As each passing day raises new concerns surrounding the implications of AI, there’s a lot of speculation from workers about what it takes to become indispensable. The thing about this is that it’s not a matter of what you do, but how you continue to do it. As someone who provides value to your industry, you need to adjust to its demands and pay attention to what’s required because that’s what’s going to set your efforts apart long-term.
Artificial intelligence is bound to make people feel that they have to be tech-savvy and understand how to leverage these new tools at maximum capacity. In reality, this may be far-fetched, because there are prerequisites and foundational skills that go beyond technical expertise for workers today and it starts with communication and problem-solving.
The way AI is evolving suggests that it needs guidance from experts, people who can identify the problems and tasks that the system will solve in the first place. With that said, nothing is slowing down the trajectory of AI anytime soon, so with these prerequisites and foundational skills locked down, here are the areas tech workers need to focus on:
Cloud Computing
This is likely the most fundamental tool needed to develop high-performing, scalable platforms and applications, especially when it comes to AI. Imagine you're a project manager building an application for a telecommunications company that monitors network performance and predicts network failures.
Two aspects of cloud computing you’ll want to focus on might include the following:
Infrastructure as a Service (IaaS): Understand how to provide and manage virtual machines, storage, and networking resources in the cloud. This is going to demand familiarity with provider offerings, such as AWS EC2, Azure Virtual Machines, or Google Compute Engine, and how to configure and scale these resources to meet the application's requirements.
Platform as a Service (PaaS): You’ll need platform-level services from cloud providers that streamline app development and deployment. This can include services like Azure App Service, AWS Elastic Beanstalk, or Google App Engine since they offer pre-configured environments for deployment without you having to worry about managing the underlying infrastructure.
Machine Learning
This arguably could have been number 1 since it’s what makes AI as versatile and convenient as it is. In 2021, of all the use cases for machine learning, improving the customer experience accounted for 57% of companies worldwide.
Two key principles of machine learning that workers should gain familiarity with include the following:
Unsupervised Machine Learning: Unsupervised learning involves training models on unlabeled data to discover patterns or groupings within that data. Clustering algorithms like k-means, hierarchical clustering, or Gaussian mixture models are good options to identify similar data points or clusters. Dimensionality reduction techniques like principal component analysis (PCA) or t-SNE also help to reduce the dimensionality of data (number of dimensions applied) while maintaining and preserving its structure.
Supervised Learning: Supervised learning is a popular approach we’re seeing with machine learning where models are trained using labelled data (opposite of unsupervised learning). Tech workers will want to understand the concept of input features and target labels, and how algorithms such as linear regression, decision trees, support vector machines (SVM), or neural networks can be applied to learn patterns and make predictions.
Data Science
Data science is interesting because it combines elements of math, statistics, computer science, and domain knowledge as a means to analyze high volumes of data and identify patterns, trends, and relationships that will then be used to make informed decisions and predictions. It's the driver behind data-driven decision making which Bloomberg identifies as “An elusive aspiration for most organizations”. This highlights the untapped potential of data science since it’s clear organizations recognize the potential value of their data but struggle to turn it into actionable insights.
Two key aspects of data science for workers to know going forward include the following:
Data mining: Remember those high volumes of data we mentioned? Well, data mining is what’s going to allow workers to identify those patterns, trends, and relationships we mentioned using algorithms and techniques. Properly leveraging data mining is what’s going to remediate that data overload and turn it into actionable insights.
Data visualization: This practice involves representing data in visual formats such as dashboards, graphs, charts, and maps. The ability to create clear and concise visual representations of data is crucial for workers to communicate findings, drive that data-driven decision-making processes, and foster a culture of data literacy within their organization. Proficiency in this is an indispensable skill…
Deep Learning
Deep learning is a subset of machine learning that trains neural networks to understand things and be able to make decisions and predictions without being directly programmed to do so. A key differentiator between machine learning and deep learning is that deep learning models excel at handling unstructured and high-dimensional data like audio, images, and text. Deep learning is something that’s going to push the envelope when it comes to what machines can achieve which makes it crucial for tech workers to understand how to leverage it in their work.
Here are two key aspects of deep learning for tech workers to focus on:
Neural Network Architectures: Understanding different types of neural network architectures is essential in deep learning. For instance, convolutional Neural Networks (CNNs) are commonly used for computer vision tasks, Recurrent Neural Networks (RNNs) are great for sequential data analysis, and Generative Adversarial Networks (GANs) are primed for generating new content. As a tech worker, it’s a great idea to study these architectures and be able to recognize what model is best for different tasks.
Training and Optimization: Deep learning models require a lot of computational resources and training to achieve high-level performance. Tech workers need to know various optimization techniques such as gradient descent, backpropagation, and regularization methods (Such as L1, L2, and Dropout) to train deep neural networks effectively. Additionally, understanding techniques like transfer learning or pre-trained models might just help leverage existing knowledge and reduce the training time for specific tasks.
Internet of Things (IoT)
IoT technology is reshaping industries across the globe and ultimately changing the way we interact with our surroundings. Above all else, IoT technology gauges where a business's systems are in terms of performance and enables them to leverage data-driven decision-making.
Two key aspects of IoT for tech workers to become familiar with:
Connectivity and Integration: IoT revolves around the premise that having various interconnected devices, sensors, and systems can create a network of objects. Workers need to understand the logistics and technology behind IoT connectivity, such as wireless protocols (e.g., Wi-Fi, Bluetooth, Zigbee), network infrastructure (e.g., edge computing, cloud platforms), and data transmission protocols (e.g., MQTT, CoAP). This is effectively going to let you design, implement, and manage IoT solutions, which ultimately leads to seamless communication and interoperability between the different components.
Industry-specific Knowledge: You need to understand how to tailor IoT solutions to the specific needs of your sector. For example, healthcare workers might use IoT applications in remote patient monitoring, while manufacturing workers may focus on IoT-enabled predictive maintenance. In essence, it’s not a one size fits all approach, but if you know the industry (Or industries) you’re serving - you can add a lot of value that will be hard to replace.
The Takeaway
People still have a lot of value to bring to the workforce that compliments the unique potential of artificial intelligence. You have to be willing to try new things and give up old methodologies to move forward. Never fall victim to thinking you know it all, and work like you can never know enough.
Written By Ben Brown
—
ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.
Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.
