When most people think about artificial intelligence (AI), they envision high-tech robots and automated processes, or, in a more romantic sense, the promise of technology finally being realized. Hardly anyone thinks about the actual people behind the technology. However, for every game-changing technology and innovative product, there is a team of humans conceiving new ideas, powering them from behind the scenes, and collaborating alongside it.
AI technologies have existed in some form for a few decades, but until recently, the possibilities were more science fiction than reality. It’s only been in the past few years that the potential of AI has started to come to life, and it is growing at an incredibly fast rate. In 2012, Google only had 2 deep learning initiatives. Now, it has over 1,000 projects.
The AI revolution and the excitement it generates can be attributed to two main factors. One is that the exponential advances in computing power, and the corresponding lowering of costs over the past decade have made it possible for both big and smaller players to run these sophisticated technologies. Another is that people, like data scientists and other innovators have learned how to utilize massive datasets to develop algorithms for deep learning systems.
Deep learning is a subset of machine learning that focuses on performing even more complex tasks, similar to a human's neural network. When you give Amazon’s Alexa commands to order snacks online or play your favorite music playlist, you are using, and contributing to, deep learning. Other examples include the recommended movies and shows you see on Netflix and the facial recognition in Facebook’s DeepFace. Vast amounts of data are needed for these technologies to identify and “learn” patterns.
The Data Science Revolution
The capabilities of AI and deep learning are impressive today, when we think about the future, they're potentially mind blowing and will almost certainly be massive disruptions to the status quo. However, there is still a long way to go. Today’s AI technologies have their limitations, and ultimately, the human engineers and data scientists behind the scenes will be the reason they overcome them. Moving AI forward will also mean improving the processes largely reliant on humans.
A recent Forbes article captured this need and declared, “The skill set of the data scientist will be rendered useless in 12-18 months.” Unless, they evolve at the same or an even faster pace than their AI counterparts, today’s data scientists may get left behind.
One key is that data scientists need to be more innovative in the way that they collect, prepare, and analyze data. In a previous blog, we mentioned that data scientists spend about 80% of their time on data prep. It is often the most unglamorous and time-consuming part of their job, but good data is critically important to developing killer AI. So, what can data scientists do to stay ahead of the curve? They can start by taking back more of their time.
AI, Data Science and the Human Cloud
The job responsibilities and tasks that data scientists perform are constantly evolving. Even the career in its modern form is young, which is why many companies are relying on younger generations to fill the large talent gap that is expanding every year.
The talent pool of data scientists is small, and the growing demand of AI solutions is draining an already shallow talent pool. In order to keep up with demand, companies need to widen the pool, and tap into the global, human cloud services to offload work that can be done, at scale, by partners whose sole focus is to improve data and free businesses up to focus on innovation.
For instance, CloudFactory’s vetted, trained, and professionally managed workforce processes and prepares data that powers analytics platforms, trains machine learning algorithms, and builds ground truth for teams that are driving the next wave of AI. This means that data science teams, product owners, and engineers are saving a ton of time — time that is far better utilized by testing new ideas and improving upon existing innovation.
Humans are the builders and the trainers of every AI technology, and humans will be the cornerstone of work for a long time to come. As AI advances, it will work to augment human intelligence in a hybrid model. Most of the automated tools and software today function with humans to get the job done faster and smarter. Some say that by 2033, close to 30% of the full-time jobs that exist today will be performed with on-demand, cloud services that use a combination of humans and automation. Even the services advertised as “fully-automated” will have humans working in the background.
The bottom line is that human labor is crucial to driving artificial intelligence. This is why CloudFactory is committed to training and supporting our global workers who devote their time to these critical tasks that further AI development. In the future, AI will rely even more on cloud labor, and our global workforce will be ready to take on the challenge.
Image via Medium
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