The main applications of AI and ML relate to predictive intelligence and decision support. For every application, the power comes not from the machines, but from the decision makers who are behind the machines, guiding their reaction to the predictions.
A scientist from the Max Planck Institute summarizes the main issue very well: “AI will change medicine. This will change the search. This will change bioengineering. It will change everything”.
And for Jack Solow, the message is even clearer “in 2011, software was eating up the world; in 2022, AI is eating software. “Any company without a viable AI strategy will be marginalized by the end of this decade.”
The challenges of artificial intelligence to follow
Artificial intelligence will take over many activities, such as searching the net, getting travel advice, and especially personal assistants and chatbots. With artificial intelligence in objects, we will no longer need to interact with them since they are able to become autonomous and learn to anticipate our intentions. More concretely, AI will free us from a number of unnecessary and time-consuming acts.
For Darpa, Artificial Intelligence is measured according to four capacities:
- That is, retrieving information from the external environment and the ability to infer things about the world via sounds, images, and other sensory input.
- i.e. self-improvement of basic functions
- i.e. autonomous adaptation to new situations and understanding of the context
- That is, making correct decisions with better answers based on available knowledge
We can summarize the stages of the deployment of artificial intelligence as follows;
- Step One – Craft Knowledge
The first wave of AI systems relies on craft knowledge. These systems, built by domain experts, contain rules that describe the basic processes and knowledge sets of specific domains.
- Step Two – Statistical Learning
Second wave AI systems are those built using machine learning techniques such as neural networks. These systems rely on statistical models that characterize a specific domain. They then feed Big Data algorithms by refining its ability to correctly predict the outcome.
- Step Three – Contextual Adaptation
The third wave of AI consists of systems capable of contextual adaptation. They are systems that construct explanatory models for classes of real-world phenomena. Third wave systems show an ability to understand what they are doing and why they are doing it.
Types of artificial intelligence can be grouped into five categories:
The 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 drugs or medical diagnosis.
The ability to set and achieve goals. For example: inventory management, demand forecasting, predictive maintenance, optimization of physical and digital networks, etc.
The 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 functionality of an AI model and the decisions it makes, there is a risk that the model will not be considered trustworthy or legitimate. XAI provides the understandability and transparency needed to enable greater confidence in AI-based solutions.
Neural networks operate on principles similar to those of human neural cells. It is a series of algorithms that capture the relationship between various underlying variables and store the data like a human brain does.
- natural language processing (NLP)
NLP is a science of reading, understanding, interpreting a language by a machine. Once a machine 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 include quantitative and therefore decisional characteristics.
Here are some examples of applications of intelligence that will be at the heart of the reinvention of business sectors:
Examples in the field of financial services
Artificial intelligence in banking is accelerating the digitization of end-to-end banking and finance processes. By implementing the power of data analytics, smart ML algorithms, and secure in-app integrations, AI applications optimize service quality and help businesses identify and combat fake transactions.
- Example of AI Chatbots
- Banking industry AI chatbots can assist customers 24/7 and provide accurate answers to their queries. These chatbots provide a personalized experience to users.
- Example of improving the customer experience
- Smart mobile apps using ML algorithms can monitor user behavior and derive valuable insights based on user search patterns. This information will help service providers provide personalized recommendations to end users.
- Example of automation and makes the process transparent
- AI applications can reduce the workload of bankers and optimize the quality of work.
- Example of data collection and analysis
- Banks can also make effective business decisions with insights from customer data and offer personalized service recommendations.
- Example of portfolio management
- Wealth and portfolio management can be done more powerfully with artificial intelligence.
- Example of risk management
- AI will help bankers identify the risks associated with granting loans.
- By using the AI-based risk assessment process, bankers can analyze borrower behavior and thereby reduce the possibility of fraudulent acts.
- Example of fraud detection
- Artificial intelligence banking applications detect risks and minimize fraudulent acts.
Examples in the field of city management
- Example of pollution control
- Predict pollution levels for the next few hours. This type of technology allows authorities to make decisions in advance to reduce their impact on the environment.
- Example of the management of parking systems
- Seat availability can be presented to waiting users, some more advanced technologies have the ability to recommend seats based on the car.
- Example of public transport management
- Enable transit riders to receive and access real-time dates and tracking, improving timing and customer satisfaction.
- Example of waste management
- Enable cities to track recycling and identify what can be recycled in the region.
- Example of traffic management
- Predict and reduce traffic, using deep learning algorithms, which can also reduce pollution created by traffic.
- Example of energy consumption monitoring
- Analyze and monitor the energy consumption of companies and citizens, with this data it can then be decided where to involve renewable energy sources.
- Example of environmental management
- Enable authorities and cities to make informed decisions that are best for the environment. Smart cities also use AI to detect CO2, which can then lead to transportation decisions.
The potential for driving sales with AI in stores is considerable:
- Intelligent product recognition and automated billing enable cashier-less stores
- AI interfaces such as chatbots and interactive screens support customer service
- Smart pricing helps manage demand and drive sales
- Predictive analytics helps in forecasting prices based on demand and seasonal trends
- Smart supply chain management and logistics improve product availability.
- Machine learning models automatically categorize and group products
- Virtual fitting rooms with smart mirrors support self-service at the highest level
- Predict customer behavior
- Improve the layout of the sales area based on the analysis of customer behavior
Examples in the field of health
Whether it’s using it to detect links between genetic codes, using surgical robots or even maximizing hospital efficiency.
For example :
- Clinical Decision Support
- Improving primary care with chatbots
- Robotic surgeries
- Virtual nursing assistants
- Accurate diagnosis aid
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Artificial Intelligence (AI) will be at the heart of all economic activities
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