Data... Lifeblood for AI
Mohsien Hassim
Seasoned Business Transformation Executive with a solid Foundation in Finance/Technology/Risk (GRC/ESG)/Security (Cyber)/Strategy and Digital Transformation. AI Researcher & Enthusiast.
We have witnessed the disruptions that the 4th Industrial Revolution or 4IR has brought to the modern world. The rapid adoption of digital strategies and technologies disrupts everything we know and touch. The move away from physical money towards digital currencies and the preference for plastic (debit and credit cards) has forced many industries to adapt to the need to have the capabilities to meet consumer demands for the mechanism to transact in non-physical means. With the move to a more digital world and the subsequent departure of manually driven interactions, there has been a burst in the growth of the data generated.
Data can be seen as?the smallest units of factual information that can be used as a basis for calculation, reasoning, or discussion. Data includes unorganised and unprocessed facts, raw numbers, figures, images, words, and sounds, derived from observations or measurements. Data is static, objective and discrete.
Big data on the other hand is data that contains greater variety, arriving in increasing volumes and with more velocity. This is also known as the three “Vs.” (source: Oracle).? These data sets are so voluminous that traditional data processing software cannot manage them. These massive volumes of data can be used to address business problems you would not have been able to tackle before. The importance of big data does not simply revolve around how much data you have. The value lies in how you use it.
Through the adoption of disruptive digital technologies, businesses have witnessed the benefits of automation and begun to realise that there is value in their data. Data is seen as the new currency and a key driver in bringing new unfound value to the business.
The reawakening of Artificial Intelligence or AI from its days of discovery in the 1950s has opened a new era that is taking the world into a new dimension. Seen by many as one of the biggest disruptions to humanity in modern times, AI promises much.
The link between data and AI is unmistakable.?High-quality data shapes AI systems into reliable interpreters, capable of navigating and deriving meaningful insights from huge datasets. Data quality is a non-negotiable in the world of AI. Data quantity is important, the more data the better, however, if the data quality is poor, it is like using contaminated fuel in your car, it is not going to get you very far.
Artificial Intelligence thrives on data.
The intrinsic dependence on data is at the heart of AI’s capability. This reliance on data is not isolated to a specific type of AI technology but spans all AI systems and technologies; from straightforward decision-making algorithms to intricate neural systems. It requires data to develop its function consistently, which is the importance of Big Data in the world of AI. Big Data in AI?helps in improving the results' efficiency. Without data, AI technology cannot do much.
Data is the lifeblood of artificial intelligence. It plays a central role in the development and efficacy of AI systems, fueling their ability to learn, adapt, and make informed decisions. Without large amounts of high-quality data, AI systems cannot reason, learn, or arrive at informed decisions.
Deep learning craves Big Data because Big Data is necessary to isolate hidden patterns and find answers without overfitting the data. With deep learning, the better-quality data you have, the better the results.
Data needs to be natural and not artificially created. Natural data is crucial for AI systems to improve. Natural data refers to information derived from the real-world environment and is unprocessed or minimally processed. Unlike synthetic data, which is artificially generated or manipulated, natural data is raw and unaltered, allowing AI systems to learn and adapt from authentic scenarios and situations.
“It's interesting because we usually seek perfect data, but when algorithms tackle real-life problems, they must understand that real life is far from perfect.”
(Alice Desthuilliers, a Sr. Product Manager at Appen)
Data quality is seen as the cornerstone of AI. For AI systems and technologies integrity and precision are core to data quality. Data must be accurate, complete, and free from corruption. Ensuring high data quality?is ongoing. Adhering to the best data collection, cleansing, and maintenance practices helps businesses maintain a consistent supply of high-quality data.?It is important not to accumulate substantial data without the involvement of the AI team. While having more data is often advantageous, merely collecting tera/giga-bytes may not prove worthy to the AI team. Businesses should involve the AI team in determining which data holds value for them as this will facilitate more efficient data collection.
A key challenge and concern with AI systems and technologies is the risk of biases. Bias in data can lead to skewed AI outcomes, which can have significant implications, especially in sensitive applications like law enforcement (think of airport passport control) or hiring. Using multiple and diverse data sources coupled with ensuring that algorithms are transparent will assist in minimising the bias in data-driven systems. Diverse data sourcing ensures that the training data represents a multitude of demographics and perspectives.
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Big data provides a vast amount of diverse and varied data. Big data contains greater variety, arriving in increasing volumes and with more velocity (the three “Vs.”). Big Data is essential for training complex AI models. Large datasets enable AI algorithms to learn intricate patterns and relationships that might not be apparent in smaller datasets, leading to more accurate and robust models.
Big data is proving to be fundamental in training sophisticated AI models with its large data sets (training data) enabling a more efficient application of advanced learning techniques such as deep learning and transfer learning. Businesses can leverage big data to train AI models across multiple applications and industries. ?
The power of AI and big data drive areas of operational efficiencies, harness the benefits of innovation and improve the levels of service to stakeholders (internally and externally).
The benefits of Cloud Computing have provided better management of big data as this allows for the optimisation of data storage organisations that are Cloud Service Providers have invested heavily in technologies including data compression, data lifecycle management, data accessibility and scalability. All these are to the benefit of businesses that have AI models, systems and technologies that rely on Big Data stored in the cloud.
Data quality, cleansing, governance and keeping data up to date (i.e., relevant) are crucial to Artificial Intelligence (AI).
Data, being the lifeblood of AI, has no space for poor data quality and data that is irrelevant and out-of-date. Ensuring that data is natural and not synthetic will enhance the outputs of AI models that are practical. AI is still undergoing continuous development and enhancement. ?By safeguarding the pureness of the ‘blood’ for AI, i.e., data, businesses can significantly improve the work to develop better AI models producing the sturdiness and effectiveness of AI systems, technologies and models.
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