Meta's Llama 3 is expected to be released in July 2024. The model is predicted to have capabilities similar to GPT-4, with a maximum of 140 billion parameters, compared to Llama 2's 70 billion parameters. Llama 3 is also expected to be more open-ended and less restricted than its predecessor, Llama 2, which has been criticized for being "too safe" due to its guardrails.
However, Meta's AI Chief, Yann LeCun, has expressed skepticism about achieving human-level artificial intelligence in the near term, emphasizing the limitations of current AI systems.
What is the difference between gpt-4 and gpt-3
GPT-4 is an advanced AI model developed by OpenAI that has several improvements over its predecessor, GPT-3. Some of the key differences between the two models include:
Understanding of Natural Language: GPT-4 has a better understanding of natural language compared to GPT-3, allowing it to generate more coherent and contextually appropriate responses.
Speed and Accuracy: GPT-4 is faster and more accurate than GPT-3, enabling it to process data more efficiently and generate more accurate language.
Handling Complex Tasks: GPT-4 can handle more complicated tasks, including multi-layered prompts and tasks that require a greater degree of context and background knowledge.
Multimodal Capabilities: GPT-4 can handle images as inputs in addition to text, making it more versatile and capable of performing tasks that require combining text and image modalities.
Parameters: GPT-4 has more parameters than GPT-3, which allows it to learn and generalize patterns from the data it's trained on more effectively.
Data: GPT-4 is trained on more recent data than GPT-3, giving it access to more current information and trends.
These improvements make GPT-4 a more powerful and capable AI tool for a variety of applications, including language translation, question-answering, and creative writing.
What are the limitations of gpt-4 compared to gpt-3
GPT-4 has several limitations compared to GPT-3:
Handling of Complex Tasks: While GPT-4 can handle more complicated tasks than GPT-3, it still struggles with certain types of problems, such as reasoning with adjacency matrices instead of lists of edges.
Algorithmic Tasks: GPT-4 has limitations in more algorithmic tasks, like communicating using a Caesar cipher with a shift of 15.
Long-Term Planning: GPT-4 has difficulty with long-term planning and may not be able to plan ahead effectively.
Hallucinations: GPT-4 can sometimes hallucinate when it lacks sufficient information to make accurate predictions.
Imagination: GPT-4 lacks imagination and cannot visualize or dream.
Identity Consistency: Some GPT-4 models may struggle with identity consistency and change their identity in conversation.
Temporal Consistency: GPT-4 may have difficulty understanding the passage of time and the concept of time.
Memory: GPT-4 may not be able to store and recall large amounts of information for long periods.
Contextual Understanding: While GPT-4 has improved contextual understanding compared to GPT-3, it still struggles with certain aspects of contextual understanding and may not always generate accurate or coherent responses.
Data: GPT-4, while having more data than GPT-3, may still face limitations in understanding and generating responses based on specific domains or specialized fields.
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