Artificial intelligence technology has many use cases in our everyday lives. Some examples include:

• Self-driving cars: AI powers the autonomous driving systems in vehicles, allowing them to perceive their environment, make decisions, and navigate safely.

• Healthcare diagnosis: AI assists medical professionals in diagnosing diseases and medical conditions by analyzing patient data and medical images.

• Virtual personal assistants: Voice-activated AI like Siri and Alexa help with tasks such as setting reminders, answering questions, and controlling smart devices.

• Recommendation systems: AI algorithms in platforms like Netflix and Amazon recommend products and content based on user preferences.

• Fraud detection: AI is used in financial institutions to identify and prevent fraudulent transactions in real-time.

• Language translation: AI-driven translation services like Google Translate enable seamless communication across languages.

• Customer service chatbots: AI-powered chatbots provide instant responses to customer inquiries on websites and apps.

Being highly technical, complex tech fields, those looking to enter the data and AI industry are recommended to pursue formal training in either area. You’re more likely to gain employer attention with recognised certifications under your belt, along with a strong portfolio of your previous experience.

Those in data science are often encouraged to specialise. Though your skills are applicable to any field, employers will typically seek out industry-specific experience, making it critical to hone your skills in your preferred sector. As mentioned, having at least a basic concept of machine learning and AI can help boost your employment value in the field.

Those in AI are required to build their knowledge on computer science, deep learning, neural networks, and machine learning to start exploring entry-level opportunities in the job or internship market. Of course, a fundamental understanding of big data and data analytics can help broaden your horizons.

On top of formal education, aspiring data and AI experts are also encouraged to expand their skills through personal practice. Plenty of online communities exist to help you learn from and collaborate with other like-minded professionals, these including Stack Overflow, Kaggle, and Dataquest for those in data science; and GitHub, Global AI Community, and r/machine learning (Reddit) for those into AI.

The types of AI depend on functionalities and capabilities:

Capabilities:

• Narrow AI: Narrow AI, also known as weak AI, is a type of AI wherein learning algorithms only perform a single task. Any knowledge gained from completing that task will not be automatically applied to others. Examples of narrow AI include speech recognition, face detection, and chess playing.

• General AI: Also known as artificial general intelligence (AGI) or strong AI, General AI is a type of AI that allows machines to learn, comprehend, and perform tasks humans. General AI mimics the human brain to solve complex problems.

• Super AI: This a hypothetical type of AI that can surpass human intelligence and ability in every aspect. In theory, super AI would be able to create, understand, and learn things that humans cannot.

Functionalities:

• Theory of mind: In AI, theory of mind is the capability to attribute to others mental states like desires, intentions, and beliefs. Often using neural networks, theory of mind enables AI to predict and understand human behavior depending on inferred mental states, which is a critical aspect for interactions between humans and AI, and social understanding.

• Self-awareness: In AI, self-awareness is a theoretical term that involves systems that possess a level of consciousness or self-recognition. This allows the systems to perceive and understand their own existence and possibly adapt their behavior depending on their self-assessment.

• Reactive machines: These refer to AI systems that operate based on pre-defined rules and responses. These systems don't have the ability to learn from past experiences, and their actions are only determined by specific, programmed conditions.

• Limited theory: Limited theory involves understanding a specific problem or domain area within the broader AI field. It involves developing theories and models designed to address particular issues without going for a comprehensive understanding of all AI aspects.

Data and AI professionals require high levels of both technical and non-technical skills.

Those in either field need a strong grasp of various programming languages, including (but not limited to) Python, SQL, Java, R, and C++. A strong knowledge of analytical tools is also necessary for data scientists, these including software such as SAS, Hadoop, Pig, Hive, and Spark. Artificial intelligence experts are expected to have an advanced knowledge of machine learning and deep learning, along with a close familiarity of programming frameworks such as TensorFlow, NumPy, and SciPy. Plenty of these technical skills may overlap between fields, helping you flexibly find work within either sector – though having experience in both areas can help bolster your opportunities on the job market.

Both professions require expertise in mathematics (i.e. algebra, statistics, probability), and excellent skills in communication, business planning, analytical thinking.