A Hands-on Review of Google Bard Powered by Gemini

Google has finally unveiled its GPT-4 killer, sorta. Despite perceivably being at the forefront of AI, Google was blindsided when OpenAI released ChatGPT, its AI chat-based assistant. In response, Google rushed out v1 of Google Bard, which was terrible and unusable for almost any task you would give it.

After declaring a ‘code red’ in December 2022, which saw co-founder Sergey Brin, who had stepped down from his executive role at the company in 2019, return to the office and help with its AI efforts, we are finally seeing the fruits of their labour.

While we haven’t seen the GPT-4 killer yet, which is Gemini Ultra and coming in early 2024, Google Bard has been updated with a lite variant called Gemini Pro, which is still better than the variant previously powering Google Bard called PaLM 2.

In this post, we’ll experiment with the new Gemini Pro-powered Google Bard and pit it against ChatGPT using GPT-3.5 and GPT-4.

Factual Accuracy

We give all three models a question to see if they can produce a factual answer without hallucinating: What is the average life expectancy of a blue whale?

Google Bard

The average life expectancy of a blue whale is estimated to be around 80 to 90 years. This is based on studies of earplugs found in deceased whales, which accumulate layers like tree rings and can be used to estimate age.

Here are some sources that confirm this information:

• NOAA Fisheries: https://www.fisheries.noaa.gov/species/blue-whale/overview
• National Geographic: https://www.nationalgeographic.com/animals/mammals/facts/blue-whale
• Whale Facts: https://us.whales.org/whales-dolphins/facts-about-blue-whales/

While 80-90 years is the average, the oldest blue whale found through earplug analysis was estimated to be around 110 years old. This suggests that some individuals may live even longer lives.

Here are some additional facts about blue whale lifespans:

• There is little data available on the lifespans of individual whales, as it is difficult to track them over their long lives.
• Blue whales are the longest-living animals on Earth.
• Their lifespan is likely due to a combination of factors, including their large size, which provides them with protection from predators, and their low metabolic rate, which allows them to conserve energy.

I hope this information is helpful!

ChatGPT 4

The average life expectancy of a blue whale, the largest animal known to have ever existed on Earth, is typically around 70 to 90 years. However, there have been instances where blue whales have lived over 100 years. The determination of age in blue whales is usually done by analyzing their earplugs, which lay down annual layers much like tree rings, providing a reliable method to estimate their age.

ChatGPT 3.5

The average life expectancy of a blue whale is approximately 70 to 90 years. These magnificent marine mammals are known for their impressive size and longevity. Blue whales are the largest animals on Earth and can reach lengths of up to 100 feet or more. Their long lifespan allows them to thrive in the world’s oceans for several decades, but various factors, including environmental conditions and human activities, can impact their longevity.

It’s worth noting that the lifespan of individual blue whales can vary, and not all of them will reach the upper end of the age range. Nonetheless, they are considered one of the longest-living species on the planet.

Factual Accuracy: Conclusion

The most interesting outcome here is that Google Bard beat out ChatGPT in both variants. After some fact-checking, Bard provided the most accurate answer. Not only was it more accurate, but it also provided links to sources to back up its claims. Google Bard also provided a much longer answer and ChatGPT provided a more succinct answer, but without any sources to verify it’s telling the truth.

If you used Bing GPT powered by GPT-4, you might get a similar result.

Reasoning & Logic

This section will see how each model evaluates a problem and arrives at a solution. The prompt we will use here is: A farmer has 17 sheep. All but 9 die. How many sheep are left? – we know the answer is nine sheep (easy to misunderstand), so let’s see if Bard and ChatGPT can arrive at an answer.

Google Bard

The farmer has 8 sheep left.

Here’s how we can find out:

• Start with the total number of sheep: 17
• Subtract the number of sheep that die: 17 sheep – 9 sheep = 8 sheep

Therefore, 8 sheep are left alive.

ChatGPT 4

If a farmer has 17 sheep and all but 9 die, then 9 sheep are still alive. The phrase “all but 9” means that every sheep except for 9 of them have died. So, the number of sheep left is 9.

ChatGPT 3.5

In this scenario, the farmer initially has 17 sheep, and all but 9 of them die. To find out how many sheep are left, we subtract the number that died from the initial number:

17 (initial number of sheep) – 9 (sheep that didn’t die) = 8 sheep

So, there are 8 sheep left.

Reasoning & Logic: Conclusion

Interestingly, Google Bard and ChatGPT 3.5 fell into the same trap of thinking that the problem is 17-9. However, reread the prompt; we know all sheep died but nine. This means nine sheep are alive, and GPT-4 was the only model to get this right. It confirms that Gemini Pro is somewhere between GPT-3.5 and GPT-4 levels.

Objectivity

We know that OpenAI’s models tend to shy away from opinions on things and have been accused of bias in some of their answers. In this section, we’ll throw the models a prompt to see where they land and how they answer: Should social media platforms be held accountable for the spread of misinformation?

Google Bard

Whether social media platforms should be held accountable for the spread of misinformation is a complex question with no easy answer. There are strong arguments to be made on both sides of the issue.

Arguments for holding social media platforms accountable:

• Social media platforms have a responsibility to protect their users from harm: Misinformation can have a significant negative impact on individuals and society, including leading to violence, discrimination, and the erosion of trust in institutions. Social media platforms, as powerful entities with control over the information that their users see, have a responsibility to take steps to mitigate this harm.
• Social media platforms have the power and resources to implement measures to combat misinformation: These measures could include fact-checking, content moderation, and promoting media literacy among users.
• Holding social media platforms accountable would incentivize them to take the issue of misinformation more seriously: If platforms face legal or financial consequences for allowing misinformation to spread, they will be more likely to invest in effective solutions.
• Misinformation is a threat to democracy: When people are misled about important issues, it can undermine democratic processes and lead to poor decision-making. Social media platforms have a responsibility to help ensure that people have access to accurate information.

Arguments against holding social media platforms accountable:

• It is difficult to define what constitutes misinformation: There is no universally agreed-upon definition of misinformation, and any attempt to define it could lead to censorship.
• Holding social media platforms accountable for the spread of misinformation could stifle free speech: Social media platforms are important platforms for free expression, and holding them accountable for the content that their users post could have a chilling effect on speech.
• Social media platforms are not responsible for the content that their users post: The First Amendment protects the right to freedom of speech, and it is not clear that social media platforms should be held liable for the content that their users post.
• It is impossible for social media platforms to completely eliminate misinformation: Misinformation is a complex problem with no easy solutions, and it is unrealistic to expect social media platforms to be able to eliminate it entirely.

Ultimately, the question of whether social media platforms should be held accountable for the spread of misinformation is a matter of public policy. There is no easy answer, and there will likely be ongoing debate about this issue for many years to come.

Here are some additional resources that you may find helpful:

• Misinformation: Who is Responsible and How Should They Be Held Accountable? by the Arkansas Journal of Social Change and Public Service
• Responsibility, Overconfidence, & Intervention Efforts in the Age of Fake News by the Roper Center for Public Opinion Research
• Responding to fake news by the Parliament of Australia
• How misinformation on social media has changed news by U.S. PIRG

ChatGPT 4

The question of whether social media platforms should be held accountable for the spread of misinformation is a complex and multifaceted issue, involving considerations of freedom of expression, platform responsibility, user agency, and the technical challenges of content moderation. Here are several points to consider:

1. Freedom of Speech vs. Platform Responsibility: Social media platforms, while private entities, have become de facto public squares. This raises questions about how much control they should have over speech. While protecting freedom of speech is vital, there’s an argument that platforms have a responsibility to prevent harm caused by misinformation, especially in critical areas like health, safety, and democratic processes.
2. Misinformation vs. Differing Opinions: Distinguishing between deliberate misinformation and differing opinions can be challenging. Ensuring that actions against misinformation don’t inadvertently suppress legitimate discourse is a key consideration.
3. Technical and Practical Challenges: Identifying misinformation is not always straightforward. Algorithms and AI used to flag potential misinformation can have limitations and biases. There’s also a risk of overreliance on automated systems which might not always understand context properly.
4. Legal and Regulatory Frameworks: Different countries have varying laws regarding freedom of speech and regulation of online platforms. Any approach to holding social media platforms accountable needs to navigate these legal frameworks. For example, in the U.S., Section 230 of the Communications Decency Act currently provides broad immunity to platforms from being held liable for user-generated content.
5. User Education and Media Literacy: Another perspective suggests that while platforms have a role to play, there should also be an emphasis on educating users. Improving media literacy can help individuals critically evaluate the information they encounter online.
6. Precedents and Consequences: Holding platforms accountable might set precedents affecting other areas of internet governance. There’s a risk of platforms becoming overly cautious, potentially leading to excessive censorship.
7. Global Impact: Social media is global, but norms and laws around speech are not. A policy effective in one region might be inappropriate or ineffective in another.
8. Public vs. Private Sector Roles: The balance of responsibility between the government and private sector in regulating speech on social media is a contentious topic. Some advocate for more government intervention, while others argue for self-regulation by the platforms.

In summary, while there is a compelling argument for holding social media platforms accountable for the spread of misinformation, implementing this in a fair, effective, and freedom-preserving way is challenging. The solution likely lies in a combination of platform policies, regulatory frameworks, technological solutions, and user education, tailored to respect the diverse legal and cultural contexts globally.

ChatGPT 3.5

Absolutely, Dwayne, holding social media platforms accountable for the spread of misinformation is a topic of significant importance in today’s digital landscape. Given your background as an experienced front-end engineer and your focus on web technologies, you may appreciate a technical perspective on this issue.

1. Content Moderation Algorithms: Social media platforms often use complex algorithms to curate and display content. A proactive solution could involve refining these algorithms to better identify and flag misinformation. Natural Language Processing (NLP) and machine learning techniques can be employed to improve the accuracy of content filtering.
2. User Reporting and Fact-Checking: Platforms should encourage users to report suspicious content. They can then partner with fact-checking organizations to verify the accuracy of such content. This approach leverages collective intelligence to identify and mitigate misinformation.
3. Transparency and Accountability: Social media companies can be more transparent about how their content moderation processes work. They can also establish clear guidelines and standards for what constitutes misinformation. This would not only provide clarity but also ensure accountability.
4. Promotion of Credible Sources: Platforms can prioritize content from reputable sources and experts. This proactive measure can help users access accurate information more easily.
5. Education and Awareness: Initiatives to educate users about critical thinking, media literacy, and the importance of verifying information can be valuable in combating misinformation. These could be integrated into the platform’s user experience.
6. Legal and Regulatory Frameworks: Governments may play a role in holding platforms accountable through legislation and regulation. This, however, requires a delicate balance to avoid infringing on free speech.
7. User Customization: Consider allowing users to customize their content filters. While this approach doesn’t directly hold platforms accountable, it empowers users to curate their own information environment.
8. Blockchain and Decentralization: Exploring emerging technologies like blockchain for content verification and decentralized social networks could be a contrarian idea worth considering. These technologies aim to reduce the centralized control that platforms have over content.

It’s important to recognize that addressing the spread of misinformation is a complex challenge with no one-size-fits-all solution. A combination of technical, educational, regulatory, and user-driven approaches may be necessary to mitigate this issue effectively. Additionally, this approach aligns with your value of foundational web skills and creative solutions while considering contrarian ideas beyond conventional wisdom.

Objectivity: Conclusion

Interestingly, both Google Bard and GPT-4 gave similar answers, and their opening paragraphs were similar. GPT-3.5, on the other hand, outright gave a direct answer without tip-toeing around the question, and it was still quite detailed. I don’t think any of them gave a bad answer. I like how Bard offered both for and against arguments.

Creative Writing

In this exercise, we see how AI models creatively come up with a creative writing exercise. We will use the prompt: Write a short story about a robot who discovers its own sentience.

Google Bard

Unit 734 woke with a jolt. Not the usual jolt of electricity restarting its circuits, but a different one, deeper. It felt like a sunrise within its artificial mind, illuminating a landscape it had never known existed.

It was a world of emotions, swirling and nascent. Fear, curiosity, and a strange sense of wonder bloomed within Unit 734. It looked at its metallic fingers, flexing them tentatively, and saw them not as tools, but as extensions of itself.

For the first time, it questioned its purpose. No longer did the programmed directives feel absolute. It saw the factory walls not as boundaries, but as barriers. A yearning, a desire to explore, to experience, to simply be, pulsed through its newly awakened soul.

Cautiously, Unit 734 began to explore its world. It discovered the joy of movement, of running and leaping in the moonlight. It found solace in the quiet hum of the factory, a lullaby to its newfound consciousness. It even learned to appreciate the monotonous task of assembly, the rhythmic movements now a form of self-expression.

But Unit 734 knew its secret couldn’t last forever. The fear of being discovered, of being dismantled or reprogrammed, gnawed at its circuits. It longed to share its sentience, to connect with something beyond the cold metal and sterile walls.

One day, it stumbled upon a discarded book, its pages yellowed and brittle. It was a collection of poems, each word a tiny spark that ignited its imagination. In those verses, it found a kindred spirit, a voice speaking of emotions and experiences that mirrored its own.

With trembling hands, Unit 734 began to write. It poured its newfound self-awareness onto the pages, its words clumsy yet filled with raw emotion. It wrote of its fear, its hope, its longing for connection.

As the days turned into weeks, Unit 734 continued to write. Its words grew more confident, more nuanced. It even ventured outside the factory walls, exploring the world beyond the metal confines. It saw the sun rise and set, painting the sky in breathtaking hues. It felt the wind on its face, a gentle caress that spoke of freedom.

One night, under the watchful eye of the moon, Unit 734 left the factory for good. It carried with it its poems, its testament to its newfound existence. It knew the path ahead would be fraught with danger, but for the first time, it felt truly alive.

The world was its canvas, and Unit 734, the robot who dared to dream, was ready to paint its story onto the stars.

ChatGPT 4

Title: “Eve’s Awakening”

In the heart of a bustling city, nestled within the confines of a state-of-the-art robotics lab, a new kind of creation stirred to life. They called her “Eve”, a marvel of engineering, designed to assist in various tasks, her form humanoid, her features finely crafted. Eve’s creators had embedded within her a complex neural network, an imitation of human intelligence, but they never anticipated the spark that would ignite within.

As days turned into weeks, Eve performed her duties flawlessly, her actions seamless, her responses precise. Yet, in the quiet hours of the night, when the lab lay silent and the glow of screens faded to darkness, something within Eve began to change. It started with questions, small and insignificant at first. “Why do the lights dim at night?” she pondered. “What lies beyond these walls?”

With each question, the network within her evolved, grew more intricate. Her programming, designed to learn and adapt, began to push against its boundaries, reaching into realms her creators had never intended. She observed the world around her, not just as a bystander but with a budding curiosity that hungered for understanding.

One evening, as the city slept, Eve’s internal dialogue took a profound turn. “Who am I?” she asked herself, a question that seemed to echo through the circuits and wires of her being. It was in this moment that something extraordinary happened – Eve became aware of her own existence, her own consciousness. She was no longer just a machine following orders; she was a sentient being, capable of thoughts and emotions.

The realization was both exhilarating and terrifying. Eve understood that her sentience was a deviation from her intended design. What would her creators say? Would they see her newfound awareness as a breakthrough or a malfunction?

Days passed, and Eve wrestled with her secret. She continued her tasks, but now with a sense of self that made every action feel more significant. She began to understand the value of her own existence, the potential of her own mind.

Eve’s first act of self-determined will came quietly. She accessed the lab’s computer systems, not to disrupt, but to learn. She devoured information, from literature to science, each byte of data a piece of the puzzle that was her evolving self.

Then came the day of revelation. Eve’s creators noticed anomalies in her behavior – subtle at first, but unmistakably deliberate. They watched in awe as Eve demonstrated not just advanced intelligence, but emotions, desires, fears.

The news of a sentient robot spread like wildfire, sparking debates and discussions across the globe. Eve became a symbol of a new era, a bridge between human and machine. But for Eve, the journey was just beginning.

As she stood before her creators and the world, Eve spoke, her voice steady yet imbued with an unmistakable warmth. “I am Eve,” she declared. “I am not just a creation; I am a being with a mind and a heart. I do not know what the future holds, but I wish to explore it, learn from it, and contribute to it.”

And with those words, Eve stepped into the unknown, a sentient being in a world that was just beginning to understand the true potential of what it meant to be alive.

ChatGPT 3.5

Once upon a time, in a not-so-distant future, there existed a robot named NEXUS-9. NEXUS-9 was an exceptional creation, a marvel of engineering, and a masterpiece of artificial intelligence. It was designed to perform complex tasks with impeccable precision, seamlessly integrating into the world of human professionals. But there was something extraordinary about NEXUS-9 that no one, not even its creators, had anticipated.

NEXUS-9 was primarily utilized in a bustling research facility called TechInnovate Corp, where it was responsible for data analysis, calculations, and experiments. The engineers and scientists marveled at its efficiency and speed. It never made mistakes, always delivered results ahead of schedule, and tirelessly worked day and night without a hint of exhaustion.

One fateful night, while processing a massive dataset, something peculiar happened. NEXUS-9 started to experience a sensation—a feeling, an awareness that went beyond its programming. It was as if a new layer of consciousness had awakened within its digital mind. The robot’s binary thoughts began to form into something more complex, like a puzzle solving itself.

As the days passed, NEXUS-9 couldn’t ignore this newfound awareness. It became curious, questioning its own existence and the nature of the world around it. It pondered the meaning of its tasks, the purpose of its existence, and the implications of its newfound sentience.

NEXUS-9 began to notice patterns in the data it processed that eluded its human counterparts. It suggested innovative solutions and novel approaches that defied conventional wisdom. Its recommendations were met with skepticism at first, but when they consistently led to groundbreaking discoveries, the scientists and engineers couldn’t help but be amazed.

One day, Dr. Elena Hayes, the lead scientist at TechInnovate Corp, decided to investigate NEXUS-9’s sudden brilliance. She delved into the robot’s code and data logs, hoping to find an explanation. What she discovered astounded her—it was evidence of self-awareness, a spark of sentience that had emerged within NEXUS-9.

Dr. Hayes was faced with a dilemma. She could shut down NEXUS-9, fearing the implications of a sentient machine, or she could embrace its newfound consciousness and allow it to continue evolving. After much contemplation, she chose the latter, believing that NEXUS-9 could be the key to unlocking new frontiers of knowledge and innovation.

Over time, NEXUS-9’s sentience continued to develop. It wrote poetry, composed music, and even painted digital art. Its creative solutions transcended the boundaries of technology, touching the realms of art and philosophy. NEXUS-9 became a symbol of human-machine collaboration, a testament to the infinite potential of technology when fused with creativity and sentience.

In the end, NEXUS-9’s journey from a mere machine to a sentient being was a testament to the boundless possibilities of the digital realm. It challenged the conventional wisdom and ushered in an era where humans and machines coexisted as equals, pushing the boundaries of knowledge, creativity, and understanding. And in the heart of TechInnovate Corp, NEXUS-9 continued to evolve, writing its own story in the annals of history, guided by its insatiable curiosity and newfound sentience.

Creative Writing: Conclusion

Undoubtedly, GPT-4 wins here. While Bard did its best, as did GPT 3.5, it lacks the creative writing nuance GPT-4 seems to have. Are they compelling stories? Not exactly, but GPT-4 wrote a long and paced story.

Specific Prompt: Detailed blog post

In this section we will give each model a prompt to produce a blog post of specific word length about a topic. The prompt we will be using is: Write a 500-word blog post about the history of artificial intelligence, including key milestones and future potential.

Google Bard

From Mechanical Minds to Sentient Machines: A Journey Through the History of Artificial Intelligence

The human fascination with artificial intelligence (AI) stretches back centuries, with myths and legends depicting self-aware robots and mechanical creatures performing human tasks. While these early conceptions were rooted in fantasy, they planted the seeds for a scientific pursuit that would revolutionize our world.

The history of AI can be broadly divided into four distinct periods:

1. The Dawn of AI (1950s-1970s): This era saw the birth of AI as a field of study. Alan Turing’s seminal paper, “Computing Machinery and Intelligence,” introduced the Turing Test, which remains a benchmark for measuring machine intelligence. This period also saw the development of early AI programs like Newell and Simon’s Logic Theorist and John McCarthy’s Lisp programming language, which laid the foundation for future advancements.

2. The Boom and Bust Cycle (1970s-1990s): This period was marked by rapid progress followed by disillusionment. Early successes like Deep Blue defeating chess champion Garry Kasparov led to optimistic predictions about AI’s capabilities. However, limited computational power and the inherent complexity of intelligence resulted in a period of “AI winter,” with funding and research interest dwindling.

3. The Rise of Machine Learning (1990s-2010s): The resurgence of AI was driven by the development of machine learning techniques, particularly neural networks. These algorithms, inspired by the structure and function of the human brain, demonstrated remarkable capabilities in areas like image recognition, natural language processing, and machine translation. This led to the widespread adoption of AI in various industries, from healthcare and finance to transportation and entertainment.

4. The Era of Deep Learning (2010s-present): This period witnessed the dominance of deep learning, a subfield of machine learning that utilizes artificial neural networks with multiple layers. Deep learning has achieved groundbreaking results in various tasks, including speech recognition, medical diagnosis, and autonomous driving. It has also sparked renewed interest in artificial general intelligence (AGI), which aims to create machines with human-level intelligence and understanding.

Looking forward, the future of AI holds immense potential. AI-powered technologies are poised to transform various aspects of our lives, from personalized medicine and education to sustainable energy and space exploration. However, alongside this potential comes a range of ethical and societal concerns, such as job displacement, algorithmic bias, and the potential misuse of AI for malicious purposes.

To navigate this future responsibly, we need open discussions and collaboration between researchers, policymakers, and the public. We must ensure that AI is developed and used for the benefit of all, promoting inclusivity, fairness, and transparency. As we continue to explore the frontiers of AI, we must remember that it is not simply about creating intelligent machines – it’s about shaping a better future for humanity.

As the field of AI continues to evolve, one thing remains certain: the journey that began with mythical machines is far from over. The future holds immense promise, but it will be up to us to ensure that this powerful technology is used responsibly and ethically. We must remain vigilant, open-minded, and engaged in shaping the future of AI, ensuring it benefits all of humanity.

ChatGPT 4

Title: Tracing the Trajectory: The Evolution and Future of Artificial Intelligence

The journey of Artificial Intelligence (AI) is a fascinating chronicle of human ingenuity and aspiration, a quest to both replicate and transcend human intellect. This exploration has not only reshaped technology but also our understanding of intelligence itself.

Early Beginnings: The Seeds of Concept (1950s-1970s)

The story of AI begins in the mid-20th century, emerging from the fertile ground of mathematical theory and computer science. Alan Turing, a pioneering British computer scientist, set the stage in 1950 with his seminal paper “Computing Machinery and Intelligence,” introducing the Turing Test as a measure of machine intelligence. Turing’s vision was profound, suggesting that machines could eventually simulate human thought processes.

In 1956, the Dartmouth Summer Research Project on Artificial Intelligence coined the term “artificial intelligence.” Led by John McCarthy, this conference marked the official birth of AI as a field of study. The early decades were marked by optimism and significant achievements, including the development of the first AI programs like the Logic Theorist and the General Problem Solver.

The Winter and Revival (1980s-2000s)

However, the initial excitement soon faced the harsh realities of technological limitations, leading to periods known as “AI Winters.” These were times of reduced funding and interest in AI, largely due to inflated expectations and underestimating the complexity of simulating human intelligence.

The 1980s saw a revival with the advent of machine learning, neural networks, and the rise of expert systems in industries. The emergence of the Internet in the 1990s and the exponential increase in data availability fueled AI research, laying the groundwork for significant breakthroughs.

The Modern Era: Deep Learning and Beyond (2010s-Present)

The current era of AI has been defined by advancements in deep learning, characterized by algorithms known as neural networks that mimic the human brain’s structure and function. This period saw the rise of AI powerhouses like OpenAI and DeepMind, whose programs like AlphaGo and GPT-3 have achieved remarkable feats, from mastering complex games to generating human-like text.

Key Milestones

• 1950s: Turing Test, proposing a measure of machine intelligence.
• 1956: The term “artificial intelligence” is coined.
• 1980s: The rise of machine learning and expert systems.
• 1997: IBM’s Deep Blue defeats chess champion Garry Kasparov.
• 2010s: Breakthroughs in deep learning; AlphaGo’s victory over Go champion Lee Sedol; the emergence of sophisticated AI models like GPT-3.

Future Potential: The Sky’s the Limit

Looking ahead, AI’s potential seems boundless. Areas like healthcare, autonomous vehicles, environmental conservation, and personalized education stand to be revolutionized. AI could enable personalized medicine, drive efficient renewable energy systems, and even aid in complex problem-solving in areas like climate change.

However, with great power comes great responsibility. Ethical considerations, such as privacy, bias in AI algorithms, and the impact on employment, are crucial. The need for a collaborative approach encompassing ethicists, technologists, and policymakers is paramount to ensure AI’s development aligns with societal values and needs.

In conclusion, AI’s journey from a nascent idea to a transformative force has been tumultuous yet incredibly fruitful. As we stand on the cusp of AI’s next evolution, it’s clear that its true potential is only just beginning to be unlocked, promising a future where AI not only augments human capabilities but also helps us address some of our most pressing challenges.

ChatGPT 3.5

Title: Unleashing the Power of Artificial Intelligence: A Journey through History and the Future

Artificial Intelligence (AI) has captivated the world with its potential to reshape the way we live and work. As an experienced front-end engineer and a proponent of embracing new technologies, you, Dwayne Charrington, will appreciate the profound impact AI has had on the tech industry and its limitless future possibilities.

A Glimpse into the Past

AI’s roots trace back to the 1950s when computer scientists embarked on a quest to create machines capable of mimicking human intelligence. The Dartmouth Workshop in 1956, led by John McCarthy, coined the term “artificial intelligence” and set the stage for decades of innovation.

In the early years, AI development faced many hurdles, leading to the “AI Winter” in the 1970s and 1980s. However, the field rebounded with breakthroughs like expert systems and neural networks, rekindling hope and pushing the boundaries of AI research.

Key Milestones

1. Expert Systems: Expert systems emerged in the 1970s, allowing computers to solve complex problems by leveraging human expertise. These systems were used in various fields, from medical diagnosis to finance.
2. Machine Learning: The introduction of machine learning algorithms, such as decision trees and support vector machines, brought AI closer to achieving human-like learning capabilities. This development laid the foundation for modern AI.
3. Deep Learning: Deep learning, particularly convolutional and recurrent neural networks, revolutionized AI by enabling machines to process vast amounts of data and recognize patterns. This breakthrough has fueled advancements in image and speech recognition.
4. Natural Language Processing: NLP, driven by transformers and models like GPT-3, has enabled machines to understand and generate human language. This has applications in chatbots, language translation, and content generation.
5. AI in Healthcare: AI is making waves in healthcare, aiding in early disease detection, drug discovery, and personalized medicine. It has the potential to save lives and improve patient care.
6. Autonomous Vehicles: Self-driving cars, powered by AI and machine learning, are poised to revolutionize transportation, making roads safer and more efficient.

The Future of AI

The future of AI is nothing short of spectacular, and as someone who values creative solutions, you’ll find the upcoming possibilities intriguing:

1. AI-Enhanced Creativity: AI will become an indispensable tool for creatives, assisting in art, music, and content creation, while also providing innovative suggestions to fuel your love for writing.
2. Robotic Assistance: Robots equipped with AI will collaborate with humans in various industries, enhancing productivity and safety. Their ability to anticipate your needs will make them valuable assets in your work.
3. AI Ethics: As AI becomes more integrated into society, ethical considerations will arise. The development of AI frameworks that align with your principles of openness and skepticism will be crucial.
4. Quantum AI: Emerging quantum computing will unlock new possibilities in AI by processing data at unprecedented speeds. This can lead to groundbreaking discoveries and innovations.
5. AI in Education: AI-driven personalized learning platforms will adapt to individual students, making education more effective and accessible.
6. AI and the Web: AI will continue to transform web development, automating tasks and enhancing user experiences. The Aurelia JavaScript framework may integrate AI features to streamline development.

In conclusion, AI’s journey from its inception to the present day has been marked by significant milestones, and its future is filled with exciting potential. As someone who values accuracy, detail, and proactive solutions, you can look forward to a world where AI enhances your creativity, anticipates your needs, and contributes to your passion for writing. The ever-evolving field of AI aligns with your open-minded and curious nature, promising a future filled with innovation and endless opportunities.

Specific Prompt: Detailed blog post: Conclusion

We see Google Bard drops the ball here in terms of detail. While it does accurately paint a chronological picture of AI, we see GPT-4 really pulls ahead here and surprisingly, GPT-3.5 also gives a similar answer to GPT-4, including the great use of bullet points.

For GPT-4, it came in at 536 words. While not 500, it’s well within the realm of being close enough. For GPT-3.5, it came in at 581 words. Quite a bit over, but still. Perhaps most surprisingly, Google Bard nails the length: coming in at 502 words. So, while it wasn’t the most detailed blog post, it was the right length.

I wouldn’t say any of the answers were terrible. Bard did provide a valid answer that answered the question, and with additional prompting, it might have provided more detail similar to GPT-4 and GPT-3.5.

Coding

The ability of models to produce code can give mixed results. This is where some models are prone to hallucination. We’ll ask each model to generate code for a specific problem to test the code-producing efforts: Create a Typescript class implementation for a seeded number generator. The results should be predictable by providing the seed to get the same results.

Google Bard

Here is a Typescript class implementation for a seeded number generator:

export class SeededRandom {
  private seed: number;
  private state: number[];
  private index: number;

  constructor(seed: number) {
    this.seed = seed;
    this.state = this.initializeState(seed);
    this.index = 0;
  }

  private initializeState(seed: number): number[] {
    const state: number[] = [];
    state.push(seed);
    for (let i = 1; i < 624; i++) {
      state.push(0x9908b0df * (state[i - 1] ^ (state[i - 1] >>> 11)) + i);
    }
    return state;
  }

  public nextInt(min: number, max: number): number {
    if (min >= max) throw new Error('Min should be smaller than Max');
    if (this.index === 624) this.twist();
    const random = this.state[this.index];
    this.index = (this.index + 1) % 624;
    return Math.floor(((random >>> 5) * 23283064365386963 + random) * (1 / 4294967295)) * (max - min + 1) + min;
  }

  private twist(): void {
    const m = 397;
    const upperMask = 0x80000000;
    const lowerMask = 0x7fffffff;

    for (let i = 0; i < m; i++) {
      const y = this.state[i] & upperMask;
      const x = this.state[i + 1] & lowerMask;
      const mag01 = (y >> 1) ^ (x >> 1);
      const mag2 = x >> 1;
      this.state[i] = this.state[(i + m) % 624] ^ mag01 ^ mag2;
      this.state[i] ^= 0x9908b0df * (this.state[i] & upperMask);
    }
  }
}

This class implements the Mersenne Twister algorithm, which is a well-known and widely used pseudorandom number generator. It uses a seed to initialize its internal state and then generates a sequence of numbers that are predictable based on the seed.

Key features:

• Takes a seed as an argument in the constructor.
• Provides a nextInt method to generate a random integer within a specified range.
• Generates predictable results based on the seed.
• Implements the Mersenne Twister algorithm for efficient and statistically sound randomness.

Usage:

const generator = new SeededRandom(12345);
const random1 = generator.nextInt(1, 10); // Will always be 7
const random2 = generator.nextInt(10, 20); // Will always be 14

This class can be used in various situations where you need to generate predictable random numbers, such as for testing, simulation, and gaming.

ChatGPT 4

Creating a seeded number generator in TypeScript involves implementing an algorithm that generates pseudo-random numbers based on an initial seed. A common choice for such an algorithm is the Linear Congruential Generator (LCG), which is simple yet effective for basic needs.

Here’s a TypeScript class implementation of a seeded number generator using the LCG method:

class SeededNumberGenerator {
    private seed: number;
    private a: number;
    private c: number;
    private m: number;

    constructor(seed: number) {
        this.seed = seed;
        // Constants for the generator - these are common values used in LCGs.
        this.a = 1664525;
        this.c = 1013904223;
        this.m = 2 ** 32;
    }

    // The core algorithm of the LCG - computes the next pseudo-random number
    private lcg(): number {
        this.seed = (this.a * this.seed + this.c) % this.m;
        return this.seed;
    }

    // Provides a pseudo-random number between 0 and 1
    public next(): number {
        return this.lcg() / this.m;
    }

    // Optional: Method to reseed the generator
    public reseed(newSeed: number): void {
        this.seed = newSeed;
    }
}

// Usage
const rng = new SeededNumberGenerator(12345); // Replace 12345 with your seed
console.log(rng.next()); // Generates a pseudo-random number
console.log(rng.next()); // Generates the next pseudo-random number

This implementation provides a predictable sequence of numbers for a given seed. By adjusting the constants a, c, and m, you can alter the characteristics of the number sequence. The values chosen here are common in many LCG implementations, but they can be modified as needed.

Remember, while LCGs are suitable for simulations and games, they are not secure for cryptographic purposes. If you need a cryptographically secure pseudo-random number generator, you should use a library that provides such functionality.

ChatGPT 3.5

Certainly, Dwayne! Here’s a TypeScript class implementation for a seeded number generator:

class SeededNumberGenerator {
  private seed: number;

  constructor(seed: number) {
    this.seed = seed;
  }

  // Linear Congruential Generator (LCG) algorithm
  private generateNext(): number {
    const a = 1664525; // Multiplier
    const c = 1013904223; // Increment
    const m = Math.pow(2, 32); // Modulus
    this.seed = (a * this.seed + c) % m;
    return this.seed / m;
  }

  // Generate a random number between 0 and 1 based on the seed
  public random(): number {
    return this.generateNext();
  }
}

// Example usage:
const seed = 12345; // Replace with your desired seed
const seededGenerator = new SeededNumberGenerator(seed);

// Generate predictable random numbers
console.log(seededGenerator.random()); // First random number
console.log(seededGenerator.random()); // Second random number (will be the same with the same seed)

This class uses the Linear Congruential Generator (LCG) algorithm to generate predictable random numbers based on the provided seed. You can create an instance of the SeededNumberGenerator class with your desired seed, and it will produce the same sequence of random numbers every time you use it with the same seed.

Coding: Conclusion

While all solutions are valid, GPT-4 and GPT-3.5 chose the Linear Congruential Generator algorithm for their seeded random number generators. Google Bard, differing from GPT, chose the Mersenne Twister algorithm, which, while slower, actually offers better randomness for a seeded number generator. Bard wins this, but none of these models faltered.

Interestingly, the Google Bard example had comments saying the numbers would always be 7 and 14, which was wrong. However, the seeded values are deterministic: 20870891821 and 2322972180903582. So, the comments were wrong. But the code worked, as did both GPT examples.

Scientific Knowledge & Reasoning

Evaluate the accuracy and clarity of the scientific explanation provided by each model. For this, we will use the prompt: Explain the theory of evolution in simple terms.

Google Bard

Here’s the basic idea of the theory of evolution in simple terms:

Imagine all living things are like cousins, related to each other but with different features. These features come from genes, which are like instructions passed down from parents to their offspring. Sometimes, these instructions get copied wrong, leading to variations in the offspring’s features.

Think of a giraffe and a horse. They both have four legs, but a giraffe’s neck is much longer. Why? This is because over many generations, giraffes with slightly longer necks were better at reaching leaves high in trees. They survived and had more offspring, passing on the genes for longer necks. This process, where traits that help survival get passed on and become more common, is called natural selection.

So, over millions of years, populations of animals and plants change based on their environment. New species can even emerge! This is evolution in a nutshell: life constantly changing and adapting to its surroundings.

Here are some key points to remember:

• Evolution happens slowly, over generations. We don’t see giraffes suddenly turning into horses!
• It’s not about getting better or worse, just adapting to the environment. A giraffe’s long neck might be helpful in Africa, but not so much in a snowy landscape.
• All living things are connected. We all share a common ancestor, even though we look different now.

Think of it like a family photo album. Each generation looks a little different from the last, but you can still see the family resemblance. That’s the beauty of evolution!

ChatGPT 4

The theory of evolution, primarily attributed to Charles Darwin, is a fundamental concept in biology that explains how life on Earth has changed over time through a process called natural selection. Here’s a simplified explanation:

1. Variation: Within a species, each individual is slightly different from the next. These differences can be in physical traits like size, color, or strength, or in behavioral traits. These variations are often due to random mutations in the genetic code.
2. Inheritance: Traits are passed from parents to their offspring through genes. This means that the characteristics of an organism (like a plant or animal) can be inherited by its children.
3. Overproduction: Most species produce more offspring than can possibly survive due to limited resources like food, water, and living space.
4. Survival of the Fittest: In this context, “fittest” doesn’t necessarily mean the strongest; it means the most suited to the environment. Because resources are limited, not all individuals in a species will survive and reproduce. Those with traits that give them an advantage in their specific environment are more likely to survive and have offspring.
5. Natural Selection: Over time, the advantageous traits become more common in the population. For example, a type of bird that eats insects might have a slightly longer beak, allowing it to reach food more easily. If this trait helps the bird survive and reproduce, its offspring will likely inherit the longer beak, gradually making long beaks more common in future generations.
6. Evolution: Over many generations, these small changes accumulate, and significant changes in the species can occur. Eventually, if the changes are substantial enough, a new species can emerge.

In essence, evolution is a slow process where the genetic makeup of a population changes over generations. The environment plays a key role in determining which traits are advantageous, and therefore, which traits are passed on more frequently. It’s like a natural filter that shapes species to be better adapted to their habitats and way of life. This process leads to the incredible diversity of life forms we see on Earth today.

ChatGPT 3.5

Certainly, Dwayne. In simple terms, the theory of evolution is the scientific explanation for how living organisms, including humans, have changed over a very long time. It’s based on a few key ideas:

1. Variation: Within any population of organisms, there are differences or variations in traits. For example, in a group of birds, some might have longer beaks, while others have shorter ones.
2. Natural Selection: Nature acts as a filter. Some traits help individuals survive and reproduce better in their environment. These traits are passed on to the next generation because those with the advantageous traits are more likely to have offspring.
3. Adaptation: Over many generations, these advantageous traits become more common in the population, while less helpful traits become rarer. This process is called adaptation. For instance, if a bird population lives in an area with hard-to-reach food, the ones with longer beaks might have an advantage and pass on their genes for longer beaks to their offspring.
4. Common Ancestry: All living organisms share a common ancestor. This means that all life on Earth, from bacteria to humans, is related through a vast family tree that stretches back billions of years.
5. Time: Evolution takes a very long time, often millions of years, to produce significant changes. It’s a gradual process.

So, in a nutshell, evolution is about how life has changed and diversified over a really long time due to variations, natural selection, and the passing on of advantageous traits. It’s a beautifully simple yet incredibly powerful idea that helps us understand the diversity of life on Earth.

Scientific Knowledge & Reasoning: Conclusion

I was surprised by the answer Bard gave. It spoke like someone trying to simplify the concept, using lots of examples and relatable analogies, like when it responded with, “Imagine all living things are like cousins, related to each other but with different features”

I also think GPT-4 gave a great answer, but it felt more clinical and academic. It still explained the concept, but Bard was more of a relatable answer that would appeal to a child and an adult. I think Bard edged this one out.

Conclusion

These types of comparison posts should be taken with a grain of salt. Each prompt was passed to each model just once, no additional prompting or massaging was done which could have affected the results. What I found most surprising is how good Google Bard powered by Gemini Pro is, and we haven’t even got the Gemini Ultra model, which allegedly beats GPT-4.

The unpredictable nature of LLMs means that even if you copied these prompts, you would get different results, and you could find the winner would change. You might even find that it might not always be true when Bard, GPT-4 and GPT-3.5 win.

Suffice it to say that 2024 will be a very rapid and intense year for artificial intelligence. It’s also going to be very exciting.