🔒 What We Lack on the Path to AGI
While CEOs of various companies argue about what AGI really is, developers have already compiled a list of problems facing AI. Solving these issues might revolutionize the field and lead us to AGI.
💬 AGI vs. AI
One definition of AGI is the ability of AI to perform most tasks that a human can do well. Although there are models today that successfully create texts or images, they are still far from being universal.
☄️ 5 Steps to AGI That Will Strengthen AI
1. Acquiring different ways of thinking (deduction, association) and memory.
2. AI should be able to learn and conduct experiments.
3. Natural language processing is necessary for interaction with humans.
4. The AI agent should gather information from reality.
5. Consciousness — the most controversial point, as even in the case of humans, it is unclear how consciously they perform actions.
🤯 What Challenges Does AI Currently Face?
⚫️ Huge Data Sets
Data sets are becoming large and complex, making traditional information processing methods ineffective. Developers propose dividing data sets into easily manageable segments and implementing RAG, a method of advanced information retrieval. This method combines LLM and a search system across all the data you have.
⚫️ Deductive Logic
The main problem with deep learning is generalized rigid reasoning; LLMs are still not capable of making inferences based on provided information. They merely predict words and their combinations. Neurophysiologists believe that the solution to this problem lies in "human-AI" interfaces.
⚫️ Hardware Limitations
Creating AGI agents will require significant computational power, necessitating the development of next-generation GPUs and TPUs. Additionally, the situation is complicated by the prices and shortages of semiconductor chips.
Other challenges facing AGI include: ethics and trust issues, model architecture, and the training data scaling crisis.
More Reading:
❓ What is AGI?
📈 10 Facts from a Former OpenAI Employee's Report on the Future of AI
#AGI @hiaimediaen
While CEOs of various companies argue about what AGI really is, developers have already compiled a list of problems facing AI. Solving these issues might revolutionize the field and lead us to AGI.
💬 AGI vs. AI
One definition of AGI is the ability of AI to perform most tasks that a human can do well. Although there are models today that successfully create texts or images, they are still far from being universal.
☄️ 5 Steps to AGI That Will Strengthen AI
1. Acquiring different ways of thinking (deduction, association) and memory.
2. AI should be able to learn and conduct experiments.
3. Natural language processing is necessary for interaction with humans.
4. The AI agent should gather information from reality.
5. Consciousness — the most controversial point, as even in the case of humans, it is unclear how consciously they perform actions.
🤯 What Challenges Does AI Currently Face?
⚫️ Huge Data Sets
Data sets are becoming large and complex, making traditional information processing methods ineffective. Developers propose dividing data sets into easily manageable segments and implementing RAG, a method of advanced information retrieval. This method combines LLM and a search system across all the data you have.
⚫️ Deductive Logic
The main problem with deep learning is generalized rigid reasoning; LLMs are still not capable of making inferences based on provided information. They merely predict words and their combinations. Neurophysiologists believe that the solution to this problem lies in "human-AI" interfaces.
⚫️ Hardware Limitations
Creating AGI agents will require significant computational power, necessitating the development of next-generation GPUs and TPUs. Additionally, the situation is complicated by the prices and shortages of semiconductor chips.
Other challenges facing AGI include: ethics and trust issues, model architecture, and the training data scaling crisis.
More Reading:
❓ What is AGI?
📈 10 Facts from a Former OpenAI Employee's Report on the Future of AI
#AGI @hiaimediaen