Before building an AI startup

Despite all the success, however, only one out 10 AI startups actually survive. In this article, you will learn about two main technical reasons responsible for the failures of an AI startup. We will also expand upon ways to overcome them.

Lack of Good Quality Data

The key to success for any AI project is having the right data. Working with outdated or insufficient information can lead you into a garbage-in/garbage out situation, which will ultimately fail your company’s goals while wasting business resources in addition!

A good example here is the failure of many AI algorithms designed for diagnosing Covid-19 using medical images. Technology wise it was plausible- if we have sufficient chest x-rays images of healthy and covid infected patients- one should be able to train one the state of the art convolution neural network architectures(like DenseNet, Inception) to detect covid infection. And since most used the technique of transfer learning-one could have done with just few thousand of images. Yet they failed:

Many models used a dataset where chest scans were either of patients lying down, or healthy standing up. This resulted in AI models which were actually detecting the position of the person and using that to predict disease.

In some cases the the training data set contained text on scans used by the hospitals while detecting Covid, here the models learned to differentiate between presence of text or its absence.

Hundreds of AI tools have been built to catch covid. None of them helped. Will Douglas Heaven, MIT Technology Review, July 2021

As you can see that the problem was doable, data was also available yet there was failure- because of insufficient information, sufficient relevant data, and correct labels. This problem can be mitigated by spending time on data exploration, and feature selection. As a rule of thumb before you discard any data- you should have a data-expert analyse it from all angles. It is important to ensure that data you have is relevant for the AI project you intend to start.

Communication between different teams

The AI/Data team cannot work in isolation, if you want to build a successful AI project, you should ensure that your data scientists, data engineers, IT engineers and subject matter experts collaborate with each other. Even if you make SOTA models, if you cannot integrate it and deploy it, it is useless. The collaboration should ensure:

• Ascertain that the output of the AI project can be well integrated into your overall technological architecture.
• Standardise the AI development process,
• Share learning and experiences via good documentation.
• Employ Data, model, DEvOps and MLOps best practices.
• Ensure that your AI solutions can be scaled.

Due to high demand of AI professionals, there is severe lack of necessary talent in the field. If you want to succeed you need experts who are well versed about the latest research in AI. There are two options you can go for either hire an AI consultant or do in house development. We suggest that you go for in-house development only if:

• You have a significant unique proprietary data. For example you are Formula One company – with unique data that you want to strategise the racing, provide recommendation to the racer while driving etc.
• You already have access to AI talent. Remember a data engineer willing to experiment with AI and an AI expert are two very different things. The experience of an AI expert can help in fine-tuning models and provide better results faster.

Maximize the Potential of AI in Your Business with NePeur’s Specialized Services

At NePeur, we understand the potential of AI in today’s business landscape. That’s why we offer a range of services to help companies like yours harness the power of AI. Our services include:

• Data Analytics: We help you assess the quality and quantity of your data, and identify and address any biases present.
• AI Consultancy: We work with business leaders to create a comprehensive AI strategy and identify the best use cases for your business.
• Custom AI Model Building: We can design and build high-efficiency, calibrated, and fair AI models tailored to your specific needs.
• AI Training for Leaders: Our custom training courses empower founders and investors of AI startups to understand the business procedures necessary for success.
• Up-skilling Your Staff: We can organize specialized training for your employees to help them master specific areas of AI, such as computer vision, natural language processing, and reinforcement learning.

If you’re ready to take your business to the next level with AI, contact NePeur today to learn more about our services and how we can help you achieve your goals

Book an appointment today