AI’s bottleneck isn’t about tech, but something else. Katherine Steiner-Dicks reports
When it comes to reading comprehension, speech recognition and image identification, artificial intelligence (AI) or more specifically language models and algorithms are outpacing humans when it comes to collecting data and piecing it together in a coherent way.
However, any company that thinks it can rely on AI to replace human brain power and ingenuity will be left behind and its data outdated.
When did AI start matching human performance?
Below is a table of when AI started matching human performance across all eight skills. As you’ll see not all areas are matched and unless new data is created by humans and driven effectively and ethically by developers, AI data will become outdated.
| Skill | Matched Human Performance | Database Used |
|---|---|---|
| Handwriting Recognition | 2018 | MNIST |
| Speech Recognition | 2017 | Switchboard |
| Image Recognition | 2015 | ImageNet |
| Reading Comprehension | 2018 | SQuAD 1.1, 2.0 |
| Language Understanding | 2020 | GLUE |
| Common Sense Completion | 2023 | HellaSwag |
| Grade School Math | N/A | GSK8k |
| Code Generation | N/A | HumanEval |
In a Visual Capital report, it discusses that some AI benchmarks could be rendered obsolete in just a couple of years if newer databases are not being updated with new and relevant data points. “This is why AI models technically haven’t matched human performance in some areas (grade school math and code generation) yet—though they are well on their way,” said the report.
The same report suggests that a key problem for AI developers is that their models “keep beating benchmark databases devised to test them, but still somehow fail real-world tests.”
“Since further computing and algorithmic gains are expected in the next few years, this rapid progress is likely to continue,” said Visual Capitalist. “However, the next potential bottleneck to AI’s progress might not be AI itself, but a lack of data for models to train on.”
When fresh data is no longer created, AI becomes obsolete.
Here are skills IT contractors should develop to avoid AI bottlenecks
- Data engineering: AI systems rely on large amounts of data to train and function effectively. IT contractors with data engineering skills will be in high demand to help businesses collect, clean, and prepare data for AI use.
- Machine learning: IT contractors with machine learning skills will be able to develop and deploy AI models that can solve real-world problems. This is a rapidly growing field with many opportunities for IT contractors.
- Natural language processing: Natural language processing (NLP) is a field of computer science that deals with the interaction between computers and human (natural) languages. IT contractors with NLP skills will be able to develop AI systems that can understand and generate human language.
- Computer vision: Computer vision is a field of computer science that deals with the extraction of meaningful information from digital images or videos. IT contractors with computer vision skills will be able to develop AI systems that can see and understand the world around them.
- Ethical AI: As AI becomes more pervasive, it is important to ensure that it is used in an ethical and responsible way. IT contractors with ethical AI skills will be able to help businesses develop and deploy AI systems that are fair, unbiased, and transparent.
In addition to these technical skills, IT contractors should also develop strong communication and problem-solving skills. These skills will be essential for collaborating with stakeholders and overcoming the challenges that inevitably arise when deploying AI systems. Read our special report on this topic here.
Who will be hiring IT contractors?
By developing these skills, IT contractors can position themselves as valuable assets to businesses that are looking to leverage AI to improve their operations and achieve their goals. One place to find potential hiring companies looking to enhance AI is private equity-backed businesses.
Especially those with investors in the tech fund space. Keep a look out for investor press releases to see where and how they are hoping to integrate AI or operational efficiencies, both of which will require highly skilled IT contractors.
