Development of products with AI

Developing Prototypes with Artificial Intelligence

 

The development of AI products can be as beneficial as it is risky. Companies, innovation teams, and inventors often focus on technological complexity over the real value that products should bring to the market. The key to a successful functional prototype with AI is not how impressive the technology is, but the impact of the solution it provides.

This shift in focus enables companies to develop successful products with AI or create technological marvels that no one is willing to pay for.

The development of AI products has become the most common service in our prototype manufacturing laboratory. The balance between future business priorities and methods for developing products with AI is what sets us apart.

3 key premises for innovating in AI products.

 

  • Not all products require AI. The development of AI solutions can be very tempting. The power of AI is exciting. However, many times AI solutions are included in innovative products without being necessary.
  • Client Participation. All AI product development processes require intensive client involvement. It is impossible to create an AI product without clean data and a thorough understanding of the problem to be solved.
  • Perfection Does Not Exist: AI product development requires learning cycles that improve outcomes, but the results are not perfect.

To create products with AI, as in any product development process, choosing the right methods and foundations is essential. To achieve a functional prototype, there are different paths. Mistakes in this selection process can compromise the product's future success in the market.

Most inventors and innovation teams lose focus on the product's actual objectives. The main challenge in the creation of an AI prototype is to choose the fastest, cheapest, and most effective method to solve the customer's problem. The most complex and technological AI solutions are sometimes not the most efficient.

Most Common AI Methods in Prototype Development.

 

  • Natural Language Processing (NLP).
  • Supervised Machine Learning.
  • Deep Learning

Prototype Development with Natural Language Processing | NLP

 

When we talk about NLP, we refer to natural language processing. It is an engine that enables product prototypes to interpret and respond as if they were a person.

The development of NLP for innovative products involves breaking down phrases or content into small blocks. The excellence in the decomposition process determines its effectiveness when processed in recurrent neural networks.

 

The development of products that aim to simulate human behavior can naturally be supported by NLP systems. However, assuming that all prototypes seeking to perfect the "machine-user" relationship must rely on neural networks is a common and serious mistake. We have examples of AI prototypes that automate and enhance the product’s interaction with humans without requiring fragment processing in neural networks.

I have doubts

Development of Products with Supervised Machine Learning.

 

Supervised machine learning enables a product to predict physical and biomechanical movements with a certain degree of accuracy. With the professional footprint of Let's Prototype in manufacturing sensor prototypes and IoT products, developing projects that aim to accurately identify physical behaviors and movements is quite common. The keys to success in the development of products with machine learning are the quantity and quality of reference data. Our experience has led us to manufacture our own sensors to capture and classify data. For example, sensors that can be attached to machines of various sizes or small wearables that can adhere to different parts of the human body, designed to capture, transmit, store, and clean data.

Learn more about machine learning product development.

To create machine learning products, it is essential to have sensors that enable the capture of high-quality data. Client involvement in the data collection process is crucial for building useful machine learning models. Once patterns are accurately identified, this type of AI product is capable of detecting identical or similar movements.

Machine learning can be used to develop products aimed at detecting movements for different purposes. We develop machine learning products that can distinguish between fishing line movements caused by wind and tides versus actual fish bites, or others in the sports world that detect padel hits or baseball swings. The refinement of machine learning products has led us to create products to detect movements, but also to suggest improvements in those movements.

ideas de productos innovadores

Product Development with Deep Learning.

 

It is important to define that deep learning can be understood as a category of machine learning. The functional principle is similar. Deep learning is often used in product development for products that need to identify similarity patterns in complex environments, such as image analysis, voice patterns, and similar functional requirements. For example, in a product that must activate certain functions based on listening to a command, a deep learning model is likely required.

The development of innovative prototypes with deep learning, as with machine learning, requires high concentrations of quality, sufficiently clean data. The process of data collection and labeling is essential for training the deep neural networks that need to be connected. Developing a custom deep learning model is costly and time-intensive. At Let's Prototype, we have established commercial agreements with top companies that already have tools allowing us to develop deep learning models more efficiently and affordably.

Hemos elegido Deep Learning para desarrollar productos IA que requieren la identificación de voz como parte crítica en su funcionamiento. En nuestra huella profesional,  han sido productos en los que la autonomía energética y las dimensiones reducidas eran condicionantes prioritarias. Para activar determinados periféricos con comandos de voz, entrenamos modelos de deep learning que permiten el sueño profundo en situaciones de inactividad. 

I have doubts

Pasos para crear un producto innovador con IA

 

The process of designing AI products, regardless of the method or final functions, follows a method that helps mitigate common mistakes. Similarly, following the steps of the AI prototype development method allows for partial validations that, far from being a waste of time or unnecessary expense, enable the building of AI models with solid foundations, clearing up major hypotheses and uncertainty elements.

Step 1 – Problem Study: Identify the functional requirements that truly form part of the future product’s value proposition.

Step 2 – Data Collection: This involves the design and development of prototypes that allow for the automation of data capture and labeling. The investment in data capture instruments will impact the time, effort, and cost associated with the raw materials needed for the development of the AI model.

Step 3 – Data Cleaning: In this stage, the goal is to clean the captured data, organize it, and transform it into optimal formats to train the AI model that will act as the brain of the prototype in development.

Step 4 – Selection and Training: The term “selection” refers to choosing the most appropriate type of AI model for the product development in question. Training involves the development of the AI architecture and its training to ensure it can perform its functions.

Step 5 – Theoretical Validation of the Model: In the data cleaning process, an isolated sample of the data used for the AI model training in Step 4 should be preserved. This isolated data will undergo a recognition test of the model, so that before continuing with the AI product development, an initial theoretical effectiveness result of the AI prototype’s brain is obtained.

Step 6 – Model Deployment: In this stage, the artificial intelligence model is deployed in the developed product. The goal is to measure behavioral effectiveness and understand potential differences between practical tests and the theoretical validations from Step 5.

Step 7 – Improvement Analysis: The result of the AI prototype is likely not perfect. It may be necessary to retrain the model, isolate new challenges, and implement a continuous improvement plan.

10 Useful Tips for Developing a Product with AI.

 

  • Consider carefully if integrating an AI model is truly the best way to give your product meaning and purpose.
  • Take the Risk. If you have decided to develop a product with AI, you must be aware that it may NOT work.
  • The effectiveness of an AI-based product will be closely related to the quantity and quality of the data. Keep collecting data!
  • Sharpen your axe! To develop an AI product, it’s essential to create an optimized prototype to maximize the amount of data collected.
  • Don’t get frustrated. The process of creating an AI product involves experimentation. You should only feel frustrated if you conduct experiments that don’t yield clear conclusions—whether positive or negative.
  • Stay focused. Technology itself is not a business opportunity. Technicians often choose the path with the greatest technical challenge. Study the chosen AI method and remember that your AI product must be a business.
  • Don’t skip steps. The theoretical validation is crucial. If the results are poor, you need to go back to Step No. 2 of the AI product creation method.
  • The devil is in the details. When everything seems finished, you will face deployment. An apparently simple cycle but full of surprises. Plan the deployment before choosing the AI model type. If possible, test untrained AI models in your product to discover the challenges and limitations of integration as soon as possible.
  • Magic doesn’t exist. The path to creating AI products is filled with challenges. Only those who persist and maintain an analytical vision will build an AI product.
  • You get what you pay for. To build an AI product, you need a team of professionals. Make sure your product is not the first AI prototype they develop. If you choose based on price, you’ll end up paying for a AI development course for the technical team, not for developing a product with AI.

Frequently Asked Questions about AI

 

The current industry is being transformed by the development of new products with artificial intelligence. This great industrial revolution, like all waves, is a source of business opportunities, but also a source of doubts and frequently asked questions about the true usefulness of AI. We have a team of experts in AI product development and we will be happy to answer your specific questions.

The cost of developing an AI product is calculated by adding the cost of raw material acquisition (the data) + complexity of the chosen model type + number of patterns where an accurate behavior is expected. For example, the cost of a machine learning model to detect and diagnose movement in a human limb costs around €30,000.

The development of an AI prototype can take about 3 months for the data collection process, 1 month for training and theoretical validation, and 1 month for deployment and practical validation. In total, the development of an AI product usually takes between 5 and 6 months.

Integration of disciplines and experiences. To create an AI product, you need industrial design, electronics, firmware development, software development, and mathematicians to be perfectly integrated within a method in the same company. Being able to see, touch, and test AI products developed by the chosen team should be a key criterion when selecting the engineering for product development. If there are missing profiles or experience, you are multiplying the already existing risk in this type of prototype by 10,000,000.

The development of innovative products with AI makes sense in almost every industry. Our proven experience is concentrated in AI prototype development for sports, medical prototypes with AI, and AI solutions for agriculture.

Do you want to turn your idea into a product?

The time to bring your ideas to life is now. We accompany you throughout the entire process: from idea to product.

 

 

 San Juan Ingenieros, S. L, is the owner of the domain www.letsprototype.com, and in accordance with the General Data Protection Regulation (EU 1679/2016), we will process your data exclusively to handle your information request. You have the right to rectify or request the deletion of your data at any time via hello@letsprototype.com.

Menu

Services

Our Parent Company

© 2024 Let´s Prototype
Privacy Policy

Industrial design and innovative product development company | Specialists in manufacturing working prototypes.