Artificial intelligence and motivation: how algorithms teach us to set goals

Imagine that your brain is a highly complex supercomputer that learns on the fly, predicts future scenarios, and manages countless processes simultaneously. For example, in just one minute, it processes about 11 million units of information coming from the senses. But here's the surprising part: consciously, you only perceive 40 units of this data. Everything else passes through the filters of the subconscious, which help us survive and make decisions.

Every second, billions of neurons in your brain exchange signals through trillions of synaptic connections. Scientists have calculated that the average adult human brain contains about 86 billion neurons, each of which can form up to 10 thousand connections with other neurons. During learning or work, the brain can create up to 700 new synapses per second.

Now imagine trying to teach artificial intelligence to do the same. The most advanced neural networks today contain billions of parameters, but even they still lag behind the brain in terms of information processing efficiency and adaptation to our life experience. Interestingly, it was the desire to understand how our brain works that inspired scientists to create machine learning algorithms. And conversely, observing how artificial intelligence works gives us a new perspective on our own cognitive processes, including motivation.

I want to share observations on how AI mechanisms resonate with human goal-setting processes, how neuroscience helps us better understand our motivation, and what lessons we can learn from technology for our own lives and work.

Machine Learning and Dopamine: Our Internal Reward System

When the Netflix algorithm recommends a series to your taste, it seems like magic. But behind this lies a clear logic: the algorithm analyzes past data, builds hypotheses, and adjusts its predictions based on feedback.

Our brain does almost the same thing, only instead of mathematical formulas, it uses dopamine—a neurotransmitter associated with the reward system. Essentially, the brain also works like a machine learning algorithm: it compares expectations with reality and adjusts its actions based on "prediction errors."

According to the work "Neuron Rewards and Decision Signals: From Theories to Data" by Wolfram Schultz (2015), the brain's dopamine system is responsible for processing prediction errors. When we get a result better than expected, dopamine levels increase, enhancing motivation.

What this means for us:

1. Set clear goals. The brain needs understandable success metrics for the dopamine system to work efficiently. For example, instead of “I want to be the best at work,” tell yourself: “I want to get a promotion by X% within a year.”

2. Give yourself frequent feedback. Just as machine learning algorithms adjust themselves after each iteration, your brain needs small progress checks.

Self-reinforcing algorithms and habits: why routine matters

AI owes much to the concept of reinforcement learning. The algorithm gets a reward for reaching a goal and tries to repeat the actions that led to it. Our brain forms habits in the same way.

Duhigg in 2016 showed that habits are formed through the “cue → action → reward” loop. Our brain uses this to minimize cognitive effort.

What this means for us:

1. Create rituals. For example, if it’s hard for you to start your workday, form a habit: every time before work, drink a cup of coffee, listen to your favorite song, and write down three tasks for the day.

2. Reward yourself. After completing a difficult task, give yourself a moment of joy—take a walk, eat something tasty, or watch an episode of your favorite show.

Deep neural networks and multitasking: where the brain wins

Deep neural networks, inspired by the structure of our brains, consist of layers that process data with varying degrees of detail. Our brain does the same, but much faster and with less energy spent. But it has one feature: it doesn’t like multitasking.

Stanford University research in 2017 shows that multitasking reduces productivity by 40% because the brain has to switch between tasks, wasting resources.

What this means for us:

1. Work in layers. Instead of trying to do everything at once, focus on one task at a time. Picture your tasks as layers in your “neural network.” Tackle them sequentially.

2. Eliminate distractions. Turn off notifications, create a clear schedule, and make sure you have time for focused work.

Feedback and learning: how not to fear mistakes

AI evolves through errors: each mistake is a valuable lesson for the algorithm. Unlike machines, we humans often fear mistakes, even though they’re exactly what help us learn.

C. Dweck in 2006 in her work on “growth mindset” theory showed that people who see mistakes as part of learning achieve better results.

What this means for us:

1. Rethink failures. Treat them as “prediction errors” in your inner algorithm that help you get better.

2. Implement feedback. For example, after finishing a project, do an analysis: what worked, what didn’t, and how you can improve the process next time.

Let’s summarize the key points

AI was created to mimic human thinking, but its work gives us valuable lessons about ourselves. We understand that motivation is not magic, but a clear mechanism related to our cognitive processes. Like AI, we learn from mistakes, adapt, and find new paths to success.

What can we learn from AI?

1. Set clear and achievable goals.

2. Give yourself regular feedback.

3. Form habits through rituals and rewards.

4. Focus on one task at a time.

5. Use mistakes as a learning tool.

Artificial intelligence and the human brain go different paths. However, their goal is the same: to learn, adapt, and be better with each new day.

Tell us in the comments, how do you set goals? Do you use any of the practices listed, or perhaps you've developed your own effective method of goal setting?

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