Google Bard vs ChatGPT

Comparison with 5 real world questions


Google Bard and ChatGPT are both AI chatbots that help automate chat tasks. The main difference between the two is their data sources. Bard is trained on an “infiniset” of data chosen to enhance its dialogue and has access to the internet in real time. ChatGPT is trained on a pre-defined set of data that hasn't been updated since 2021.

Bard uses Google's own LaMDA language model, while ChatGPT uses its own GPT-3.5 model. Bard is built on data provided in recent years, while ChatGPT is based on slightly older data, restricted in its current GPT3 model to data collected prior to 2022.

Comparison with 5 real world questions

We'll compare the two chatbots with ChatHub, a chatbot client designed to use multiple chatbots in parallel. We'll ask 5 questions to both chatbots and see how they respond.


1. Generate a short story about a dragon and a lost kingdom in 200 words.


In the realm of Eldria, a lost kingdom forgotten by time, a lonesome dragon named Silvyrus rested atop an ancient castle. The once-lively streets were empty, homes hollow, only echoes of laughter and cheer haunted the silent air. The dragon, the last guardian, guarded the fallen kingdom, preserving its memories like hidden treasures.

One day, a brave young girl named Elara, determined to restore her ancestral land, ventured into Eldria. The sight of Silvyrus startled her, yet she showed no fear. Sensing her courage, Silvyrus allowed her close, intrigued by her audacious spirit.

Elara pleaded with the dragon, asking for his help to restore the kingdom. Silvyrus, initially dismissive, was eventually moved by Elara's passion. They embarked on a quest, uniting scattered Eldrians, reviving ancient lore, rebuilding homes, and rekindling the kingdom's spirit.

Against all odds, the kingdom gradually came back to life. Silvyrus and Elara, with their unyielding efforts, revived Eldria. The dragon roared, not in solitude, but in jubilation, and Elara's heart echoed with the kingdom's newfound joy. The lost kingdom was lost no more, and the dragon, once the kingdom's reminder of despair, became its beacon of hope.

Google Bard

Once upon a time, there was a dragon who lived in a cave in the mountains. The dragon was very old and very wise, and it had seen many things in its long life. One day, a young man came to the cave. The young man was looking for a lost kingdom, and he hoped that the dragon could help him find it.

The dragon listened to the young man's story, and then it told him that the lost kingdom was located in a faraway land. The dragon also told the young man that the journey would be long and dangerous, but that he would be successful if he was brave and determined.

The young man thanked the dragon for its help, and he set off on his journey. The journey was long and difficult, but the young man never gave up. Finally, he reached the lost kingdom. The young man was overjoyed, and he entered the kingdom with a sense of awe.

The lost kingdom was beautiful and peaceful. The people were kind and welcoming, and they were happy to see the young man. The young man lived in the lost kingdom for many years, and he was loved by all. He never forgot the dragon who had helped him find the lost kingdom, and he was always grateful for its wisdom and guidance.

One day, the dragon came to the young man and told him that it was time for it to die. The dragon said that it had lived a long and happy life, and that it was now ready to pass on. The young man was sad to see the dragon go, but he knew that the dragon was right.

The dragon died peacefully in the young man's arms. The young man buried the dragon in a secret place, and he never forgot the dragon's kindness and wisdom. The young man lived a long and happy life, and he always remembered the lost kingdom that the dragon had helped him find.

2. What are the implications of the Schrödinger's Cat thought experiment in quantum mechanics?


The Schrödinger's Cat thought experiment is a famous illustration of the strange principles that underlie quantum mechanics, particularly the concept of superposition and quantum measurement problem. It was originally proposed by physicist Erwin Schrödinger in 1935.

The thought experiment goes as follows: a cat is placed in a sealed box with a radioactive source, a Geiger counter, and a vial of poison. If the Geiger counter detects that a single atom has decayed, it triggers the smashing of the vial, releasing the poison and killing the cat. Quantum mechanics allows for the atom to be both decayed and not decayed until it is observed. Therefore, the cat is both dead and alive until someone opens the box to look. This absurd situation was Schrödinger's attempt to critique the prevailing interpretation of quantum mechanics at the time.

Here are some of the implications of this thought experiment:

  1. Superposition: According to quantum mechanics, particles can exist in multiple states at once - called a superposition of states. Only when we measure it, the particle 'chooses' its state. Until the box is opened, the cat is both alive and dead. This reflects the quantum state of the atom: both decayed and not decayed.

  2. Observer Effect and Quantum Measurement Problem: Quantum systems appear to behave differently when observed, as demonstrated by the famous double-slit experiment. In Schrödinger's Cat experiment, the cat's life or death is determined when it is observed. This implies that the act of observation affects the observed.

  3. Copenhagen Interpretation and Many-Worlds Interpretation: The Copenhagen interpretation suggests that the act of measurement forces the particle (or in this case, the cat) to 'collapse' into a single state. The Many-Worlds Interpretation, on the other hand, suggests that both outcomes occur but in different 'branches' of reality, splitting the universe into two: one where the cat is alive and another where it is dead.

  4. Macroscopic and Microscopic: One of the key messages of Schrödinger's Cat is the stark difference between the quantum world (microscopic) and the classical world (macroscopic). Our everyday intuition doesn't seem to apply to quantum phenomena. The experiment underscores this clash and the strangeness of quantum mechanics.

  5. Limitations of Classical Physics: The thought experiment shows that classical physics can't explain all the phenomena in the universe, particularly at the quantum level. This was one of the key motivations behind the development of quantum mechanics.

In essence, Schrödinger's Cat is a metaphor for the mysteries and paradoxes of quantum mechanics. Although it doesn't provide answers, it's a tool for understanding the questions at the heart of quantum theory.

Google Bard

Schrödinger's Cat is a thought experiment that illustrates a paradox of quantum superposition. In the thought experiment, a hypothetical cat may be considered simultaneously both alive and dead, while it is unobserved in a closed box, as a result of its fate being linked to a random subatomic event that may or may not occur. This thought experiment was devised by physicist Erwin Schrödinger in 1935 in a discussion with Albert Einstein to illustrate what Schrödinger saw as the problems of the Copenhagen interpretation of quantum mechanics.

The Copenhagen interpretation is a widely accepted interpretation of quantum mechanics that states that the act of observation collapses the wave function of a quantum system, causing it to assume a definite state. This means that, until the cat is observed, it is both alive and dead.

Schrödinger's Cat is often used to argue against the Copenhagen interpretation, as it seems absurd to believe that a cat can be both alive and dead at the same time. However, the Copenhagen interpretation has been supported by a great deal of experimental evidence, and there is no currently known alternative that can explain all of the data.

The implications of Schrödinger's Cat are still being debated by physicists today. Some believe that the thought experiment shows that the Copenhagen interpretation is incorrect, while others believe that it simply highlights the strangeness of quantum mechanics. Regardless of one's interpretation, Schrödinger's Cat remains a powerful tool for thinking about the nature of reality.

Here are some of the implications of Schrödinger's Cat thought experiment in quantum mechanics:

  • The Copenhagen interpretation of quantum mechanics may be incorrect.
  • Quantum mechanics may be incompatible with our everyday understanding of reality.
  • The act of observation may play a role in determining the outcome of a quantum event.
  • The universe may be in a state of superposition until it is observed.

Schrödinger's Cat is a thought-provoking experiment that has challenged our understanding of reality for decades. It is a reminder that quantum mechanics is a very different theory from classical physics, and that we still have much to learn about the nature of the universe.

3. Can you translate the sentence 'The weather is beautiful today' into French, German, and Italian?


Sure, here are the translations:

French: 'Le temps est beau aujourd'hui.' German: 'Das Wetter ist heute schön.' Italian: 'Il tempo è bello oggi.'

Google Bard

Sure, here are the translations of the sentence "The weather is beautiful today" into French, German, and Italian:

  • French: Il fait beau aujourd'hui.
  • German: Das Wetter ist heute schön.
  • Italian: Il tempo è bello oggi.

I hope this helps! Let me know if you have any other questions.

4. How do I cook a perfect medium-rare steak?


Here's a simple step-by-step process for cooking a perfect medium-rare steak:

  1. Season the Steak: About 40 minutes before you're ready to cook, remove the steak from the refrigerator. This allows it to come to room temperature, which helps in achieving more even cooking. Pat the steak dry with paper towels and season generously on both sides with salt and pepper.

  2. Preheat the Pan: Heat a heavy-bottomed skillet (cast-iron is great for this) over high heat until it's very hot. You can also add a small amount of oil, but this is optional as a well-marbled steak will render its own fat.

  3. Sear the Steak: Place the steak in the pan. It should sizzle loudly. Sear for about 2-3 minutes on one side, or until it develops a rich brown color and crust. Flip the steak and sear the other side for 2-3 minutes.

  4. Lower the Heat and Continue Cooking: Lower the heat to medium and continue cooking the steak for an additional 3-5 minutes, depending on the thickness of the steak and your preferred level of doneness. For a medium-rare steak, aim for an internal temperature of 130-135°F (54-57°C).

  5. Check the Temperature: Use a meat thermometer to check the steak's internal temperature. Insert it into the side of the steak, not from the top.

  6. Rest the Steak: Once the steak reaches your desired temperature, remove it from the pan and let it rest on a cutting board or warm plate for at least 5 minutes. This allows the juices to redistribute throughout the steak.

  7. Serve: After resting, slice the steak against the grain and serve.

This method will result in a medium-rare steak that is warm and still red in the center. If you prefer your steak less or more cooked, adjust the cooking time accordingly.

Remember, it's always better to err on the side of undercooking since you can always cook the steak more if needed. Overcooking, however, can't be undone.

Google Bard

Here are the steps on how to cook a perfect medium-rare steak:

  1. Choose the right steak. The best steaks for cooking medium-rare are ribeye, New York strip, or filet mignon. These cuts of meat are well-marbled, which means they have a good amount of fat running through them. The fat helps to keep the steak moist and flavorful.
  2. Bring the steak to room temperature. This will help the steak cook more evenly. Remove the steak from the refrigerator at least 30 minutes before cooking.
  3. Season the steak. Simply season the steak with salt and pepper. You can also add other herbs and spices, such as garlic powder, onion powder, or paprika.
  4. Heat your pan. Use a heavy-bottomed pan, such as a cast-iron skillet. Heat the pan over medium-high heat until it is very hot.
  5. Add the steak to the pan. Do not add any oil to the pan. The fat in the steak will be enough to prevent it from sticking.
  6. Sear the steak for 2-3 minutes per side. This will create a nice crust on the outside of the steak.
  7. Reduce the heat to medium-low. Cook the steak for an additional 3-4 minutes per side, or until it reaches the desired level of doneness.
  8. Remove the steak from the pan and let it rest for 5 minutes. This will allow the juices to redistribute throughout the steak.
  9. Slice and serve. Enjoy!

Here are some additional tips for cooking a perfect medium-rare steak:

  • Don't overcrowd the pan. If you're cooking more than one steak, cook them in batches so that they have enough room to sear properly.
  • Don't poke the steak with a fork. This will release the juices and make the steak dry.
  • Let the steak rest before slicing. This will allow the juices to redistribute throughout the steak.

With a little practice, you'll be able to cook a perfect medium-rare steak every time!

5. What are the latest developments in AI and machine learning as of 2023?


I'm sorry for the confusion, but as an AI developed by OpenAI, my training only includes information up until September 2021, and I don't have the ability to update or access real-time information. As of my last training cut-off, there were several trends in AI and machine learning:

  1. Self-supervised learning: This is a form of unsupervised learning where the data provides the supervision. This trend was gaining traction with applications in language models, like GPT-3.

  2. Few-shot learning: Another big trend, few-shot learning refers to the ability to understand and execute tasks with a minimal number of examples.

  3. Transformer architectures: The success of transformer-based models, especially in natural language processing tasks, was a major development.

  4. Explainable AI: There was also a growing interest in making AI and ML models more transparent, explainable, and interpretable.

  5. Ethical AI: Along with explainability, there was an increasing focus on the ethics of AI, including minimizing bias, respecting privacy, and ensuring fairness.

  6. Hybrid models: These models combine different approaches, like the integration of symbolic AI with neural networks.

  7. Edge AI: Running AI algorithms on edge devices (like IoT devices) was also becoming more popular.

  8. AI in healthcare: The application of AI in the healthcare industry, for drug discovery, disease prediction, and telemedicine, was a promising area.

For the latest developments in AI and machine learning as of 2023, I'd recommend checking the latest research papers, blog posts from leading AI research groups (like OpenAI, DeepMind, Facebook AI, etc.), AI-focused news outlets, and conference proceedings (like NeurIPS, ICLR, ACL, etc.).

Google Bard

Here are some of the latest developments in AI and machine learning as of 2023:

  • Large language models: Large language models (LLMs) are AI models that have been trained on massive datasets of text. They can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. LLMs are becoming increasingly powerful and are being used for a variety of applications, such as generating realistic chatbots, writing news articles, and translating languages.
  • Computer vision: Computer vision is a field of AI that deals with the ability of computers to see and understand the world around them. Computer vision is used in a variety of applications, such as self-driving cars, facial recognition, and medical image analysis.
  • Natural language processing: Natural language processing (NLP) is a field of AI that deals with the ability of computers to understand and process human language. NLP is used in a variety of applications, such as machine translation, chatbots, and sentiment analysis.
  • Deep learning: Deep learning is a type of machine learning that uses artificial neural networks to learn from data. Deep learning has been used to achieve state-of-the-art results in a variety of tasks, such as image classification, natural language processing, and speech recognition.
  • Explainable AI: Explainable AI (XAI) is a field of AI that deals with the ability of computers to explain their decisions. XAI is important for ensuring that AI systems are fair and unbiased, and that they can be trusted by humans.

These are just a few of the latest developments in AI and machine learning. As AI continues to evolve, we can expect to see even more exciting and transformative applications of this technology in the years to come.


Google Bard and ChatGPT are both AI chatbots that help automate chat tasks. The main difference between the two is their data sources. Bard is trained on an “infiniset” of data chosen to enhance its dialogue and has access to the internet in real time. ChatGPT is trained on a pre-defined set of data that hasn't been updated since 2021.

You can also compare Google Bard and ChatGPT easily with ChatHub.