A.I.: Demystifying the Types and Real-World Applications

Integrating cutting-edge technologies, new digital tools, or using tech for good is pivotal to gaining an edge in today’s business world. Among those technologies exists Artificial Intelligence (AI), a transformative force!

This post is about the four basic types of AI and their applications in different industries.

Let’s get to it!

The Different Types of Artificial Intelligence

Narrow or Weak A.I.

Definition: Narrow A.I. is designed for specific tasks and operates within a limited domain.

Examples:

1. Virtual Assistant “Smart Speakers” (Siri, Alexa): These A.I. systems excel at natural language processing and voice recognition. AKA: smart devices and virtual assistants.

General or Strong A.I.

Definition: General AI possesses human-like cognitive abilities and can perform a wide range of tasks at human levels of intelligence.

“True” General AI or Strong A.I. isn’t here yet. At least, to my knowledge, there aren’t products on the market that use it. When that happens, leave a comment if you find something! Nonetheless, Narrow A.I. applications generally can perform specific tasks or functions at levels surpassing human capabilities. These A.I. systems are designed for specialized tasks and excel because they process vast amounts of data and perform repetitive tasks with high precision and speed.

For instance, A.I. applications in fields like image recognition, natural language processing, and specific data analytics tasks have demonstrated the ability to outperform humans.

Machine Learning

Definition: Machine learning (ML) focuses on developing algorithms that allow computers to learn from and make predictions or more data-driven decisions.

Examples:

1. Healthcare Diagnosis: ML models analyze patient data to assist in detecting disease and predicting possible medical outcomes. For instance, Merative (previously known as IBM Watson) for Oncology uses machine learning to analyze large volumes of medical papers, clinical trial data, and patient records to provide oncologists with treatment recommendations for cancer patients. This helps health professionals make more informed decisions about personalized cancer treatments.
2. E-commerce Recommendations: Some online retailers, like Amazon, use ML to provide personalized product suggestions to customers.

Deep Learning

Definition: Deep learning employs neural networks with many layers to analyze and make decisions about complex data.

Examples:

1. Autonomous Vehicles: Deep learning technology is pivotal for object detection and recognition. Companies like NVIDIA develop deep neural networks that enable vehicles to identify and classify objects on the road, such as pedestrians, cyclists, and other vehicles. This technology enhances the ability to navigate complex traffic scenarios safely.
2. Speech Recognition: Deep learning is essential for automatic speech recognition systems in healthcare. For instance, deep learning algorithms convert spoken medical notes and patient records into text in medical transcription services, increasing efficiency and reducing the risk of errors. Companies like M*Modal use deep learning for accurate and timely medical transcription.

How A.I. Gets Used in Different Industries

Let’s examine how to apply these A.I. types across industries. Please note that while some sources directly support the examples, others provide broader insight into an industry’s use of A.I. technologies.

Automotive Industry

• Machine Learning: In this example, artificial intelligence helps with quality control and defect detection. Manufacturers like BMW employ computer vision systems powered by A.I. to inspect each car for imperfections during production. These A.I. systems can quickly identify and flag defects in paint, welding, or other critical components.
• Narrow A.I.: Is employed for advanced driver assistance systems (ADAS). Think adaptive cruise control and lane-keeping assistance. Various automakers, including Ford, use these AI-powered systems. For instance, Ford’s Co-Pilot360 technology includes features like adaptive cruise control with stop-and-go, which uses A.I. algorithms to maintain a safe following distance from the vehicle ahead and can even bring the car to a complete stop in traffic.

A.I. in Healthcare

• Machine Learning: Gets used in specific health facilities to help improve predictive analytics in the early detection of sepsis. Hospitals also use machine learning to continually monitor patient vital signs and laboratory results. Plus, these algorithms can detect subtle changes that may indicate the onset of sepsis, allowing for early intervention and potentially saving lives.
• Deep Learning: Deep learning is pivotal in medical image analysis, particularly radiology. For example, companies like Siemens Healthineers use deep learning algorithms to enhance the accuracy of medical image interpretation. These algorithms can detect subtle anomalies in X-rays, MRIs, and C.T. scans, aiding radiologists in diagnosing conditions such as fractures, tumors, and neurological disorders. This technology leads to more precise diagnoses and improved patient care.

Retail

• Machine Learning: The retail industry loves dynamic pricing optimization. For example, airlines like Delta (As of 8/30/24 this link is no longer accessible: https://news.delta.com/nothing-artificial-about-intelligence-deltas-industry-first-machine-learning-platform-minimizes) use ML to adjust ticket prices based on various factors, including seat availability, booking patterns, and weather conditions that might affect travel demand. This dynamic pricing strategy maximizes revenue while ensuring competitive pricing. At the same time, some say unchecked algorithmic pricing can lead to price-fixing and imply the need for stricter pricing regulations.
• Narrow A.I.: Chatbots as virtual shopping assistants are common in retail. Retailers such as Sephora have integrated chatbots into their digital platforms. Sephora’s chatbot, accessible via its mobile app, assists customers in selecting beauty products, providing personalized recommendations based on skin type, preferences, and prior purchases. Overall, this AI-driven virtual assistant enhances the shopping experience, making product selection more convenient and tailored to individual needs.

Finance

• Machine Learning: A good example is algorithmic trading. Investment firms like Citadel Securities use ML algorithms to analyze vast datasets of financial market information. These algorithms can also identify subtle market trends and patterns, enabling automated trading systems to execute high-frequency trades precisely and quickly. Overall, it enhances trading strategies and optimizes investment portfolios.
• Deep Learning: In finance, deep learning helps with fraud detection. For example, credit card companies like Visa leverage deep learning algorithms to detect real-time fraudulent transactions. These algorithms can also analyze numerous transaction parameters, including location, transaction history, and spending patterns.

Manufacturing

• Narrow A.I.: Another application of Narrow A.I. in manufacturing is supply chain management. AI-driven supply chain systems optimize inventory levels, shipping, and production schedules. For instance, companies like Toyota use A.I. to improve supply chain efficiency and reduce costs. Below is how they plan to do part of that in-house.
• Machine Learning: ML helps perform predictive maintenance. For example, aerospace companies like Boeing use machine learning algorithms to monitor the health of aircraft engines. These algorithms analyze sensor data, like temperature, pressure, and vibrations, to predict when maintenance is needed. By proactively addressing potential issues, maintenance may be able to be scheduled more efficiently to reduce downtime and help ensure safety and reliability.

Please note

First, this post was published before Boeing’s most recent lawsuit. I’m not going to focus on their abhorrent response. Instead, let’s reflect on the lost lives (passengers and whistleblowers).

Secondly, let’s acknowledge that A.I. isn’t the best option in every scenario and that #AIFOMO is a thing.

Thirdly, I’m also unsure if they used it in their 737s before the lawsuit, but they are now (as of 5/24/24).

Also, I am uncertain if they use it in Starliner. Here are some additional articles as of 6/24/24:

• A Korean Air Boeing plane dropped 25,000 feet in 5 minutes
• Feds Recommend Criminal Charges. . . Over Safety Issues
• Astronauts stranded in space due to multiple issues with Boeing’s Starliner — and the window for a return flight is closing.
  • Update: As of 8/13/24, both astronauts are still stranded. Some reports say they might be up there until February 2025.

Now that I’m off my Soapbox, below is a great video by Toyota (for a Narrow AI example)!

A.I. in Marketing

• Machine Learning: Can help make content for web strategies. For example, ML methods can assist with natural language processing tasks like generating text and optimizing blog content. The kicker, these A.I. tools, for the most part, don’t require complex algorithms. Instead, they are meant to aid people, not replace us. P.S. If you’re curious and want an example, here are my thoughts on blogging with an A.I. tool.
• Narrow A.I.: This type can be harnessed for content generation. Furthermore, content marketing platforms like Clearscope utilize A.I. to analyze vast amounts of online content related to specific topics. These algorithms can identify gaps in existing content, recommend better keywords, and generate content briefs for writers. This streamlines content creation and ensures that content is comprehensive, informative, and optimized for search engines.

The Takeaway: Businesses Should Embrace A.I. Opportunities

In its various forms, A.I. plays a crucial role in multiple industries. It enhances efficiency, customer experience, and decision-making.

Overall, embracing A.I. can give organizations a competitive advantage. We can navigate complexities like never before, overcome digital transformation challenges, and even use AI for social impact. AI’s role in business ops will become more prominent, offering endless possibilities for innovation, growth, and challenges. Make no mistake: its effect is profound, from autonomous vehicles and disease diagnosis to personalized shopping experiences in retail, which will no doubt impact management or upend entire industries.