The autonomous world is one of the most important developments in human history. From self driving cars to retail stores to warehouses, AI and machine learning are now an integral part of our everyday life, and the next frontier for AI is to enable machines to have even more autonomy. An essential component to bring us into this next phase will require taking a data-centric AI approach utilizing visual data.
Make a shift from our current model-centric approach to AI to a data-centric AI approach.
The Data-Centric AI Approach
A model-centric approach to AI keeps data fixed and focuses on the parameters of the model to improve performance. The model-centric approach is all about how to change or improve the model to improve performance, rather than improving the data.
On the other hand, a data-centric AI approach involves building AI systems with high-quality data, so the AI can “learn” from the data. In the data-centric approach, the focus is on how to change or improve data to improve performance, rather than the model. With data-centric AI, data is engineered to best build the AI system.
With a data-centric AI approach, data quality is prioritized over quantity. This emphasis on data quality helps circumvent common challenges that arise when deploying AI infrastructure. Additionally, as AI models improve, focusing on the quality of data will become even more critical for developing and deploying higher accuracy models. Through a data-centric approach, the development of software tools and practices can focus on making data more efficient, reliable, and systematic. For a wide swath of industries, this turn to more efficient, reliable, and systematic data will maximize the return on data, cut operational and employee labor costs, and improve the efficacy of products, services, and systems.
In order to keep up with the sheer volume and variety of data today and in the future, it’s time to embrace a data-centric approach to AI and software development.
How Do We Best Use Visual Data?
Visual data is the representation of data in a graphical, pictorial, or video format. Visual data is used in applications across a wide range of industries, including security and surveillance, medical, automotive, construction, transportation, retail, and many more.
Identifying and understanding visual data sets through computer vision is set to explode in use across a wide range of businesses, organizations, and industries. The AI in computer vision market reached $11.34 billion globally in 2020 and is expected to grow from 2021 to 2028 at an annual growth rate of 7.3%, according to Grand View Research. Additionally, the advanced computer vision market is expected to reach $49 billion by 2022, according to Forbes.
Whether it’s identifying cancer cells in an organ scan in a doctor’s office or opening up smartphone applications through facial recognition software, computer vision has thousands of uses when it comes to visual data.
Previously, computer vision could only perform limited functions and required lots of manual coding to perform tasks like database creation, image interpretation, and capturing content.
Now, the latest deep learning computer vision models have achieved above human-level accuracy and performance in real-world image recognition tasks like facial recognition, object detection, and image classification.
Computer vision has flourished thanks to:
Thanks to its speed, objectivity, and potential for automation, computer vision can now surpass human capabilities to identify, assess, and analyze large quantities of visual data. This makes computer vision critical to our future, as this kind of AI can help us inspect products, babysit infrastructure, and spot problems, flaws, and issues in everything from products to systems to software.
However, the major problem facing deep learning right now is that while the demand for labeled data is infinite, the lack of labeled data in the enterprise is the major bottleneck to progress.
If the focus shifted to more high-quality data that is consistently labeled, it would unleash the potential and value of AI for a huge range of industries, including healthcare, retail, automotive, manufacturing, city planning, and more.
That’s where Akridata comes in.
What is Akridata Data Explorer? Exploring An AI Platform for Visual Data
Akridata Data Explorer is a revolutionary AI platform built for exascale visual data and AI training.
Akridata Data Explorer allows users to import vast visual data sets and allows users to explore their data in clusters based on feature embeddings, search their data based on points of interest, analyze the data to identify the causes of model inaccuracies, and compare novel data across visual data sets to reduce training time and resources required to improve model training and accelerate model accuracy.
With Akridata, users can easily identify novelty visual data sets and compare label quality across sources. Users can easily surface and explore interesting data clusters to continue improving data sets. With Akridata, it’s easy to access the right data in minutes or hours vs. hours or days, which frees up data scientists to handle more critical tasks and encourages efficient scaling.
By giving data science teams the tools they need to quickly and efficiently create better training data sets, they can accelerate model accuracy and efficiency and achieve greater results. Placing a larger emphasis on the data will be essential to continue advancing AI capabilities.