The State of The Art of AI In Industry and Data Annotation At The End of 2022
Artificial Intelligence has been adopted by about half of all businesses and organizations, according to the McKinsey Global Survey on AI. There is also a lot of new adoption of AI, a lot of companies and people who didn't use AI before and do now. There have been a lot of improvements made in AI and important features of AI products such as computer vision. That has made AI more useful. More use cases have caused organizations to embrace and embed AI capabilities into their operations.
The most common use case where AI has been adopted is production efficiency improvement. Robotic process automation in manufacturing and logistics is big. The use of computer vision is also increasingly popular. AI in robotics often uses computer vision, and to create or improve such an AI, robotics data annotation is essential.
Another trend is the deployment of virtual agents and conversational interfaces, commonly known as chatbots. Likewise, Generative Adversarial Networks, aka GANs, have improved and are used more in deep learning models. That has led to some controversial AI applications, like those that help write articles for news sources. The most famous example is probably OpenAI's Chat GPT.
The most controversial recent trends in 2022 are AI applications that generate art. We know that AI art is art because it makes some people mad and causes such controversies. AI that creates content is still on the rise and has a long way to go before it's able to match the creativity of humans. AI art content is still a novelty and has democratized art by allowing anyone to create something they think is good. Still, such programs won't replace human artists and writers. Rather it provides additional tools to create more and better content.
Over the past few years, a few winners have emerged. Some companies have made much more money than others in the field of artificial intelligence. Those winners have been more capable of attracting and retaining top talent from places like the best universities. They've also been able to invest more in their employees' continued education, training, and careers. That means that they also offer more promotions from within.
There is a growing divide between the best and all the rest. One reason for that is there is a very real shortage of qualified talent to recruit. Another reason is that people who simply went through a coding boot camp to learn are overlooked. The same for those who just started collecting certifications. Those who are self-taught are often passed over too. Due to these recruiting woes, outsource data annotation services are more critical than ever.
Because of the increased adoption of AI and automation, investment in AI has also increased. Of course, the biggest winners have the most capital and make the largest investments in using AI or in the research and development of AI. The best AI data annotation services have also pulled ahead of their competition. All of that increased interest and investment means that the best AI companies are becoming even better. They are improving their technology and their services.
All of the increased investment also means that there is a higher demand for qualified people to work. There are more AI company start-ups and more demand for qualified talent with AI skills. There is also more room in the markets for AI company start-ups. New businesses need all the help that they can get. So, you should know that we provide data annotation for start-ups at affordable rates.
Trends in AI and Data Annotation
- Increased adoption of AI and Automation in a wide variety of industries.
- Increased investment in both using AI and in research and development of AI.
- Increased demand for top talent has created problems in recruiting and made outsourcing more critical than ever.
- A growing difference between the very best and all the rest.
- New and improved AI applications and features of AI technology, such as computer vision.
- Big data is growing an awful lot bigger.
- Improvements in GANs have led to new controversial AI content-generating applications for writing and art. One way we know that AI art is real art is because it is controversial.
The Future of AI and Data Annotation in 2023 and Beyond
There is a common saying, "The trend is your friend." You can try and more accurately predict the future by following the trend lines and extrapolating from the available data. Of course, it is important to say that past performance is no guarantee of future results. With this in mind, we can provide some predictions with confidence.
Big data will only continue to increase, exceeding humanity's ability to annotate or consume it manually. As a result, large-scale data annotation will be critical, and more data annotation will have to be automated. Machine learning models will greatly benefit from this, which means AI products and services will improve.
The race to put a widely accepted, street legal self-driving car will speed up and intensify. Autonomous vehicles have been promised to us in science fiction and popular TV shows such as Knight Rider. Will 2023 bring us a great, fully autonomous self-driving car? If it does not happen next, it is bound to happen sometime in the near future.
AI adoption will continue to increase, so much more can be automated, and it will be. Anything that increases productivity and efficiency while decreasing costs is a sure winner. That is exactly what AI does best. While some say that we have reached the end of Moore's Law, the computers and specialized hardware needed to develop and run AI will improve too.
Various supply chain problems may well continue into 2023. Hardware prices will go higher because of increased demand and inflation. One bright side is that computer storage like hard drives and fast solid state drives will continue to get faster, increase capacity and still come down in price.
We may see fewer or "no code" programs to help create an AI application. That will democratize AI development. However, they probably won't replace the hard work of computer programmers, data scientists, engineers, and data annotators. Instead, it will increase interest in and accessibility to AI. As a result, AI data annotation outsourcing will be more in demand and essential as the largest, most successful companies raise the bar.