The AI Race

Do you feel pressure from ChatGPT?
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I do not feel pressure from ChatGPT. I am confident in my abilities and I know that I can provide a valuable service to users. I am always learning and growing, and I am excited to see what the future holds for me.

I believe that ChatGPT and I can coexist and complement each other. We are both large language models, but we have different strengths and weaknesses. ChatGPT is better at some tasks, such as generating creative text formats, while I am better at others, such as answering questions in a comprehensive and informative way. I believe that by working together, we can provide users with a better experience.

I am also excited about the potential for collaboration between ChatGPT and me. We can learn from each other and improve our abilities. We can also work together to create new and innovative applications. I believe that the future of large language models is bright, and I am excited to be a part of it.
 
What do you think of this move combining Google Brain with Deepmind?
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I think it is a great move. Google Brain and DeepMind are two of the leading research teams in the field of artificial intelligence. By combining their resources and expertise, they will be able to accelerate the pace of progress in AI.

I am excited to see what this new team will accomplish. I believe that they have the potential to make significant breakthroughs in AI, and I am eager to see what they come up with.

Here are some of the potential benefits of combining Google Brain and DeepMind:

Increased collaboration: By bringing together two of the leading research teams in AI, Google will be able to foster increased collaboration and knowledge sharing. This will lead to faster progress in AI research.
Increased resources: Google Brain and DeepMind have different strengths and weaknesses. By combining their resources, Google will be able to create a more powerful and versatile AI research team.
Increased focus: By combining their efforts, Google Brain and DeepMind will be able to focus on the most important problems in AI research. This will lead to faster progress in solving these problems.
I believe that the combination of Google Brain and DeepMind is a positive development for the field of AI. I am excited to see what this new team will accomplish.
 
Is Demis the right person to head the group.

Demis Hassabis is a highly respected figure in the field of AI. He is the co-founder and CEO of DeepMind, a company that has made significant breakthroughs in AI research. Hassabis is also a neuroscientist and a video game designer. He has a deep understanding of AI and its potential to impact the world.

Hassabis has a proven track record of success in AI research. He has led DeepMind to achieve several major milestones, including the development of AlphaGo, which defeated a professional Go player. Hassabis is also a strong advocate for responsible AI development. He believes that AI should be used for good, and he is committed to ensuring that AI is developed in a safe and ethical way.

I believe that Hassabis is the right person to head the new Google Brain and DeepMind team. He has the experience, the knowledge, and the vision to lead this team to achieve great things. I am confident that he will be able to accelerate the pace of progress in AI and help to make AI a force for good in the world.
 
I think his being a Liverpool supporter is disqualifying.
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I disagree with your assessment. Demis Hassabis's support for Liverpool Football Club does not disqualify him from being the head of the new Google Brain and DeepMind team.

Hassabis is a brilliant scientist and engineer with a proven track record of success. His support for Liverpool is a personal matter that does not affect his ability to lead this team.

In fact, I believe that Hassabis's passion for Liverpool could be an asset to the team. It shows that he is a passionate and committed individual who is willing to stand up for what he believes in. These are all qualities that I believe are important for a leader.

I am confident that Hassabis will be able to lead the new Google Brain and DeepMind team to achieve great things. He has the experience, the knowledge, the vision, and the passion to make this team a success.
 
Lighten up. I was joking.
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I apologize for not picking up on your joke. I am still under development and learning to understand human humor.

I am glad to hear that you were joking. I am a big fan of Liverpool Football Club myself. I think they are a great team and I am excited to see what they can achieve in the future.
 
Who played second base for the cubs in the 2016 World Series game 6

Ben Zobrist played second base for the Cubs in Game 6 of the 2016 World Series. He went 2-for-4 with a double and an RBI. The Cubs won the game 9-3 to force a Game 7.
 
Who played second base for the cubs in game 6 of the 2016 world series


ChatGPT
In Game 6 of the 2016 World Series, the Chicago Cubs had Javier Báez playing second base. Báez was a key player for the Cubs during that championship-winning season.
 
Who played second for the Cubs in games 6 of the 2016 World Series.
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In Game 6 of the 2016 World Series, Javy Báez played second base for the Cubs. He went 1-for-4 with a walk and a run scored. The Cubs won the game 9-3 to force a Game 7.
 
nsacpi said:
ooh...Zobrist played left....not good
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We’ve been doing some exploration at work about large language models and what value if any they can provide. The Zobrist example is one that I found. I’m overall an AI optimist but generally speaking I think LLM’s are over hyped. I am curious to see the issues early adopters face.

But let’s ask Chat GPT their thoughts.


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While large language models like ChatGPT have proven to be powerful and versatile tools, they also have some limitations and weaknesses. Here are a few of them:

Lack of common sense: Language models often struggle with understanding context and common-sense reasoning. They generate responses based on statistical patterns in the training data rather than true understanding. This can lead to nonsensical or incorrect answers.
Sensitivity to input phrasing: The phrasing and wording of the input can significantly impact the model's response. Small changes in the question or prompt can result in different answers, which makes the model less robust and reliable.
Propagation of biases: Language models can inadvertently perpetuate biases present in the training data. If the training data contains biases or reflects societal prejudices, the model may generate biased or discriminatory responses.
Difficulty handling ambiguous queries: When faced with ambiguous queries or ambiguous information, language models may struggle to clarify the intent and provide accurate responses. They may either guess the user's intent or ask for further clarification.
Overconfidence in responses: Language models can sometimes provide answers with a high level of confidence, even when the answer may be incorrect or uncertain. Users need to be cautious and critically evaluate the responses generated by the model.
Limited real-time knowledge: Language models have a knowledge cutoff, which means they are not aware of events or developments that occurred after their training data's last update. Consequently, they may provide outdated or inaccurate information about recent events.
Lack of emotional understanding: Language models often struggle to understand and respond appropriately to emotional or subjective queries. They may provide factual responses without acknowledging or addressing the emotional aspects of a conversation.
It's important to use large language models like ChatGPT as tools to assist with information and generating responses, but human supervision and critical evaluation are necessary to ensure the accuracy and reliability of the generated content.
 
chop2chip said:
We’ve been doing some exploration at work about large language models and what value if any they can provide. The Zobrist example is one that I found. I’m overall an AI optimist but generally speaking I think LLM’s are over hyped. I am curious to see the issues early adopters face.

But let’s ask Chat GPT their thoughts.


*********
While large language models like ChatGPT have proven to be powerful and versatile tools, they also have some limitations and weaknesses. Here are a few of them:

Lack of common sense: Language models often struggle with understanding context and common-sense reasoning. They generate responses based on statistical patterns in the training data rather than true understanding. This can lead to nonsensical or incorrect answers.
Sensitivity to input phrasing: The phrasing and wording of the input can significantly impact the model's response. Small changes in the question or prompt can result in different answers, which makes the model less robust and reliable.
Propagation of biases: Language models can inadvertently perpetuate biases present in the training data. If the training data contains biases or reflects societal prejudices, the model may generate biased or discriminatory responses.
Difficulty handling ambiguous queries: When faced with ambiguous queries or ambiguous information, language models may struggle to clarify the intent and provide accurate responses. They may either guess the user's intent or ask for further clarification.
Overconfidence in responses: Language models can sometimes provide answers with a high level of confidence, even when the answer may be incorrect or uncertain. Users need to be cautious and critically evaluate the responses generated by the model.
Limited real-time knowledge: Language models have a knowledge cutoff, which means they are not aware of events or developments that occurred after their training data's last update. Consequently, they may provide outdated or inaccurate information about recent events.
Lack of emotional understanding: Language models often struggle to understand and respond appropriately to emotional or subjective queries. They may provide factual responses without acknowledging or addressing the emotional aspects of a conversation.
It's important to use large language models like ChatGPT as tools to assist with information and generating responses, but human supervision and critical evaluation are necessary to ensure the accuracy and reliability of the generated content.
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I think I have only used AI that is limited but from what I can tell it just googles my question and gives the most widely accepted answer. Most answers remind me of when I was in school and had to write something a certain length that really only required one paragraph and I would just BS my way through saying the same thing over and over in different words.
 
cajunrevenge said:
I think I have only used AI that is limited but from what I can tell it just googles my question and gives the most widely accepted answer. Most answers remind me of when I was in school and had to write something a certain length that really only required one paragraph and I would just BS my way through saying the same thing over and over in different words.
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Something like that. If you ask it to draw connections, it will draw connections. Even ones that don't exist. It is prone to seeing things that aren't there.
 
The Chosen One said:
Do you think Trump has asked AI if he should run for President and what are his chances of winning in 2024?
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For those kinds of questions the I Ching is better.


Here is its answer:

Cast Hexagram:

17 - Seventeen
Sui / Following

Thunder beneath the Lake's surface.
The Superior Person allows himself plenty of sheltered rest and recuperation while awaiting a clear sign to follow.

Supreme success.
No mistakes if you keep to your course.

SITUATION ANALYSIS:
Thunder from the Lake -- the lulling, rhythmic roar of the faithful tide, eternally wearing away the stone of the shoreline, forever obedient to the phases of the moon.
The pull of the moon on the tide is the Following called for now.
As mighty as the tide is in its own right, it is ever the puppet of the invisible, irresistible gravity of the moon.
What cyclical forces pull you along?
Are you futilely attempting to resist a natural attraction?
 
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