Category: Features

Love is Found

“What gives us hope in life is the sense that love is not found in predictable symmetry or straight edges. The closest we can get to perfection are the moments we can’t put into words, but can still feel when nothing is left.”

Excerpt From: James Wadman. “Diamondis.” James Wadman, 2017. iBooks.

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To The Earth

By James Wadman



If you asked me about the future of humanity I inevitably think of our place in the stars. It is natural that the minds of the conscious wander toward the things in our universe that are larger than life. But now more than ever, the debate seems to be is our destination in the sky or in circuitry of virtual realities? First we must answer, is the fate of humanity in progress or the dust of our self-inflicted ashes? And if we can cohesively strive for progress, we might find that our fate is infinite, both in the space that exists above us and in the space between transient pulses of binary gates.

Inevitably, though, we are meant to leave this earth. Will we do it together, or will we wave goodbye to lives left behind?

To the Earth is a testament to the first step mankind took toward this goal, and an optimistic gesture toward the flightpath of our ascension into the stars.

Achilleus is the collaborative project of Eric Averetta and James Wadman. A symbol of transcendence, Achilleus is a return to authentic trance as it first was intended. “To the Earth” is a testament to the first step mankind took toward this goal, and an optimistic gesture toward the flightpath of our ascension into the stars. This video was composed using mirrored time lapse footage in Austin, Texas. There are automated 180 degree and 45 degree mirrors with zoom-movement in exported footage.

To Have Lived at All

From a dream 12/04/2016:

It was any ordinary day. I go to work, I try to admire the world and others, I do my best. I was working on writing an exam for my job in the chemistry program at the university when my vision began to fade. At first I thought nothing of this. Growing up with syncope, lapses of consciousness were not unfamiliar occurrences to me. However, this time it was sustained and came with a harsher, prolonged separation from reality. I thought I might be in trouble, so I ran to find someone.

I pleaded to the first person I found to call an ambulance in shaky, inarticulate words as I fell to the ground. My eyes were closing, but I told myself I only needed to breathe. I might fall asleep on the ground of a university hallway, but I would wake up at the destination of the ambulance that was en route to save me. And sure enough, I did.

I woke up in a hospital bed beside overjoyed family members, none of whom I recognized. A man took my hand and I was surprised at how his hand engulfed mine, as if he were a giant with a friendly face. When he moved closer I could see in the reflection of the window that I was not myself — or at least not who I was before the fall. I was a child and this was my father. My life, the life of James Wadman, the neurobiologist, chemistry specialist, writer, and musician was the dream of a young boy in the midst of his chemotherapy treatment.

I was still looking in my reflection in the window when the world faded again. This time, I woke up in bed at home.

When the clock runs out of time, I learned that it is a blessing to have lived at all.

Conscious Talks: Neural Systems, Neural Networks, & AI

Artificial Intelligence: From Neural Systems to Neural Networks, by James Wadman

This is a very casual meditation on the state and future of artificial intelligence from the perspective of a neurobiologist.

I want to clarify that I will use two different phrases to make it easy to distinguish between biology and computer science. In this piece, “neural systems” refers to the biological nervous system and the associated molecular biology. “Neural networks” refers to artificial intelligence that mimics (for now) neural systems on the macro-scale of neurons. I will argue that neural networks should operate on a more fundamental scale, where instead of focusing on macro-scale neurons, we should focus on the underlying molecular-scale (genetic) processes. To address the hardcore physicists: yes, we can and will at some point go one step further and operate neural networks on an atomic (dare I say quantum) scale, but this will come only after we fully understand molecular-scale neural networks. More on this later.

First let me ask, what are you trying to accomplish with designing an artificial intelligence system? One of the more common applications of artificial intelligence is to compose an algorithm that can evaluate and improve itself. Let’s call this AIa. This utilizes simplified (although not necessary simple) versions of neural networks that take into consideration the connectivity between neurons using a circuit-structured and feedback mentality. Another form of artificial intelligence includes algorithms that can make sense of large, often abstract, data structures and draw conclusions from the chaos that humans would not see. Let’s call this AIb. This form might also use neural network feedback tools, but focuses on data rather than the plasticity of the algorithm. Both of these take simple human logic characteristics, such as deduction and compare/contrast, and apply them to data that is far too vast for an ordinary human to sift through. Either of these examples are only as complex as the data itself or the results desired. In other words, the more abstract the data or the more precise the conclusions, the more challenging the algorithms. The differences between the two can be understood by comparing them to behavioral traits in humans. While AIa is based on the principles of learning (the brain adapts to experience to create intelligence), AIb is mimics intuition (the brain extrapolates direct conclusions from abstract concepts). However, one can write these algorithms without a fundamental understanding of how actual biological intelligence works.

If, however, our goal for artificial intelligence is to make a true intelligent entity we have a major problem. We should not expect to fully replicate human intelligence or consciousness through neural networks until we fully understand neural systems. Ironically, we also cannot fully understand neural systems without our work so far in using artificial intelligence algorithms in the manner of the first two examples I discussed above. AIa and AIb are integral tools to understanding the complexity of neural systems with incredible capacity and utility in modern computing. But they are not yet acceptable for making true consciousness.

In the wake of my seemingly pessimistic sentiments, I want to make clear that our only barrier to artificial intelligence is our knowledge of the problem itself and the complexity of our algorithms. Once we really understand what we are doing, the solution will be obvious. For now, we should be working toward optimizing functional use algorithms (AIa and AIb) to build the foundation for true intelligence systems.

AIa and AIb are powerful algorithms for modeling molecular biology and are therefore the keys to understanding the problem, not the solution. Neural networks are designed to solve problems or enhance algorithms by simulating neuron-neuron interaction and plasticity. Let us not power to quickly forward in attempts to create functional artificial intelligence algorithms before truly understanding the capacity of learning and ingenuity. Concisely stated, the solution to creating an algorithm for artificial consciousness is to create realistic neural networks based on the underlying molecular neural systems. Go beyond just neuron plasticity, incorporate genetics and cell signaling, and build upon neuron replication.

Artificial intelligence has so far undervalued multi-stage memory consolidation and altogether neglected the ironic cohesion between the unconscious and conscious minds. We are therefore taking blind shots into a darkened room that is likely far larger than the echoes that call back. This comes as a contrast to my often optimistic perspective on technology and our ascension toward the future, so what is my intention in saying this? Quite simply I am suggesting that more biologists should program and more programmers should study the brain’s fundamental biology. Everything in biology is a product of the molecular processes that govern the expression, activation, and inhibition of genetics. It would be foolish, therefore, to limit neural networks to neuron-neuron interactions when their actual function involves far more complicated interactions. Human intelligence might be a function of synaptic interactions, but we cannot ignore the fact the synaptic interactions are the function of intracellular molecular processes. At the present moment, we are too limited by our knowledge of what happens “under the hood” of neurons to properly model intelligence through a synaptic model. We should develop accurate libraries for how biological systems work to the point where neural networks in computer science are equally complex and efficient as neural systems in human conscious reflections, unconscious reflexes, and intelligence.

It is already inherent to computer algorithms that the efficiency of problem solving far exceeds the efficiency of a human with a pen and paper. That is to say, given any complex question and a computer program with the algorithm and a person to knows how to solve the problem, the computer program will nearly always win. Artificial intelligence, even in its early stages of simply replicating human thought, will create far more capable “thinkers.” However, artificial intelligence entities will not be restrained by genetics, evolution, and neurochemistry. We will be able to take strides from there to improve upon the biological disadvantages of natural neural system to the point where we will eventually determine strategies for enhancing the basic framework of thought beyond that of the capacity of humans.

I believe that the future of artificial intelligence will progress in stages that address both our strides so far and the distance left before us to the end goal. Its success will be based on genuine understanding and the pursuit of knowledge not driven by profit or social gain. However, in the end artificial intelligence will be profitable, but the benefits beyond just currency are what will make the true difference. Our moral, intellectual, and sensible views of modern structure will be forever improved.

Boundless Wonder

By James Wadman

Society has misguided us into believing that boundless wonder has an expiration date in our lives. The difference between a child and a parent is experience, not the capacity for desire, curiosity, or even intelligent thought. I can’t be the only person to suggest that some of the most brilliant ideas are those voiced by children.

Anatomically, there are differences between the adult and child brains. Throughout late childhood and adolescence, the brain undergoes what is called “synaptic pruning,” a molecular phenomenon where unused circuits in the brain grow weaker and the useful circuitry grows stronger. For many of us that means that logical thought and responsibility increases as a function of growing up, while playful thought and uninhibited pleasure seeking is tamed by rationality. In modern society, synaptic pruning is an important product of our evolution. However, it is also an inherent limitation that forces us to cling to our biological history.

And for this I will suggest that maintaining the characteristics of a child’s brain can prove to be effective for a genuinely happy life, even if it would seem that this is fundamentally impossible given the previously discussed anatomical differences. Embrace your dreams, the chance to see new things in the world, and express yourself freely. Growing up, we learn to be responsible in a world that becomes increasingly competitive. Don’t let your curiosity fall victim to the false notion of growing up.



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As long as the universe exists, there is something to be created and something to be explored. James Wadman
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