Where humans win, AI loses.
Discover and understand with simple examples the areas where Human Intelligence excels beyond artificial intelligence so that you can determine where to focus to outperform in AI times.
🪣 1. Intro.
🪣 2. Who is this article for?
🪣 3. AI: Human-Like Appearance, but Not Quite Human.
🪣 4. Top 4 Human Skills AI Can’t Match.
- AI and the Need for Massive Data vs Human Efficiency and Elegance.
- AI: Stuck Critical Thinking and assess veracity.
- AI Struggles with Moral Understanding and Ethical Decision-Making.
- AI lacks the formation of personal opinions and subjective perspectives.
🪣 5. Where and How Humans Need to Focus to Surpass Machines.
🪣6. End Note.
🪣 Annexe
- Special Thanks to Ángel González.
2. Article’s Sources.
3. Do not miss other AI articles you might be interested in.
3. About the Author.
🪣 1. Intro
Discover and understand the areas where Human Intelligence excels beyond artificial intelligence, to know which areas you and your children need to delve into to be prepared in this new technological world.
Today, the supposedly revolutionary advances in artificial intelligence evoke both concern and optimism.
Optimism stems from intelligence being the means through which we solve problems. The concern arises from fears that the most trendy form of artificial intelligence, machine learning, may distort our science and degrade our ethics by incorporating a fundamentally flawed conception of language and knowledge into our technology.
Although I am also a passionate advocate for technology — believing that its proper use will give us more time to connect as humans and allow us to focus on areas where we add the most value, thus evolving into a superhuman state — it is true that there are also fears. In this article, we will explore the areas where human capabilities surpass those of artificial intelligence.
This article draws from various sources, but it is primarily based on an article titled “The False Promise of ChatGPT,” recently published by Noam Chomsky (Laureate Professor at the University of Arizona and Institute Professor Emeritus in the Department of Linguistics and Philosophy at MIT), Ian Roberts (Professor of Linguistics at Downing College, University of Cambridge), and Jeffrey Watumull (Director of Artificial Intelligence at Oceanit) for subscribers of The New York Times.
🪣 2. Who is this article for?
This article is aimed at individuals unsure how to embrace change with artificial intelligence, guiding them to identify areas where they can excel beyond machines from a very positive approach.
And if you feel like you need to understand some fundamental AI concepts, then this free course for people with zero knowledge of non-tech people, is for you:
It also targets parents seeking to prepare their children for a future where education needs to be reoriented. And I also encourage you to read this article:
🪣 3. AI: Human-Like Appearance, but Not Quite Human.
The various solutions of AI like ChatGPT from OpenAI, Bard from Google, and Sydney from Microsoft have been lauded as pioneering advancements in the realm of general artificial intelligence.
Here is a list of Open-Source and Proprietary Gen AI Solutions:
* Open-Source Gen AI Solutions
OpenLLaMA
Developed by Berkeley AI Research, OpenLLaMA is an open-source reproduction of Meta’s LLaMA model with parameters of 3B, 7B, and 13B. It’s known for its performance in various tasks, although it has limitations in code generation tasks.
Falcon-Series
Created by the Technology Innovation Institute (TII) in Abu Dhabi, the Falcon-Series includes Falcon-40B and Falcon-7B models. These models are optimized for various language tasks and are designed for scalability and efficiency.
MPT-Series
Developed by MosaicML, the MPT-Series are decoder-only large language models trained on diverse data sets. These include MPT-Instruct and MPT-Chat, which are tailored for task-oriented and conversational applications respectively.
FastChat-T5
A chatbot model fine-tuned from the Flan-T5-XL model, FastChat-T5 is designed for commercial applications that require language understanding and interactive responses.
Meta’s LLaMa2
An open-source model from Meta, LLaMa2 is available in various configurations ranging from 7B to 70B parameters and can be accessed through platforms like Hugging Face.
BigScience’s BLOOM
A multilingual model developed collaboratively by the AI research community, emphasizing transparency and openness.
Vicuna and Alpaca
Fine-tuned versions of the LLaMa architecture, offering competitive performance levels for various applications.
* Proprietary Gen AI Solutions
OpenAI’s GPT-4
A highly advanced model with approximately 1.8 trillion parameters, GPT-4 excels in problem-solving and content generation. It is widely used in applications like Duolingo Max and Microsoft’s Bing Chat.
Google’s PaLM 2
Known for its robust language processing capabilities, PaLM 2 is used for tasks requiring precise language interpretation and generation.
Anthropic’s Claude
Another proprietary model focusing on safe and reliable AI interactions.
Cohere
Provides proprietary models aimed at natural language understanding and generation for business applications.
Amazon’s Proprietary Models
Amazon’s models integrate seamlessly with its cloud services, offering scalable and efficient AI solutions for enterprises.
IBM Watsonx.ai
Offers a suite of proprietary models that provide diverse functionalities across different business applications.
We find ourselves at a juncture where these “mechanical minds” surpass human brains not only quantitatively in terms of processing speed and memory capacity but also qualitatively in terms of intellectual insight, artistic creativity, and any other distinctive faculties of human beings.
As useful as these programs may be in certain specific domains (such as computer programming or suggesting rhymes for light verse), we know from the science of linguistics and the philosophy of knowledge that they differ greatly from the way humans reason and use language. These differences impose significant limitations on what these programs can achieve, encoding them with inherent flaws impossible to eradicate.- Noam Chomsky
The False Promise of ChatGPT
Jeffrey Watumull, Noam Chomsky, Ian Roberts
March 2023
🪣 4. Top 4 Human Skills AI Can’t Match.
- AI and the Need for Massive Data vs Human Efficiency and Elegance.
Unlike ChatGPT and similar AI systems, the human mind diverges significantly in its approach to processing information. While AI often operates as a heavy statistical pattern-matching machine, ingesting vast amounts of data to extrapolate the most probable response in a conversation or the likely answer to a scientific question, the human mind operates with surprising efficiency and elegance. It functions adeptly with small amounts of information, focusing not merely on inferring raw correlations between data points but on eliciting deeper explanations. This ability allows humans to comprehend complex situations, innovate creatively, and engage in nuanced reasoning beyond mere statistical inference.
Noam Chomsky, Ian Roberts, and Jeffrey Watumull explain this with an elementary example in their article:
Humans, even from infancy, can learn and use language effectively with limited exposure. A young child can acquire the complex grammatical structure of their native language simply by listening to those around them, and developing innate grammatical rules. This contrasts with artificial intelligence models, which require vast amounts of data to generate pattern-based responses without truly understanding the underlying grammar.-
The False Promise of ChatGPT
Jeffrey Watumull, Noam Chomsky, Ian Roberts
March 2023
2. AI: Stuck Critical Thinking and assess veracity.
Conversational languages powered by Generative AI lack the fundamental ingredients of true intelligence. Their most profound flaw is the absence of the capability that is crucial to any intelligence: the ability to state not only what is the case, what was the case, and what will be the case — that is, description and prediction — but also what is not the case and what could or could not be the case.
True intelligence involves a deep understanding of context and the ability to generate hypotheses, assess possibilities, and provide explanations. While Generative AI can generate responses based on patterns in data, it does not possess the ability to critically evaluate or hypothesize beyond the data it was trained on. This limitation prevents it from achieving the level of nuanced understanding and reasoning that characterizes human intelligence.
Therefore, despite the impressive capabilities of AI in tasks such as language processing and data analysis, it falls short in areas requiring genuine explanatory power and critical thinking.
Suppose you are holding an apple in your hand. Now you let the apple go. You observe the result and say, “The apple falls.” That is a description. A prediction might have been the statement “The apple will fall if I open my hand.” Both are valuable, and both can be correct. But an explanation is something more: It includes not only descriptions and predictions but also counterfactual conjectures like “Any such object would fall,” plus the additional clause “because of the force of gravity” or “because of the curvature of space-time” or whatever. That is a causal explanation: “The apple would not have fallen but for the force of gravity.”
That is thinking.
The False Promise of ChatGPT
Jeffrey Watumull, Noam Chomsky, Ian Roberts
March 2023
ChatGPT Can Remember Everything but… Can’t Tell Fantasy from Reality
ChatGPT and similar programs are, by design, unlimited in what they can “learn” (which is to say, memorize); they are incapable of distinguishing the possible from the impossible. For this reason, the predictions of machine learning systems will always be superficial and dubious. Because these programs cannot explain the rules of English syntax, for example, they may well predict, incorrectly, that “John is too stubborn to talk to” means that John is so stubborn that he will not talk to someone or other (rather than that he is too stubborn to be reasoned with).
The False Promise of ChatGPT
Jeffrey Watumull, Noam Chomsky, Ian Roberts
March 2023
3. AI Struggles with Moral Understanding and Ethical Decision-Making.
Human intelligence includes the ability to think about ethical problems and consider moral principles and the situation when making decisions.
Jeffrey Watumull, Noam Chomsky, and Ian Roberts explain this with an understandable example in their article, when people debate about making Mars more like Earth, they think about how it could affect nature and ethics. Humans can think about what’s right or wrong in the long run. AI doesn’t have its own moral rules, so it can’t think about ethical questions like people can, as simple as this.
For example, these are other cases I’ve thought about now that we are highly sensitized to climate change
- Example: When deciding whether to cut down a forest for urban development, humans can weigh the economic benefits against the environmental impact and the moral responsibility to preserve nature for future generations. They might conclude that while development is beneficial, it should be done sustainably to protect ecosystems.
- AI Limitation: An AI system can analyze data and provide statistical outcomes, but it cannot independently assess the moral implications of destroying habitats or the long-term environmental impact without predefined ethical guidelines.
Regarding medical Ethics, my father suffered from Amyotrophic lateral sclerosis (ALS), which is a progressive neurodegenerative disorder that affects motor neurons in the brain and spinal cord, leading to muscle weakness, atrophy, and eventually paralysis. Over time, individuals with ALS lose the ability to initiate and control voluntary movements, including speaking, eating, and breathing, ultimately resulting in respiratory failure.…
- Example: In medical decision-making, a doctor might consider not only the clinical effectiveness of a treatment but also the patient’s quality of life, personal values, and ethical concerns about end-of-life care. For instance, a physician might decide against an aggressive treatment for a terminally ill patient if it would cause undue suffering without significant benefit.
- AI Limitation: An AI system can recommend treatments based on medical data and probabilities but cannot consider the patient’s personal values and ethical concerns, potentially leading to recommendations that do not align with the patient’s wishes or moral considerations.
4. AI lacks the formation of personal opinions and subjective perspectives.
Humans develop their opinions based on personal experiences, values, and beliefs. When dealing with complex issues, such as the use of AI in moral decisions, individuals can form informed positions that reflect their ethical principles. AI, on the other hand, lacks consciousness and subjectivity, is restricted to neutral processing and combining information without personal perspective or emotional input.
Which can lead to several implications, here are two easy-to-understand examples:
Customer Service Interactions:
- Example: When you contact customer service with a complaint, a human representative can understand your frustration, empathize with your situation, and provide a personalized response. They might say, “I understand how frustrating this must be, and I’m here to help resolve this issue for you.”
- AI Limitation: An AI-based customer service bot can provide scripted responses based on keywords but cannot genuinely empathize or offer personalized opinions. It might respond with, “I see you are having an issue. Please try restarting your device,” without understanding the emotional nuance behind your complaint.
Creative Collaboration:
- Example: In a brainstorming session for a new marketing campaign, human team members can offer unique ideas based on their personal experiences, preferences, and perspectives. One might suggest, “Let’s create a campaign that focuses on the nostalgia of childhood summers, as many people have fond memories of that time.”
- AI Limitation: An AI tool can generate ideas based on data patterns and past successful campaigns but cannot draw from personal memories or subjective experiences. It might suggest, “Based on data, summer-themed campaigns perform well,” without the depth of personal insight.
🪣 5. Where and How Humans Need to Focus to Surpass Machines.
Humans should focus on perfectionism in the 4 Human Capabilities Beyond AI I have just mentioned. In my eyes, the most challenging one is the Critical Thinking.
Critical thinking is the practice of resisting assumptions,
considering alternative perspectives,
and doingYOUR OWN RESEARCH
to form an objective conclusion.Mar Fernández Parra
Unlike AI, which follows programmed algorithms, humans can apply critical thinking to navigate complex, ambiguous situations and innovate solutions.
As artificial intelligence (AI) continues to advance, humans must focus on developing the capabilities where we excel beyond the reach of AI. This blog post explores why enhancing human strengths, particularly critical thinking, is essential and how we can strive to be the best in class in this area.
Why do Human Capabilities Matter in the Age of AI?
AI is transforming industries and automating tasks that once required human intervention. While AI excels in data processing, pattern recognition, and executing repetitive tasks, it lacks the nuanced understanding and creative problem-solving abilities that humans possess. This distinction highlights the importance of nurturing human capabilities to maintain a competitive edge.
So if you are here reading this, probably you might wonder…How to Develop Critical Thinking Skills?
1. Embrace Lifelong Learning
Continuous education is vital. Engage in courses, workshops, and reading materials that challenge your thinking and expand your knowledge base.
2. Practice Reflective Thinking
Take time to reflect on your decisions and thought processes. Ask yourself why you arrived at a particular conclusion and consider alternative viewpoints. Reflective thinking helps in recognizing patterns in your reasoning and improving your decision-making skills.
3. Read from Diverse Sources ( THIS IS KEY!!)
To avoid the pitfalls of algorithm-driven content, make a conscious effort to read from a variety of sources. This includes books, academic journals, reputable news outlets, and even viewpoints that differ from your own. Diverse reading broadens your perspective and enhances your critical thinking.
My very personal tip is
Surround yourself with people who are brighter than you, both in your private life and at work — individuals who challenge and inspire you. Seek out intelligent mentors and specialists in your team from whom you can learn. Travel and engage with people from diverse cultures, learning languages to connect deeply with others and understand their perspectives. Extend connections beyond real-life interactions to also online communities, e.g. as my connections with Web3 friends and NFT communities. Embrace individuals with different viewpoints who genuinely care about you, actively listen, and seize opportunities to grow and learn together.
4. Engage in Debates and Discussions
Participate in discussions that expose you to diverse perspectives. Debates and dialogues with others can reveal different angles on a topic, helping you to refine your arguments and understand opposing views better.
5. Solve Complex Problems
Tackle problems that require more than just straightforward answers. Engage in activities like puzzles, strategic games, or case studies that demand critical thinking and strategic planning. These exercises enhance your ability to think critically under pressure.
6. Seek Feedback
Regular feedback from peers, mentors, or supervisors can provide valuable insights into your thinking processes. Constructive criticism helps you identify areas for improvement and develop a more robust critical thinking approach.
🪣 6. End Note
In today’s digital age, the real challenge I witness is the type of non-diverse content we consume.
It seems paradoxical how the internet has brought accessibility to information. As a professional in online business development for 23 years, I’ve witnessed a before-and-after in the world with the internet era. However, in today’s age of social media, we often consume content curated by algorithms on social media platforms.
These algorithms prioritize engagement and often promote content that aligns with our existing beliefs, creating echo chambers. This can limit our exposure to diverse perspectives and hinder our critical thinking abilities. Moreover, the content we consume is often influenced by the interests of the companies behind these platforms, making it even more crucial to seek information from multiple sources.
social media has modified and simplified our language through short tweets, memes, and emojis. While these forms of communication can be effective for quick interactions, they often lack the depth and nuance needed for thorough understanding and critical analysis. This trend towards brevity and simplicity can make it challenging to engage in complex discussions and develop well-rounded perspectives.
Dear readers, thank you very much for reading this far. Your interest in developing yourselves as better individuals and preparing for a bright future compared to machines is greatly appreciated. I hope this article positively contributes to raising awareness about the importance of developing critical thinking. See you in the next article! And I closing this article with a video by Noam Chomsky…
🪣 ANNEXE
1. Special Thanks to Ángel González. My 1st Source.
Thanks to this post that my web3 friend Ángel published on Linkedin, I learned about “The False Promise of ChatGPT,” by Noam Chomsky, Ian Roberts and Jeffrey Watumull.
Without Angel’s post, this post would not have been possible.
Thanks, Ángel for sharing!
2. Article’s Sources.
https://lingbuzz.net/lingbuzz/007587
https://tinkogroup.com/artificial-intelligence-vs-human-brain/
https://aiindex.stanford.edu/report/
https://www.instagram.com/p/C2VuJK6u416/?utm_source=ig_embed&utm_campaign=embed_video_watch_again
https://www.linkedin.com/pulse/diversity-dictionary-critical-thinking-theotherbox/
3. Do not miss other AI articles you might be interested in.
3. About the Author.
Web 2.
https://www.linkedin.com/in/marfernandezparra/
AI
🪣 About BlueBucket BRAND
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