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Aquaculture Artificial Intelligence: A History for the Curious

Artificial intelligence (AI) has become a popular buzzword this year, but many struggle to understand its true meaning and how it applies to specific industries like aquaculture. However, in the rapidly evolving field of aquaculture, AI is not just a passing trend but a transformative force that presents countless opportunities.

By tapping into the capabilities of AI-powered data intelligence, companies can venture into uncharted territories and unleash limitless possibilities. To fully comprehend the monumental impact of AI, it is essential to take a step back and delve into its origins, various forms such as machine learning, and its specific applications in aquaculture.


What is Artificial Intelligence?

Before diving deeper into the applications and impact of AI, it's essential to clarify some commonly used terms that are often misunderstood or used interchangeably: AI, Machine Learning, and Deep Learning. At its core, AI is the ability of a machine to imitate human behavior. It serves as the umbrella term under which Machine Learning and Deep Learning fall. Machine Learning is a specialized application of AI that enables a system to learn and improve automatically from experience, without being explicitly programmed. Deep Learning, on the other hand, is a subset of Machine Learning that employs complex algorithms and deep neural networks to train a model. Think of it as the cutting-edge technique that allows machines to process data in a way that resembles the human brain's neural networks.



The Quick History of Artificial Intelligence

The concept of AI may not seem as futuristic as it appears. In fact, shows like "The Jetsons" predicted the rise of AI over 60 years ago. The mathematical and scientific foundations of AI have been in place for decades, but it was only in 1955 that the term "Artificial Intelligence" was officially coined by John McCarthy. Around the same time, the first artificial neural network was designed at Princeton, laying the groundwork for future advancements. However, the exponential increase in computing power over the last 50 years has truly been a game-changer in the evolution of AI. This surge has enabled the translation of complex mathematical theories into large-scale computing applications. Especially in the last decade, we have witnessed an incredible scaling-up of AI technologies, turning what was once the stuff of science fiction into a tangible reality today.




Understanding the Tools of AI: Supervised Learning & Generative AI

At the end of the day, AI is essentially a collection of tools designed to imitate human behavior. While the term may conjure images of robots and futuristic landscapes, the reality is much more nuanced. Two significant types of AI that have been shaping the landscape are Supervised Learning and Generative AI. The former has been the focus for the last decade, while the latter represents the future of AI.

Supervised Learning: The Backbone of Commercial AI

Over the last 10 years, the majority of work in commercial settings has revolved around supervised learning. This approach has scaled significantly with the advent of big data, and its success hinges on our ability to label data correctly. In simple terms, supervised learning is about predicting an output based on a given input.

For instance, given input A, what will output B be? This concept is evident in various applications, from playing chess against a computer to the advancements in targeted advertising. The process involves several components: you need labeled data and categories to identify, which are then fed into a model training algorithm. This algorithm generates a predictive model that can map any new data to those labeled categories.




Generative AI: The Future of AI

Generative AI is poised to be the next big boom in artificial intelligence. It builds upon supervised learning by continuously predicting the next word in a sequence, enabling the generation of long-form text. A familiar example of this is ChatGPT, where a prompt generates a detailed output. Essentially, generative AI takes supervised learning to the next level by using it to predict subsequent words, thereby creating coherent and contextually relevant text. It's considered a large language model trained on hundreds of billions of words, making it incredibly powerful and versatile.


The Implications of AI for Aquaculture

So, what do these advancements in AI mean for the aquaculture industry? The impact is nothing short of transformative. Over the last decade, advancements in tooling have significantly lowered the barriers to applying AI in aquaculture. Gone are the days when generating a machine learning model required an astronomical amount of code and specialized expertise. Today's tooling has evolved to a point where AI can be applied across various industries, including those that may not be as large or as traditionally tech-focused as others.

Take, for example, the application of supervised machine learning in aquaculture. If we want to build a model to differentiate between small and large fish, the process would be quite similar to the general approach we discussed earlier in the context of supervised learning. We would start by gathering a large dataset of different photos of fish, both small and large. These photos would serve as the labeled data that the machine learning algorithm uses for training. Once the training model is developed, test data can be fed into it to generate a predictive model. This model would then be capable of analyzing new images of fish to determine whether they are large or small, thereby aiding in inventory management, feeding schedules, and overall farm efficiency.

The advent of AI and machine learning tools has opened up a world of possibilities for aquaculture, making it more efficient, sustainable, and data-driven than ever before.


The Speed of AI Development and Its Impact on Aquaculture Data

One of the most remarkable aspects of AI's evolution is the accelerating speed at which solutions can be developed today, particularly for industries like aquaculture. This rapid development is not just about the technology itself but also about its myriad applications that are revolutionizing aquaculture practices.

Applications in Aquaculture: Cameras & Beyond

When it comes to aquaculture, AI has a range of use-cases that extend far beyond the obvious. These include improving feeding efficiency, using cameras for feed identification, estimating biomass, and even inventory counting. However, it's worth noting that most discussions about AI in aquaculture often revolve around camera-based applications. Why is that? The answer lies in the quality of data. In the world of AI, garbage in equals garbage out; if you feed the model poor data, you'll get unreliable results. Aquaculture, compared to other industries, has historically lacked extensive data sets.

Tidal's AI Camera Technology

Cameras have become a favored tool on farms because they provide high-quality, reliable data. With imagery, what you see is a direct representation of what's happening in the real world. This level of accuracy is why camera-based applications have been at the forefront of AI adoption in aquaculture. The imagery not only provides a rich data set but also allows for more precise decision-making, whether it's determining the right feeding schedules or assessing the health of the aquatic population.



Manolin's Data Intelligence: Taking Aquaculture AI to the Next Level

While there's a growing focus on using AI and machine learning to optimize various aspects of aquaculture, Manolin believes that there's even more that can be done, particularly in cleaning up existing and historical data. We recognize the importance of taking a comprehensive approach to performance evaluation that goes beyond solely relying on camera data. In order for companies to effectively integrate all of their data sources, a holistic view is necessary.


That's where Manolin's Data Intelligence platform comes into play, serving as the industry's "janitors" to clean up data comprehensively, enabling aquaculture farms and suppliers to apply it in new and unique ways.

Data Enrichment-App-Graphic


Cleaning, Enriching, and Transforming Data

Manolin takes a continuous approach to cleaning up existing data from various sources, including lab data, inventory data, feed data, and environmental data. This meticulous cleaning process allows for actionable results on the other end. For instance, Manolin's platform can generate disease forecasting models that farms can use to understand their risk factors for diseases that could be crippling. The platform also accelerates scientific research in aquaculture, as evidenced by their collaboration with Veramaris on EPA + DHA research, which scaled up the most recent findings by over 100,000 times compared to the last decade's research.




Unlocking New Possibilities

The benefits of investing in data intelligence with Manolin are boundless. From developing better growth models to accelerating the science behind aquaculture, Manolin's platform opens up a world of possibilities for both farms and suppliers. By transforming raw data into actionable insights, Manolin is not just optimizing current practices but also paving the way for future innovations in the aquaculture industry.

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