In addition to new programming languages, machine learning and natural language processing, technology professionals should focus on problem solving and communication.
Perhaps counterintuitively, the most important skills are not all related to technology. In addition to new programming languages, machine learning and natural language processing, technology professionals should focus on problem solving and communication. That's because the evolution of AI will be less like a new tool and more like an assistant that requires instruction, direction, and correction.
As AI continues to evolve, so must those who work closely with it. The following critical skills are essential for technology workers to learn now and in the years to come.
1. Machine Learning
Machine learning (ML) is the practice of training machines to learn from data. This goal is achieved by exposing them to a large set of data 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 algorithms and ML 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 performed 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, technology professionals must become familiar with several 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 based on trial and error.
AI technology workers should familiarize themselves with the various tools that help develop 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 performing tasks involving 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.
Technical workers interested in AI must be skilled in deep learning techniques. They include convolutional neural networks (CNNs), which can process and analyze grid-structured data, recurrent neural networks (RNNs), which can process sequential data, and generative adversarial networks (GANs), which can generate new data samples. Workers must 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 an essential skill for technology workers in AI because it provides the foundation for many ML techniques, including regression analysis, hypothesis testing, probability theory, Bayesian inference, experimental design, model evaluation and validation, sampling, time series analysis, anomaly detection and estimation, and confidence intervals. AI technical workers must learn the statistical skills necessary to perform all of these techniques.
4. Data Science
Data science involves gathering insights from large, complex data sets. It makes use of statistical analysis and ML processes. Technical workers who want to perform data science must be qualified in the following procedures:
- Data mining is the process of discovering patterns, relationships, and insights from large volumes of data. To achieve this goal, technology professionals must extract usable information from data sets with computational techniques and statistical algorithms. The goal of data mining is to discover hidden patterns, trends, and associations that can be useful in identifying anomalies, making predictions, and making data-driven decisions.
- Data cleaning (also known as data cleansing or data scrubbing) is the process that ensures that data is reliable, accurate, and suitable for analysis in other applications. Includes identifying and correcting or removing errors, inconsistencies, inaccuracies, and discrepancies in a data set. This step is important in data science because poor data quality can result in problems during data collection, input, storage, and processing.
- Data analysis is the process of reviewing data with the goal of discovering useful information, drawing conclusions, and making informed decisions. It involves a variety of techniques, tools and methods for understanding patterns, relationships and trends within a data set. The step is critical to collect meaningful information from raw data.
- Data visualization is a visual representation of data and information derived from it. Data visualization uses tables, 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 the results of data analysis 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 purpose 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.
AI technology professionals 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 technology professionals are already familiar with several 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-performance AI applications. In particular, Python is widely used due to its simplicity and versatility, and R is popular among data scientists for statistical analysis.
6. Natural Language Processing
Natural language processing (NLP) allows machines to understand human language. An example of NLP usage that most people have 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 where the virtual assistant converts spoken words to text using automatic speech recognition (ASR) technology
- Natural language understanding (NLU) where the virtual assistant analyzes the structure, syntax, and semantics of the query to accurately understand the user's request
- Intent recognition where the virtual assistant determines the specific action or information the user seeks by identifying the intent behind their request, such as providing weather information in response to the question “What’s the weather like today?”
- Entity recognition where the virtual assistant identifies specific information mentioned in the user input, such as the phrase “Mexican restaurant” in the query “Find a Mexican restaurant nearby”
- Dialogue management where the virtual assistant tracks the context and history of conversations and responds appropriately based on recent and past interactions.
- Natural language generation (NLG) where the virtual assistant generates human-like responses
Tech workers should familiarize themselves with the NLP techniques listed above. They should also familiarize themselves with NLP libraries such as NLTK, spaCy, and Stanford CoreNLP.
7. Robotics
Robotics is another important area of AI, so technology workers must become familiar with programming, planning, and controlling robot movements. Robot programming is the process of creating instructions or codes that allow 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 assigned goals.
Planning movement means determining a reasonable path or trajectory for a robot to move from its current position to reach another position, avoiding obstacles and taking into account its own limitations. This skill involves calculating and optimizing the robot's movements to enable movements that are efficient and safe for both the robot and other nearby humans and machines.
Robot control refers to the management and regulation of a robot's behavior, actions, and operations. It encompasses a focus on controlling individual robot components as well as higher-level decision making and coordination. The process requires the implementation of algorithms, hardware and software systems that allow robots to interact with their environments, perform tasks and perform specific actions.
AI technology workers should also learn about robotic platforms. They include Robot Operating System (ROS) and Gazebo.
8. Cloud Computing
Technology professionals can use cloud computing to create 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 offer enhances your services, increases customer satisfaction, and encourages higher spending.
These recommendation systems use a huge 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 collecting data about user preferences, browsing history, purchasing behavior, ratings, and product interactions
- Machine learning models that make use of AI algorithms – such as collaborative filtering, content-based filtering, and deep learning models – to analyze collected data and better understand user preferences and make improved recommendations
- Real-time processing where 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 using AI applications, including the deployment of infrastructure and the ability to scale resources according to demand
- Load balance a process used to handle high volume of user requests and ensure smooth operation
- A/B testing a testing methodology used to evaluate the effectiveness of different algorithms or recommendation strategies
- Continuous learning and improvement where 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 haven't already, should familiarize themselves 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
Problems solution
Workers in most industries benefit from excellent problem-solving skills, but this skill is especially useful in technology, where much of the work involves developing new products and services, a process that doesn't always go as planned. As AI is a new area, problem solving is even more important. AI technology workers must be able to think creatively to develop solutions to complex problems.
One situation where AI technology professionals could use problem-solving skills is an image classification task. For example, when given a large dataset 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”. Professionals working on this task can use problem solving to understand the problem, select the right tools to solve it, deploy those tools, and resolve any obstacles that arise.
Communication
As with problem solving, communication is highly useful in almost every job in any industry. But the ability to explain complex AI concepts is particularly useful when working with AI, especially when non-technical stakeholders and team members are involved. Tech workers must be able to present their ideas clearly, both verbally and in writing. Effective communication is particularly useful when working as 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 technology workers in AI because it allows them to determine the suitability of various algorithms and models for specific tasks. It also allows them to identify biases in data and evaluate the ethical implications of AI applications.
Strong critical thinking skills enable technology professionals 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 the development of AI applications. As with any type of app development, teams come together to use their diverse skills to create the final product. AI technology professionals may have experience in data science, domains, software, or other areas, and should 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 cede control to the group while working toward a common goal.
Best Ways to Acquire AI Skills
For technology workers, the need to evolve is nothing new. Technology has been changing rapidly over the past 40 years and the pace is only increasing. In the rapidly evolving landscape of AI, the importance of developing and honing a diverse set of skills is essential. AI technology workers must take a multidimensional approach, combining technical knowledge with interpersonal 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 some ways to obtain the necessary skills:
- Formal education such as earning a degree or certification in computer science, data science, or an AI-related field
- Online courses and tutorials including through online learning platforms such as Coursera, Udacity and edX, which allow students to study at their own pace
- Open source projects and communities that contribute to open source AI projects and expose technology professionals to real-world applications and collaborative development through platforms like GitHub
- Kaggle Competitions (based on the popular data science platform Kaggle), which allows technology professionals to solve real-world problems, explore diverse data sets, and compare their skills against others
- Practical projects such as creating applications and AI 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 technology workers to apply their skills in practical settings.
Finally, AI technology workers must adopt an attitude of continuous learning and professional development. It includes reviewing current research articles, following blogs and publications about AI, attending conferences, and participating in webinars or workshops to continually understand the state of the technology.
If you liked this, be sure to check out our other articles on AI.
- Could we develop a system similar to JARVIS?
- Has AI Killed the Developer Star? No, it gave us superpowers!
- Why Edge AI is the future
- The Ethics of AI in Software Development
- 3 Tips for Finding a Great AI Development Partner
Source: BairesDev
