Artificial Intelligence: What It Is, Why It Matters for Managers and How you should approach it in 2024Estimated Reading Time: just 15 min

Artificial intelligence (AI) is the field of computer science that develops and studies intelligent machines and software that can simulate human thinking and behavior. AI has various sub-fields, goals, and applications in different domains, such as web search, recommendation systems, speech recognition, self-driving cars, creative tools, and strategy games.

AI is not a new concept, but it has gained a lot of attention and progress in recent years, thanks to the advances in data, computing power, and algorithms. AI can help managers solve complex problems, improve decision making, enhance customer experience, optimize processes, and innovate products and services.

However, AI also poses some challenges and risks, such as ethical, social, legal, and security issues. Managers need to be aware of the potential impacts of AI on their business, customers, employees, and society, and adopt responsible and trustworthy practices to ensure the benefits outweigh the harms.

In this article, we will explore some of the key concepts, methods, and applications of AI, and provide some tips and resources for managers who want to learn more and leverage AI in their work.

What is Artificial Intelligence?

Artificial intelligence (AI) is a broad term that refers to the ability of machines and software to perform tasks that normally require human intelligence, such as reasoning, learning, planning, problem-solving, perception, and creativity.

AI can be classified into two main categories: narrow AI and general AI.

• Narrow AI is the type of AI that is designed to perform a specific task or function, such as playing chess, recognizing faces, or translating languages. Narrow AI is also known as weak AI or applied AI, because it does not have the full range of human cognitive abilities. Most of the current AI applications fall into this category.
• General AI is the type of AI that can understand and perform any intellectual task that a human can do, such as reasoning, learning, and creativity. General AI is also known as strong AI or artificial general intelligence (AGI), because it aims to achieve human-level intelligence or beyond. General AI is still a hypothetical and elusive goal, and there is no consensus on when or how it will be achieved.

AI can also be classified into two main approaches: symbolic AI and sub-symbolic AI.

• Symbolic AI is the approach that uses symbols, rules, and logic to represent and manipulate knowledge and reasoning. Symbolic AI is based on the assumption that human intelligence can be formalized and abstracted into symbols and rules that can be manipulated by machines. Symbolic AI is also known as classical AI or good old-fashioned AI (GOFAI), because it was the dominant paradigm in the early days of AI research. Examples of symbolic AI include expert systems, logic programming, and knowledge representation.
• Sub-symbolic AI is the approach that uses numerical values, statistical models, and neural networks to represent and learn from data and experience. Sub-symbolic AI is based on the assumption that human intelligence can be modeled and simulated by machines that can process large amounts of data and learn from patterns and feedback. Sub-symbolic AI is also known as connectionist AI or neural AI, because it relies on artificial neural networks as the main technique. Examples of sub-symbolic AI include machine learning, deep learning, and natural language processing.

What is a dataset and why it matters in Artificial Intelligence projects?

A dataset in artificial intelligence is a collection of data that can be used to train, test, or evaluate an AI system or algorithm. A dataset can consist of various types of data, such as text, images, audio, video, or numerical values. A dataset can also have different formats, such as CSV, JSON, XML, or HDF5.

A dataset is an essential component of any AI project, as it provides the input and output for the AI system to learn from and perform on. A good dataset should be relevant, representative, reliable, and robust for the AI task and domain. A bad dataset can lead to poor performance, bias, or errors in the AI system.

Some examples of datasets in artificial intelligence are:

• The MNIST dataset, which contains 70,000 handwritten digits for image recognition⁴
• The IMDB dataset, which contains 50,000 movie reviews for sentiment analysis⁵
• The LibriSpeech dataset, which contains 1,000 hours of speech for speech recognition.
• The Artificial Intelligence Patent Dataset, which identifies U.S. patents and publications that contain one or more of eight AI component technologies.

There are many sources and platforms that provide free or open datasets for machine learning and artificial intelligence, such as Kaggle, Google Dataset Search, UCI Machine Learning Repository, and USPTO.

Most of the time, building a relevant dataset is the most difficult part of an AI project therefore it is a mandatory milestone to be evaluated with your technical team before starting one.

What are Machine Learning, Deep Learning, Supervised Learning and Data Science?

Machine learning, deep learning, supervised learning and data science are terms that are often used interchangeably, but they have different meanings and applications. In this article, we will explain what they are, how they work and how they relate to each other.

Machine learning, deep learning, supervised learning and data science are related but distinct fields that have different meanings and applications. Machine learning is a branch of artificial intelligence that uses statistical methods to improve the performance of an algorithm in identifying patterns in data. Deep learning is a subset of machine learning that uses artificial neural networks to process data. Supervised learning is a type of machine learning that uses labeled data to train algorithms to classify data or predict outcomes. Data science is the science of extracting value from data, using various tools and techniques, including machine learning, deep learning and supervised learning.

Machine Learning

Machine learning is a branch of artificial intelligence that uses statistical methods to improve the performance of an algorithm in identifying patterns in data. Machine learning algorithms can learn from data and make predictions based on it, without being explicitly programmed to do so. Machine learning can be used for a wide range of tasks, such as spam filtering, face recognition, natural language processing, image analysis, data mining, self-driving cars and more.

Machine learning can be divided into two types of problems: supervised learning and unsupervised learning. Supervised learning uses labeled data to train algorithms to classify data or predict outcomes. Unsupervised learning uses unlabeled data to discover hidden structures or patterns in the data. There are also other types of learning, such as semi-supervised learning, reinforcement learning and self-supervised learning, that combine aspects of both supervised and unsupervised learning.

Some of the common machine learning algorithms are linear and logistic regression, k-nearest neighbors, decision trees, support vector machines, neural networks, clustering, principal component analysis and association rules.

Deep Learning

Deep learning is a subset of machine learning that uses artificial neural networks to process data. Neural networks are composed of layers of interconnected nodes that simulate the functioning of the human brain. Each node takes an input, applies a weight and a bias, and produces an output. The output of one layer becomes the input of the next layer, until the final layer produces the desired output.

Deep learning can handle complex and high-dimensional data, such as images, texts, sounds and videos, and can automate the feature extraction process, reducing the need for human intervention. Deep learning can perform tasks such as image recognition, speech recognition, natural language generation, machine translation, text summarization, sentiment analysis, face detection, object detection, style transfer, generative adversarial networks and more.

Some of the common deep learning architectures are feedforward neural networks, convolutional neural networks, recurrent neural networks, long short-term memory, gated recurrent units, attention mechanisms, transformers, autoencoders, variational autoencoders, generative adversarial networks and deep reinforcement learning.

Supervised Learning

Supervised learning is a type of machine learning that uses labeled data to train algorithms to classify data or predict outcomes. The labeled data consists of input-output pairs, where the output is the correct answer or the desired outcome for the input. The algorithm learns from the training data and then applies what it has learned to new data.

Supervised learning can be used for tasks such as email spam filtering, credit card fraud detection, face recognition, sentiment analysis, stock price prediction, medical diagnosis and more.

Data Science

Data science is the science of extracting value from data. It combines mathematics and statistics, specialized programming, advanced analytics, artificial intelligence and machine learning with domain-specific skills to discover actionable insights hidden in the data. Data science can be used for various purposes, such as business intelligence, customer analytics, product development, recommendation systems, social media analysis, natural language processing, computer vision, bioinformatics and more.

Data science involves several steps, such as data collection, data cleaning, data exploration, data visualization, data modeling, data evaluation and data communication. Data science requires the use of various tools, such as programming languages (e.g. Python, R, SQL), frameworks (e.g. TensorFlow, PyTorch, Scikit-learn), libraries (e.g. NumPy, Pandas, Matplotlib), platforms (e.g. Google Cloud, AWS, Azure) and software (e.g. Jupyter, RStudio, Tableau).

What can Artificial Intelligence do today?

Machine learning can do many things today, such as:

• Recognize faces, objects, and scenes in images and videos. For example, TensorFlow is an open source machine learning platform that provides tools and libraries for building image recognition applications¹.
• Analyze text and speech and generate natural language responses. For example, Simplilearn is an online learning platform that offers courses on machine learning and natural language processing².
• Recommend products, services, and content based on user preferences and behavior. For example, social media platforms use machine learning to suggest friends, pages, and posts that users might like².
• Detect fraud, spam, and anomalies in data and transactions. For example, IBM is a technology company that provides solutions for machine learning and data analytics³.
• Predict outcomes and trends based on historical and current data. For example, Google DevFest is an event that showcases machine learning applications for forecasting and decision making¹.

Machine learning is a powerful and versatile technology that can solve many real-world problems and create new opportunities for innovation. However, machine learning also has some challenges and limitations, such as:

• Data quality and availability. Machine learning models depend on large and diverse datasets to learn and perform well. However, data can be noisy, incomplete, biased, or scarce, which can affect the accuracy and reliability of machine learning models.
• Ethical and social implications. Machine learning can have positive and negative impacts on society and individuals, such as privacy, security, fairness, accountability, and transparency. Machine learning developers and users need to be aware of the ethical and social implications of their work and follow best practices and guidelines to ensure responsible and beneficial use of machine learning.
• Explainability and interpretability. Machine learning models can be complex and opaque, making it difficult to understand how they work and why they make certain decisions. Explainability and interpretability are important aspects of machine learning that can help users trust and evaluate machine learning models and outcomes.

Generally speaking, when starting an AI project, always evaluate with your technical team what is feasible. A rule of thumb is that you can do what involves learning a simple function and the presence of structured/standardized data to train the algorithm. A simple function is one that you would be able to perform very fast such as recognizing an object by looking at it.

How to lead your company into the AI era

To lead your company into the AI era you can find inspiration in the AI Transformation Playbook by Landing AI that is a guide for enterprises that want to become strong AI companies. It was written by Dr. Andrew Ng, a leading AI expert and the founder of Landing AI. The playbook consists of five steps:

• Execute pilot projects to gain momentum. The first few AI projects should be feasible, meaningful, and measurable, and should help the company gain familiarity and confidence with AI.
• Build an in-house AI team. The company should recruit and retain AI talent, create a centralized AI team, and foster a culture of collaboration and innovation.
• Provide broad AI training. The company should educate its employees, especially executives and managers, about the basics and potential of AI, and empower them to identify and propose AI opportunities.
• Develop an AI strategy. The company should align its AI vision with its business goals, identify its AI advantage and differentiators, and prioritize and allocate resources for AI initiatives.
• Develop internal and external communications. The company should communicate its AI successes and challenges, both internally and externally, and build trust and engagement with its stakeholders.

The playbook also provides examples, best practices, and tips for each step, as well as a checklist for assessing the company’s AI readiness and progress. You can download the playbook for free from this link.

For more information and examples on how to organize and execute your first pilot project, you can refer to Dr Andrew Ng’s article on Harvard Business Review titled How to Choose Your First AI Project, which provides some practical guidelines for selecting and executing a pilot AI project that can create value and build up your company’s AI capabilities.

What are the steps of an AI project?

An AI project is a complex and iterative process that involves various steps and components. There is no one definitive structure for an AI project, but a general framework can be described as follows:

• Define the problem and the goal: This is the first and most important step of any AI project. It involves identifying the specific problem that the AI system will solve, the desired outcome, and the evaluation criteria. This step also requires understanding the domain, the stakeholders, and the ethical implications of the project. In this phase it is important select the most promising initiatives by running

  • A Technical Diligence by AI Experts to understand feasibility, required dataset, timeline and costs
  • A Business Diligence by Domain Experts to understand added value for the business

• Collect and prepare the data: This is the step where the AI system gets the raw material for learning and inference. It involves finding, acquiring, cleaning, labeling, and transforming the data that is relevant and representative of the problem and the goal. This step also requires ensuring the quality, diversity, and security of the data.
• Build and train the model: This is the step where the AI system learns from the data and generates predictions or outputs. It involves choosing, designing, implementing, and testing the appropriate AI technique (such as deep learning, natural language processing, computer vision, etc.) and the corresponding architecture, algorithm, and parameters. This step also requires optimizing the model performance, validating the results, and debugging the errors.
• Deploy and monitor the model: This is the step where the AI system is integrated into the real-world environment and delivers value to the end-users. It involves deploying the model to the target platform, ensuring its scalability, reliability, and security, and providing user interfaces and feedback mechanisms. This step also requires monitoring the model performance, updating the data and the model, and addressing any issues or changes.

For more information and examples on how to organize and execute an AI project, you can refer to the following web sources:

• How to Organize Deep Learning Projects – neptune.ai
• How to Set Your AI Project Up for Success – Harvard Business Review

A subset of AI projects is Data Science that aims to analyze data and extract insights or knowledge from it. A Data Science project can have different goals, such as exploring a phenomenon, predicting an outcome, recommending actions, or optimizing a system.

Therefore, a Data Science project structure is similar to AI projects and some of the key aspects are:

• A clear and concise problem statement that defines the scope, objectives, and expected outcomes of the project.
• A well-documented and reproducible data pipeline that covers the steps of data collection, cleaning, transformation, analysis, and visualization.
• A modular and maintainable code base that follows coding standards, uses version control, and includes unit tests and documentation.
• A trained and validated machine learning model (if applicable) that meets the performance criteria and can be deployed and monitored in production.
• A comprehensive and informative report or presentation that communicates the results, insights, and recommendations of the project to the stakeholders.

The Roles of an Artificial Intelligence Team

There are several roles that can be involved in an AI project, here you will find the most common and a description of their main activities:

• Software engineer: A software engineer is a programmer who designs, develops, tests, and maintains software applications. They use various programming languages, frameworks, and tools to create software products that meet the requirements and specifications of the clients or users. They also collaborate with other engineers, designers, and stakeholders to ensure the quality and functionality of the software. A software engineer may work on different aspects of software development, such as front-end, back-end, web, mobile, embedded, cloud, etc
• Machine learning engineer: A machine learning engineer is a specialized software engineer who applies machine learning (ML) techniques and algorithms to build, deploy, and optimize ML systems and models. They use data science, software engineering, and computer vision skills to create ML solutions that can learn from data and make predictions or recommendations. They also monitor and evaluate the performance and accuracy of the ML systems and models, and update them as needed. A machine learning engineer may work on various domains and applications of ML, such as natural language processing, computer vision, speech recognition, recommender systems, etc
• Machine learning researcher: A machine learning researcher is a scientist who conducts research and experiments on ML theory and methods. They use mathematical, statistical, and computational skills to develop new ML algorithms, models, and frameworks, or improve existing ones. They also publish their research findings in academic journals, conferences, and workshops, and contribute to the advancement of the ML field. A machine learning researcher may focus on specific areas or topics of ML, such as deep learning, reinforcement learning, generative models, etc.
• Applied machine learning scientist: An applied machine learning scientist is a hybrid role that combines the skills and responsibilities of a machine learning engineer and a machine learning researcher. They use both engineering and research skills to design, implement, and evaluate ML systems and models that solve real-world problems. They also conduct experiments and analysis to validate and improve the ML solutions, and communicate the results and insights to the stakeholders. An applied machine learning scientist may work on various types of ML problems, such as supervised, unsupervised, semi-supervised, or self-supervised learning.
• Data scientist: A data scientist is an analyst who uses data to generate insights and support decision making. They use statistical, analytical, and visualization skills to collect, process, and explore data, and to discover patterns, trends, and correlations. They also use ML skills to build and test predictive or prescriptive models that can answer questions or solve problems. They also communicate their findings and recommendations to the business and product leaders, and educate the organization on the use and value of data. A data scientist may work on various types of data, such as structured, unstructured, or streaming data.
• Data engineer: A data engineer is a developer who builds and maintains the data infrastructure and pipelines that enable data collection, storage, processing, and analysis. They use software engineering, database, and cloud skills to create scalable, reliable, and secure data systems and platforms. They also collaborate with data scientists, machine learning engineers, and other data users to ensure the quality, availability, and accessibility of the data. A data engineer may work on various types of data technologies, such as SQL, NoSQL, Hadoop, Spark, Kafka, etc.
• AI product manager: An AI product manager is a leader who oversees the development and delivery of AI products or features. They use product management, business, and AI skills to define the vision, strategy, and roadmap of the AI product, and to align it with the customer and market needs. They also work with the engineering, design, and research teams to plan, prioritize, and execute the AI product development, and to measure and evaluate the AI product performance and impact. An AI product manager may work on various types of AI products, such as chatbots, voice assistants, image recognition, etc.

Based on the maturity of your organization you may need all of these roles or just a few of them. If the company is executing pilot projects to gain momentum you may just need a Machine learning engineer or a Data Scientist adding Software and Data engineers in later stages where the AI Strategy is more clear.

Conclusion

Here is a possible SEO-optimized conclusion for the article:

Artificial intelligence is a fascinating and powerful technology that can transform the way we work, learn, and live. In this article, we have explored some of the innovative topics related to AI, such as:

• How AI can help managers improve their decision-making and leadership skills
• How AI can enhance the learning experience and outcomes for students and educators
• How AI can create new opportunities and challenges for businesses and society

I hope you have enjoyed reading this article and learned something new about AI. If you want to learn more about AI and how it can benefit you, we recommend you check out these resources:

• AI for Managers: A Practical Guide
• AI in Education: The Ultimate Guide
• AI and Business: Opportunities and Risks

Thank you for reading and feel free to leave your comments and questions below. I would love to hear your thoughts and feedback on AI and its applications.