With 2023 right around the corner, it is a great time to look forward and see what to expect in 2023. Global spending from governments and corporations on AI is going to exceed five hundred billion dollars. The total market cap may reach one trillion dollars.
One significant trend to follow is the democratization of AI. That is to say, we expect more applications using AI to reach the hands of ordinary people and regular end users. We can also look forward to plenty of room in the market for competition. Many more companies will adopt AI or innovate their own AI.
Accessibility to the market will likely increase as more tools like SwayAI and Akkio come out. This will allow anyone to create their own enterprise-level AI application, even with little to no programming experience. In addition, other tools like GitHub Copilot will increase the productivity of experienced programmers.
These coming trends will also increase the demand for labeling tools for machine learning. Behind the development of most of the best AI is machine learning. For that work, databases and labeling tools are required.
Generative AI that takes existing information and creates something new and unique is going to come to the forefront. Generative AI can create in a way that is similar to human creativity.
Generative AI algorithms take pictures, videos, sound, text, and even computer code and uses what it learns from these to create entirely new unique products and content.
Image labeling tools for object detection will improve because they are vital for AI in the automotive industry. These are especially important for creating an AI for autonomous vehicles, whether self-driving cars, drones, or satellites in space. As a result, demand for these AI and the tools used to develop them will only increase.
In 2023 efforts to solve the "black box" problem of AI will increase. The black box problem needs to be overcome for AI to be more trusted and accepted. The black box problem is the basic inability to explain how an AI works and comes to the correct decisions. Open Source software solutions will gain more traction and popularity as a part of solving this looming black box problem.
Solving this black box problem and embracing open software solutions will also increase the demand for medical AI. Furthermore, due to the global shortage of medical experts, medical AI will only become more critical in the coming years. Therefore, it is essential for both doctors and patients to be able to trust medical AI. Solving the black box problems is what will make that possible.
AI will augment human labor. Of course, human labor will always be needed. For example, humans will still need to label data and use a manual image labeling tool to do that. However, AI labeling tools will also increase their productivity and the quality of their work. As a result, humans working together with AI will become more common in 2023 and beyond.
The 5 Most Important Expectations for 2023
- Government and Corporate spending will top $500 billion
- The democratization of AI
- The rise of Generative AI
- Solving the Black Box problem with Open Source solutions
- AI will augment human labor and increase productivity
- Remember that these expectations of advancement demand for AI labeling tools and services will also increase in 2023.
AI Labeling Tools and Services Drive Future AI Innovations and Trends
In 2023 machine learning data labeling tools and the other tools and services we offer will continue to drive these advancements and set expectations.
Semantic segmentation labeling tools and Open Source solutions will gain popularity. Semantic segmentation labels each and every pixel with a corresponding class. That provides more in-depth metadata and context for every object in a picture or frame of a video.
That is important for AI in a wide range of industries and AI applications. For example, semantic segmentation is needed for medical AI and AI for self-driving cars. Unfortunately, those are both kinds of AI that also currently suffer a lack of trust due mainly to the Black Box problem. An Open Source solution to this problem is expected to be developed by someone in 2023 or sooner.
AI labeling tools will be needed to create that Open Source solution, whatever that looks like. Of course, it is often easier and better to leave using such tools to the experts. Outsourcing the data creation, data collection, and labeling that you need leaves you free to create the next significant trend in AI for 2023.