As AI continues to evolve, so do workers’ fears that this technology will replace them. But as Nacho De Marco, CEO of BairesDev, said in a recent Forbes Technology Council post, this way of analyzing the matter is inaccurate. He mentions that technology like AI will further change how we work and says that, “Employers value workers who can adjust to new situations, learn new skills and take on new challenges.” Given that reality, the best thing tech workers can do to protect their jobs is to gain new skills.
Perhaps counterintuitively, the most important skills aren’t all technology-related. In addition to new programming languages, machine learning, and natural language processing, tech workers should be focusing on problem-solving and communication. That’s because the evolution of AI will be less like a new tool and more like an assistant requiring instruction, direction, and correction.
As AI continues to evolve, so must those who work closely with it. The following critical skills are essential for tech workers to learn now and in the coming years.
1. Machine Learning
Machine learning (ML) is the practice of training machines to learn from data. This goal is accomplished by exposing them to a large data set and teaching them to recognize patterns, make predictions, and perform tasks.
Autonomous vehicles are good examples of machines that have been trained in this way. These vehicles rely on ML algorithms and models to navigate roads, detect obstacles, and make driving decisions without human involvement. For these machines, training involves exposure to large amounts of data, including images, sensor readings, and examples of actions taken by human drivers. Vehicles learn to identify patterns and make predictions based on this data.
ML involves several steps: data collection, data cleaning and preparation, model selection, feature extraction, model training, and evaluation. To achieve proficiency in ML, tech workers should become familiar with various ML algorithms, including supervised learning, which is based on labeled data, unsupervised learning, which is based on unlabeled data, and reinforcement learning, which is based on trial and error.
Tech workers in AI should become familiar with the various tools that help in developing ML models. They include TensorFlow, Keras, and Scikit-learn.
2. Deep Learning
Deep learning is a type of ML that uses artificial neural networks (ANNs)—a class of ML models based on the structure and function of the human brain—to simulate the human brain. These models are used to identify complex relationships and patterns in data. They are powerful tools for accomplishing tasks that involve pattern recognition, regression, classification, and generative modeling.
Deep neural networks are neural networks with multiple hidden layers. These models can perform higher-level tasks such as learning hierarchical representations and analyzing large data sets.
Tech workers interested in AI should be skilled in deep learning techniques. They include convolutional neural networks (CNNs), which can process and analyze data structured in grids, recurrent neural networks (RNNs), which can process sequential data, and generative adversarial networks (GANs), which can generate new data samples. Workers should also be familiar with deep learning frameworks, some of which are the same as those used for ML. They include TensorFlow, PyTorch, and Keras.
3. Statistics
Statistics is considered a core skill for tech workers in AI because it provides the basis for many ML techniques, including regression analysis, hypothesis testing, probability theory, Bayesian inference, experimental design, model evaluation and validation, sampling techniques, time series analysis, anomaly detection, and estimation and confidence intervals. Tech workers in AI should learn the statistical skills needed to perform all of these techniques.
4. Data Science
Data science involves gleaning insights from large and complex data sets. It makes use of statistical analysis and ML processes. Tech workers that want to perform data science must be skilled in the following procedures:
- Data mining is the process of discovering patterns, relationships, and insights from large volumes of data. To reach this goal, tech workers must extract usable information from datasets with computational techniques and statistical algorithms. The goal of data mining is to discover hidden patterns, trends, and associations that might be useful for identifying anomalies, making predictions, and making data-driven decisions.
- Data cleaning (also known as data cleansing or data scrubbing) is the process of ensuring data is reliable, accurate, and suitable for analysis for other applications. It includes identifying and correcting or removing errors, inconsistencies, inaccuracies, and discrepancies in a data set. This step is important in data science because low data quality can result in issues during data collection, entry, storage, and processing.
- Data analysis is the process of reviewing data with the goals of discovering useful information, drawing conclusions, and making informed decisions. It involves a variety of techniques, tools, and methods to understand patterns, relationships, and trends within a data set. The step is critical for gleaning meaningful information from raw data.
- Data visualization is a visual representation of data and information derived from it. Data visualization uses charts, graphs, maps, and other visual formats to transform raw data and information derived from it into image-based formats that are easy to understand, interpret, and communicate. Data visualization is especially useful for communicating data analysis results to a range of technical and non-technical stakeholders.
- Data reporting is the process of presenting data in a structured and organized way to convey information, insights, and findings. It involves summarizing and communicating the results of data analysis in the form of reports, dashboards, presentations, or data visualization elements. The goal of data reporting is to provide a clear and meaningful representation of data to support decision making and communication within an organization or to external stakeholders.
Tech workers in AI should also become familiar with data management tools, including SQL and NoSQL databases. In the following video, futurist Bernard Marr emphasizes the importance of data science and discusses recent trends.
5. Programming Languages
Many tech workers are already familiar with a variety of programming languages. In the age of AI, this skill is even more important, because languages such as Python, R, Java, and C++ are widely used to develop high-performing AI applications. In particular, Python is widely used because of its simplicity and versatility, and R is popular among data scientists for statistical analysis.
6. Natural Language Processing
Natural language processing (NLP) enables machines to understand human language. One example of the use of an NLP that most people have already experienced is virtual voice assistants such as Amazon’s Alexa, Apple’s Siri, Google Assistant, and Microsoft’s Cortana. These virtual assistants use NLP to understand and respond to user commands given in natural human language.
When a user interacts with a virtual voice assistant, NLP algorithms analyze and interpret their speech. The process includes the following tasks:
- Speech recognition in which the virtual assistant converts spoken words into text using automatic speech recognition (ASR) technology
- Natural language understanding (NLU) in which the virtual assistant analyzes the structure, syntax, and semantics of the query to understand the user’s request accurately
- Intent recognition in which the virtual assistant determines the specific action or information the user seeks by identifying the intention behind their request, such as providing weather information in response to the query, “What’s the weather like today?”
- Entity recognition, in which the virtual assistant identifies specific pieces of information mentioned in the user’s input, such as the phrase “Mexican restaurant” in the query, “Find a Mexican restaurant nearby”
- Dialog management in which the virtual assistant tracks context and conversation history, then responds appropriately based on recent and previous interactions.
- Natural language generation (NLG) in which the virtual assistant generates human-like responses
Tech workers should become familiar with the NLP techniques listed above. They should also become familiar with NLP libraries such as NLTK, spaCy, and Stanford CoreNLP.
7. Robotics
Robotics is another key area of AI, so tech workers should become familiar with robot programming, motion planning, and control. Robot programming is the process of creating instructions or code that enable robots to perform specific tasks on their own or with human supervision. Programmers must define the sequence of actions, behaviors, and decision-making processes that a robot needs to follow to achieve its assigned objectives.
Motion planning means determining a reasonable path or trajectory for a robot to move from its current position to arrive at another position while avoiding obstacles and taking into consideration its own limitations. This skill involves computing and optimizing robot motions to enable movements that are efficient and safe for both the robot and other humans and machines nearby.
Robot control refers to the management and regulation of a robot’s behavior, actions, and operations. It encompasses focusing on the control of individual robot components as well as higher level decision making and coordination. The process requires the implementation of algorithms, hardware, and software systems that enable robots to interact with their environments, execute tasks, and perform specific actions.
Tech workers in AI should also learn about robotics platforms. They include robot operating system (ROS) and Gazebo.
8. Cloud Computing
Tech workers can use cloud computing to build scalable AI applications. An example of this type of application is the recommendation system used by large e-commerce platforms such as Amazon. These platforms use AI algorithms to provide personalized recommendations to users. This offering enhances their services, increases customer satisfaction, and encourages higher spending.
Such recommendation systems use a tremendous amount of data about users and the products they prefer. These systems rely on a variety of processes, including those listed here:
- Data collection, the process of gathering data about user preferences, browsing history, purchase behavior, ratings, and interactions with products
- Machine learning models, which make use of AI algorithms—such as collaborative filtering, content-based filtering, and deep learning models—to analyze the collected data to better understand user preferences and make improved recommendations
- Real-time processing in which AI applications process recommendations in real time to provide dynamic responses to user actions
- Distributed computing, the use of AI applications and distributed computing frameworks to manage the computational demands of processing large data sets
- Infrastructure and resource scaling, the use of AI applications, including the deployment of infrastructure and the ability to scale resources according to demand
- Load balancing, a process used to handle the high volume of user requests and ensure smooth operation
- A/B testing, a testing methodology used to evaluate the effectiveness of different recommendation algorithms or strategies
- Continuous learning and improvement, in which AI applications incorporate feedback loops and mechanisms for continuous learning and improvement, so they can adapt to changing user preferences and improve over time
Tech workers, if they’re not already, should become familiar with the processes listed here, as well as cloud computing platforms and technologies. Major platforms include AWS, Azure, and Google Cloud, and technologies include Docker and Kubernetes.
9. Non-technical Skills
Problem-Solving
Workers in most industries benefit from excellent problem-solving skills, but this skill is especially useful in tech, where much of the work involves developing new products and services, a process that doesn’t always go as planned. Because AI is a new area, problem-solving is even more important there. Tech workers in AI should be able to think creatively to develop solutions to complex problems.
One situation in which tech workers in AI could use problem-solving skills is an image classification task. For example, when given a large data set of images, the goal might be to develop an AI model that can accurately classify new images into predefined categories, such as “house,” “car,” “lamp post.” The professionals working on this task may use problem-solving to understand the problem, select the right tools to solve it, deploy those tools, and troubleshoot any obstacles that arise.
Communication
As with problem-solving, communication is highly useful in just about every job in any industry. But the ability to explain complex AI concepts is particularly helpful when working with AI, especially when non-technical stakeholders and team members are involved. Tech workers should be able to present their ideas clearly, both verbally and in writing. Effective communication is particularly useful when working on a team creating AI applications.
Critical Thinking
Critical thinking involves the ability to accurately analyze and evaluate information resulting in informed decisions. Therefore, it is another critical skill for tech workers in AI because it enables them to determine the suitability of various algorithms and models for specific tasks. It also enables them to identify biases in data and evaluate the ethical implications of AI applications.
Strong critical thinking skills enable tech workers to approach complex problems logically and analytically, resulting in the design of effective AI systems and the ability to address the challenges they present.
Collaboration
As mentioned above, collaboration is a frequent practice in AI application development. As in any kind of application development, teams come together to use their various skills to create the end product. Tech workers in AI may have expertise in data science, domains, software, or other areas, and they must be able to work well with those who have different skills. Effective collaboration involves active listening, knowing what information is important to present, and the ability to relinquish control to the group while working toward a common goal.
Best Ways to Gain AI Skills
For tech workers, the need to evolve is nothing new. Technology has been changing rapidly over the last 40 or so years, and the pace is only increasing. In the quickly evolving landscape of AI, the importance of developing and honing a diverse set of skills is essential. Tech workers in AI must embrace a multidimensional approach, blending technical expertise with soft skills and a commitment to lifelong learning.
The ability to adapt, collaborate, think critically, and navigate ethical considerations is essential as AI continues to shape the world. Here are a few ways to gain the necessary competencies:
- Formal education, such as pursuing a degree or certification in computer science, data science, or AI-related fields
- Online courses and tutorials, including through online learning platforms like Coursera, Udacity, and edX, which allow learners to study at their own pace
- Open-source projects and communities, which contribute to open-source AI projects and expose tech workers to real-world applications and collaborative development through platforms like GitHub
- Kaggle competitions (based on the popular data science platform Kaggle), which allow tech workers to solve real-world problems, explore diverse datasets, and benchmark their skills against others
- Hands-on projects, such as creating AI applications and projects from scratch, help reinforce concepts and develop practical skills.
- Collaborative learning includes joining AI-focused communities, forums, or meetups and provides opportunities to learn from peers, share knowledge, and collaborate on projects.
- Industry internships and work experience include securing internships or work opportunities with organizations involved in AI and allowing tech workers to apply their skills in practical settings.
Finally, tech workers in AI must adopt an attitude of continuous learning and professional development. It includes reviewing current research papers, following AI blogs and publications, attending conferences, and participating in webinars or workshops to continually understand the state of the technology.
If you enjoyed this, be sure to check out our other AI articles.
