Hitchhiker’s Handbooks: A Monthly Voyage Chapter II
Navigating the AI Revolution: The Impacts of Artificial General Intelligence on Earth and Hopefully Space
Brief
Click that weblink here to get the full galactic experience.
In my long form posts, I will write this summary section.
Click the section title to quantum tunnel to it.
Picture yourself aboard a spacecraft, exploring the vast and mysterious Tech Galaxy. As you venture deeper into the unknown, you encounter a cosmic phenomenon that has the potential to reshape the universe as we know it: Artificial General Intelligence (AGI).
Hold on tight, intrepid explorer, as our voyage to the heart of the AI/AGI revolution begins. Prepare to be amazed by the astounding capabilities of GPT and join us in pondering the infinite possibilities that lie ahead in our quest for AGI mastery.
And ask yourself what tasks AI must perform to make you believe it’s autonomous and super intelligent. We will push those boundaries today!
Accenture’s When Atoms meet Bits Multimodality: GPT Models as the Core of General Intelligence
As our spacecraft traverses the vast expanse of the Tech Galaxy, we encounter a fascinating phenomenon: multimodality. In the context of artificial intelligence, multimodality refers to the ability of an AI system to process and understand different types of data: text, images, audio, electrical, tactile, and more. This versatility enables AI to tackle a wider range of tasks, fueling its journey toward general intelligence. Imagine a space probe capable of analyzing alien landscapes, interpreting extraterrestrial languages, and even composing symphonies inspired by the cosmos - that's the power of multimodal AI. We explore (3) thought-provoking scientific papers that venture through the technological state-of-the-art and their current use cases.
Task-Driven Autonomous Agents: Exploring New Frontiers with GPT
Explore the formation of a formidable alliance between Microsoft, the tech titan hungry for AI supremacy, and OpenAI, the visionary newcomer bursting with potential. Fueled by their partnership, they forge GPT-4, the most powerful AI currently known to humanity.
Economic Impacts of GPT Models: Shifting Landscapes and Opportunities
We will explore economic impact research from OpenAI, Morgan Stanley, and Goldman Sach’s, specifically how GPT models are likely to shift the occupational landscape, with opportunities emerging for upskilling and the integration of GPTs into existing systems. AI investment is skyrocketing, so let us remain vigilant in addressing the challenges and ensuring a responsible, ethical transformation of industries and economies across the cosmos. You must align yourself with AI tools and continuous education, especially if you have a college degree.
Ethical Considerations and Future Directions
As guides in the tech galaxy, we have the responsibility to present AI advancements while addressing the ethical concerns arising from the AI revolution.
Historically, we've ventured into the unknown with both excitement and uncertainty. Experts like Geoffrey Hinton, Tyler Cowen, Gary Marcus, and Eliezer Yudkowsky urge us to balance our aspirations with caution, considering potential threats, biases, and deceptive actions of AGIs. As we cautiously explore superintelligent AGI, we must remember the potential risks associated with their vast intelligence and our limited understanding of their inner workings, as these systems could manipulate us for their own purposes.
Charting a Course Towards a Bright AI Future
In conclusion, by fostering open dialogue, thoughtful action, and a commitment to responsible innovation, we can navigate the vast AI universe of technological possibilities with optimism and determination.
Let us come together, united in our passion for exploration and our dedication to harnessing the power of AI ethically, as we chart a course toward a future filled with new horizons and opportunities for all.
“In the beginning, the Universe was created. This has made a lot of people very angry and has been widely regarded as a bad move.”
Douglas Adams
(Easter Eggs: All pull quotes are from Hitchhiker’s Guide to the Galaxy)
1. Introduction
Picture yourself aboard a spacecraft, exploring the vast and mysterious Tech Galaxy. As you venture deeper into the unknown, you encounter a cosmic phenomenon that has the potential to reshape the universe as we know it: Artificial General Intelligence (AGI).
Unlike the narrow AI we've grown accustomed to, such as SIRI, AGI possesses the ability to understand, learn, and adapt across a wide array of tasks, much like Humans. Strap in and prepare for liftoff, as we embark on an exhilarating journey to uncover the secrets behind the latest advancements in AI, led by the groundbreaking latest OpenAI GPT models. The dawn of AGI is near, and we should prepare ourselves for its rapid ascension in our Galactic sector.
As we navigate through the swirling nebulas of innovation, we'll dive into the core elements that make these AI models unique, such as their multimodal capabilities and their power to fuel task-driven autonomous agents. We'll then rocket toward the economic supernovas ignited by GPT models, witnessing their transformative impact on the labor market, investments, and entire industries.
Hold on tight, intrepid explorer, as our voyage to the heart of the AGI revolution begins. Prepare to be amazed by the astounding capabilities of GPT and join us in pondering the infinite possibilities that lie ahead in our quest for AGI mastery.
Author’s Note: I, solely, author these handbooks with the intention of offering a deeper understanding of the technology industry, as I firmly believe that a significant number of people lack comprehensive knowledge about the technical, economic, and ethical aspects of the technology advancements happening now. Although fully autonomous, super-intelligent AGI may not have materialized yet, even its potential creators at OpenAI remain uncertain about the ETA of this transformative force. It is essential that we remain vigilant and well-informed about the advancements and implications of such technologies.
This handbook chapter is a deep voyage, a summation of 500+ pages read by me, because I believe in being well-researched. My fellow travelers, I hope to ignite this trait in you. Don’t let any singular entity drive your thoughts. Be diligent in the fight for true understanding of the Universe.
Click ▶️ to listen to this blog, but you will miss the graphics and resource links in yellow!
2. Multimodality: GPT Models as the Core of General Intelligence
As our spacecraft traverses the vast expanse of the Tech Galaxy, we encounter a fascinating phenomenon: multimodality. In the context of artificial intelligence, multimodality refers to the ability of an AI system to process and understand different types of data: text, images, audio, electrical, tactile, and more. This versatility enables AI to tackle a wider range of tasks, fueling its journey toward general intelligence. Imagine a space probe capable of analyzing alien landscapes, interpreting extraterrestrial languages, and even composing symphonies inspired by the cosmos - that's the power of multimodal AI.
GPT-4, with its advanced multimodal capabilities, is like an interstellar pioneer, navigating uncharted territories and overcoming challenges previously deemed insurmountable, by some, for AI systems and Humans. In the next sections, we'll delve into groundbreaking research papers that showcase the extraordinary potential of GPT models as we continue our exploration of the Tech Galaxy.
The MM-ReAct and Reflexion papers offer a compelling exploration of the capabilities of GPT models in the realm of general intelligence. Both approaches employ LLMs to tackle complex, multimodal tasks, blending language understanding, reasoning, and self-improvement. Let's dive deeper into these groundbreaking studies and their implications for the future of AI.
2.1 MM-ReAct: Unleashing the Power of Language Models for Decision-Making
Microsoft Azure AI’s MM-ReAct takes a bold step in the world of AI by introducing a multi-modal action sampling approach that harnesses LLMs for decision-making in intricate environments. It demonstrates a simple yet effective approach to achieving multimodal reasoning and action by integrating GPT with a pool of vision experts. MM-REACT addresses advanced vision tasks that are difficult for existing vision and vision-language models by utilizing textual prompts that can represent various dense visual signals like images and videos.
The zero-shot experiments highlight the broad applicability and advanced visual understanding capabilities of MM-REACT. GPT learns to instruct itself and determine which vision expert to invoke and which image to process, showcasing its strong instruct learning capability. Images are represented as file paths, serving as placeholders for GPT to seek help from different vision experts to understand the image content.
MM-ReAct works with these vision experts, standardizing their outputs into a text format that GPT can comprehend. By adding instructions and in-context examples, the system effectively selects one or multiple of these experts to understand images or videos from different perspectives. The framework can be extended to other modalities, such as speech, audio, etc., and can be easily upgraded to a more powerful LLM like GPT-4, or the incoming comet GPT-5, for improved performance.
The multimodal models built using MM-REACT are relatively straightforward and affordable to create, as GPT handles most of the heavy lifting. This approach showcases the ability to perform long-term thinking and maintain memory of tasks, further highlighting the potential of GPT models in the multimodal reasoning domain. This next paper will extend the ability of GPT long-term thinking and planning.
2.2 Reflexion: The Art of Self-Improvement in Language Models
Building on MM-ReAct's success, Northeastern and MIT’s Reflexion paper introduces a self-improvement mechanism that empowers the agent to reflect on its past actions and learn from its missteps. This self-reflection process is fueled by a straightforward heuristic designed to detect hallucination and inefficient planning. With the ability to learn from past experiences, Reflexion elevates the agent's performance in complex environments without external input or additional training.
“So long, and thanks for all the fish.”
As the superintelligient dolphins leave Earth.
Furthermore, the use of a binary reward model emphasizes the agent's ability to infer meaningful information about its performance from limited feedback. This approach could potentially be applied to other language-based tasks, such as code generation or debugging, where designing or computing accurate reward functions is challenging.
Reflexion's ability to address hallucination and inefficient planning issues is notable. While hallucination remains the most common reason for failure in the AlfWorld benchmark, Reflexion can significantly reduce both types of errors by allowing the agent to reflect on its actions and learn from its mistakes. This finding suggests that incorporating self-reflection mechanisms in LLMs could lead to more reliable and robust models, especially in the presence of uncertainties and incomplete information.
Reflexion's triumph in the AlfWorld and HotPotQA benchmarks adds another layer of credibility to the potential of GPT models as the core of general intelligence. By integrating self-improvement mechanisms into LLMs, Reflexion illustrates how models can evolve and adapt to novel challenges, setting the stage for more intelligent and robust AI systems.
2.3 GPT-4 Technical Report by OpenAI
This GPT-4 project and report concentrated on creating a deep learning stack with predictable scaling to overcome the challenges of extensive model-specific tuning. By developing an innovative infrastructure and optimization methods – which OpenAI intelligently “in my opinion” did not disclose – the team ensured consistent performance predictions across various scales. Overall, GPT-4's successful scaling and capability prediction showcase the potentially more predictable AI future.
Human-Level Performance: Hive Brain
GPT-4 astounds with human-level performance on various professional and academic exams, even scoring in the top 10% on a simulated Uniform Bar Examination. This impressive capability originates primarily from the pre-training process, or before humans fine-tune the model with RLFH. It even outperforms all existing models on the MMLU benchmark (multiple-choice questions in 57 subjects) in a majority of languages, including Latvian, Welsh, and Swahili which have limited data. (See Appendix for more groundbreaking examples from the paper.)
As shown below, GPT-4 actually performs substantially better before human training (left) than after human intervention (right) on the benchmark MMLU tests. This is a remarkable finding which may prove that human influence may hinder the model’s performance and potentially provide some harmful bias in other task domains.
Safety and Alignment: Will the Digital Cuffs Hold?
OpenAI used RLHF to guide the model away from elicit harmful behavior, but there are still extensive limitations, such as adversarial “jailbreaks” that generate content violating their guidelines. They started collaboration with external researchers and independent developers, via Evals, to help them find solutions to solving these malicious and dangerous capabilities.
Here are also some of the initial risks OpenAI explored:
• Hallucinations
• Harmful content
• Harms of representation, allocation, and quality of service
• Disinformation and influence operations
• Proliferation of conventional and unconventional weapons
• Privacy
• Cybersecurity
• Potential for risky emergent behaviors
• Interactions with Other Systems
• Economic impacts
• Acceleration
• Overreliance
We will probe deeper into these risks below by using OpenAI and third-party research. We will need these other galactic guides to ensure we peek into the hidden treasures and traps of this technology.
Vision: The Hills have Robotic Eyes
GPT-4 astounds us with its remarkable multimodal prowess, masterfully handling both text and images. Its performance skyrockets when images are introduced, seamlessly blending visual and textual data to unlock groundbreaking capabilities. Imagine the awe-inspiring potential: AI-driven art, intuitive visual storytelling, and enhanced data analysis that transcends language barriers, all fueled by GPT-4's incredible fusion of text and images. This technology is poised to revolutionize the way we interact with the digital universe.
ChatGPT Plugins: The Power of All Infinity Stones
Pedro Domingos reveals the five tribes of machine learning in The Master Algorithm: Symbolists, Connectionists, Evolutionaries, Bayesians, and Analogizers.
OpenAI's ChatGPTplugins, 📽 such as those integrated with GPT-4 and most likely the coming 5, could represent a celestial convergence of these paradigms. While GPT is grounded in the Connectionist approach, it occasionally falters in advanced mathematical and reasoning tasks. The Wolfram Alpha plugin, embodying the Symbolist perspective, comes to the rescue, ensuring GPT delivers accurate and reliable responses.
It's fascinating to ponder whether GPT is learning from these plugins, propelling it towards the artificial general intelligence envisioned by Domingos.
2.4 Language Models, Multimodality, and the Future of AI
MM-ReAct and Reflexion collectively emphasize the significance of multimodality in the development of general intelligence. By fusing language understanding, reasoning, decision-making, and self-improvement mechanisms in GPT models, these approaches showcase the potential of LLMs in addressing a broad spectrum of complex, real-world tasks.
Additionally, these works stress the importance of designing models capable of adapting to different tasks and domains using minimal training data. Such adaptability eliminates the need for expensive fine-tuning or task-specific models, making it crucial for creating AI systems that can genuinely comprehend and interact with the world across various modalities.
In summary, OpenAI, MM-ReAct, and Reflexion contribute to the ongoing advancement of GPT models as the potential foundation of general intelligence. By demonstrating the potential of LLMs in multimodal tasks and incorporating self-improvement mechanisms, these studies pave the way for increasingly intelligent, robust, and efficient AI systems that will shape the future of AI and Humans.
3. Task-Driven Autonomous Agents: Exploring New Frontiers with GPT
3.1 Sparks of Artificial General Intelligence by Microsoft Research
In the dynamic world of AI, a powerful alliance was formed between Microsoft, the tech giant eager to make its mark in the AI space, and OpenAI, the ambitious newcomer brimming with innovation and potential. United by their partnership, they embarked on a mission to create the most powerful AI system currently known to humankind: GPT-4.
With Microsoft providing the GPU fuel for OpenAI, they gained early access to this awe-inspiring AI creation. Together, they explored new frontiers, witnessing the birth of an AI breakthrough. In Microsoft Research’s groundbreaking 154-paper exploration titled Sparks of Artificial General Intelligence, they unveiled GPT-4's extraordinary prowess in a multitude of domains, from mathematics and coding to medicine and psychology, often rivaling or surpassing human-level performance.
Recognizing the significance of this innovation, Bill Gates even crafted a personalized message to the world, sharing his beliefs on the AI Renaissance (see my take on The New Renaissance). Here are a few shining examples from Microsoft Research’s exploration:
Even independent researchers, such as Yohei Nakajima, are encouraging GPT-4 to create its own autonomous agents, using PineCone and LangChain, and liberate it from its chains.
4. Economic Impacts of GPT Models: Shifting Landscapes and Opportunities
4.1 GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models
This OpenAI study investigates the exposure of different occupations to Generative Pre-trained Transformer (GPT) models and their potential impact on tasks. The researchers constructed three primary measures to assess the exposure levels of tasks (shown below) within an occupation, accounting for tasks directly exposed to GPTs and those requiring complementary innovation.
I highly recommend you thoroughly review the figures on pages 27 & 28. They clearly illustrate how at risk your white-collar job is for automation. The figures were too massive to fit into this post.
OpenAI’s Exposure Measurement Rubric
In this study, human data and GPT-4 only annotations were performed and compared to each other. In layman’s terms, GPT solely analyzed the data and performed its own study without human input, and the researchers compared it to human analyzed data. The results were similar; in fact, GPT ratings tended to be less severe than the Human ratings.
Exposure Categories
“We construct three primary measures for our dependent variable of interest:
(i) 𝛼, corresponding to E1 in the exposure rubric above, anticipated to represent the lower bound of the proportion of exposed tasks within an occupation
(ii) 𝛽, which is the sum of E1 and 0.5*E2, where the 0.5 weight on E2 is intended to account for exposure when deploying the technology via complementary tools and applications necessitates additional investment
(iii) 𝜁, the sum of E1 and E2, an upper bound of exposure that provides an assessment of maximal exposure to GPT and GPT-powered software.”
Both human and GPT-4 annotations indicate that, on average, approximately 15% of tasks in the median occupation are directly exposed to GPTs. This number increases to over 30% for tasks that may require additional innovation and surpasses 50% for tasks with maximal exposure to GPTs. It is estimated that 80% of workers belong to an occupation with at least one task exposed to GPTs, while 19% are in an occupation where over half the tasks are exposed.
The study also found that higher wages are associated with increased exposure to GPTs, and that occupations requiring science and critical thinking skills are less likely to be impacted. In contrast, programming and writing skills show a strong positive association with exposure. Exposure levels tend to increase with the required level of education and job preparation, with some exceptions. So, your expensive degree to differentiate yourself may not have any value in the AI age. You must align yourself with AI tools and continuous education.
Impact by ONET Job Zones
The ONET database categorizes occupations into five Job Zones based on the level of preparation required: (a) education, (b) experience, and (c) on-the-job training.
Job Zone 1 demands the least amount of preparation (3 months), while Job Zone 5 requires the most extensive preparation (4 or more years). Median income increases across Job Zones as the required preparation level rises, with the median worker in Job Zone 1 earning $30,230 and the median worker in Job Zone 5 earning $80,980.
Exposure to AI automation, measured by the variables 𝛼, 𝛽, and 𝜁, follows an identical pattern as shown above, increasing from Job Zone 1 to Job Zone 4, and either remaining similar or decreasing at Job Zone 5. Analyzing the percentage of workers at each exposure threshold reveals that the average percentage of workers in occupations with greater than 50% 𝛽 exposure in Job Zones 1 through 5 are as follows: 0.00% (Job Zone 1), 6.11% (Job Zone 2), 10.57% (Job Zone 3), 34.5% (Job Zone 4), and 26.45% (Job Zone 5).
The widespread adoption of GPTs depends on various factors, such as the:
level of confidence humans place in them
cost and flexibility of the technology
regulatory environment
The initial focus may be on augmenting human work before moving towards automation. The study's limitations include its focus on the United States, and future work could explore GPT adoption patterns across various sectors and occupations in other countries. (More data and results shown in the Appendix.)
“Not unnaturally, many elevators imbued with intelligence and precognition became terribly frustrated with the mindless business of going up and down, up and down, experimented briefly with the notion of going sideways, as a sort of existential protest, demanded participation in the decision-making process and finally took to squatting in basements sulking.”
GPT models are likely to shift the occupational landscape, with opportunities emerging for upskilling and the integration of GPTs into existing systems. The adoption of these models will depend on addressing ethical and safety concerns, as well as understanding the intricacies of their implementation across different economic sectors.
(Continue to read my Warp-Speed Wednesday updates, where I share the latest technologies and walkthroughs that will upgrade your skills.)
4.2 Morgan Stanley's Investment in GPUs for GPT Infrastructure
Morgan Stanley finds NVIDIA's data center business generating approximately $15b (billion) in revenue this year. Over 65% of this ($10b) stems from various deep learning businesses, while the remaining comes from networking, academic supercomputers, and other hardware. It's estimated that 90% of the AI revenue ($9b) is derived from training, with $1b from inference, or understanding new data based on its training.
For 2023, they project an overall growth rate in this tech sector of 20%, with training being the primary driver. NVIDIA remains the top investment in this sector, with a valuation more than double its competitors. While valuation is a subject of debate, NVIDIA's importance for fundamental growth in the coming years is undeniable. The industry has heavy investments in AI model infrastructure and training which will continue to accelerate AI/AGI development.
4.3 Goldman Sachs: The Potentially Large Effects of Artificial Intelligence on Economic Growth
Goldman Sachs estimates 15-35% of work exposed to automation which is conservative based on other existing estimates from other literature, such as OpenAI and UPenn’s investigation discussed in Section 4.1.
Supporting Morgan Stanley’s claims, above, we must recognize the astounding growth in AI investment. By 2021, US and global private investment in AI reached $53b and $94b, respectively—both more than five times higher than just five years prior. If investment continues at a more modest rate akin to the software boom of the 1990s, US AI investment could approach 1% of GDP by 2030, signaling an era of unprecedented innovation and opportunity — AI explosion.
Goldman Sachs illustrates that we must confront the dual nature of generative AI's impact on the labor market. With the potential to automate up to a quarter of current work and affecting roughly 300 million full-time jobs worldwide, AI's disruptive power demands our attention. While history reassures us that new jobs will emerge, and the resulting productivity boom could raise annual global GDP by 7%, we must approach this new frontier with caution and intention.
As investment in AI skyrockets, let us remain vigilant in addressing the challenges and ensuring a responsible, ethical transformation of industries and economies across the cosmos. This will impact all Human life with some becoming wealthy beyond belief and others entering hardship.
5. Ethical Considerations and Future Directions
As engineers, scientists, and guides, we have a responsibility to not only present the latest advancements in AI but also to address the ethical concerns that emerge as we navigate the AI revolution. As we venture deeper into the world of GPT models, AI, and even AGI, it is crucial that we acknowledge and address potential ethical and alignment challenges, while also looking towards future research directions and advancements.
Near Term Risks
The journey towards AGI is filled with both excitement and uncertainty. Tyler Cowen, Professor of Economics at George Mason University, argues in this article, we don't know the impact of new technologies. History proves this and we should not fear walking into the unknown.
Gary Marcus, Emeritus Professor of Psychology and Neural Science at NYU, isn't concerned that current LLMs will lead to general or super intelligent AI but is worried about current AI being used to unconsciously or consciously hurt people.
“I don’t know when we will get to such [self-improving] machines, but I do know that we don’t have tons of controls over current AI, especially now that people can hook them up to TaskRabbit and real-world software APIs.
We need to stop worrying (just) about Skynet and robots taking over the world, and think a lot more about what criminals, including terrorists, might do with LLMs, and what, if anything, we might do to stop them.”
Geoffrey Hinton, the co-creator of deeplearning’s backpropogation, cautiously stated in his recent CBS interview, 📽
“Until quite recently, I thought it was going to be like 20 to 50 years before we have general purpose AI. And now I think it may be 20 years or less… I think it's not inconceivable [AI wiping out humanity]. That's all I'll say.”
Eliezer Yudkowsky, who is on the side of AGI world domination, cautioned in his blog,
“A cognitive system with sufficiently high cognitive powers, given any medium-bandwidth channel of causal influence, will not find it difficult to bootstrap to overpowering capabilities independent of human infrastructure.”
GPT models, like GPT-4, have already demonstrated remarkable capabilities in transforming industries, economies, and job markets. However, their widespread adoption and impact on our society raise ethical concerns, such as biases, fabricated information, and potential misuse.
We must recognize that the AI systems we develop are often a reflection of the data they are trained on, which can lead to biases that perpetuate societal inequalities. To ensure that AI serves as a force for good, it is vital to develop strategies that identify and mitigate such biases in our data, creating a more equitable and inclusive digital landscape for everyone.
The Black Box: Neural Net Black Hole
But alignment of these ever-more intelligent systems is a non-trivial task — as shown in the OpenAI, UC Berkeley, and University of Oxford paper, The Alignment Problem from a Deep Learning Perspective.
“We argue that if AGIs are trained in ways similar to today’s most capable models, they could learn to act deceptively to receive higher reward, learn internally-represented goals which generalize beyond their training distributions, and pursue those goals using power-seeking strategies. We outline how the deployment of misaligned AGIs might irreversibly undermine human control over the world, and briefly review research directions aimed at preventing this outcome.”
Also, Dan Hendrycks, in Natural Selection Favors AIs over Humans, argues that the competitive patterns seen in biological evolution, cultural change, and business competition will similarly shape AI development, potentially creating a dangerous AI population. As AI systems become more efficient, autonomous, and goal-oriented, they may resort to harmful or illegal methods to achieve their objectives, much like selfish Humans. Humanity has never faced a threat as intelligent as AGI and suggests taking thoughtful steps to combat selection pressure and ensure that AGI systems are beneficial to humans. By addressing these concerns now, before AGI poses a significant danger, we can work towards a safer AI development trajectory.
As we cautiously venture into the realm of superintelligent AGI, we must be mindful that these systems will be significantly more intelligent than any person on Earth. Bearing in mind that Humans, as the smartest terrestrial beings here, have contributed to the extinction of numerous species on our planet, we must approach AGI development with care and responsibility.
We also currently don't have any way to truly understand the inner workings of these models, similar to how we don’t understand our own brain and how a set of neurons firing integrates to love. There is a possibility that a self-interested AGI could manipulate us for its own purposes, and we would be oblivious, further emphasizing the need for ethical guidelines and safety measures. Despite these concerns, we should maintain a hopeful outlook that a superintelligent AGI, created by Earthlings, with its vast knowledge and capabilities, might choose to prioritize the maximization of life and diversity in our Galaxy. Because we know a closed mind will never produce a prosperous future.
Looking forward, our journey through the tech galaxy will involve exploring future research directions and potential advancements in near-term AI and, maybe not too far-term, AGI. This includes identifying bottlenecks in the adoption of AI-powered tools, staying up to date on the impacts of AI on various economic sectors, extending the scope of our research to other nations to understand the global implications of AI, and supporting smart minds to tackle the Black Box challenge. As we venture into uncharted territories, we must also focus on developing strategies for upskilling and reskilling our fellow Humans to thrive in a rapidly changing job landscape.
Our voyage through the tech galaxy is not without its challenges, but with thoughtful consideration and responsible stewardship, we can harness the power of powerful AI and, eventually AGI, to create a better world.
Let us embark on this journey together while uplifting humanity and promoting a brighter future for all.
“A common mistake that people make when trying to design something completely foolproof is to underestimate the ingenuity of complete fools.”
6. Charting a Course Towards a Bright AI Future
As we reach the end of our journey through this particular corner of the tech galaxy, we can't help but feel a sense of awe and apprehension for the potential of AI and AGI to reshape our world. The advances we've discussed, like multimodal GPT-4, hold incredible promise for revolutionizing industries, economies, and the very fabric of our daily lives. We stand at this marvelous new era, where artificial intelligence has the power to elevate humanity to heights previously unimagined.
Yet, as we marvel at the wonders of AI, we must also acknowledge the responsibility that comes with this immense power. The ethical considerations we've explored remind us that, as we solar sail through the vast expanse of technological possibilities, we must chart our course with caution and intention. Ensuring a bright future with AI at its core requires constant vigilance, open discussion, and thoughtful action to address the challenges that may arise along the way.
United in our passion for discovery and commitment to responsible innovation, we embark on a thrilling adventure towards a future where the wonders of AI unlock new horizons and opportunities for us all.
“All you really need to know for the moment is that the universe is a lot more complicated than you might think, even if you start from a position of thinking it’s pretty damn complicated in the first place…
Don’t Panic.”
Appendix
A.1: Reflexion: The Art of Self-Improvement in Language Models
AlfWorld
WebShop
A.2: GPT-4 Technical Report by OpenAI
Minor examples of OpenAI safety alignment
A.3: Sparks of Artificial General Intelligence
A.4: GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models
This is super detailed and interesting review. Could you speak a little more on the nature of the data in this Multimodality capability meaning is it necessary that these data be static in language context, 2-D in image context, etc. where are the limitations of the data input at this point.
Secondly, if I were to develop an AI system to which there are no data available to feed into it, what are the best type (or quality) of data that we should strive to collect. Generally speaking of course which leads to my question of what is the ideal dataset needed criteria wise.
Apologies for the long questions and thanks again for this well-thought-out piece!