Imagine a world where machines can diagnose diseases, personalize your shopping experience, or even drive you to work. Applied AI, unlike theoretical AI research, focuses on taking existing artificial intelligence concepts and putting them to practical use in real-world scenarios.
Whether you’re a business owner, a tech enthusiast, or simply curious about the future, understanding Applied AI is key to transforming your work, business, or lifestyle. In this article, we’ll break down the essentials – what applied AI is, its key characteristics, the difference between applied AI and general AI research, examples, and the Four Laws of AI.
What is Applied AI?
Applied AI is the field of artificial intelligence concerned with implementing AI solutions to solve specific problems and achieve tangible results. Applied AI uses well-established AI techniques like machine learning, natural language processing, and computer vision, and applies them to address challenges in various industries.
Key Characteristics of Applied AI
Applied AI specialists are domain experts who use high-quality data and artificial intelligence to solve problems with a focus on achieving practical results.
Focus on Outcomes
Applied AI prioritizes achieving practical results and measurable benefits. The goal is to create AI systems that can perform specific tasks efficiently and effectively.
Domain Expertise
Applied AI specialists often have a deep understanding of the particular industry or problem they’re trying to solve. This allows them to tailor the AI solution to the specific needs and constraints of the domain.
Data-Driven Approach
Applied AI relies heavily on data. The success of an applied AI project hinges on the quality, quantity, and relevance of the data used to train and refine the AI model.
Examples of Applied AI
- Fraud Detection: Banks use AI algorithms to analyze financial transactions and identify patterns indicative of fraudulent activity.
- Medical Diagnosis: AI systems can analyze medical images like X-rays and mammograms to aid doctors in early disease detection.
- Personalized Recommendations: E-commerce platforms leverage AI to recommend products to users based on their past purchases and browsing behavior.
- Self-driving Cars: Autonomous vehicles rely on a combination of AI techniques like computer vision and deep learning to navigate roads and perceive their surroundings.
Applied AI vs. General AI Research
While applied AI focuses on practical applications, general AI research strives to develop artificial intelligence that can mimic human intelligence in its entirety. General AI research is a theoretical field that explores fundamental questions about consciousness, learning, and reasoning in machines. The key difference between applied AI and general AI research lies in their goals and approaches. Here’s a breakdown:
Applied AI
- Focus: Practical applications and solving real-world problems.
- Approach: Data-driven and domain-specific.
- Goal: Achieve measurable results and improve efficiency.
General AI Research
- Focus: Understanding intelligence and creating human-like AI.
- Approach: Theoretical and broad exploration of AI concepts.
- Goal: Achieve Artificial General Intelligence (AGI) – machines that can think and learn like humans.
Two analogies to further clarify the difference
Imagine building a car.
- Applied AI: This would be like designing a car for a specific purpose, like a fuel-efficient commuter car or a rugged off-road vehicle. You’d use existing engineering principles and focus on the specific needs and constraints of the chosen purpose.
- General AI Research: This would be like trying to understand the fundamental principles of how a car works, how it interacts with its environment, and how it “learns” to navigate different terrains. The goal isn’t to build a specific car yet, but to gain a deeper understanding of what makes a car function well.
You could also look at it this way: Skynet vs. the Terminator.
Prior to becoming self-aware and genocidal, Skynet, the highly advanced computer system of Terminator movie franchise fame, is a decent example of general AI research. Skynet was initially intended to coordinate unmanned military hardware for the US government. After achieving AGI (Artificial General Intelligence), Skynet developed T-units, also known as terminators, to solve specific problems, namely, exterminating humanity and John Connor. That’s applied AI.
The Four Laws of AI
The previous example of Skynet and the Terminator is meant to be entertaining and shouldn’t concern anyone. The Four Laws of AI were developed to prevent any such scenario and should be integrated into each AI system, from pre-training to inference. The laws include:
- Avoid harming humans
- Be helpful, honest, and accurate
- Be transparent and protect privacy
- Stay secure and functional
Terminator on hold
The world of science fiction is rife with intelligent machines, from the cute and helpful ‘bots of Star Wars to the nigh unstoppable Terminators. While achieving human-level AI (Artificial General Intelligence) remains a theoretical goal, applied AI is here today, transforming businesses and our daily lives.
By harnessing the power of existing AI techniques, applied AI offers practical solutions to real-world problems, making our lives easier, safer, and more efficient, without all the danger and drama. As a leader in applied AI, Stefanini provides safe, effective, and reliable AI tools for organizations looking to integrate LLMs into their workflows and enhance employee productivity.
