AsianScientist (Jun. 16, 2022) – Working with machine learning algorithms is not a simple case of clicking a button to instruct the computer to predict the next top-performing stock or generate art based on a short textual description. Rather, it relies on statistical algorithms that must be developed by data scientists and machine learning engineers.
Yet, this is an area where there is a shortage of engineers to create machine learning algorithms or people with the necessary skills to analyze and use the data to meet the growing demand for AI in many organizations.
No-code AI/ML platforms are replacing complicated AI code with easy-to-use accessible interfaces. Now, without needing to engage a team of AI/ML engineers, organizations are empowered to bring the power of AI/ML to the forefront of their core business operations.
Such no-code AI/ML tools can be a tremendous boon for small businesses—it reduces the barrier of entry to a full suite of AI-enabled capabilities, including prediction and classification tasks, while simplifying the deployment and maintenance of AI/ML solutions; directly addressing the manpower crunch associated to this constantly developing field.
Filling in the blanks
Data lies at the heart of AI/ML applications—machine learning is a subset of AI that enables a machine to automatically learn from historical data without any explicit programming. Instead of programmatic commands, data is used as input to build a statistical model. Insufficient training data can impact the model’s ability to identify underlying patterns within the data—leading to decreased model reliability, robustness and resilience in dealing with situations that are not represented in the input data. Conversely, providing too much data can produce an undesirable effect – where irrelevant data upsets the ability to uncover useful statistical patterns.
Despite the importance of high-quality data in building machine learning applications, ethical concerns, logistical issues, privacy laws and many other technological and regulatory bottlenecks may impede data acquisition. For instance, analysing consumer data to enhance revenue models might be an issue in the insurance industry as customers are not always willing to disclose personal information. This could lead to significant data gaps and biases, weakening the overall legitimacy of the ML model.
To overcome this obstacle, Singapore-based innovators have developed a synthetic data generation engine to help fill the gaps. In this technology offer, an ML algorithm learns and captures the complexities of scarce but real datasets. Subsequently, it churns out synthetic data that is just as complex as the data it aims to replicate. The data is generated quickly as well—up to 10,000 rows of eight columns in just eight minutes.
This synthetic form of data generation solves the challenge of data scarcity and difficulties of data acquisition while obfuscation techniques preserve the privacy of the information.
Some industries face inherent difficulties in acquiring credible data with which to train their AI applications. For example, the rapid and erratic evolution of consumer behaviour during the beginning stages of the COVID-19 pandemic wreaked havoc on product-demand data used by market researchers to track emerging trends. Furthermore, in such fast-changing environments, traditional market analyzes—commonly run for months on end—struggle to keep pace.
In such situations, market analysis tools like the AI-powered Consumer Packaged Goods (CPG) Product Innovation could lend a helping hand. By gathering massive amounts of data from various sources like social media and e-commerce platforms, search engines and product reviews, this technology offer generates unbiased insights into consumer behaviour. Such insights can empower market research teams to make more informed decisions with respect to market positioning or product promotion.
Not only does this data-driven technique help discover trends and predict the future growth trajectory of a particular product, it can also evaluate the viability of new product concepts before they launch. Through the identification of white-space opportunities, companies could innovate new products to address the unspoken, unmet needs of customers, forging a new stream of revenue and emerging as a market disruptor.
Beefing up quality control
When it comes to preparing data that are used for the training of ML models used in quality control systems, labelling unstructured data is one of the most tedious and laborious steps of data preparation due to the sheer volume of images that require manual annotation.
Inconsistencies and inaccuracies stemming from human errors during the data-labelling process could spell disaster for companies in high-precision medical, pharmaceutical or semiconductor industries.
An AI-enabled data-labelling feature is built-in to this manufacturing defect detection platform, accelerating the process of training ML models which can identify defects in the assembly line more consistently and rapidly than human inspectors. In this technology offer, multiple AI models are evaluated to determine the best performer, which is then automatically deployed for its purpose.
In addition, the AI platform also provides classification transparency to improve customer trust, and the ability to alert end-users when model degradation occurs. Together, these benefits could eliminate production errors, reduce manual labour and provide opportunities for improvement and innovation through insights gained from inspections.
These technology offers present highly accessible, easy-to-use and fuss-free no-code AI/ML platforms that can give start-ups and small businesses the resources they need to develop, scale-up, deploy and maintain their products and services.
For more empowering technology offers, visit IPI’s Innovation Marketplace here.
Asian Scientist Magazine is a content partner of IPI.
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