The main applications of AI and machine learning relate to predictive intelligence and decision support. For each application, the power comes not from the machines, but from the decision makers behind the machines, directing their reaction to the predictions.
A scientist at the Max Planck Institute sums up the main issue well: “Artificial intelligence will change medicine. This will change research. This will change bioengineering. It will change everything.”
And for Jack Solow, the message is even more blunt “In 2011, software was eating the world; in 2022, AI is eating software.” Any company without a viable AI strategy will be sidelined by the end of this decade.
Artificial intelligence challenges to follow
Artificial intelligence will take over many activities, such as searching the net, getting travel advice, especially personal assistants and chatbots. With artificial intelligence in objects, we will no longer need to interact with them because they are able to become independent and learn to anticipate our intentions. Concretely, artificial intelligence will free us from a number of unnecessary and time-consuming work.
For Darpa, AI is measured according to four capabilities:
- that is, retrieving information from the external environment and being able to infer things about the world from sounds, images, and other sensory input.
- No self-improvement of basic functions
- i.e. independent adaptation to new situations and understanding of context
- That is, making correct decisions with better answers based on the available knowledge
We can summarize the stages of the spread of artificial intelligence as follows;
- The first step – the craft of knowledge
The first wave of AI systems relied on literal knowledge. Created by domain experts, these systems contain rules that describe the basic operations and knowledge sets of specific domains.
- The second step – statistical learning
The second wave of AI systems are those built using machine learning techniques such as neural networks. These systems are based on statistical models that characterize a specific domain. Then they feed the big data algorithms by improving their ability to correctly predict the outcome.
- The third step is contextual adaptation
The third wave of AI consists of systems that are able to adapt to context. They are systems that construct explanatory models of classes of real-world phenomena. Third wave systems demonstrate the ability to understand what they are doing and why they are doing it.
Types of AI can be categorized into five categories:
Ability to solve problems by logical deduction.
The ability to present knowledge to the world. For example: trading in financial markets, forecasting purchases, preventing fraud, creating medicines or medical diagnoses.
The ability to set goals and achieve them. For example: inventory management, demand forecasting, predictive maintenance, physical and digital network optimization, etc.
Ability to understand spoken and written language. For example: real-time translation of spoken and written languages, smart assistants, or voice control
Without explanations behind the internal function of the AI model and the decisions it makes, there is a risk that the model will not be considered trustworthy or legitimate. XAI provides the understanding and transparency needed to enable greater confidence in AI solutions.
Neural networks operate on similar principles to those found in human neurons. It is a series of algorithms that capture the relationship between different underlying variables and store the data as the human brain does.
- natural language processing (NLP)
NLP is the science of reading, understanding and interpreting language by machine. Once the device understands what the user intends to communicate, it responds accordingly.
Using computer vision means that the user inputs an image into the system and what they receive as output can involve quantitative characteristics and thus decision making.
Here are some examples of intelligence applications that will be at the heart of reinventing business sectors:
Examples in the field of financial services
Artificial intelligence in banking accelerates the digitization of end-to-end banking and financial processes. By implementing the power of data analytics, intelligent machine learning algorithms, and secure in-app integration, AI applications improve service quality and help businesses identify and combat fake transactions.
- An example of an AI chatbot
- AI chatbots for the banking industry can assist customers 24/7 and provide accurate answers to their queries. These chatbots provide a personalized experience for users.
- An example of improving customer experience
- Intelligent mobile apps that use ML algorithms can monitor user behavior and extract valuable insights based on user search patterns. This information will assist the Service Providers in making tailored recommendations to end users.
- An example of automation and makes the process transparent
- AI applications can reduce the workload of bankers and improve the quality of work.
- An example of data collection and analysis
- Banks can also make effective business decisions with insights from customer data and provide personalized service recommendations.
- Portfolio management example
- Wealth and portfolio management can be made more robust with artificial intelligence.
- An example of risk management
- Artificial intelligence will help bankers identify risks associated with granting loans.
- Using an AI-based risk assessment process, bankers can analyze borrower behavior and thus reduce the possibility of fraudulent actions.
- Example of fraud detection
- AI banking applications detect risks and reduce fraudulent actions.
Examples in the field of city management
- An example of pollution control
- Anticipate pollution levels over the next few hours. This type of technology allows authorities to make decisions in advance to reduce their impact on the environment.
- Example of managing parking systems
- Available seats can be offered to waiting users, some of the more advanced technologies have the ability to recommend seats based on the vehicle.
- An example of public transportation management
- Enabling transit riders to receive, track, and access real-time appointments, improving timeliness and customer satisfaction.
- An example of waste management
- Enable cities to track recycling and identify what can be recycled in the area.
- Traffic management example
- Predict and reduce traffic using deep learning algorithms, which can also reduce traffic pollution.
- An example of monitoring energy consumption
- Analyzing and monitoring the energy consumption of companies and citizens, using this data can then determine where renewable energy sources are involved.
- An example of environmental management
- Empowering authorities and cities to make informed decisions that are appropriate for the environment. Smart cities also use artificial intelligence to detect carbon dioxide, which can then inform transportation decisions.
The potential to increase sales with AI in stores is huge:
- Intelligent product recognition and automated billing enable stores without a cashier
- Artificial intelligence interfaces such as chatbots and interactive displays support customer service
- Smart pricing helps manage demand and increase sales
- Predictive analytics helps forecast prices based on demand and seasonal trends
- Intelligent supply chain management and logistics improve product availability.
- Machine learning models automatically classify and group products
- Virtual fitting rooms with smart mirrors support self-service at the highest level
- Anticipate customer behavior
- Improve sales territory planning based on customer behavior analysis
Examples in the field of health
Whether it’s using it to discover connections between genetic codes, using surgical robots or even increasing hospital efficiency.
- Clinical decision support
- Improving primary care with chatbots
- Robotic surgeries
- Virtual nursing assistants
- Accurate diagnostic assistance