From conversational bots providing self-service customer support to fully autonomous content creation with Generative AI for marketing and movies, there has been an accelerated advancement of the AI ecosystem worldwide.
In fact, research estimates that there will be a USD 1.33 trillion worth of market size awaiting AI tech by 2030.
While on one hand, the adoption of AI is increasing, there is also a scenario where enterprises often make mistakes in handling their AI strategies. The misfit of requirements and capabilities of AI models has been exhibited by several organizations and has even led to heavy losses by pioneer companies like Google.
In 2023, their generative AI tech, then called BARD, provided an incorrect response to a question asked in a promotional advertisement. Journalists were quick to pick up the mistake and very soon the blunder became viral resulting in Google’s parent company Alphabet witnessing a massive loss of shareholder trust as evident in the day’s trading results. Their shares nosedived in value following the incident leading to over USD 100 billion being lost in market value. This shows that even the most prolific names in AI tech can lose their ground and make the wrong decisions.
A Deeper Understanding of AI Models is the Key
One of the primary reasons why enterprises often struggle with inaccuracies and inefficiencies in their AI adoption cycle is because of the lack of a clear understanding of which architecture they need to pick for their AI models. In simple terms, the architecture referred to here is the approach of building an artificial neural network that processes information and predicts outcomes or results for a given set of inputs and scenarios. In the world of deep learning, there are primarily two architecture approaches that enterprises can adopt - Dense Architecture and Sparse Architecture.
When it comes to deep learning, choosing between dense and sparse architecture can go a long way in helping businesses reshape their AI capabilities. Even better, the desired levels of model performance, interpretability, and efficiency are determined directly by the architecture approach they adopt.
Let us have a detailed analysis of the benefits and drawbacks of sparse and dense architecture to help in finalizing the most optimal approach for your AI needs.
What is Dense Architecture in AI?
Dense architecture is regarded as the simplest form of neural network design wherein every neuron has a direct link to every neuron in the previous layer of the network. It is a straightforward neural network design and is widely used as the stepping-stone model for AI development. Complex representations of data states can be easily mapped using dense architecture, thanks to the tight connectedness it exhibits.
From image classification to natural language processing, dense architecture-powered models can be flexibly used in a wide range of computing tasks. It empowers organizations with the benefits of versatility, ease of implementation, and most importantly, expressiveness thanks to the interconnectedness of neurons. Very complex patterns of relationships between data entities can be mapped in this network easily.
What is Sparse Architecture in AI?
Sparse architecture is a more selective neural network constructed for better interpretability. In this architecture, only a subset of neurons in one layer is connected to the neurons in the next layer. In other words, neurons are sparsely connected leading to faster computational time as fewer neuron processing cycles have to be executed for each scenario. Sparse architecture holds a close resemblance to how the human brain is mapped biologically in its neural infrastructure. From an interpretability perspective, sparse networks offer better visibility into the factors used for decision-making as it is less thickly populated in terms of neural connections.
Which is Better – Dense or Sparse Architecture?
From the literal definitions laid down for dense and sparse architecture, we can see that both have their own unique positioning when it comes to uncovering contextual responses to data about scenarios. While for beginners, dense architecture makes sense, there will be an increasing preference for sparse architecture in more advanced AI models.
Let us understand it with a simple comparison example wherein the AI model is represented by a school classroom. Now if we choose to go forward with a dense architecture, then every student in the classroom will be friends with everyone else and hence they all talk with each other. In the case of sparse, only a few students will be friends and speak to each other. In an ideal scenario, sparse architecture is the better model here because of the following factors:
Efficiency: If everyone talks to everyone else, it can get noisy and chaotic. If only some people talk, it's easier to focus and get things done.
Regularization: If everyone talks to everyone else, they might start repeating the same things. If only some people talk, it can help prevent this.
However, it is not always right to assume that sparse architecture is the better option. If sparsity increases too much, then there is a possibility that the network may not capture all relevant insights and there is a potential for information loss as well. Furthermore, the design of sparse architecture is a challenge as there needs to be careful consideration of connectivity patterns.
For very complex tasks that involve decision points supplied by all neurons, dense architecture makes more sense despite being more expensive to run and maintain.
Conclusion – Making the Choice Depends on Your Needs
Artificial Intelligence (AI) is no longer an experimental technology that is used by a small group of beta users. Over the past couple of years, enterprises worldwide in almost all sectors ranging from retail to airlines have successfully managed to integrate AI capabilities into their mainstream digital offerings. So, getting the right models in place is extremely important for better results.
The choice of dense or sparse architecture for your AI models should be made after balancing several factors such as efficiency, complexity of tasks involved, cost of computation covered in your budgets, interpretability, availability of key resources, etc. Making such detailed analysis and subsequent decisions on the architecture front could be a source of major delays in your business because of inadequate expertise in the AI domain. This is where a knowledgeable partner like Wissen can help make a huge difference. Get in touch with us to know more.
