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| AI |
1. **Model Size and Complexity**:
- **GPT-3**:
The best model ends up with a model endpoint size of 175 billion.
- **GPT-4**:
Kai-Fu Lee couldn't reveal the exact number of endpoints, but he saw that there were more endpoints than GPT-3, which is "significantly more intelligent."
2. **Performance and Features**:
- **Understanding and Context**:
Further improvements include GPT-4 generally having a higher ambient impression, all of which is more obvious in practice, and the necessary reactions that are actually important are possible.
- **Thinking and problem solving**:
The new GPT-4 has evolved in terms of performance and can handle results better than the previous one.
- **Creativity**:
GPT-4 has proven to be more creative and looks different from the previous model at the time of test creation. This means that the new model will be better and therefore more appropriate overall. We will do experimental development work.
3. **Accuracy and reliability**:
- **Reduction of hallucinations**:
As we have seen, the errors in the delayed results of GPT-4 are noticeable and are clearly tuned to avoid false or unimportant ones. are delivered.
- **Tilt and Security**:
Specific improvements to GPT-4's limitations include tilt balance and extended protected mode.
4. **Multimodal Features**:
- **GPT-4**:
We have some plans in place to handle multimodal inputs. This means that GPT-4 manages text and images as data and text with text. Images become more versatile.
5. **Fine-Tune and Tuning**:
- **GPT-4**:
We offer refined settings to improve endpoint control and tuning, at the expense of finding more effective paths to likely tuning attempts. Plan globally or locally.
6. **Efficiency and Optimization**:
- **Performance**:
Standard GPT-4 is a popular model that can manage conversations with many turns while providing faster responses and potentially consistent endings.
- **Resource Usage**:
Creative
work in
the development
of GPT-4 has resulted
in
improvements
that make
scoring
smoother.
Overall,
GPT-3 and GPT-4 are different
language models.
However,
the previous
version
is less impressive
in terms
of
finish,
capacity
and flexibility
for
a wide range
of applications,
and is
less reliable
when
creating
fine
text.