What is artificial intelligence (AI)?
“Artificial intelligence as any task performed by a program or a machine that, if a human carried out the same activity, we would say the human had to apply intelligence to accomplish the task”
Minsky and McCarthy (1950)
AI is a combination of software technologies designed to replicate the brain’s decision-making functions. The human brain is made up of neural networks and AI is based on artificial neural networks that are designed to recognise speech and objects and independently generate actions.
The objective of AI is to create artificial neural networks that automatically build graphs that represent the software interpretation of human memory resulting in an AI program that learns from and educates itself.
History of AI Patents
Artificial intelligence (AI) emerged in the 1950s, with the first mention of the term coming during the Dartmouth Summer Research Project on Artificial Intelligence in 1956. Since that time innovators and researchers have published over 1.6 million AI-related scientific publications and filed patent applications for nearly 340,000 AI-related inventions.
(Source: World Intellectual Property Organisation 2019)
Characteristics of AI programs or machines
- Problem solving
- Knowledge representation
Examples of AI in practice
- Google maps – Using anonymized location data from smartphone, that can analyse the speed of movement of traffic at any given time.
- Uber and Lyft – ridesharing apps.
- Siri – uses machine-learning technology to learn, predict and understand natural-language questions and requests.
- Alexa – recognises speech, understands commands, answers questions and facilitates smart homes.
- Tesla – self-driving vehicles with predictive capabilities.
- com – predictive purchasing and delivery planning.
- Netflix – predictive viewer preferences.
What are some of the areas AI is used for?
The use of AI is rapidly spreading across many sectors including, but not limited to:
- Marketing – Product and content recommendations, Visual search & image recognition, Search engines, Social monitoring & analysis, reactive product pricing, chatbots, audience targeting.
- Vision – Augmented reality.
- Image Processing – facial recognition, environment mapping, and human motion detection.
- Aviation – combat and training simulators, mission management aids, support systems for tactical decision making. AI is also used to help designers in the process of creating conceptual designs of aircraft.
- Computer science – solving difficult problems in computer science including the development of neural net topologies.
- Education – student tutoring, creating lessons, problems, and games to tailor to the specific student’s needs, and providing feedback.
- Finance – Algorithmic micro trading of shares on stock markets. Market analysis and data mining to assist with investment practices. Personal finance using AI to assist people with their personal finances. Portfolio management, Robo-advisors provide financial advice and portfolio management with minimal human intervention. Underwriting using machine learning algorithms to develop credit risk models that predict a consumer’s likelihood of default.
- Agriculture – Crop and soil monitoring uses new algorithms and data collected on the field to manage and track the health of crops and to predict the time it takes for a crop to ripen and ready for picking.
- Natural Language Processing – automatic language translation, speech recognition, sentiment analysis, handwriting recognition and question answering.
- Engineering – Automation of many low-level engineering tasks.
- Legal – Assisting with due diligence and research, providing additional insights and through analytics, case preparation.
Commercial platforms useful in building an AI application
The following available apps are often used by developers in creating new AI applications:
The Wit.ai platform features special mechanisms that transform user voice requests into text. Wit.ai is used by developers that work with iOS, Android, Node.js, Raspberry Pi, Ruby, Python, C, Rust, and the Windows Phone.
An open source platform using Python programming language. Melissa features imbedded voice recognition mechanisms, suitable for the development of voice AI applications.
Clarifai processes data received through cameras built in user devices to identify images received from external sources. Clarifai is used by developers working with Python, Java, and Node.js.
TensorFlow is a library created by Google with an open source code and applies pre-installed databases, as well as unique user interaction experiences.
Programming languages used to code an AI application
Developing AI software requires a knowledge of certain programming languages.
How to get started with AI?
The easiest way to experiment with AI-related services is via the cloud as all of the major tech firms offer AI services, from the infrastructure to build and train your own machine-learning models through to web services that allow you to access AI-powered tools such as speech, language, vision and sentiment recognition on demand.
AI applications are on the increase and offer many opportunities for developers. AI requires an understanding of the technical complexities, development skills and a knowledge of different platforms and languages.