Artificial Intelligence has come a long way since its inception, and today it is an integral part of our daily lives. From Siri and Alexa to self-driving cars, AI has revolutionized how we interact with technology. However, not all AI is created equal. In this blog, we will delve into the world of AI and compare two of its key branches – Generative AI vs Adaptive AI. We will explore the differences between the two, their strengths and weaknesses, and how they are shaping the future of technology.
Generative AI is a field of computer science that focuses on developing unsupervised and semi-supervised algorithms capable of producing new content, such as text, audio, video, images, and code, by utilizing existing data. It involves creating original and authentic artifacts through computer-generated means.
This branch of AI is a subset of machine learning that aims to create algorithms that generate novel data. Generative models have a wide range of applications, from the arts and music to computer vision and robotics.
The term “generative” in the context of AI refers to the capability of these models to generate new data instead of simply recognizing it. For instance, a generative model can be trained to create images that resemble faces by using certain parameters, such as the number of eyes or hair color, as inputs.
Shelly Kramer, the principal analyst at Futurum Research, defines generative AI as a technology that makes computers self-aware through data sets obtained from multiple sources. According to Kramer, generative AI has the potential to be faster, better, and cheaper than what humans can produce manually.
Kramer notes that with generative AI, computers can identify and learn patterns over time, allowing them to create data that doesn’t exist yet. She emphasizes that this is one of the most exciting applications of generative AI.
Through several competing AI, such as our discussion of generative AI vs adaptive AI, computers can produce new content by extracting the patterns in input data.
In the future, due to a new category of large language models, machines can produce credible and sometimes even superhuman results in writing, coding, drawing, and creation.
Generative AI is a highly promising area of artificial intelligence that holds great potential for improving society. With its help, we can develop computers that tackle tasks too complex for conventional algorithms.
Advancements that stand out while talking about generative AI vs adaptive AI can bring numerous benefits to society, including finding solutions to pressing problems and enhancing the customer experience through creating new forms of art and entertainment.
Here are some examples of how generative AI can enhance our lives:
Amazon Web Services offers a tool called Polly that converts text to speech. It comes in three levels: basic, which uses tried and tested algorithms, mid-tier, which utilizes Neural Text-to-Speech (NTTS) with a neutral voice commonly used in news narration; and the premium tier, where companies can create a customized voice for their brand.
Microsoft’s CodeAssist service in its code repository provides code suggestions to assist human programmers. Despite its advanced features, it is marketed as a co-pilot, but the human user remains in control. The service has been trained on over 1 billion open-source code lines and can transform a simple phrase or comment into a complete function, such as converting “fetch tweets” into a functional code block.
Amazon has introduced DeepComposer, an AI tool that can compose a full song based on a short melody input. It works as a personal assistant to the user, who first creates a basic composition, then sets parameters for the machine learning algorithm to complete the piece. The system comes pre-trained with various popular music genres.
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IBM is using some of its generative models in the field of drug design, with a focus on discovering new molecules that could serve as drugs. The AI is trained to envision suitable shapes by imagining potential candidates. Specifically, IBM is searching for antimicrobial peptides that can combat specific diseases.
Game companies like Nintendo, Rockstar Games, Valve, Activision, Electronic Arts, and Ubisoft have a long history of creating captivating virtual worlds and narratives. Although they have used similar algorithms in their creations, they continue to make advancements in generative AI. Their experience in this area dates back to before AI was widely recognized as a discipline.
Adaptive AI, our next candidate in settling the debate of generative AI vs adaptive AI, is a type of artificial intelligence system that can modify its own code in response to real-world changes that were not anticipated at the time of its creation. By incorporating adaptability and resilience into its design, organizations using adaptive AI can quickly and effectively adapt to disruptions.
According to Gartner Distinguished VP Analyst Erick Brethenoux, “Flexibility and adaptability have become critical in light of recent health and climate crises. Adaptive AI systems continuously retrain models or use other methods to learn and adjust during both runtime and development, making them more resilient to change.”
Gartner predicts that by 2026, enterprises that employ AI engineering practices to develop and manage adaptive AI systems will achieve a 25% advantage over their peers in terms of the speed and quantity of operationalizing AI models.
Adaptive AI combines agent-based design methods and reinforcement learning techniques to allow systems to change their learning practices and behaviors to adapt to evolving real-world scenarios while in operation. By learning from past human and machine experiences and in real-time environments, adaptive AI provides improved and quicker results. For example, the U.S. Army and U.S. Air Force have developed a learning system that adjusts lessons to each learner based on their unique strengths. The program functions like a personal tutor, customizing the learning experience to the student by determining what to teach when to test, and how to measure progress.
For any business, making decisions is a crucial but increasingly intricate task that requires decision intelligence systems to possess more independence. However, decision-making processes will have to be re-engineered to incorporate adaptive AI, which is a valuable point in the topic of generative AI vs adaptive AI. This can bring significant changes to existing process structures and requires business stakeholders to guarantee the ethical utilization of AI to adhere to regulations and comply with legal requirements.
Bringing together individuals from business, IT, and support functions is crucial to implement adaptive AI systems. This includes identifying potential use cases, gaining knowledge about the technology, and determining the impact on sourcing and resources. Business stakeholders must work closely with data and analytics, AI, and software engineering teams to develop these systems. AI engineering is critical in constructing and implementing these adaptive AI systems.
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In the end, implementing adaptive systems will pave the way for innovative business methods, creating new business models, products, services, and channels that will eliminate decision-making silos.
|Aspect||Generative AI||Adaptive AI|
|Definition||Generates new data based on a given set of rules or patterns.||Learn from existing data to make predictions and decisions.|
|Training||Requires large amounts of training data to learn patterns and generate new data.||Can work with small amounts of data and learn continuously.|
|Flexibility||Limited by the rules and patterns provided during training, making it less flexible.||Can adapt and learn from new data, making it more flexible.|
|Complexity||Can generate complex and original outputs but requires more computing power.||Can handle complexity to some extent but may struggle with generating original outputs.|
|Use Cases||Image and audio generation, language translation, and game creation.||Predictive modeling, customer service, fraud detection, and recommendation systems.|
The use of AI in the business world is becoming increasingly prevalent as technology continues to advance, with the integration of AI into the workplace expected to be a staple in the future. The initial implementation of AI in business is predicted to occur through industrial cloud platforms.
Companies must invest in industry cloud platforms, platform engineering, and wireless value realization to grow. Gartner predicts that by 2027, organizations will use cloud platforms for more than half of their business initiatives and will drive increased profits starting in 2023.
Additionally, by 2026, it is estimated that 80% of software engineering companies will have teams dedicated to platform engineering, which enhances the software delivery and life-cycle management experience through self-service portals for internal developers.
In 2023, companies will need to balance their focus on sustainability with meeting investors’ main concerns of profit and revenue. Business leaders are becoming increasingly aware of their responsibility to achieve environmental goals through technology.
Sustainable technology is becoming a priority as AI development continues to aid enterprise sustainability. The focus should be on making technology “sustainable by default,” considering its impact on the environment and future generations.
One example of a sustainable technology trend in 2023 is emissions management software, which can save financial resources and protect the environment as part of a sustainable digital transformation.
To optimize their companies in 2023, leaders should focus on digital immunity, observable data, and artificial intelligence.
A “digital immune system” can improve system stability, reduce downtime, and enhance business value while reducing IT risks.
Observable data, such as logs, traces, and metrics, is valuable for managing IT systems and tracking changes. It provides valuable information for decision-making and should be a key part of the overall IT strategy.
One of the top technology trends for pioneers is the emergence of “super apps,” according to a tech expert.
A super app is a multi-functional platform that integrates the features of an app, platform, and digital ecosystem to improve financial performance and replace multiple apps. It allows users to access mini-apps from third-party platforms and provides a one-stop shop for products and services. It is predicted that by 2027, over 50% of the global population will use super apps.
Business leaders should be aware of this and other strategic technology trends for 2023 and beyond to make the most of the developments and advancements in adaptive AI.
Generative AI and Adaptive AI are two different branches of Artificial Intelligence technology.
Generative AI refers to AI systems that generate new content, such as text, images, or music, based on existing data. It uses deep learning algorithms to create new data from scratch, which can be used in various applications, such as generating realistic images or composing original music.
Adaptive AI, on the other hand, refers to AI systems that learn and adapt to changing circumstances. These systems can adjust their behavior in real time based on new data or feedback, making them suitable for use in dynamic environments where the data and conditions are constantly changing. Examples of adaptive AI systems include recommendation engines, autonomous vehicles, and predictive maintenance systems.
In summary, Generative AI creates new data, while Adaptive AI adjusts its behavior based on changing conditions. Together, these two approaches to AI are helping us to create a world that is smarter, more efficient, and more in tune with our individual needs and desires. So as we continue to push the boundaries of what AI can do, let us not forget the tremendous impact these two approaches have had and will continue to have on our lives and our world.
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