products, model training is an essential requirement. Automated Machine Learning (AutoML) is a great example of how model creation can be made accessible to a larger number of people. AutoML analyses a dataset and runs a range of algorithms to determine which one performs best. However, developers using AutoML should receive sufficient training to ensure a robust model. It is also important to understand the deterministic results that the model produces.

In the example of an AI image classification machine, it is possible that healthy samples may be incorrectly labelled as infected. This raises the question of what action should be taken if results that do not match any of the expected types are produced when another disease’s scan is entered. The most illustrative example of this is facial recognition technology, where it is unclear how the system will categorise an individual that is not part of its training set. In order to ensure the accuracy of the AI system, it is essential that the developer has the confidence to answer such questions.

It is essential that the constructed model is free from any form of prejudice. Unfortunately, we are prone to producing unintentionally biassed data sets due to our own personal prejudices. This bias is often apparent when a dataset is overly populated with one particular demographic, such as when there are more males than females or more people of a certain skin colour. A further example of this bias can be seen in datasets used for facial recognition that are populated with more white surgical masks than colourful ones. It is inevitable that any model trained on such skewed data will reflect the same biases, which is why it is important that these issues are addressed and prevented as far as possible.

Marketplace

At the opposite extreme is the concept of a data science or artificial intelligence (AI) marketplace, where it is possible to purchase and sell algorithms and models. Kaggle is the foremost example of a platform which hosts competitions to identify the most successful model, with the reward of appealing prizes. Nevertheless, these exchanges have their own limitations arising from the misuse of the data, algorithms, or models made available.

Methods for expanding access to AI-relevant data

Below, we’ll go through the four main pillars of democratising AI.

Make it possible for people to get in at a reasonable price

In order to facilitate the development and utilisation of artificial intelligence, it is essential to ensure that data, algorithms, storage, model creation, and the market are accessible at an affordable cost. One way to achieve this is by providing open access to datasets on sites such as Kaggle. Additionally, there are numerous AI algorithms available in the GitHub libraries that may be utilised free of charge.

It is not feasible to provide these datasets and algorithms free of charge. However, they should be reasonably priced to ensure availability to the greatest number of people. Charging users thousands of dollars for access to datasets or algorithms is unjustified and defeats the purpose of “democratising AI” by making it inaccessible to the majority.

Achieve Abstraction

The second step in the process is to ensure that the data is abstracted in such a way that people with limited knowledge of SQL queries or complex terminal commands can make use of it. To ensure that the democratisation of Artificial Intelligence is achievable for all, it is essential to confirm that the necessary components for AI development can be acquired from the company which is advocating this mission. To achieve this, it is important to limit the amount of coding required in order to access the data.

Allow manipulation of stack items

Step three in the process is to gain mastery over the individual components of the technology stack. Users should be able to make informed decisions regarding what elements of the stack to use, when to implement them, and how to utilise the outcomes.

Given Google Colab’s pre-existing packages, users do not need to install any additional software to begin training models. Its efficient hardware support, such as GPUs and RAM, makes it an ideal platform for developing Artificial Intelligence (AI) models, particularly complex neural networks. Furthermore, users can easily take screenshots of the model’s categorization reports or download it as an h5 file for use in their own applications. Restricting access to such control over AI models can have a negative effect on decentralisation of AI.

Verify ownership claims

Inspecting the ownership status is the fourth stage. Here are some inquiries that need to be resolved:

  • Who are the data’s rightful owners?
  • Who owns the data—those who produce it or those who analyse it and develop conclusions?
  • Does it have a single proprietor, or may it be owned jointly, by many people?

It is important that all four stakeholders involved in the democratisation of Artificial Intelligence (AI) — users and the companies that are facilitating this process — take into account the implications of cost. If two datasets are available for the same problem statement, but one costs $250 while the other costs $10 (or is free), customers will naturally opt for the lower-cost alternative (Source-B).

In order to maximise the success of their business, many companies have adopted the strategy of providing datasets for free initially, then charging customers for their use thereafter. To ensure maximum customer satisfaction and loyalty, it is important for companies to be open and honest about their data policy and the measures taken to prevent misuse or abuse of the datasets. By being transparent in this way, companies are more likely to gain the trust of their customers and gain a lasting competitive advantage.

In recent years, there has been increasing apprehension about the misuse of Artificial Intelligence (AI) models. This includes the misapplication of algorithms and misinterpretation of mathematical results. It is therefore essential to educate those who casually use data on the proper methods of collecting and disseminating information.

Furthermore, it is essential that advanced users are provided with an explanation of the mathematical principles underpinning the outputs of the various AI models. In order to facilitate the effective utilisation of the newly available AI components, it is necessary to not only make the data, models and algorithms accessible online, but also to provide a comprehensive guide that outlines how to best utilise them. Such an approach would ensure that people are knowledgeable on the effective use of the technology.

System for promoting democracy

AI professionals, including developers, testers, and maintainers, should possess a comprehensive understanding of the field and demonstrate a deep commitment to ethical AI. Furthermore, their expertise in the field should be unparalleled. In order to prevent potential misuse, abuse, prejudice, and other issues associated with AI, leaders in the field should implement training, governance, and IP management strategies, as well as open-sourcing initiatives.

Training

Users must be provided with suitable data science training to guarantee the secure utilisation of AI. For example, a given dataset should be appropriately divided into train, test, and validation sets. This will allow users to gain the necessary expertise to ensure that the AI is being used safely, and that the data is being handled correctly.

A closer inspection of the diagram below reveals how the given picture dataset was divided into training, testing, and validation sets. We started by allocating 80% of the dataset for training, with the remaining 20% set aside to serve as a test set. Subsequently, the training dataset was further split into a validation set (20%) and a training set (80%). This split ratio is based on the conventional 80-20% division.

The validation dataset is used to assess the performance of the model on unseen data. Firstly, the model is trained using the data from the training set. Subsequently, the model is evaluated on the validation dataset in order to measure its accuracy. Finally, the model is tested on the test dataset to validate its predictive capabilities. This process of model evaluation is referred to as the “validation set approach”.

Subdividing a dataset into two separate populations is a popular approach for the purpose of training and testing models. By allocating the first half of the dataset to training and the remaining half to testing, only one step is required to effectively separate the two populations. Failure to do so can lead to a model that is not properly fitted and thus, produces inaccurate results.

Administration and management

It is essential to have a clear understanding of who owns the data, who has authority over it, and who has the rights to the conclusions that may be drawn from it. Shadow AI, or AI developed using data that is not managed by teams within an organisation that are responsible for data quality, is a cause for concern. If data that was initially collected for the purpose of creating a particular AI model becomes open-source, it is essential to create AI/ML models using data that is properly monitored, secured, and comprehended.

For successful model development, it is essential to have appropriate validation metrics, such as accuracy, and to be able to explain findings. It is also important to identify and remove any biassed models before developing and deploying them in the cloud. Furthermore, models whose outcomes are difficult to interpret or cannot be explained in a deterministic manner should be avoided. By following these principles of governance, organisations can ensure they are developing and deploying trustworthy and reliable models.

IPR stands for intellectual property.

The democratisation of intellectual property (IP) rights related to Artificial Intelligence (AI) components is essential in order to ensure equitable access to digital services. Despite the potential for anonymous processing of sensitive data, many businesses are reluctant to utilise cloud-based picture and audio processing due to the uncertainty surrounding who owns the data. Technological innovations such as cloud services can certainly facilitate the democratisation of data ownership, but the real impetus for democratisation lies in the need for establishing clear ownership protocols for digital assets.

Open-sourcing

Companies that are committed to democratising AI should ensure that their customers have the liberty to reproduce, customise, and distribute their software, as well as the source code, for whatever purpose they deem appropriate. To put it another way, in order to make AI more accessible, it must be made open source in a manner that does not expose users’ private data, proprietary information, or the market’s dynamics to potential risks.

With the recent democratisation of Artificial Intelligence (AI), anyone is now able to attempt AI development, resulting in a significant decrease of both time and money needed to include support for GPUs. Nevertheless, with more and more AI components being made available online, there is an increased risk of these models being abused. To combat this potential issue, it is recommended to adhere to a democratisation framework. Doing so allows us to address this difficulty head-on.

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