What is artificial intelligence and when did it all start?

Artificial intelligence (AI) refers to the simulation of human intelligence in machines, enabling them to perform tasks that typically require human intelligence, such as learning, problem-solving, and decision-making. AI encompasses various technologies, including machine learning and deep learning, which allow systems to learn from data and improve over time. Its goal is to mimic cognitive functions and augment human capabilities, impacting almost all branches of industry.

The era of AI began in the 1940s with the programmable computer, laying the groundwork for machines to process information. Alan Turing, a key figure, proposed a test for machine intelligence in 1950 (the Turing Test).

The term “artificial intelligence” itself was coined in 1956 by John McCarthy, who also spearheaded the development of an early AI programming language. Early efforts focused on symbolic AI, using logical rules to mimic human reasoning. The 1960s saw a rise in funding and complex systems when researchers like Marvin Minsky and John McCarthy made pioneering contributions. The 1970s saw the development of expert systems, which applied knowledge from specialists to solve specific problems. However, progress slowed in what was dubbed the “AI winter” due to funding constraints and unmet expectations. The field regained momentum in the 1980s with advancements in machine learning (including backpropagation algorithm and neural networks), inspired by models of the human brain. This approach uses algorithms that can learn and improve without explicit programming.

The 1990s saw major milestones like Deep Blue, the IBM chess computer that defeated world champion Garry Kasparov in 1997. Deep Blue’s victory showed that AI systems could rival and even surpass human experts in complex games like chess, raising awareness about the capabilities and potential of AI technologies. The late 2000s brought about breakthroughs in natural language processing (NLP) with systems like IBM Watson which showed potential in natural language understanding and question answering. A significant resurgence of interest and progress in deep learning techniques occurred during the last decade and created great impact across various domains. Deep learning has enabled the development of new applications and technologies in computer vision. DeepMind AlphaGo defeated the human world champion in Go in 2016 demonstrating the power of AI in mastering complex games. Remarkable progress occurred in NLP where models like Google’s BERT achieved state-of-the-art performance in various NLP tasks.

Why is artificial intelligence used at all?

The amount of information we create is exploding. AI excels at handling massive datasets, uncovering patterns and trends that would overwhelm human analysts.

AI is used extensively today for a variety of reasons. First, AI enables the automation of tasks that traditionally require human intervention, leading to increased efficiency and reduced costs across industries. This includes manufacturing processes, customer service, administrative tasks, etc. Furthermore, AI algorithms can analyze vast amounts of data quickly and identify patterns that humans might miss. This is particularly valuable in fields like finance, healthcare, and marketing for making data-driven decisions.

AI powers recommendation systems that personalize user experiences based on preferences and behavior. This is seen in streaming platforms, e-commerce websites, and social media. NLP allows machines to understand and generate human language, facilitating chatbots, virtual assistants (like Siri and Alexa), and language translation services.

AI is also crucial for developing autonomous vehicles, drones, and robots that can navigate and make decisions independently. Furthermore, AI is revolutionizing healthcare with applications in medical imaging, diagnostics, drug discovery, and personalized treatment plans. AI-powered systems can also detect anomalies and patterns indicative of fraud or cybersecurity threats, enhancing overall security measures. In addition to these most important reasons, there are many other reasons for using AI.

Overall, AI is used to improve efficiency, decision-making, and innovation across various sectors, ultimately aiming to augment human capabilities and create new opportunities for growth and development. Some tasks are simply too complex or repetitive for us. AI can tirelessly analyze a lot of data or optimize factory production, freeing people for more creative or strategic work. However, ethical considerations, such as bias in algorithms and privacy concerns, must be carefully addressed as AI adoption continues to expand.

Examples of application of artificial intelligence in our lives

Examples of AI in medicine

AI is transforming medicine with innovative applications that improve diagnosis, treatment, and patient care. Here are concrete examples of AI’s impact on healthcare:

Medical Imaging: AI algorithms analyze medical images such as X-rays, CT scans, and MRIs to assist radiologists in detecting abnormalities. For instance, AI-powered systems can identify early signs of diseases like cancer or stroke, leading to faster and more accurate diagnoses.

Robot Surgeons with Steady Hands:  AI-powered robotic surgical systems are becoming increasingly sophisticated. They assist surgeons with precise movements and minimally invasive procedures, leading to faster recovery times for patients.

Drug Discovery: AI is accelerating drug discovery by predicting the efficacy and potential side effects of new compounds. Machine learning models analyze large datasets to identify promising drug candidates, reducing the time and cost associated with traditional drug development.

Personalized Treatment Plans: AI can analyze a patient’s medical history, genetics, and lifestyle to create a personalized treatment plan. This can lead to more targeted and effective therapies, with reduced side effects.

Virtual Health Assistants: Virtual health assistants like chatbots provide patients with instant access to healthcare information and assistance. They can answer common medical questions, schedule appointments, and provide medication reminders, improving patient engagement and reducing the administrative burden on healthcare providers.

Remote Monitoring and Predictive Analytics: AI monitors patient data collected from wearables and other devices to detect early signs of deterioration or disease progression. This enables proactive interventions and reduces hospital readmissions.

Mental Health Support:  AI chatbots can offer initial screenings and basic mental health support. They can also analyze language patterns in conversations to identify signs of depression or anxiety, helping people connect with the care they need.

Epidemiology and Public Health: AI analyzes population-level data to track disease outbreaks, predict public health trends, and optimize resource allocation during health crises.

Examples of AI in autonomous driving

In most cases, a traffic accident occurs due to the driver’s mistake, most often due to disobeying traffic rules and traffic signals, driver inattention, misjudgment, fatigue, consumption of opiates, and the like. This is precisely why many Advanced Driver-Assistance Systems (ADAS) are being developed to help the driver while driving with the aim of increasing the safety of all road users. As an increasing number of ADASs are developed, so does the level of vehicle autonomy, and the ultimate goal of the automotive industry is to develop a fully autonomous vehicle in which there would be no need for a driver.

On the road to self-driving cars, AI plays a critical role in navigating the complexities of our streets. Here are concrete examples of AI applications in autonomous driving:

Perception Systems: AI-powered perception systems use cameras, LiDAR (Light Detection and Ranging), and radar sensors to detect and classify objects in the vehicle’s surroundings. Machine learning algorithms enable accurate identification of pedestrians, vehicles, cyclists, and road signs, crucial for safe navigation.

Decision-Making Algorithms: AI algorithms process real-time data to make driving decisions such as steering, acceleration, and braking. These algorithms analyze complex scenarios, predict potential hazards, and determine optimal driving maneuvers based on safety and efficiency considerations.

Simultaneous Localization and Mapping (SLAM): AI-driven SLAM technology creates detailed maps of the environment while simultaneously tracking the vehicle’s position within these maps. This enables autonomous vehicles to navigate unfamiliar routes and adapt to dynamic road conditions.

Driver Monitoring Systems: AI-based systems monitor driver behavior and attention levels using cameras and sensors. These systems can detect signs of fatigue, distraction, or impairment, ensuring the safe operation of autonomous vehicles.

Natural Language Interaction: AI enables natural language interaction between passengers and autonomous vehicles. Voice-controlled interfaces allow passengers to request routes, adjust settings, or access entertainment options hands-free.

Examples of AI in the banking sector

AI is revolutionizing the banking sector with numerous applications that enhance efficiency, security, and customer experience. Here are some concrete examples:

Fraud Detection: Banks use AI algorithms to analyze vast amounts of transaction data in real time, identifying unusual patterns that could indicate fraud. Machine learning models learn from historical data to detect emerging fraud trends more accurately than traditional methods.

Credit Scoring and Risk Assessment: AI can streamline the loan application process. By analyzing your financial data and creditworthiness, AI can provide loan approvals faster and even personalize loan options based on your specific needs.

Chatbots and Virtual Assistants: Gone are the days of limited banking hours. AI-powered chatbots can answer your questions 24/7, handle basic transactions, and even provide financial advice – all at your convenience, anytime and anywhere.

Personalized Banking: AI algorithms analyze customer data to offer personalized product recommendations, such as tailored investment plans or loan options, based on individual financial profiles.

Algorithmic Trading: Banks use AI for high-frequency trading, employing complex algorithms to make split-second trading decisions based on market conditions and historical data.

These AI applications optimize operations, mitigate risks, and enhance customer interactions, driving the banking sector towards greater efficiency and competitiveness.

Examples of AI in the agricultural sector

AI is sprouting in the agricultural field, helping farmers optimize yields and resources:

Precision Farming: AI-powered technologies like drones equipped with multispectral cameras collect and analyze data to monitor crop health. This data helps farmers identify areas needing irrigation, fertilizer, or pest control, allowing for targeted interventions and resource optimization.

Smart irrigation:  AI-powered irrigation systems monitor soil moisture levels and weather forecasts. They can then adjust watering schedules automatically, ensuring crops receive the exact amount of water they need, reducing water waste, and optimizing growth.

Robot Farmers:  AI-powered robots are taking on tedious tasks like harvesting fruits and vegetables. These tireless workers can operate 24/7, improving efficiency and reducing reliance on manual labor.

Livestock Monitoring: AI-powered sensors track animal health metrics like activity levels and feeding patterns. This data helps farmers identify signs of illness early and optimize breeding practices for better livestock management.

Supply Chain Optimization: AI optimizes supply chain logistics by analyzing data on weather forecasts, market demand, and transportation routes. This ensures the efficient distribution of agricultural products, reducing waste and costs.

These AI applications demonstrate how technology is revolutionizing agriculture, making farming more efficient, sustainable, and resilient to environmental challenges.

Examples of AI in the recommendation systems

AI is the secret sauce behind those “recommended for you” features you see everywhere online. Here’s how it personalizes your shopping experience:

Buying recommendations: Companies like Amazon and Netflix use AI to analyze customer behavior and preferences. AI algorithms process past purchase history, browsing patterns, and demographic data to suggest products or movies that match individual tastes, improving user engagement and sales.

Music and Video Streaming Services: Platforms such as Spotify and YouTube leverage AI to recommend songs and videos based on listening/viewing history and user feedback. AI models understand user preferences, suggesting new content and creating personalized playlists.

Social Media Platforms: AI algorithms on platforms like Facebook and Instagram analyze user interactions, content preferences, and social connections to recommend relevant posts, groups, or accounts to follow, enhancing user experience and retention.

News and Content Aggregators: News aggregators use AI to analyze the articles you read and the topics you share.  They then recommend similar stories, keeping you informed about the subjects you care about most – or introducing you to new perspectives. Think about apps like Flipboard and Google News…

Online Advertising: AI enhances ad targeting by analyzing user data to deliver personalized advertisements. Platforms like Google Ads and Facebook Ads use AI to optimize campaigns, showing relevant ads to users most likely to convert.

Travel and Accommodation Booking: AI-powered travel platforms like Expedia and Airbnb recommend destinations, accommodations, and experiences based on past bookings and user preferences, facilitating personalized trip planning.

These AI-driven recommendation systems improve user satisfaction, increase engagement, and drive business revenue by delivering targeted content and products tailored to individual preferences and behaviors.

As it was said at the beginning of this text, AI is increasingly penetrating every pore of the industry. We have singled out only a few of them here, aware of the fact that we have only touched a drop in the ocean.

Follow our blog in the future, where we will analyze and explain the specific applications of AI in various fields and bring you new interesting things.

Author
Prof. Mario Vranješ, PhD

Text and/or images are partially generated by artificial intelligence