What is the difference between AI and generative AI?
Both AI and generative AI are powerful technologies that can help you reshape your business, cut down on costs, and optimize operations, if applied to the right problem.
Let’s see which issues each technology can address and which challenges it presents.
Understanding artificial intelligence
Artificial intelligence specializes in analyzing large amounts of data very fast and performing complex tasks that typically require human intelligence. AI algorithms study the data, analyze it, and make decisions based on the rules and patterns they’ve discovered. Additionally, this technology helps with data optimization, anomaly detection, and data clustering.
As mentioned in the introduction, AI has several subtypes:
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Machine learning. These algorithms are trained on structured, semi-structured, and unstructured data to discover patterns and make decisions and predictions based on them.
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Natural language processing. NLP can extract data from unstructured human language. It enables machines to understand written or spoken human language.
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Computer vision. These models can interpret visual information. They can analyze and extract insights from images and video and react to it with actions or recommendations.
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Robotic systems. These are (semi-)autonomous machines that are trained to perform different tasks and interact with the environment.
AI is versatile and can take over different duties, depending on what you train the algorithm to do.
For instance, one AI model can help your management team make informed business decisions, another can spot malfunctioning in a factory machine, a third one operates a self-driving vehicle, and a fourth one guards you against cyberattacks by detecting anomalies in your business data access.
Where to use AI?
You can deploy AI in any context where the algorithm can learn patterns and make decisions based on them. Here are some example applications:
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Supporting business decisions as AI analyzes large amounts of historical data and discovers patterns that can escape the human eye
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Automating repetitive manual tasks to improve efficiency
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Operating autonomous vehicles with advanced navigation and decision-making capabilities
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Detecting anomalies in cybersecurity by monitoring data access and network penetrations, as well as spotting abnormalities in manufacturing equipment for predictive maintenance
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Enhancing security measures through facial recognition and biometric authentication technologies
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Transcribing spoken language accurately with speech recognition technology
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Powering recommendation engines to personalize product suggestions on eCommerce websites
Check out our elaborate guide on how to implement AI in business (with a free eBook).
Limitations
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Some AI algorithms are designed and trained to perform a specific task and can’t adapt by themselves to novel situations. When faced with variations, like a novel category of input data, these algorithms require retraining to accommodate the changes.
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AI can overfit to the training data, meaning the algorithms excel at solving specific problems and fail when faced with unfamiliar data
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Some AI algorithms, such as classic machine learning models, can’t handle unstructured data without pre-processing
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Most AI models look into a specific issue in isolation, without understanding the surrounding context. And even when you can teach an algorithm to consider the context, it’s rather costly and requires extensive computational power.
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Even though AI mimics human intelligence, it doesn’t have human-level reasoning capabilities
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AI models heavily depend on the training data and will contract any bias present there
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Deep learning models can’t explain how they came up with the output, which can be unacceptable in some applications, such as AI-infused medical or manufacturing software. But there is a possibility to move towards explainable AI when needed. These algorithms are less powerful, but you will know where the results came from.
Understanding generative AI
Generative AI’s main purpose is to create new content, such as text, music, images, etc. that looks like it was created by humans. It’s trained on large sets of data to discover patterns and produce something that is novel but still abides by the rules the technology has learned from the training dataset.
Even though many consider this content as original, generative AI models tap into large volumes of human creativity to produce “their own” work. As you will see below, this can cause copyright disputes.
What’s unique about generative AI algorithms?
Gen AI doesn’t just learn patterns. Instead, the technology delves into training data to learn features that it can combine and substitute on its own.
In the case of sequence analysis, generative AI models are largely based on transformer architecture, which introduces the notion of “attention.” This means that algorithms can receive an enormous dataset as an input, we are speaking about billions of text pages, and still maintain a connection not only between sentences, but between chapters and even books to detect complex patterns. This ability does not only apply to text but can be transferred to analyzing DNA sequences, music, and more.
Where to use generative AI?
You can apply gen AI to business use cases that require imagination and creativity. Here are some examples:
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Generating art, such as songs, music, drawings, fashion item designs
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Producing synthetic datasets for research purposes and AI model training
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Designing new products
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Writing research articles and code scripts
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Creating product demonstration videos and other material
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Customizing marketing campaigns to individual users
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Suggesting novice drug compounds
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Summarizing complex texts in a more comprehensible manner
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Studying evidence to generate court arguments in the legal sector
Limitations
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Generative AI can cause serious copyright disputes. Before creating content independently, algorithms analyze large volumes of human-created content. As a result, Gen AI content sometimes resembles the training data way too closely. You might have heard of a music-generating algorithm that was trained on Drake’s and The Weekend’s songs. It produced music that was well received by fans but had to be destroyed due to copyright issues. Similar cases have happened with other artists.
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Algorithms have the potential to expose sensitive information. This includes, for instance, revealing patient data in healthcare settings.
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Generative AI models can hallucinate, meaning that they can confidently give you a reasonable answer that is factually incorrect. For instance, Stack Overflow reviewed some of AI’s responses to technical questions and found that the answers were often incorrect.
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Gen AI, devoid of self-awareness, can come up with bizarre and even offensive comments. A case in point is Microsoft’s generative AI chatbot, which, during a conversation with technology reporter Matt O’Brien, repeatedly called him fat and ugly and even compared him to Hitler. This incident highlights the algorithms’ potential sensitivity and the critical need for safeguards in AI communication.
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It’s challenging to verify the information produced by Gen AI models since they don’t cite sources. Moreover, these models currently lack features equivalent to explainable AI.
Summary of generative AI vs. AI
To summarize, artificial intelligence is more like a well-informed strategist that excels at analyzing data and making decisions. Generative AI is an artist that produces novel and creative content.
| AI | Generative AI | |
|---|---|---|
| Functionality |
Performing a broad spectrum of tasks that require human intelligence |
Generating new content |
| Focus |
Analytics and prediction of future events |
Creativity and imagination, as it can produce completely new things |
| Pattern usage |
Discovers patterns to make predictions |
Discovers patterns to combine them in new, original forms |
| Creativity and innovation |
Good at analytics but lacks creativity |
Excels at creative tasks |
| Training dataset size |
Models can be smaller, therefore the dataset can be smaller too |
Large datasets because the models are typically large |
| Learning approach |
Supervised/unsupervised/semi-supervised/reinforcement learning |
Reinforcement learning with user feedback/unsupervised |
| Broad vs. narrow specialization |
Narrow focus as it concentrates on one specific task, such as predicting customer churn or classifying images |
Broad focus as the same model can generate cooking recipes, summarize books, and produce technical reports |
| Interpretability |
There is explainable AI, but deep learning models are still a black box |
No explanation provided |
| Computational resources |
Fewer resources required when the models are smaller |
Computationally intensive, as the models are large |
| Accuracy |
There are standards for what’s correct and what is not. The results are objective and verifiable. |
Accuracy relies on human interpretation, which means there is no clear benchmark. What is good enough for one person might not be accepted by another. |
AI vs. generative AI in different industries
Take a look at how generative AI versus AI applications differ in these three example sectors.
Healthcare
Artificial intelligence has many diverse applications in the medical sector. Here are the most prominent ones:
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Enabling robot-assisted surgeries and robot nurses
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Automating administrative tasks, such as transcribing consultations and entering patient details into EHRs
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Helping radiologists with tumor detection and diagnosis
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Assisting in clinical trials by recruiting participants, monitoring their adherence, and more
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Supporting remote patient monitoring together with medical IoT
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Detecting prescription errors
Additionally, AI is one of the key technologies enabling smart hospitals.
Generative AI, as we already established, focuses on producing new content, and its applications are more on the creative side. Deploy generative AI if you want to accomplish this:
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Generating different training scenarios for students and interns
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Coming up with synthetic medical data
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Designing new molecules and novel drug compounds
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Enabling doctors to query patient medical records
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Composing patient feedback surveys
For more inspiration, refer to our recent article on generative AI use cases in healthcare.
Real-life examples of AI in healthcare
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Mass General Cancer Center together with MIT developed Sybil, an AI system that can detect breast cancer. The model works with low-dose chest computed tomography scans, predicting whether a patient would develop breast cancer in the next six years.
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AiCure offers an AI-powered interactive medical assistant that can spot clinical trial participants who are likely to violate the trial’s rules. This solution also enables participants to capture a video of themselves taking the medication as proof of adherence.
Real-life examples of generative AI in healthcare
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Researchers at the University of Toronto built a model that can generate novel realistic proteins. They evaluated the potential of the resulting proteins with another AI tool, OmegaFold, and were pleasantly surprised to see that most of the sequences folded into real protein structures.
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Another research team developed a generative AI model that can create realistic synthetic patient data with the desired properties for clinical trials.
Retail and eCommerce
If we look at generative AI vs. AI in retail, classic AI can supply virtual and physical store owners with powerful analytics, hardworking robots, and tireless store monitoring. Here are more detailed applications of AI in retail:
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Assisting customers with in-store navigation
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AI-powered robots for delivery packing and restocking
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Self-driving delivery vehicles
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Spotting shoplifting and sweethearting events through computer vision
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Enabling self-checkout
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Better-informed customer segmentation, product recommendations, and price optimization
Generative AI, on the other hand, can attract customers and optimize internal operations through more creative tasks, such as:
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Crafting customized marketing campaigns
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Creating SEO-oriented content to attract traffic to your eCommerce store
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Offering virtual fitting rooms for clothes, shoes, and accessories, in tandem with immersive technologies
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Forecasting demand
You can find more information on Gen AI applications in retail on our blog.
Real-life examples of artificial intelligence in retail
We have two exciting examples in our portfolio:
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ITRex helped a large retailer build an AI-driven business intelligence platform that enabled the client’s employees to capture and analyze data from the entire organization, create complex reports, and visualize data without learning technical skills.
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Our team implemented an ML-based solution for checkout-free shopping. It uses computer vision and cameras attached to the ceiling to monitor consumers’ movements and identify items they grab from the store shelves. This system can turn any store into a checkout-free format without the need to redesign the space.
Generative AI examples in retail
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Carrefour deployed a ChatGPT-driven chatbot to suggest personalized shopping tips to consumers based on their budget and purchase history
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Walmart uses a generative AI-powered system to forecast demand and predict which products customers will need at each Walmart store
Media and entertainment
Media and entertainment is a creative sector, so this is where generative AI can shine. But this is also where the copyright issues discussed earlier can get even more concerning. Here is what the technology can do:
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Generating art, screenplays, music, and articles
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Editing videos based on user preferences
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Summarizing long-reads, podcasts, sports events, and other lengthy content
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Generating video metadata, like captions and descriptions
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Designing new immersive games, as well as new settings and characters for existing games
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Engaging the audience through chatbots and voice interactions
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Generating realistic backgrounds and visual effects for movies
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Producing virtual reality settings
The classic AI also still has interesting applications in this field, as there is lots of data to analyze to improve viewer engagement and satisfaction. Here are some use cases:
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Analyzing user behavior and preferences to recommend personalized content
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Detecting copyright infringement
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Gauging customer sentiment on social media
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Enhancing video quality by reducing noise and improving resolution
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Predicting content trends
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Content filtering as AI algorithms can spot and block inappropriate text and videos
Artificial intelligence example in the media sector
Netflix employs AI algorithms to analyze user data and deliver content recommendations based on information, such as actors, genre, the user’s viewing habits, and more. Netflix claims that around 80% of all watched content is suggested by its AI recommendations system.
And there is a project from our portfolio when a leading social networking app developer turned to ITRex to build an ML-powered automated content policing solution. We developed a computer vision model that could analyze live streams and take corresponding actions and we utilized MLOps best practices to speed up the algorithm’s deployment.
Generative AI example in the media and entertainment sector
Generative AI from Runway contributed extensively to producing the movie “Everything Everywhere All at Once,” where it created realistic background elements and visual effects. This film won seven Academy Awards.
Final thoughts
As you can see from the examples above, artificial intelligence can be a valuable addition to your company if you are looking for solid analytical power, need help with decision making, want to use AI-powered robots, or automate tedious, monotonous manual tasks. But if you want a technology that offers creativity and imagination, and can produce something new, generative AI is a better fit.
From the technical point of view, generative AI is more complex as it aims to imitate human thinking, while AI’s goal is to perform concrete tasks that the models are trained on. Also, in generative AI, there is no clear cut for what’s correct and what is not. Its performance is harder to evaluate, as it depends on human interpretation.
Speaking of the budget, generative AI consumes more computational resources, and it’s more expensive to build, train, and fine-tune. You can find more information on how much it costs to implement AI on our blog. We don’t have similar numbers for generative AI yet, so stay tuned to learn more about the topic. But we can already say that building a Gen AI model from scratch would be overwhelming. For the sake of comparison, estimates show that OpenAI trained ChatGPT-3 on around 45 terabytes of text data. This is equivalent to one million feet of bookshelf space. That would cost several millions. Therefore, you will probably have to fine-tune an existing model rather than create one from scratch.
But generative AI is relatively new. Should you trust it at all?
We keep hearing about generative AI bloopers, like that time when someone asked it to explain why butter is good for building skyscrapers, and the algorithm gladly generated arguments supporting this claim. Yes, these things happen. But keep in mind that this technology makes its decisions based on mathematical models, not on context understanding, empathy, and social norms. Generative AI can be very good at the tasks that it was built to do.
Finally, it doesn’t always have to be generative AI vs. AI. Generative AI can work hand in hand with other AI subtypes to produce even more powerful solutions to your business problems. Consult an AI development company to understand which solution works best for you or how to combine both technologies for optimal results.
