Computer Vision is Not Generative AI, It’s Practical AI

Gmail finishes your sentences. Spotify learns the music you love. Alexa and Siri can answer the question: What’s the difference between generative AI and computer vision? (But don’t close out of this screen yet! We can tell you a few things Siri and Alexa won’t but you’ll have to keep reading to find out what.)

Gmail, Spotify, Alexa, and Siri all use artificial intelligence (AI) to help make your life a little easier and a little more enjoyable. These popular AI tools have been around for years, quietly adjusting to your preferences and habits behind the scenes. While a bit unsettling to see an ad for the drink you were just talking about appear in your social media feed, we’ve been living with AI for a while. 

So why, suddenly, is AI in nearly every news headline (and not just in the business and technology sections)? AI is going through a bit of a renaissance and is the not-so-new “It” technology. 

Much of the renewed interest in AI for businesses is thanks to the release of Open Source’s ChatGPT in November of 2022. Short for Generative Pre-trained Transformer, the GPT in ChatGPT introduced the general public not to AI but to a specific type of AI – generative AI. 

AI is a very broad term (like the universe). Generative AI is slightly less broad, but still very broad (like a galaxy). As with any broad, umbrella term, the meanings of both AI and generative AI have become somewhat convoluted. 

We’re here to help you understand the differences between AI, generative AI, and computer vision (in a more comprehensive, and slightly more entertaining manner than Alexa or Siri).

What is AI? 

AI is an incredibly broad term that encompasses all artificial intelligence (non-human intelligence). It refers to the development of computer systems that can perform tasks that typically require human intelligence, or as Demis Hassabis, Co-Founder and CEO of Deep Mind puts it, “AI is the science of making machines smart.” 

AI allows machines to understand natural language, recognize patterns, make decisions, and learn from experiences. AI technologies aim to simulate human-like thinking processes, enabling machines to process information, adapt to different situations, and improve their performance over time.

AI encompasses a wide range of techniques and approaches, such as machine learning and deep learning. Machine learning involves training algorithms on data to enable them to make predictions or decisions. Deep learning is a subset of machine learning that involves neural networks with interconnected layers, inspired by the structure of the human brain. These neural networks can recognize complex patterns in data, which is particularly useful for tasks like image and speech recognition.

Because it is such a broad field, AI has diverse applications across various industries, including manufacturing, retail, construction, healthcare, finance, transportation, and entertainment. AI allows computers to analyze large datasets, automate repetitive tasks, provide personalized recommendations, and even simulate human-like interactions through chatbots and virtual assistants.

Because the implications of artificial intelligence are so significant, the field, while not new, has been garnering serious attention. But all the attention has diluted and confused exactly what AI is and what it can do. To better understand the role of AI in business, it’s important to understand the different types of AI and their functions. 

Types of AI

Teaching machines to think and react like humans is complex and as such, is generally organized into three categories: Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), and Artificial Superintelligence (ASI).

These categories define what the AI can do. ANI means the machine can only perform a narrowly defined set of tasks. AGI includes AI capable of “thinking” as humans do. It can perform the same general tasks as humans. ASI is capable of performing tasks beyond what a human could do. 

To get more granular, AI is divided into four main types: reactive machines, limited memory, theory of mind, and self-aware. 

• Reactive Machines – The most commonly cited example of a reactive machine in AI is IBM’s Deep Blue which famously beat chess champion Garry Kasparov in a highly publicized game in 1997. Deep Blue is reactive because it operates based on predefined rules and strategies – though it can perform complex algorithms to predict outcomes far more quickly than a human. It can only react or predict what to do next based on past experiences. It cannot learn or improve based on new experiences.

• Limited Memory – As the name suggests, limited memory AI allows machines to build on past and present data. Essentially, it improves and gets smarter as it receives more data. Limited memory AI systems have some capacity to retain information from previous interactions and use that information to inform their responses. These systems are a step beyond reactive machines in terms of their ability to adapt and provide contextually relevant answers. Thanks to advances in deep learning, limited memory AI has become more advanced. Self-driving cars, with the ability to recognize and processes other vehicles, people, traffic lights, and other obstructions in the road, are an example of limited memory AI. 

• Theory of Mind – Theory of mind refers to empowering machines with the ability to understand the more emotional components of human behavior. Theory of mind AI would allow machines to understand intentions and predict behavior but this type of highly advanced AI is not yet possible.

• Self-aware – Like theory of mind, self-aware AI does not yet exist. It would give machines a sense of self and their state of being. Theoretically, a machine could develop personal preferences, favorites, and sense how they make others feel.

Different types of AI - Reactive Machines, Limited Memory, Theory of Mind, Self-Aware

Benefits of AI:

The explosion of AI advancements has afforded innovative businesses significant benefits. AI can be leveraged across industries and job functions to: 

• Drive costs down and revenue up
• Provide greater personalizations to customers 
• Better predict customer behavior
• Get actionable insights
• Reduce time on repetitive tasks

But AI is not a one-size-fits-all miracle cure. Companies need to move beyond simply investing in AI technology and really become educated in what type of AI will yield the most benefit to their bottom line. What might work well on a factory floor may not be very useful in an office building. 

What is Generative AI?

Generative AI is another umbrella term that refers to any AI system that creates content (audio, visual, text, etc.). It includes large language models, image generation, code generation, and audio generation.  

Like other AI systems, generative AI analyzes data to make predictions but goes a step further to create new data based on the data it’s been trained on. For example, ChatGPT is trained on massive amounts of data, this helps the algorithm create new data based on its training data. While powerful, it lacks true understanding, relying on learned patterns. ChatGPT is reactive, with no memory of past interactions.

Similarly, image-generation AI systems learn from large datasets to generate new images, which can be artistic, realistic, or even innovative. However, like other generative AI models, they are limited by their training data.

While creating something new from nothing may seem like magic, generative AI is not a magic wand. It uses complex algorithms to make predictions based on the data it’s been trained on.

Generative AI is ideal for creating new content, like social media posts, articles, and images. It can create voice and audio recordings like when talking to Siri or Alexa. It can even create code to help with website development. In this way, generative AI can help companies save time, boost creativity, and free staff from mundane tasks.  

However, it’s important to understand generative AI does have some limitations. It is limited by training data and might generate inaccurate, unexpected, or incoherent responses. It does not have any access to real-time data. 

Why is generative AI creating such a buzz? 

Anyone who’s played around with generative AI tools knows the results can be impressive. Have you seen the Pope in a leather jacket on a motorcycle? Or created a 2,000-word article in seconds? 

If it seems like generative AI suddenly and dramatically exploded onto the scene in November 2022, it did. Literally overnight, generative AI became accessible to the public, didn’t require any special training, was useful, and cost-effective. Not surprisingly, it was quickly and widely accepted. 

Since then, businesses have seen immediate results especially when it comes to creating content, supporting interactions with customers, and drafting computer code based on natural-language prompts. 

Because of the widespread popularity of generative AI tools, businesses have been scrambling to adopt and leverage the power of this technology. As more and more businesses use generative AI, the risks and limitations are becoming more pronounced – mainly around privacy concerns. 

Although some organizations are looking to establish their own large language models to better control data privacy, this is neither a quick nor easy solution.  Other organizations are eager to establish AI solutions but it’s becoming harder to navigate the frenzied landscape to understand the solutions that will most effectively address their pain points. Just like the once buzzworthy terms “cloud” or “IoT,” the meaning of generative AI is becoming blurred. 

Businesses looking for real-time data and visual insights into their business operations need a different kind of AI: computer vision.

What is Computer Vision? 

Computer vision is a field of artificial intelligence and computer science that focuses on enabling computers to interpret and understand visual information from the physical world, just as humans do. 

Computer vision detects people, objects, and events in real-time leveraging existing cameras. This kind of immediate feedback allows businesses to address issues as they happen to improve productivity and safety.  In this way, computer vision provides practical AI solutions to businesses looking for greater visual insights into processes. 

Unlike generative AI, computer vision does not create any new information but rather highlights opportunities for process and operational improvements in existing areas of business. Because computer vision acts like an extra set of eyes, it is ideal for any business needing real-time data – making it applicable to nearly any industry. 

Computer Vision Use Cases

Computer vision's applications are diverse and continue to expand as technology advances. Its ability to interpret visual data in real-time has shown significant value in improving processes, enhancing decision-making, and increasing efficiency across industries. This kind of practical AI is transforming how enterprises do business. 

Retail

As retailers continue to juggle online and brick-and-mortar sales in a very competitive market, they are looking for every advantage to stand out. Computer vision can help.

Occupancy counting:

Retailers don’t have an accurate, reliable way to count customers in their stores. Manual counting or relying on motion sensor cameras is tedious and prone to errors leading to missed revenue opportunities and negatively impacting customer experience. 

Computer vision automates accurate occupancy counts in real-time to create a better customer experience. Because it can distinguish between customers, employees, and delivery personnel, managers can better allocate staff and attend to shoppers promptly.

Speed of Service:

Customers do not like waiting in line. In fact, 86% of retail customers have left a store due to long wait times resulting in billions of dollars of lost sales due to line abandoners.

Computer vision allows retailers to track and monitor patrons in lines at checkout – in real-time – and send immediate alerts when wait times pass a certain threshold or if lines get too long.

Foot traffic analytics:

Foot traffic metrics are undeniably important, but most retailers lack real-time data about where customers go in-store and which products and services they’re engaged with. 

Computer vision can be used to track foot traffic, discover hot spots, and monitor pick-ups and put-backs to see what products customers engage with, better manage product placement and optimize staffing decisions – in real-time.

Frictionless-Checkout:

As labor shortages persist, optimizing staff and automating tasks is paramount. With computer vision, retailers can eliminate the need for staff at checkouts with accurate and quick frictionless checkouts. 

Computer vision enables self-checkout systems by identifying items placed on the conveyor belt, without relying on barcode scanning, and automates the payment process so customers can get in and out hassle-free.

Computer vision detecting people inside a store.

Manufacturing

Like retail, manufacturing is another industry seeing tremendous value in computer vision. Some of the most notable use cases include:

Anomaly and Defect Detection:

Minimize delays and reallocate staff to other manufacturing functions with computer vision. Catch issues as they arise and get immediate alerts to avoid slowdowns and increase ROI.

Package and Label Detection:

Relying on manual labor for label and package detection can lead to costly errors and delays due to labor shortages. 

Computer vision can automate package and label detection with unbeatable accuracy allowing manufacturers to reduce costs, better allocate staff, and increase efficiencies throughout their entire production line.

Safety Monitoring:

Manufacturing jobs have the highest rate of workplace injuries resulting in billions of dollars of lost revenue. Complex machinery with moving parts, sharp edges, and hot surfaces makes them inherently dangerous.

Computer vision can improve workplace safety by monitoring hazardous areas, tracking personal protective equipment compliance, and better managing machine usage in real-time. With immediate alerts about safety issues, factory floor supervisors can address issues before or as they occur.

Volumetric Space Detection for Distribution and Trucking:

Inefficient shipping and delivery processes cost trucking companies over $27.5 billion per year. Empty or partially full trucks result in avoidable sunken costs.

But with real-time data, computer vision can accurately determine truck capacity and usage customized for unique business needs. Get immediate alerts about unused space to better manage materials delivery capacity and boost ROI.

Restaurants

Like retail, the restaurant industry (particularly fast casual (FCR) and quick service restaurants (QSR)) have undergone significant changes since the pandemic. Ordering has expanded to in-person, pick-up, restaurant delivery, or third-party delivery. As a result, operators are looking for technology to improve speed of service and customer satisfaction. Computer vision can help. 

Behind-the-counter operations:

QSRs and FCRs lack specific insights into how long it takes for orders to be fulfilled – especially with custom orders. The current data does not allow operators to sufficiently address choke points.

Because computer vision captures real-time data about order fulfillment, operators can better understand where choke points exist in the line and exactly how long fulfillment takes. Managers can be sent immediate alerts about choke points in the food line to improve speed of service and customer satisfaction.

Speed of Service:

The majority of orders at QSRs and FCRs. are now placed online for off-premise dining, making speed of service increasingly important and more complex.

With computer vision, operators can track and monitor orders from the moment the order is placed to the time it is completed.

Occupancy counting:

While many restaurant managers can reference historical data about peak times, this information can be unreliable and lead to inefficient staffing decisions.

Computer vision allows restaurant operators to automate customer counting to get accurate, real-time data. Managers could then track exactly how many customers are in the store and how long they’ve been waiting for food. Better allocate staff and improve customer experience.

Drive-thru vehicle ID, counting, and analysis:

With 70% of revenue coming from drive-thru service, it’s critical for QSRs to fulfill orders quickly, correctly, and safely. Computer vision can improve these processes with real-time vehicle ID and tracking to optimize drive-thru speed of service, traffic patterns (such as line abandonment), and order fulfillment. 

Computer vision can also send immediate alerts to managers when drive-thru lines get too long to reduce wait times and further enhance customer experience.

Industrial

Robotics and Automation:

Computer vision guides robots in complex tasks, like picking and placing items, assembly, and navigation.

Safety Enhancement:

Monitoring work environments using computer vision helps ensure compliance with safety regulations and prevent accidents.

Energy Efficiency:

Analyzing energy consumption patterns in industrial facilities can help identify opportunities for energy savings.

alwaysAI computer vision can detect hazardous spill in real-time.

Automotive

Autonomous Vehicles:

Computer vision plays a critical role in self-driving cars by interpreting the vehicle's surroundings and making real-time driving decisions.

Driver Monitoring:

Monitoring driver behavior and attentiveness to enhance safety and prevent accidents.

Agriculture

Crop Monitoring:

Using drones and cameras to monitor crop health, detect diseases, and optimize irrigation and fertilization.

Precision Farming:

Applying the right amount of resources to specific areas, improving crop yield and resource efficiency.

What AI is best for me?

Before deciding which AI solution is best for your business, it’s important to identify which problems are most important to solve.  In addition to these 7 questions, determine what you hope to gain by AI adoption. Most notably: are you looking for real-time data or do you need help with generating creative content? 

Computer Vision and Generative AI Chart

Artificial intelligence (AI) has surged in popularity due to advancements like generative AI, which creates content. While this technology has been getting the lion’s share of publicity recently, computer vision, which offers real-time data, offers equally exciting benefits to enterprises across industries. Generative AI generates innovative content based on patterns from existing data, while computer vision interprets and analyzes visual information for efficiency and safety enhancements across industries such as retail, manufacturing, restaurants, and more. 

Deciding between generative AI and computer vision depends on specific needs, with generative AI excelling in creativity and content, and computer vision delivering immediate value through visual analysis. Staying informed about these AI types is crucial for harnessing their transformative potential in businesses, navigating AI's growing influence with clarity and purpose.