Overview

Chatbots are becoming indispensable tools in the fast changing field of artificial intelligence (AI) for automation and communication in a variety of industries. One of Google’s most recent AI developments, Google Gemini, seeks to go beyond what chatbots are capable of. But Google Gemini is not without its problems, as are many sophisticated AI systems; one such issue is the phenomena referred to as “AI hallucinations.” This paper explores Google Gemini’s strengths and limitations, focusing especially on its hallucination susceptibility—a crucial problem that affects the dependability and credibility of AI-generated responses.

Google Gemini: What is it?

The most recent iteration in Google’s line of AI chatbots is called Google Gemini. Google Gemini is a cutting-edge machine learning (ML) and natural language processing (NLP) tool created by a group of top AI experts. The objective of this development was to construct a chatbot with high accuracy and relevancy that could comprehend text messages and produce responses that seemed human.

How to Test Google Gemini 

Research and accuracy

Gemini’s connection to the open internet should give it an advantage over ChatGPT 3.5 in terms of accuracy. It seems, however, that access to the open internet isn’t helping Gemini be more accurate. 

When asking Gemini to look up papers on the relationship between homeschooling and neuroplasticity, it played things safe. Gemini correctly stated that there aren’t many studies that look at this relationship and recommended I Google “neuroplasticity and learning” or “brain-based learning.”

Gemini also recommended a video titled How Does Neuroplasticity Apply to Homeschooling? but when clicking on the YouTube link, it took me to a different video. When searching through the video transcript, the word “homeschooling” or other related terms never surfaced.

When I pressed Gemini to cite some papers, the ones it did recommend couldn’t be found via a Google Search, which suggests it was hallucinating.

Microsoft Copilot (in creative mode) and Claude performed the best, taking in data from multiple sources and finding links between them. Both also found the nuances in the complexities of teaching environments and made note of how results could vary for a variety of factors. And, unlike Perplexity, it only cited scholarly and reputable sources.

ChatGPT 3.5, which isn’t connected to the open internet, didn’t cite any papers on this topic, but did cite others on the effects of COVID-19 and the brain. Perplexity did cite some papers and sites, but didn’t do a great job of synthesizing that information. Claude performed the best, citing papers that existed and finding potential links in research between said papers.

If you do use Gemini for research, be sure to double-check and verify.

Summarizing articles

Unlike ChatGPT 3.5, Gemini can actually summarize articles. Entering too much text into ChatGPT 3.5 usually forces it to error out. But with Gemini, there’s no need to copy and paste the text of the entire article. Just add a link to the article and Gemini can summarize it.

That said, the quality of that summary is rather useless when directly linking. When asked to summarize an article I wrote earlier this year about the effects of ChatGPT on technology at CES 2024, Gemini spouted off two sentences and missed all the key points I made in the piece.

Gemini fared much better when I copied and pasted the text of the article directly into the chatbot. Here, Gemini did grab more of the key points I made. While it still ultimately lacked the impact of reading my piece in its entirety, in a pinch, it gave enough details for someone to get a good gist. 

Claude, Copilot and Perplexity performed on par with Gemini, gathering the basic gist but leaving out the main thrust of the article. 

Writing emails

AI chatbots have been described as autocomplete on steroids. To an extent, that’s true. These word calculators can take simple prompts written in plain language and calculate the right words to arrange in a sentence-like structure. 

Gemini performed well in writing emails to a boss asking for additional time off. It put together a professional-sounding email that also took into consideration floating holiday policies. However, most employees likely won’t refer to the employee handbook when just asking for a day or two off. In reality, this version of the email would likely be too formal, almost robotic, and may tip off its recipient that it was written by AI. 

When asked to make the email sound more casual, Gemini brilliantly shortened the text and added the appropriate exclamation points so that the tone didn’t come off as stodgy. 

Granted, ChatGPT 3.5, Claude and Perplexity also accomplished writing simple emails with great ease. But what about more complicated topics, ones that delve into morals, capitalism and the role of consent?

In asking Gemini to write a pitch email to my editor about such topics, it did a solid job of putting together a pitch that I feel would raise some curious eyebrows. Comparatively, ChatGPT 3.5 also did an adequate job of crafting this pitch, but the language was so banal and pedantic that it would too easily come off as AI-generated. It lacked a sense of wonder and excitement that would prompt an editor to want to know more. Perplexity, too, came off as robotic.

Claude performed the best in this test, not only adding a compelling headline, but capturing the weirdness of parasocial relationships and striking curiosity with the reader. It needed some minor tuning, but was honestly good enough to be worthy of a reply. 

Copilot outright refused to write the pitch, saying the topic was too sensitive. 

Among Google Gemini’s salient features are:

• Advanced Natural linguistic Processing (NLP) Capabilities: Gemini can absorb and comprehend complicated linguistic inputs, enabling it to have more complex and nuanced conversations.
• Seamless Integration: The chatbot’s usefulness is increased by its seamless integration with Google’s ecosystem, which includes Google Assistant, Google Search, and other services.
• Real-time Translation: One of its most notable advantages is the capacity to translate between languages in real-time, which makes cross-language communication easier.
• Contextual Understanding: Gemini is made to keep context intact during protracted discussions, facilitating more meaningful and cogent exchanges.

Building on the successes of its predecessors, Google Gemini seeks to decrease errors and enhance the user experience by providing more accurate and contextually relevant responses.

Comprehending Artificial Intelligence Hallucinations

When an AI model produces results that are not based on its training data or actual facts, this is known as an AI hallucination. These delusions might contain anything from slightly false information to entirely made up details. In this context, the term “hallucination” refers to the AI’s propensity to produce stuff that appears reasonable but is actually false or illogical.

Various Causes of Hallucinations.

• Data Restrictions: Although AI models are trained on enormous datasets, they are not perfect. The model could produce inaccurate results when given strange or unclear inputs.
• AI is based on predictive algorithms and pattern recognition, or predictive modeling. These algorithms can yield results that seem reasonable but are not factually correct.
• Complex Questions: Complex or context-rich inquiries may make it more difficult for the AI to produce reliable results, which could result in hallucinations.

It’s important for developers and users to understand the causes of hallucinations since it illustrates the drawbacks and dangers of depending too heavily on AI for important activities.

Evaluation of Google Gemini’s Performance

Google Gemini has demonstrated outstanding results in a number of domains:

• Accuracy: Gemini is a trustworthy tool for routine questions and chores since it consistently responds to interactions in a contextually appropriate and correct manner.
• Context Management: The chatbot has made great progress in preserving context throughout protracted discussions, enabling more organic and flowing exchanges of ideas.

Nonetheless, reports of hallucinations have been made, especially in situations when the questions are unclear or complex. 

These delusions have the potential to compromise the chatbot’s dependability, particularly in situations where precision is crucial. As examples, consider:

• Fabricated Facts: Gemini may produce answers that contain information that isn’t in its training set, which could result in false information.
• Nonsensical Outputs: Occasionally, the chatbot generates answers that don’t make sense or aren’t related to the question.

Google Gemini is still a strong and useful tool in spite of these difficulties, especially when used in settings where possible hallucinations can be controlled.

Google Gemini and Other Chatbots: A Comparison

In contrast to other top chatbots, Google Gemini offers a number of notable benefits and parallels.

• Google Gemini vs. GPT-4 from OpenAI: GPT-4 is well-known for its vast knowledge base and sophisticated language skills. Although Google Gemini is great at real-time translation and integrating with the Google environment, GPT-4’s size and flexibility allow it to be used for a wider range of purposes.
• Google Gemini vs. BlenderBot from Meta: BlenderBot prioritizes social connection and personality in its rich and captivating talks. On the other hand, despite having comparable hallucination issues, Google Gemini offers better integration and usefulness within Google’s service suite.

Hallucinations are also experienced by GPT-4 and BlenderBot, demonstrating that this is not only a Google Gemini problem but a common one in the AI industry.

User Experience and Input

User opinions about Google Gemini have been mixed.

• Positive Reviews: A lot of users like the chatbot’s sophisticated capabilities, which improve the efficiency and smoothness of conversations. Examples of these features include its contextual understanding and real-time translation.
• Unfavorable Reviews: A few users have complained about the chatbot’s sporadic hallucinations, which might spread false information and make it harder to trust the AI’s answers.

Even while Google Gemini is commended for its features and integration, users are nevertheless wary, especially when information integrity is crucial. This conflicting response emphasizes how crucial it is to keep making improvements and educating users about the limitations of AI chatbots.

Taking Google Gemini’s Hallucinations Seriously

Google is actively utilizing a number of tactics to solve the problem of hallucinations in Gemini:

• Refining Training Data: Google hopes to lower the probability of hallucinations by increasing the caliber and range of the training data.
• Tighter validation guidelines ensure accuracy by implementing stringent procedures to double-check the chatbot’s data.
• Reinforcement learning with human feedback helps the model grow over time by learning from its errors.
• These initiatives enhance AI-generated content’s reliability, ensuring Google Gemini is a trusted tool for consumers.

Practical and Ethical Consequences

There are serious moral and practical issues when AI models such as Google Gemini exhibit hallucinations.

• Ethical Concerns: AI hallucinations have the ability to disseminate false information, causing people to make bad decisions with possibly dangerous outcomes. Transparency on AI’s limitations and proactive risk mitigation are essential to ensuring its ethical use.
• Practical Implications: Inaccurate responses might harm a company’s reputation and result in losses of money if it uses chatbots for customer support.

Putting human control and verification procedures in place can aid in risk management.

It is essential for the proper development and application of AI technology to comprehend and deal with these ramifications.

Prospects for Google Gemini in the Future

Google has big ambitions for Gemini going forward:

• Future improvements are planned to improve contextual understanding and lessen hallucinations, with the overall goal of enhancing the chatbot’s skills.
• Technological advancements in hybrid AI models and advanced neural networks enhance performance and dependability.
• Google Gemini leads chatbot innovation, setting new benchmarks for AI-driven communication.

In summary

With its sophisticated features and integration potential, Google Gemini is a major advancement in chatbot technology. But there is still a serious problem with AI hallucinations that needs to be solved. Advancing technology and Google’s efforts to enhance Gemini’s accuracy promise more reliable user interactions. Maximizing AI’s benefits while minimizing its threats requires an understanding of its limitations and ethical concerns.

FAQs

Q: Google Gemini: What is it?

A: Google Gemini, a sophisticated AI chatbot, enhances conversational skills using advanced machine learning and NLP methods.

Q: AI hallucinations: what are they?

A: AI hallucinations occur when a chatbot generates responses not based on real-world knowledge or its training, leading to inaccuracies..

Q: What distinguishes Google Gemini from other chatbots?

A: Google Gemini, featuring real-time translation and seamless Google integration, competes with OpenAI’s GPT-4 and Meta’s BlenderBot.

Q: What moral issues do AI hallucinations raise?

A: AI hallucinations can disseminate false information, resulting in poor judgments and a decline in confidence in AI systems. This presents moral dilemmas for both users and engineers.

Q: What steps is Google doing to solve Gemini hallucinations?

A: Google is reducing hallucinations in Gemini by enhancing validation, augmenting training data, and incorporating human input.

Key Takeaway 

• Google Gemini is a state-of-the-art chatbot with sophisticated functionality.
• Artificial intelligence hallucinations pose a serious problem since they can produce false or distorted information.
• Google is continuously pursuing technology innovations and enhanced validation procedures to reduce hallucinations.
• Applications of AI must be carefully considered and overseen due to the ethical and practical implications of hallucinations.
• Google Gemini’s upcoming improvements should improve its dependability and performance, securing its place in the rapidly changing field of AI-driven chatbots.