By replicating the human capability of forgetting superfluous data, psychological Artificial Intelligence will revolutionize algorithmic accuracy. Our brains are extraordinarily adept at adapting and creating explanations for unstable or uncertain circumstances. When faced with a new situation, our minds quickly formulate an interpretation - if this understanding is disproven by extra data, we can generate another explanation without delay. This process of discarding outdated knowledge and reforming our understanding is known as forgetting.
The ability of humans to forget irrelevant data efficiently enables us to interact with an uncertain environment, providing the basis for decision-making and problem-solving skills. Unfortunately, most AI systems lack this capability, which can lead to numerous issues, such as overfitting or bias.
Human cognition often works differently, unlike machine learning, which views reasoning as an activity with a predetermined set of labels and perceives the world as a fixed space of possibilities. Machine learning has successfully accomplished remarkable results when used in steady and clearly defined situations like chess or computer games, yet without these conditions, machines frequently fail to achieve their desired outcomes.
This is mainly because AI systems cannot forget and relearn from the sum of their experiences. In order for Artificial Intelligence to truly adopt the complex human process of forgetting, it must rely on a combination of techniques.
A few approaches that could be taken include:
- Using networks to adaptively weigh data points based on their relevance in the current context.
- Utilizing memory recall techniques that enable the AI to relearn concepts as new information becomes available.
- Deploying reinforcement learning algorithms that will allow the AI to focus solely on current tasks at hand.
Each approach can help hone AI’s ability to forget, allowing it to more accurately interpret and respond to complex environments. By understanding the importance of forgetting in human cognition and implementing appropriate forgetting strategies, AI systems can become more adaptable and less prone to overfitting or bias.
In 2008, Google released Flu Trends with the goal of predicting flu-related physician visits using big data. Unfortunately, this web service fell short when it came to forecasting the 2009 swine flu pandemic. Despite multiple adjustments to improve its algorithm's accuracy, Google eventually had no choice but to abandon the project completely by 2015.
In such fluctuating times, the brain behaves differently. It often forgets surplus information and instead focuses on recent events - a feature coined 'intelligent forgetting.' If an algorithm were to rely only on one data point like that of predicting flu-related doctor visits in the next week equal to those from last week, it would have minimized Google Flu Trends' mistakes by half.
Artificial Intelligence that is psychologically informed, with elements such as causal reasoning, intuitive psychology, and physics at its core, will soon be seen in 2023 as the key to solving challenging problems. Intelligent forgetting – just one component of this type of AI – will allow us to create smarter machine-learning algorithms than ever before by leveraging the remarkable functions of the human brain.
The Max Planck Institute, Microsoft, Stanford University, and the University of Southampton have already begun integrating psychology into their algorithms to make more accurate predictions about human behavior. These researchers can now accurately anticipate decisions such as recidivism and consumer purchases.
Researchers once believed that the more transparent an AI system was, its predictions were less reliable. This false assumption stated that complex challenges always require intricate solutions; however, psychological AI has disproved this notion as it is both explainable and accurate in its forecasts.
By the end of 2023, this notion will be long gone. As the flu prediction example shows, sometimes simpler psychological algorithms can offer more precise predictions than complex ones. Psychological AI gives us a new opportunity to move away from complex, obscure systems and contemplate if more simplistic, understandable psychological AI can provide an equally accurate answer.
Deep learning will soon be viewed as a dead end. Without human intervention and intuition, it will become evident that this type of computerized learning cannot adapt to unpredictable environments; additional processing power increases speed but not intelligence. We'll finally come to understand the limits of machine-controlled problem-solving.
Self-driving cars are an exceptional illustration of the limits that have been hit in achieving so-called level 5 vehicles - those capable of navigating safely under any circumstances without requiring a human driver.
Elon Musk will undoubtedly reevaluate his previous forecast that self-driving cars are nearby. Instead, he will concentrate on the more attainable and exhilarating level 4 vehicles; these automobiles can traverse autonomously without human interference in specific environments, such as highways or tailor-made cities designed for autonomous driving.
The widespread implementation of level-4 cars will not only lead to a redesign of our cities, making them more secure and dependable – it may also protect us from potential distractions while driving. We can quickly adapt to their limitations even if machines encounter some difficulties.
Using psychological elements in Artificial Intelligence will have become the norm. We can make more accurate predictions due to our understanding of how people think and behave. This knowledge can revolutionize many industries and save lives through increased safety with self-driving cars.
The rise of psychologically informed AI will help us make quicker, more accurate decisions and solve challenging problems with greater reliability. Finally, we'll be able to move beyond the limits of deep learning, allowing us to progress further in our use of technology.
