From Neutral Networks to Neuromorphic: A Deep Dive into AI Evolution
Artificial intelligence (AI) is changing the world, and companies need to get ready. To do this, companies must check the skills of their employees and train them to learn new skills about AI. If companies don’t do this, they may get left behind.
Artificial Intelligence (AI) is used to make decisions and find patterns in data, but it can’t do anything without the right information to learn from. It’s also important to remember that AI doesn’t understand what it’s doing – it’s just following instructions.
In 2012, AI became important when a program named AlexNet won a contest by sorting millions of pictures into 1000 categories better than the other competitors.
The Artificial Intelligence (AI) that did this job uses something called a neural network, which is a system that tries to work like a human brain. But our brains are much more advanced than any AI system. Unlike AI, human brains can think creatively, understand what they’re doing, and change depending on what they’re thinking about.
Even though AI can’t do everything our brains can do, it’s still very powerful. Artificial Intelligence (AI) can beat humans at complex games like chess and go. It can even write stories and answer difficult questions.
But AI systems require a lot of computing power and energy. As we make AI systems more advanced, they need even more resources.
AI is used in many industries, like healthcare and transportation. As technology continues to improve, AI will likely to become even more powerful.
There’s also a lot of money being invested in AI – the global artificial intelligence (AI) market size was estimated at US $ 119.78 billion in 2022 and it is expected to hit US $ 1,519 billion by 2030.
In the future, we may be able to make AI more like our brains. By studying how our brains work, scientists are creating new types of AI that work in a similar way. These new systems, called neuromorphic networks, could lead to even better AI. But even these systems are still much simpler than a human brain.
Our brains have many different parts that all work together. To make AI even more advanced, we might need to create different types of AI to mimic different parts of our brains.
Training these new types of AI could be less expensive than traditional AI systems. Instead of needing to see millions of examples to learn, these new AI systems can learn from just a few examples.
Within the next few years, these systems might be used in everything from healthcare to space exploration. These advancements might eventually lead to artificial general intelligence (AGI), which is AI that can do anything a human can do.
Just like the internet and computers changed the world, AI is expected to have a big impact on our lives. Companies need to start preparing for this change now by training their employees in AI.
Advanced Thoughts Version:
As the rapidly evolving landscape of artificial intelligence (AI) transforms the world, organizations must promptly adapt to the unavoidable shift.
An internal audit of the current skill set within the firm will reveal the necessary competencies that need reinforcement. Failing to embrace this shift towards AI may result in a strategic disadvantage.
Artificial intelligence gained significance in 2012, with AlexNet’s ground-breaking performance in the ImageNet challenge, showcasing the capabilities of AI and the potential it holds.
The fundamental concept behind AI technologies is the neural network, often compared to the human brain’s functionality.
This similarity, while instructive, simplifies the multifaceted complexity and efficiency of the human brain, which possesses qualities such as consciousness, creativity, and adaptability – attributes yet to be replicated in AI.
Despite the absence of these traits, AI has exhibited expertise in areas such as strategic gaming, complex problem-solving, and sophisticated storytelling.
However, this growing complexity demands high-performance computing resources and energy, a factor that escalates with each advancement in neural network sophistication.
AI, with its subdivision including machine learning, has permeated sectors like healthcare, finance, manufacturing, and transportation, reflecting its versatility. With technological advancements proceeding at an accelerating pace and funding pouring into AI research and development, the AI sphere is poised for substantial growth.
The escalating trend towards larger neural networks indicates a growing demand for expanded capabilities.
One of the promising emergent technologies that could spearhead the next wave of AI evolution is neuromorphic processing. These dedicated circuits emulate the brain’s dynamic cells and neural architecture, facilitating learning capabilities.
Neuromorphic cortical models of AI focus on replicating the functions of the neocortex, responsible for complex cognitive processes, and they operate more efficiently than conventional computers.
Given that the human brain comprises numerous interconnected structures, it may be beneficial to develop distinct neural networks to copy specific brain functions.
The thalamus, hippocampus, and cerebellum each contribute different cognitive face, and their simulation could enhance AI systems’ abilities. For instance, replicating the thalamus’s function could refine AI’s sensory data processing, while the hippocampus’s function in spatial navigation and long-term memory formation could enhance AI learning and memory capabilities.
Additionally, emulating the cerebellum’s broad connections to all neocortex regions could facilitate simultaneous data processing and learning in AI systems.
While the neuroscience understanding of these brain regions remains incomplete, current knowledge serves for developing models that can clarify open questions and provide insight through experimentation.
Eventually, cortical neuromorphic neural networks could replace the traditional neural networks, marking a significant milestone in AI’s evolution.
The training methods for these new AI systems depart substantially from conventional techniques. These networks can learn from a minimal number of examples, making their deployment cheaper.
They also have the potential to create more accurate outcomes through continuous learning. Future applications for these technologies could enfold a wide range of fields from speech recognition to robotics.
The development of such networks could pave the way for artificial general intelligence (AGI), the ultimate frontier in artificial intelligence that could catalyze significant economic growth and enhance human creativity and safety.
This monumental shift could mirror the transformations brought about by the development of the internet and computing technologies.
Given the dynamic nature of the AI field, organizations must be proactive in empowering their workforce to confront future challenges.
It is crucial to invest in personnel training on this emergent technology to tackle its potential across the organization.
Can AI system work as efficient as human brain?
AI systems and human brains have different strengths and weaknesses. Here are some key differences between AI and human intelligence:
- AI is best suited for handling repetitive, data-driven tasks and making data-driven decisions.
- AI-based machines are fast, more accurate and consistently rational.
- AI systems are already much better than people at logically and arithmetically correct gathering and processing.
- AI can identify informational patterns that optimize trends relevant to the job.
- AI never gets physically tired and as long it’s fed data, it will keep working.
- The next generation of AI may be 1000 times more energy efficient as the computer chips are working like the human brain.
- Human use the brain’s computing power, memory and ability to think.
- Human skills such as creativity, critical thinking, emotional intelligence and complex problem-solving still need to be more valuable and easily replicated by AI.
- Human intelligence has a much more powerful thinking capacity than artificial intelligence and can have a great problem-solving skills depending on the situation.
- Humans can solve a variety of problems and learn to solve ones we haven’t encountered before.
- Humans posses intuitive, emotional and culturally sensitive abilities that AI lacks.
- Humans can make rational decision that AI cannot.
- Humans can identify and correct problems in a system when AI can not.
What are neutral networks, and how do they relate to the human brain?
Neutral networks are the core concept of AI technologies. They ae designed to mimic the functioning of the human brain to some extent, but this is a simplified interpretation. Human brains are far more complex and efficient, possessing characteristics like consciousness and creativity that AI has yet to replicate.
What could be the possible impact of the development of artificial general intelligence(AGI)?
The development of AGI, Artificial Intelligence(AI) systems with human-like logical capabilities, could revolutionize our economy, technology and society. AGI could increase productivity, drive technological breakthroughs, transform social services like healthcare, education and enhance safety in hazardous tasks.
However, it also raises concerns about job displacement, ethical issues and potential misuse. Balancing these opportunities and challenges will requite thoughtful policies, workforce initiatives and ethical AI research.