Today, there is an influx of companies moving towards the next phase of AI where machine learning runs on the back of information to bolster human capabilities.
As we continue to lean towards AI globally, demand for expertise will only multiply.
It is possible through collective efforts to apply known technology models to create an interest in the digital sector and secure a future for humanity, especially the youth.
Incorporating Artificial Intelligence (AI) across fundamental business functions is becoming a necessity for the survival of organisations. Hence, forcing companies to rely even more heavily on data to make core decisions.
Today, there is an influx of companies moving towards the next phase of AI where machine learning runs on the back of information to bolster human capabilities.
Machine Learning algorithms are dependent on the data provided. They make connections, cultivate understanding and execute decisions based on the training data provided. Therefore, the higher the quality of training data, the superior the function of the model.
Therefore, data intended for training algorithms needs to be augmented and labelled.
Now, if one stores a huge amount of well-structured data in the dataset, it might not necessarily be labelled to work as a training dataset for the intended model.
For instance, self-driving vehicles require more than pictures of the designated road, the algorithm enabling vehicle autonomy needs annotated images of each vehicle, pedestrian and street sign.
This increases the importance of accurate data in developing artificial intelligence and business models tailored for their end goals while analysing how humans and machines interact within working environments.
It is this need to efficiently acquire accurate training data that fuels most tech teams to commit to the advance of data annotation technology and amplifying of technology development.
As we continue to lean towards AI globally, demand for expertise will only multiply. Hence securing apt talents and skills is vital to excel in this climate.
As seen with our company’s mode of hiring, we seek to identify talent all over the East Africa region, the introduction of a diverse workforce ecosystem—a wide-ranging portfolio of workers, gig workers, talent networks, and service providers who allow for flexibility, exploration of various capabilities, and the potential for discovering different economic models.
Many challenges
In addition, we enable the acquisition of digital skills that enable our team to prepare for the future of work while exposing them to new approaches to work backed by a global-minded perspective alongside championing gender equality. Yet even with such promise and potential, the world of AI has to contend with a myriad of challenges.
It not only necessitates learning new skills, but it also requires adapting to a new culture.
AI-fuelled enterprises operate in ways not accustomed to different workforces around the globe.
Driving forces
As the AI-fuelled model becomes the norm, workers will need to adapt towards a more advanced state where humans and machines collaborate in ways that only recently seemed to exist in the world of fiction.
In identifying alternative ways to attain preferred outcomes, automation is increasingly becoming one of the main driving forces to achieve said results and change the ways in which work gets done.
It is clear leading organisations are committed to making digital transformations tenable.
More companies are striving to inspire new opportunities outside their original domain into the rest of the globe.
We have confidence in the world of infinite opportunities made available by technology.
It is possible through collective efforts to apply known technology models to create an interest in the digital sector and secure a future for humanity, especially the youth.
Greg Moser, VP, global delivery, at Sama (formerly Samasource)
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