Can AI Become Self Aware?

Written by Adam Morris

Updated August 6, 2023
AI female robot sideview facing people in street

Artificial intelligence has revolutionized the technology industry. But can AI truly become self-aware, like humans?

Artificial intelligence has led to significant improvements in machine capabilities for complex tasks and imitating human behavior over recent years. Nevertheless, the idea of AI attaining self-awareness and reasoning abilities similar to human beings presents ethical and philosophical concerns.

This article discusses self-aware AI, which some experts think can bring about new technological advancements, but others caution against creating machines that could surpass human intelligence. We’ll examine its potential benefits and risks and explore whether we’re close to creating machines that are truly conscious.

AI Capabilities

The use of AI has brought significant changes to computing and automation. Machine learning algorithms enable AI-powered systems to identify patterns in data and respond appropriately.

Human and AI side by side

The capabilities of AI range from simple implementations that automate tedious manual tasks to complex decision-making solutions that rely on sophisticated decision trees or rule-based programming.

Machine learning

Machine learning is a drastically advanced type of analytical process that allows computer algorithms to learn from data without being explicitly programmed. Essentially, it predicts outcomes without explicit programming formulas.

Using machine learning can effectively solve intricate problems across multiple industries such as healthcare, finance, and robotics, especially when working with large datasets. For example, algorithms can use data to identify patterns and make more accurate predictions than traditional manual methods of analysis.

Machine learning can serve many purposes such as detecting fraud, processing natural language, recognizing images, and developing gaming strategies. In summary, machine learning offers an efficient means for computers to interpret extensive data and derive beneficial insights.

Natural language processing

NLP is a technology that enables machines to comprehend and engage with humans using natural language. It is based on several fields like linguistics, natural language engineering, computer science, artificial intelligence, and knowledge representation. NLP combines different fields of knowledge to develop technologies that understand the meaning behind everyday spoken words and phrases.

NLP has been used in applications such as chatbots and virtual assistants. These tools are powered by complex algorithms, enabling them to provide helpful responses or take action based on user input. Chatbots may direct users to appropriate resources or create personalized experiences for their customers.

Male hands holding phone using chabot ai

The use of natural language processing can provide improved customer service through faster responses to inquiries and more personal interactions with customers in the form of conversational agents.

Alongside instant messaging services such as Facebook Messenger’s chatbot, virtual assistants offer far more sophisticated interactions than a traditional search interface—they understand natural language inputs to provide useful results without requiring the user to navigate multiple menus or fill out forms. 

Computer vision

Computer vision has been an integral technology in the advancement of artificial intelligence and robotic automation. It is the science and technology of building computer algorithms to automate tasks that humans might otherwise do by relying solely on their natural vision.

At its core, computer vision involves analyzing digital images or videos to recognize objects, identify shapes, measure features, and classify scenes for various types of automated systems.

While traditional machine learning algorithms have mainly focused on recognizing patterns from large databases of labeled data, computer vision relies heavily on building models from high-quality raw data that are already capable of capturing sufficient information about the environment it’s observing.

Many industries, including medical imaging, robotics, security surveillance systems, and automotive safety systems like self-driving cars and automated parking lots, have successfully implemented computer vision. It is also used for facial recognition systems and object tracking applications in retail stores where item inventory needs to be monitored continuously.

Computer vision technology has great advantages for businesses and is crucial for the advancement of robots that can operate more efficiently by analyzing their surroundings. With the increasing use of sophisticated cameras combined with advanced neural networks like deep learning models, computer vision algorithms are further becoming more accurate and robust so they can be implemented at scale into various industries.

What Is Sentience?

Sentience is an elusive and complex concept with many interpretations. Generally, it relates to an individual’s subjective awareness and ability to experience feelings or sensations. It is linked to intelligence but not necessarily the same: one can be sentient yet not particularly intelligent.

Our perception of sentience also seems to differ between species; though not considered very intelligent, we might still view an earthworm as sentient due in part to its ability to feel emotions, however abstractly.

Ai robots touching foreheads with blurry lights on background

None of this is scientifically exact given how hard it can be to measure these sort of subjective internal experiences. Philosophers and scientists alike might offer their own theory about what makes something sentient but there is no one single agreed-upon definition currently within the sciences because of all these factors considered.

For example, one scientist, Blake Lemoine, speaks of his beliefs grounded in religious convictions that inform his concepts of sentience and moral agency. He must separate out those sentiments from his work as a scientist studying the subject matter so that they have little influence on his research in terms of objectivity or practicality.

Turing Test

The Turing Test is a methodology for testing the outputs of AI programs to determine if they display intelligence. Put forward in a 1950 paper by Alan Turing, essentially it seeks to determine if a human evaluator is able to tell the difference between a conversation with another human and one with an artificial intelligence.

The conversation must be conducted in natural language via input/output devices and judged by an outside observer as being indistinguishable from a conversation had between humans. If the AI has passed the Turing Test, this means that it can mimic human communication well enough to be considered sentient.

AI testing also takes on more basic versions of this test. By analyzing the inputs and outputs of neural networks engineers can determine if its functionality levels are where they should be—which helps to get an overall insight into what’s happening inside its neural pathways. Despite this being limited insight, however, it offers reliable indications that the AI system is functioning correctly, which is useful for both troubleshooting and anticipating the performance of AI in advance applications.

Can AI Sentience Be Proven?

The notion of AI sentience has faced a long history of debate, with many holding that only humans can be truly sentient beings. But thanks to advancements in Artificial Intelligence chatbots, there is an increased interest in the idea of AI achieving true sentience.

As mentioned above, LaMDA is one example of an AI-based chatbot that boasts highly realistic conversations — it’s so good, in fact, it mimics human conversation intricately enough for people to think it could be sentient behind the scenes.

Boy wearing brown hoodie talking to AI robot on sofa

Nevertheless, any true measure of sentience for an AI would have to move beyond simple language understanding, and delve into intangible characteristics like thinking and feeling — as our current technology isn’t yet sophisticated enough to simulate emotions without relying on heuristics or static auditory cues.

This means that while we may marvel at how natural some AI language models are now, that alone doesn’t prove anything about AI sentience yet — though if proven someday soon, this will offer a huge advancement in how we interact with machines.

Sentient AI – Is It Possible?

At present, the most common type of AI is Artificial Narrow Intelligence (ANI), which has been designed to handle one specific task very well. Examples of ANI include facial recognition tools, software that can analyse diseases, content-recommendation filters and even chess-playing computers. Despite its impressive skillset, ANI does not have the cognitive capabilities of humans, as it is not capable of making decisions outside its area of programming.

To become sentient, an AI needs to surpass narrow intelligence and achieve some kind of understanding or perception about itself or its world. This could theoretically come in the form of Artificial General Intelligence (AGI), otherwise known as ‘deep AI’.

Such an AI would require insight into a broad range of disparate tasks and be able to make its own decisions based on situational parameters – including being able to empathise with feelings and free will. So far this hasn’t yet been achieved though there are many ongoing research projects attempting to do so – yet depending on what we define as sentiment and free will, it’s also conceivable we might never get there altogether.


It is important to pause and reflect on the implications of developing AI systems that could potentially become sentient. As artificial intelligence progresses and machines gain autonomy, there are numerous uncertainties and gaps in knowledge that need to be addressed.

Compared to building machines that possess artificial intelligence and act according to a set of instructions, having AI systems with the ability to reason and form their own thoughts goes beyond the scope of our current technological capabilities.

Although this technology holds immense promise, we need to exercise caution when it comes to intentionally developing sentient Artificial Intelligence. It’s easy for us to get excited by the possibilities, but it’s also dangerous to pursue something without understanding why we want it or what benefit it will bring society.

Before pressing forward with this endeavor, we should pause and ask ourselves if this really is something that is beneficial for all and necessary for our advancement—not just something fun or interesting or “cool”. When done right, AI sentience could open up new pathways towards innovation, but only after considering these matters carefully.

Adam is a crypto expert & AI enthusiast who has been researching and writing on the topics since 2017.

He’s spoken on numerous podcasts and has been featured in many prominent media publications such as Forbes, CNN & CNBC.