TL;DR: The term “foom,” coined in the AI safety discourse, describes a scenario where an AI system undergoes rapid, explosive self-improvement, potentially surpassing human intelligence. This article explores the origins of “foom,” its implications for AI safety, and the ongoing debate among experts about the feasibility and risks of such a development.
The concept of “foom” emerges from the intersection of artificial intelligence (AI) development and safety research. Initially popularized by Eliezer Yudkowsky, a prominent figure in the field of rationality and AI safety, “foom” encapsulates the idea of a sudden, exponential leap in AI capabilities. This leap could hypothetically occur when an AI system reaches a level of intelligence where it can start improving itself, leading to a runaway effect where its capabilities rapidly outpace human understanding and control.
Origins and Context:
Eliezer Yudkowsky and AI Safety: Yudkowsky’s work, particularly in the realm of machine intelligence research, significantly contributed to the conceptualization of “foom.” His concerns about AI safety and the potential risks associated with advanced AI systems are foundational to the discussion.
Science Fiction and Historical Precedents: The idea of machines overtaking human intelligence is not new and can be traced back to classic science fiction literature. However, “foom” distinguishes itself by focusing on the suddenness and unpredictability of this transition.
The Debate:
Feasibility of “Foom”: Experts are divided on whether a “foom”-like event is probable or even possible. While some argue that AI systems lack the necessary autonomy and adaptability to self-improve at an exponential rate, others caution against underestimating the potential advancements in AI.
Implications for AI Safety: The concept of “foom” has intensified discussions around AI safety, emphasizing the need for robust and preemptive safety measures. This includes the development of fail-safes and ethical guidelines to prevent or manage a potential runaway AI scenario.
“Foom” remains a hypothetical yet pivotal concept in AI safety debates. It compels researchers, technologists, and policymakers to consider the far-reaching consequences of unchecked AI development. Whether or not a “foom” event is imminent, the discourse around it plays a crucial role in shaping responsible and foresighted AI research and governance.
TLDR: OpenAI has released a comprehensive guide on prompt engineering, detailing strategies for optimizing interactions with large language models like GPT-4.
OpenAI has recently unveiled a detailed guide on prompt engineering, aimed at enhancing the effectiveness of interactions with large language models, such as GPT-4. This document serves as a valuable resource for anyone looking to refine their approach to working with these advanced AI models.
The guide emphasizes six key strategies to achieve better results: writing clear instructions, providing reference text, and others. These techniques are designed to maximize the efficiency and accuracy of the responses generated by the AI. By experimenting with these methods, users can discover the most effective ways to interact with models like GPT-4.
This release is particularly notable as some of the examples and methods outlined are specifically tailored for GPT-4, OpenAI’s most capable model to date. The guide encourages users to explore different approaches, highlighting that the best results often come from combining various strategies.
In essence, this guide represents a significant step forward in the realm of AI interaction, providing users with the tools and knowledge to unlock the full potential of large language models.
Prompt engineering is a critical aspect of interacting with AI models, particularly with sophisticated ones like GPT-4. This guide delves into various strategies and tactics for enhancing the efficiency and effectiveness of these interactions. The primary focus is on optimizing prompts to achieve desired outcomes, ranging from simple text generation to complex problem-solving tasks.
Six key strategies are highlighted: writing clear instructions, providing reference text, specifying the desired output length, breaking down complex tasks, using external tools, and testing changes systematically. Each strategy encompasses specific tactics, offering a structured approach to prompt engineering.
For instance, clarity in instructions involves being precise and detailed in queries, which helps the AI generate more relevant and accurate responses. Incorporating reference text into prompts can significantly reduce inaccuracies, especially for complex or esoteric topics. Specifying output length aids in receiving concise or elaborately detailed responses as needed.
Complex tasks can be made manageable by splitting them into simpler subtasks. This not only increases accuracy but also allows for a modular approach to problem-solving. External tools like embeddings for knowledge retrieval or code execution for accurate calculations further enhance the capabilities of AI models. Systematic testing of changes ensures that modifications to prompts actually lead to better results.
This guide is a comprehensive resource for anyone looking to harness the full potential of AI models like GPT-4. It offers a deep understanding of how specific prompt engineering techniques can significantly influence the quality of AI-generated responses, making it an essential tool for developers, researchers, and enthusiasts in the field of AI and machine learning.
DeepMind’s latest innovation, FunSearch, marks a significant leap in the realm of mathematical sciences through the application of Large Language Models (LLMs). Published on December 14, 2023, by Alhussein Fawzi and Bernardino Romera Paredes, this groundbreaking technology represents a paradigm shift in how we approach and solve complex mathematical and computational problems.
Breaking New Ground with LLMs
LLMs, known for their ability to read, write, and code, traditionally assisted in problem-solving by combining various concepts. FunSearch, however, takes this a step further by not only assisting in problem-solving but also making novel discoveries in mathematical sciences. This is particularly noteworthy because LLMs, despite their capabilities, have been prone to producing factually incorrect information, commonly referred to as “hallucinations”. FunSearch counters this by pairing a pre-trained LLM with an automated evaluator to filter out these inaccuracies, allowing the system to evolve initial ideas into verifiable new knowledge.
Innovative Approach: Evolutionary Method and Practical Applications
The core of FunSearch’s methodology is an evolutionary process powered by LLMs. It starts with a user-defined problem described in code, initiating a cycle where the LLM generates new program ideas, which are then automatically evaluated and refined. This iterative process results in a self-improving loop, enhancing the quality of solutions over time. Remarkably, FunSearch has been instrumental in finding new solutions to the cap set problem – a long-standing challenge in mathematics – and improving algorithms for the bin-packing problem, demonstrating its practical utility in diverse applications.
Beyond Traditional Computing: The Advantages of FunSearch
What sets FunSearch apart from conventional computing methods is its ability to generate programs that elucidate the process of arriving at solutions, rather than just the solutions themselves. This characteristic aligns closely with the scientific method of explaining phenomena and discoveries. Moreover, FunSearch prefers compact programs, reducing complexity and enhancing the interpretability of its outputs. This feature not only aids in understanding but also in refining and improving solutions through collaborative human-machine interactions.
Addressing Real-World Challenges
FunSearch’s versatility extends beyond theoretical problems to practical challenges like the bin-packing problem, crucial in various industrial applications. Its ability to generate tailored programs that outperform existing heuristics highlights its potential in optimizing real-world systems efficiently. Unlike other AI approaches that might require significant resources, the solutions provided by FunSearch are easily deployable, offering immediate practical benefits.
Looking Forward: The Future of LLM-Driven Discovery
As LLMs continue to evolve, so will FunSearch. Its current success is just the beginning, with plans to expand its capabilities to tackle a broader spectrum of scientific and engineering challenges. This advancement positions FunSearch and similar LLM-driven technologies as future mainstays in solving complex problems in science and industry.
Peloton, once a shining star in the fitness industry, has experienced a significant downturn, with its co-founder and CEO John Foley at the center of the narrative. Founded in 2012 by Foley, Peloton achieved rapid growth, particularly during the COVID-19 pandemic, as demand for at-home fitness solutions skyrocketed. This period marked the company’s transformation into a key industry player, reshaping home exercise norms.
However, Peloton’s success story took a turn under Foley’s leadership. The company faced substantial challenges, notably in supply chain management and product safety. The recall of a product following a tragic accident involving a child, coupled with shipping delays and pricing issues, marked the beginning of its decline. These operational hurdles were exacerbated as the pandemic eased and people started returning to gyms. Additionally, a series of public relations blunders further tarnished Peloton’s reputation and financial standing.
Foley’s leadership style and decisions have been under scrutiny. His choice to host an extravagant holiday party amid company-wide troubles was particularly controversial, raising doubts about his decision-making. His reluctance to consider selling the company and the complexity of Peloton’s product lines further compounded the challenges. Foley’s personal finances, deeply intertwined with Peloton’s stock, also suffered as the company’s shares plummeted.
In a strategic move, Peloton appointed Barry McCarthy, the former CFO of Netflix and Spotify, as its new CEO. Foley transitioned to the role of executive chairman, maintaining significant influence in the company’s decisions. This change was part of a larger strategy to revitalize and stabilize Peloton. Despite these efforts, the company’s market valuation and stock prices have drastically decreased from their peak, highlighting the depth of its challenges.
In summary, John Foley’s tenure at Peloton was marked by remarkable innovation and significant setbacks. His vision drove the company to new heights, but his later decisions and operational issues led to its difficulties. The leadership change and restructuring are aimed at addressing these challenges and steering Peloton towards recovery.
In the dynamic landscape of animation and design, keeping abreast of the latest tools and technologies is paramount. Rive and Adobe Animate (formerly Flash) stand out as two popular choices for crafting captivating animations and interactive content. This article delves into the nuances of these platforms, comparing their features, usability, and suitability for different project types.
The Evolution from Flash to Adobe Animate
Adobe Animate, the rebranded version of the once ubiquitous Flash, revolutionized web animation. It enabled designers to craft interactive content and animations integral to websites and games. However, its dependence on a proprietary plugin and non-compliance with evolving web standards led to a gradual decline in its popularity.
Rive: Embracing Modern Web Standards
Rive emerges as a contemporary tool for animation and design, prioritizing compatibility with modern web standards. It facilitates the creation of captivating animations and interactive content, exportable to a multitude of platforms, including web, Android, iOS, and others. Rive’s commitment to web standards and multi-platform optimization positions it as a formidable tool for today’s designers.
Comparative Analysis: Rive vs Adobe Animate
Technology: Adobe Animate operates on a proprietary plugin system, whereas Rive is engineered with contemporary web standards, offering greater versatility and efficiency.
Compatibility: Adobe Animate faces limitations in supporting modern web standards and is incompatible with many mobile devices. Rive is crafted for seamless functionality across diverse platforms.
Features: Rive boasts advanced features like real-time collaboration, a domain where Adobe Animate falls short.
Learning Curve: Rive’s user-friendly interface and workflow cater to beginners, simplifying the learning process compared to Adobe Animate.
Choosing the Right Tool for Your Needs
Both Rive and Adobe Animate have their distinct advantages and limitations. The optimal choice hinges on specific project requirements and user preferences. Rive is an excellent choice for modern, versatile tool requirements, aligning with web standards. Conversely, Adobe Animate might appeal to users familiar with its interface and specific features. Understanding these tools’ capabilities enables designers to make informed decisions, ensuring their projects resonate with the intended audience and leverage the latest in animation technology.
1. Action and Risk-Taking: Optimists often dive into projects with enthusiasm, willing to take risks that might intimidate others. This bold approach can lead to significant breakthroughs and innovations.
2. Enhanced Learning and Creativity: Optimism fuels a positive mindset that is conducive to learning and creativity. Optimists are more likely to engage in creative problem-solving, seeing potential where others might see dead-ends.
3. Resilience: Optimism is a key component of resilience. It allows individuals to bounce back from setbacks and maintain a positive outlook, even in the face of adversity.
4. Networking and Support: Optimists tend to attract a network of supportive and like-minded individuals. This positive energy not only fosters personal growth but also creates a support system that can be crucial in challenging times.
5. Experimentation and Adaptability: An optimistic disposition encourages experimentation and adaptability. Optimists are more likely to try new things and adapt to changes, viewing them as opportunities rather than threats.
Downsides of Realism
1. Limiting Opportunities: Realists might miss out on opportunities due to their focus on practicalities and limitations. This perspective can sometimes prevent them from taking chances that could lead to significant gains.
2. Stifled Creativity: A realistic viewpoint might inhibit creativity. By focusing too much on what is practical or probable, realists may overlook innovative solutions.
3. Demotivation: Realism can lead to demotivation, especially if the focus is constantly on the hurdles or the less-than-ideal aspects of a situation.
4. Relationship Challenges: Realists might face challenges in relationships, particularly with more optimistic counterparts, due to their pragmatic and sometimes pessimistic outlook.
5. Inflexibility: A strong focus on realism can result in inflexibility, as adapting to new or unforeseen circumstances may be more difficult for those who are deeply rooted in practical realities.
Downsides of Optimism
1. Overlooking Risks: Optimists might overlook or underestimate risks, leading to decisions that are not well-informed or prepared for potential challenges.
2. Unrealistic Expectations: Excessive optimism can foster unrealistic expectations, setting one up for disappointment if things do not pan out as hoped.
3. Disregard for Practical Limitations: Optimists may sometimes disregard practical limitations, leading to overcommitment or the pursuit of unfeasible goals.
4. Potential for Disappointment: High expectations fueled by optimism can lead to disappointment if outcomes do not match the optimistic projections.
5. Misjudging Situations: A highly optimistic outlook can sometimes lead to misjudging situations, underestimating difficulties, or failing to plan adequately for possible setbacks.
Benefits of Realism
1. Risk Awareness: Realists are typically more aware of risks, allowing for better preparedness and contingency planning.
2. Practical Problem Solving: A realistic approach fosters practical problem-solving skills. Realists tend to focus on tangible solutions that are feasible and grounded in reality.
3. Motivation from Tangible Goals: Realists are often motivated by tangible, achievable goals. This focus on realistic objectives can drive consistent progress and achievement.
4. Constructive Relationships: Realism can lead to more constructive relationships, particularly in professional settings, as realists tend to set clear, achievable expectations.
5. Strategic Flexibility: Realism does not necessarily mean rigidity. In fact, a realistic outlook can foster strategic flexibility, allowing individuals to adjust plans and strategies based on practical assessments of changing circumstances.
Both optimism and realism have their distinct advantages and disadvantages. Striking a balance between the two can lead to a more well-rounded approach to life and decision-making, combining the best of both worlds: the hope and drive of optimism with the groundedness and practicality of realism.
INVERT:
Charlie Munger, the renowned investor and vice chairman of Berkshire Hathaway, is well-known for his concept of “inversion,” a problem-solving strategy. Inversion involves approaching a problem backward or from the opposite end of the traditional starting point. It’s about looking at what you want to avoid and then figuring out how to avoid it. This concept can be applied to the balance between optimism and realism, particularly in decision-making and strategic planning.
Applying Inversion to Optimism and Realism
For the Overly Optimistic:
Inversion Focus: Instead of focusing on what could go right, they should consider what could go wrong. This helps in identifying potential pitfalls and risks they might typically overlook.
Risk Management: By considering the worst-case scenarios, optimists can create contingency plans, thus grounding their optimism with a dose of realism.
Setting Realistic Goals: By inverting their tendency to set overly ambitious goals, they can aim for targets that are challenging yet achievable, reducing potential disappointment.
For the Overly Realistic:
Inversion Focus: Instead of constantly looking at the limitations and risks, they should contemplate what opportunities might be missed by being too cautious.
Encouraging Creativity: By considering the best-case scenarios, realists can open themselves up to more creative and innovative solutions, breaking free from the confines of their usual pragmatism.
Expanding Comfort Zones: Inverting their focus on practicality can lead to taking calculated risks, potentially leading to greater rewards.
Practical Steps for Inversion
Reflection and Self-Awareness: Acknowledge your natural inclination towards either optimism or realism. Self-awareness is key in understanding how to apply inversion.
Seeking Alternative Perspectives: Engage with individuals who hold a different outlook. For optimists, this means consulting with more pragmatic individuals, and vice versa for realists.
Scenario Analysis: Actively practice thinking through both positive and negative outcomes of any given situation. For optimists, emphasize the negative outcomes more, and for realists, the positive ones.
Balance in Decision Making: Strive for decisions that incorporate both the hopefulness of optimism and the groundedness of realism. This might mean tempering high expectations with practical considerations or infusing a realistic plan with a bit more ambition and vision.
In essence, Munger’s inversion idea, when applied to the dichotomy of optimism and realism, encourages individuals to step outside their comfort zones and adopt a more balanced, comprehensive approach to thinking and problem-solving. This can lead to more robust, well-rounded decisions and strategies, both in personal and professional contexts.
In a riveting interview, Sam Altman, CEO of OpenAI, robustly addresses Elon Musk’s criticisms, discusses the challenges of AI development, and shares his vision for OpenAI’s future. From personal leadership lessons to the role of AI in democracy, Altman provides an insightful perspective on the evolving landscape of artificial intelligence.
Sam Altman, the dynamic CEO of OpenAI, recently gave an interview that has resonated throughout the tech world. Notably, he offered a pointed response to Elon Musk’s critique, defending OpenAI’s mission and its strides in artificial intelligence (AI). This conversation spanned a wide array of topics, from personal leadership experiences to the societal implications of AI.
Altman’s candid reflections on the rapid growth of OpenAI underscored the journey from a budding research lab to a technology powerhouse. He acknowledged the challenges and stresses associated with developing superintelligence, shedding light on the company’s internal dynamics and his approach to team building and mentorship. Despite various obstacles, Altman demonstrated pride in his team’s ability to navigate the company’s evolution efficiently.
In a significant highlight of the interview, Altman addressed Elon Musk’s critique head-on. He articulated a firm stance on OpenAI’s independence and its commitment to democratizing AI, contrary to Musk’s views on the company being profit-driven. This response has sparked widespread discussion in the tech community, illustrating the complexities and controversies surrounding AI development.
The conversation also ventured into the competition in AI, notably with Google’s Gemini Ultra. Altman welcomed this rivalry as a catalyst for advancement in the field, expressing eagerness to see the innovations it brings.
On a personal front, Altman delved into the impact of his Jewish identity and the alarming rise of online anti-Semitism. His insights extended to concerns about AI’s potential role in spreading disinformation and influencing democratic processes, particularly in the context of elections.
Looking forward, Altman shared his optimistic vision for Artificial General Intelligence (AGI), envisioning a future where AGI ushers in an era of increased intelligence and energy abundance. He also speculated on AI’s positive impact on media, foreseeing an enhancement in information quality and trust.
The interview concluded on a lighter note, with Altman humorously revealing his favorite Taylor Swift song, “Wildest Dreams,” adding a touch of levity to the profound discussion.
Sam Altman’s interview was a compelling mix of professional insights, personal reflections, and candid responses to critiques, particularly from Elon Musk. It offered a multifaceted view of AI’s challenges, OpenAI’s trajectory, and the future of technology’s role in society.
Renowned investor and thinker Naval Ravikant attributes his remarkable success to a simple yet powerful habit: reading for 1-2 hours every day. This dedication to reading has not only shaped his worldview but also contributed significantly to his professional achievements. In a recent compilation of insights, Ravikant shares 43 invaluable reading tips, offering a glimpse into the mindset that has propelled him to the forefront of success and innovation.
Embrace Reading for Pleasure: Ravikant advocates for reading materials that genuinely interest you, as this nurtures a love for reading itself.
Explore Controversial Literature: He encourages delving into books that face opposition or banning, suggesting these often hold significant insights.
Quality over Speed: He emphasizes the importance of absorbing quality literature slowly and thoughtfully.
Investing in Knowledge: Ravikant regards spending on books not as an expense but as a crucial investment.
Revisiting Great Works: He advises re-reading and even re-buying books that have a lasting impact.
Intelligent Reading: As one’s understanding deepens, reading becomes a slower, more thoughtful process.
Depth over Brevity: Books that can be speed-read, he asserts, are likely not worth the time.
Reading as a Fundamental Skill: He views reading as the ultimate skill that can open doors to endless knowledge and opportunities.
Reading as a Vacation: Ravikant finds reading to be a fulfilling and peaceful way to spend one’s leisure time.
Restful Reading vs. Audio Learning: He differentiates between the efficiency of reading in stillness and learning through audiobooks while in motion.
Ravikant’s tips continue, covering a broad spectrum of advice that underscores the transformative power of reading. From advocating for self-directed learning to challenging oneself with complex texts, his insights reflect a deep appreciation for the written word and its capacity to enrich one’s life and mind. His approach to reading is not just as a pastime, but as a strategic tool for personal growth and intellectual development.
Naval Ravikant’s reading tips are a testament to the profound impact that a dedicated reading habit can have on an individual’s success and intellectual growth. His advice spans from choosing engaging literature to viewing reading as a key investment in one’s future, offering a comprehensive guide for anyone looking to enhance their knowledge and thinking through the power of books.
Adobe has recently announced a significant change impacting Creative Cloud users. Starting February 1, 2024, Adobe will discontinue the Creative Cloud Synced files feature for both free and paid personal users. This decision excludes those associated with Creative Cloud for Enterprise or Creative Cloud for Teams business accounts.
Understanding Creative Cloud Synced Files
Creative Cloud Synced Files allowed users to save and sync files across devices. This feature was integrated into various Adobe mobile applications, including Photoshop Express, Rush, and Fresco. Users could access their files through a local folder or via the Creative Cloud website.
Post-Discontinuation Scenario
Once discontinued, personal users will lose the ability to sync files through Creative Cloud. However, Adobe assures continued support for other cloud storage solutions like Cloud Documents, Creative Cloud Libraries, Lightroom Cloud storage, and Frame.io cloud storage.
Impact on Personal Users
The discontinuation means the Creative Cloud desktop synchronization service and the folder and file-sharing capabilities will cease. Files in the local Creative Cloud Files folder will remain but won’t be copied to cloud storage. Shared files and folders will also become inaccessible.
Why is Adobe Making this Change?
Adobe aims to focus more on creative collaboration, steering away from generalized cloud storage services. The company is investing in services that support creative work across devices and apps and enable collaboration.
Alternatives to Creative Cloud Synced Files
Adobe offers several cloud-based solutions tailored to different needs:
Cloud Documents: Compatible with apps like Photoshop, Illustrator, and Adobe Express, Cloud Documents update work across devices.
Creative Cloud Libraries: Useful for organizing and sharing brand and creative assets within Creative Cloud apps.
Frame.io: Offers web and mobile sharing, review, and approval of creative files.
Notification to Users
Users will be notified via email at least 90 days before the discontinuation process starts. The Creative Cloud Files folder on local devices will be renamed, but users can modify it as needed.
Next Steps for Users
Users are advised to back up their files and may consider syncing them with Adobe-supported storage solutions or third-party cloud storage providers before the discontinuation date.
Special Note for Business Users
This change has a separate timeline for enterprise or team accounts, with further details available from Adobe.
Adobe’s decision reflects its evolving strategy focusing on enhancing creative collaboration and offering specialized cloud solutions. Users are encouraged to explore alternative Adobe cloud services to continue their creative endeavors seamlessly.
The scientific community has long sought to reconcile the principles of biological evolution with the immutable laws of the universe as defined by physics. Historically, these two realms have existed in a state of separation, with evolutionary biology focusing on the selection and adaptation of life forms, and physics dealing with the fundamental laws governing matter and energy. However, a groundbreaking approach known as Assembly Theory (AT) is poised to bridge this gap, offering a novel perspective that integrates these seemingly disparate fields.
Assembly Theory: A New Perspective on Object Formation
Assembly Theory fundamentally redefines the concept of an ‘object’ in physical terms. Traditional physics views objects as fundamental, indivisible entities, akin to the ancient concept of atoms as the smallest unit of matter. In contrast, AT views objects as composites, defined not just by their current state but by their entire formation history. This perspective allows for the examination of objects as evidence of selection within specific boundaries, providing a more dynamic view of matter that includes its evolutionary history.
The core component of AT is the “assembly index,” a measure that represents the minimal number of steps required to construct an object from basic building blocks. This index, along with the concept of copy number (the number of identical or near-identical objects produced by evolutionary processes), aids in understanding the complexity and functionality of objects in a measurable and experimentally verifiable manner.
Implications of Assembly Theory in Evolution and Chemistry
AT’s implications are far-reaching, particularly in understanding how novel forms emerge and are selected for in evolutionary processes. In the realm of chemical systems, for instance, the assembly index offers insights into molecular evolution and the detection of life signatures. It provides a new lens through which to view the generation of novelty and selection, effectively blending aspects of biology and physics. The theory also emphasizes the role of historical contingency and the limitations imposed by resources and memory in the development of complex structures.
Unifying Selection and Novelty Generation
One of the critical insights of AT is its focus on path-dependent processes. Unlike traditional views which often attribute evolutionary changes to random mutations or fluctuations, AT proposes that selection in evolution is influenced by previously existing structures and environmental factors. This view aligns with the concept of natural selection but adds a layer of historical and physical context to it, providing a more nuanced understanding of how complex biological structures and functions have evolved.
The Future of Assembly Theory
The potential applications of AT extend beyond molecular biology and into various fields, including polymer science, cell morphology, computer programming, and even human languages and cultural phenomena like memes. The challenge lies in constructing assembly spaces that have clear physical meanings and can be experimentally validated.
Assembly Theory offers a revolutionary approach to understanding the evolution of complex matter, emphasizing the importance of selection history and the physical feasibility of constructing objects. By blending the principles of physics with the processes of biological evolution, AT provides a framework that could lead to a new understanding of matter and life.
Further Exploration
For those interested in diving deeper into the concepts discussed, the following links provide valuable resources for further exploration:
These resources offer a wealth of information for those seeking to deepen their understanding of the fascinating interplay between physics, chemistry, and biology as it relates to the evolution of complex life and matter.
