Machine Learning
& Artificial Intelligence
Next-gen software systems are not only functional, but they're also intelligent and perceptive —almost human-like in their abilities.

When most people talk about AI today, they're often referring to learning-based systems, which are essentially systems that become proficient (even human-like) at performing particular tasks — for example, self-driving cars and customer service bots. Learning-based systems "learn" by processing a vast amount of data (often labeled data, known as supervised learning), whereas at other times, systems learn by competing against itself or other simulated agents.

Among all learning-based systems, in particular, neural networks have helped usher in a new wave of breakthroughs thanks to a technique called deep learning, recently creating a lot of hype, confusion, excitement, and panic for companies looking to find a differentiating edge or niche.
Applied machine learning is a multi-stage process.

First, we need to have the data to learn from it, so gathering the data from various relevant sources (databases, knowledge graphs, etc.) is the first stage. Then, the data needs to be analyzed. Analysis includes not only understanding what the data can tell us, but also which features of the data are most applicable to the problem that we are trying to solve. While deep learning has been touted for its extraordinary ability to automatically identify the relevant features, in practice however, one will find that some hand-picked feature engineering and preprocessing goes a long way.

For example, suppose that we are trying to determine whether two faces in two different images or videos are the same. The naive approach is to train a deep neural network, e.g. via a triplet loss function such that the Euclidean distance of images from the same identity are closer than the Euclidean distance of images from different identities. However, the theoretical size (and thus required parameters) to automatically "learn" those features may result in a network that is intractable, either resulting in the training becoming too expensive or the deployment nearly impossible (such as in resource constrained environments where edge machine learning models may be desirable).
Being able to "crop" to only the face (i.e., preprocessing the images or video frames) can enable the final model to not only be more accurate, but perhaps even more lightweight and faster, many times requiring fewer parameters to achieve greater accuracies.

This is why, for example, in Kaggle competitions, the top solutions usually have a feature selection and preprocessing stage.
Another example, suppose we are looking at 3D scans of an organ to determine the incidence of cancer: preprocessing the data by identifying nodules in advance can enable the neural network focus more wholly on areas of importance, thereby significantly improving the final accuracy with the same number of parameters than otherwise. Then, it is time to train and evaluate a particular model.

This stage often involves research, usually scouring publications for similar or relevant solutions that can be reapplied for a new use case. For example, if we were trying to detect whether certain abnormally shaped cells are precursors to cancer, we may look into the larger body of research that exists around melanoma, and reapply similar ML models as a starting point.

We may apply transfer learning from other datasets, seeing if some general learned features can be reapplied for the new use case. Throughout this entire process, we are constantly validating and evaluating the model, finetuning the hyper-parameters, analyzing the confusion matrix, and perhaps even performing grid searches. Finally, the model needs to be deployed to the cloud, where it can process requests and scale independently together with the rest of the software application — the app then not only seamlessly integrates with the deployed model, but it also continuously collects data for future iterations and training of the model. We guide clients through every step of the multi-stage process.
How does a neural network learn?
One common type of question that we get is of the form: "How does the neural network 'think?' How does the neural network arrive at its answer? Why does the neural network have that particular set of bias/weights?" This is a complex question to answer, and there's a lot of research being done to understand how a neural network "thinks" or "sees" or "hears," but first it's important to understand how a neural network learns. When you are a baby just learning to walk, you undergo a complex process of experimentation / trial and error. Your mind is actively trying to minimize error — essentially, you are falling, getting up, falling, and learning. In an analogous (but not exact) manner, when we train neural networks, our goal is to either minimize or maximize an objective function, such as minimizing a cost/error function, essentially "training" a neural network to respond in a particular manner when it is fed some particular input. One way to achieve this is through a process called supervised learning, where we have labelled data of what the "right" answers are, and we train the neural network by having it take a sample and "guess" what the answer is, and if it's wrong, "learning" from it (i.e. minimize the error function). In essence, what a neural network is actually learning is how to map particular inputs to particular outputs, and it does this by "inferring" what the answer could be for a particular training sample and then (for example) performing gradient descent to update the entire network to "inch" towards (hopefully) the global minimum of the error function. With enough training data, the network will get good at "generalizing" to new use cases. That is a basic introduction to neural networks in a nutshell.
It should be noted, however, that AI is not just machine learning...
AI also includes more traditional approaches like rule-based expert systems (RBES) and behavior trees, often found in software systems that rely on domain-specific logic. For example, industrial automation has often relied on rule-based systems, and many video games model complex NPC or enemy behaviors via behavior trees. Surya Tech has a lot of experience with both modern machine learning systems and more traditional AI systems, but we especially value the potential that machine learning brings to our various clients. No matter which venture or product that we embark on with our partners, we ensure that data collection and data-driven design is always a central, guiding principle in the engineering of all of our products. In fact, the very page that you are browsing is collecting data — what pages you view, what actions you take, your IP address (and thus location, assuming you are not on a VPN), even how far you scroll — all this data is collected and used in conjunction with our marketing efforts.
Thus, it should come as to no surprise that data is a valuable new currency. Unfortunately, we find that a vast number of businesses underestimate the value of their data.
In fact, many of our current clients viewed data to be central to critical business decision-making, but that was where their fascination with data ended. We find that clients have no idea truly how valuable their data is, and we work with clients across numerous industries to unlock the hidden potential of the treasure trove that they are sitting on. Moreover, when it comes to startups and new companies, we find that clients recognize the value of collecting data, but unsurprisingly, data isn't a central focus in the initial engineering of the product. However, that is a big mistake. Software and hardware / IoT systems need to prioritize on data collection from the onset, as proper analytics and data-driven insights can help startups avoid costly mistakes and ensure that they are getting the best possible return on investment (at a time period when every dollar counts). We help and guide clients through the proper development of data-driven systems, collecting, aggregating, and reporting on data at massive scale, and then training ML models to learn to automate numerous processes that once required human intervention. Whether you're in agriculture, medicine, the auto industry, retail, manufacturing, or even the government, AI has the potential to enhance how you work and engage with technology.
At Surya Tech, our focus is on applied data science — mainly, turning mainstream and bleeding-edge research into practical and novel solutions for our clients and their respective industries.
If clients wish to patent new ideas that we collaborate on, we will help you with that too, from software to hardware!
We've helped clients automatically identify when crops are ready for harvest using machine learning and robotics (shaving off hundreds of hours per week versus manual walk-by inspections), we've helped clients in the staffing industry build AI systems to handle scheduling, recruiting, and payments, and we've helped a client in the auto industry build a patented real-time digital auction system.
No matter your industry, we'll guide you through the research and development process that comes with innovation — we will be your partner every step of the way, from conceptualization and ideation to prototype and design to MVP and beyond, helping you tackle the larger scope of your business as well, from the legal aspects (terms and conditions, privacy policies, patents and trademarks) to marketing (integration with Google Analytics, Hotjar, Facebook / Snapchat pixels, Instagram, blogging / content marketing, and beyond).
Essentially, we help clients:
1. Identify novel research papers and publications that solve an analogous or similar problem.
2. Build innovative solutions, reapplying cutting-edge research to your industry's problems, from hardware / IoT, cloud, and AI.
3. Help you through any legal requirements (FDA, HIPAA, etc.)
We become your partners, not just engineers — we develop a passion for every project that we decide to tackle.
We work with
How we help
Identify novel research papers and publications that solve an analogous or similar problem.
Build innovative solutions, reapplying cutting-edge research to your industry's problems, from hardware / IoT, cloud, and AI.
Help you through any legal requirements (FDA, HIPAA, etc.)
We become your partners, not just engineers — we develop a passion for every project that we decide to tackle.
Contact Us