A Brief History
To study intelligence we use both neuroscience and artificial intelligence to explore what it means to be human and how to better understand complex life.
Machine Learning (ML) and Artificial Neural Networks (NNs) trace their roots to the 1950s. The design of Neural Networks was sparked by our own brain's architecture. Advancements in computing power and data availability led to their resurgence in the 1980s, propelling rapid progress. Today, ML and NNs find widespread applications across industries, and ongoing research focuses on interpretability, fairness, and efficiency. The future promises further advancements to tackle complex real-world challenges and fuel technological innovations.
Introducing the Artificial Neuron
This is the perceptron's decision gate. It evaluates the weighted inputs and determines how much information to relay forward.
An artificial neuron, commonly referred to as a perceptron, is like the brain cell of artificial intelligence. Just as our brain has billions of neurons that process and transmit information, artificial neural networks use these perceptrons to compute and learn.
The perceptron takes in data, weighs its importance, decides what's noteworthy, and then sends its decision onward. These foundational units knit together to form vast artificial neural networks, enabling machines to learn and make decisions like us!
Just as our brain receives information through our senses, the artificial neuron gets certain data or 'input values'.
These are the perceptron's tools for valuing data. They assign significance to each input and make adjustments to ensure the output is accurate.
This is the final decision or signal the perceptron sends out based on its evaluation of the inputs.
Biologically Feasible Computing
As AI embeds itself deeply in today's world, we see the rise of Large Language Models (LLM), Machine Learning (ML), and other intricate algorithms. Yet, the dominance remains with narrow AI and supervised learning. The pinnacle of AI evolution points towards General AI, aiming to mirror human-like learning. This aspiration leads to 'biologically feasible computing,' an approach that aligns AI design closer to our neural frameworks.
As we inch towards this convergence, our foremost commitment should be to ethical governance, ensuring corporate giants adhere to responsible AI practices. In harnessing AI's potential responsibly, not only do we unlock technological marvels but also possibly decode the mysteries of human cognition.
Human attention is integral to our daily life but is being significantly challenged by technology, particularly social media and short-form video platforms. These digital realms habituate our brains to seek instant gratification, causing fragmented information consumption and a decline in focused attention. In the field of artificial intelligence (AI), there's a parallel effort to mimic human attention with artificial neural networks. These networks prioritize data and allocate resources dynamically, much like our focus shifts. Yet, AI grapples with emulating complex nuances of human attention, such as emotional and contextual factors. This juxtaposition highlights the importance of understanding the changing dynamics of human attention and its intricate relationship with AI.
Language is an essential part of human life, allowing us to connect with others, express ourselves, and understand the world. A mosaic of syntax, semantics, and pragmatics, human language is an extraordinary phenomenon where meaning arises not just from words, but also from context, tone, and nonverbal cues. In contrast, artificial intelligence interacts with language through algorithms, replicating human-like responses without truly grasping its emotional and experiential depth. This capability raises philosophical questions about the nature of understanding and consciousness. It invites us to ponder whether AI, trained on patterns, can truly understand language as we do or if it merely imitates these patterns. In the backdrop of advancing AI, we're compelled to reflect on our unique human qualities mirrored in our use of language. The evolution of AI challenges us to further cherish and explore the complexities of human language and communication.
LAW & JUSTICE (Neurolaw)
Neuroscience stirs profound implications for the realms of law and justice, igniting a paradigm shift in our understanding of criminal behavior, culpability, and rehabilitation. This convergence, known as 'neurolaw', not only challenges our long-held notions of free will and responsibility but also provokes us to rethink the ethical boundaries of leveraging brain-based evidence in the courtroom. Can we hold individuals accountable for actions driven by their neural circuitry? Are our legal systems ready to incorporate this nuanced understanding of the human mind? The intersection of neuroscience and law beckons us to explore these questions.
MEMORY & LEARNING
Memory and learning, as essential facets of human cognition, mold our perception and knowledge acquisition. Our learning process, which extends beyond mere memorization to include abstraction, allows us to interpret new information. AI strives to mirror the depth and complexity of human memory and learning, but can it truly grasp the nuanced interaction of attention, emotion, and context? The biases and limitations of human memory spur us to question the accuracy of our recollections and the nature of truth. Memory, in defining our sense of self and crafting our personal narratives, also provokes reflection on our cognitive processes. As we explore memory and learning in humans and AI, we are invited to contemplate knowledge, the trustworthiness of our memories, and the significant role memory plays in shaping our reality.
Music, an inherent human expression, weaves together our emotions, cognitive growth, and cultural bonds, reflecting our collective consciousness. We, as humans, craft music by comprehending and responding to rhythm, melody, harmony, and tone, all of which echo our emotional states and cultural identities.
Concurrently, the advent of AI systems like OpenAI's MuseNet is revolutionizing the music creation process, as they generate compositions that seemingly rival human creativity. However, AI derives its music from patterns and algorithms, devoid of the human experience's emotional richness and cultural depth. Can AI, lacking human consciousness, truly create art, and does its music carry equivalent artistic value? As we stand, the emotional resonance and cultural narrative integral to human-composed music remain exclusive to our species. Despite AI's capabilities, its role in music creation remains an imitation of the human symphony of emotions and creativity.
THEORY OF MIND
The Theory of Mind (ToM), a key concept in cognitive science, refers to our ability to understand and attribute mental states such as beliefs, intentions, and desires, to ourselves and others. ToM's relevance extends to Artificial Intelligence (AI), serving as a cornerstone in developing AI systems that can comprehend and respond to human behavior. Such AI models, capable of understanding the human perspective, contribute to a more intuitive human-AI interaction, improving the effectiveness and safety of applications like personalized AI assistants, autonomous vehicles, and social robots.
An Action Potential
The web of neuronal pathways “fire” and they become reinforced through repetition. The “firing” of a neuron is called an action potential. Action potentials are nothing more than a transient shift in the membrane potential of a neuron triggered by the rapid inflow or outflow of negative or positive ions across its membrane.
A synapse is the tiny gap between neurons where information is conveyed. At the synapse, electrical signals are transformed into chemical signals to bridge the gap. Once the signal reaches the opposite end, it reverts to its original electrical state.
Electrical characteristics of individual cells play a crucial role in impulse transmission and intercellular communication. When neurons receive or transmit information, they transmit electrical impulses along their axons, which can range in length from a micron to over a meter.