When people have a basic understanding of cellular automata (CA), their most immediate association is likely with John Horton Conway’s Game of Life. Since Martin Gardner introduced the game in his column on mathematical games in Scientific American in 1970, it has grown in popularity and fosters both academic and casual interest. The game has a strong following of hobbyists and researchers alike, who have explored and developed a range of dynamic machines within the confines of the Game of Life, many of which are intricate and involve features like universal computers and self-replicators. Other well-known examples of CA include Rule 110, developed by Stephen Wolfram, which was proven to be Turing complete by Matthew Cook in 1998.
The idea of cellular automata can be traced back to discussions between John Von Neumann and Stanislaw Ulam at Los Alamos National Laboratory in the 1940s. Although Conway’s Game of Life is the most well-known set of cellular automata rules, the initial 29-state system developed by Von Neumann and Ulam laid the groundwork for Von Neumann’s universal constructor, a machine that functions within his cellular automata world and is capable of replicating itself.
What Are Cellular Automata?
Cellular automata are essentially a group of cells, each having one or more states that undergo changes based on a set of rules that depend on the local context of the cell. Although these cells are usually arranged in a 2D grid, their placement can be arbitrary. In addition, although states are typically discrete, both states and rules can also take on continuous values and be differentiable. Many of the most fascinating CA universes appear to mimic physical occurrences like growth, diffusion, and flow, and they have been likened to fields by physicists who view them as the computer scientist’s version of this concept. CA is frequently utilized as a foundation for physics models, and some researchers believe that this resemblance goes beyond a surface-level comparison.
Tiny Universes: Simulating Physics and Building Machines in Cellular Automata
Since the 1960s, there has been significant research interest in cellular automata (CA), resulting in a steady increase in the number of published papers. CA have been used to model various phenomena, including chemical reactions, diffusion, turbulence, and epidemiology, and are a fundamental aspect of complexity research. Due to their resemblance to natural phenomena and life-like growth and self-organization, CA have found numerous applications in modeling and simulation. Additionally, CA rules can often lead to systems capable of universal computation, from the simple 1D Rule 110 to the more complex 29-state Von Neumann system.
In CA universes, we’ve observed Turing complete computers and self-replicating universal constructors. Surprisingly, achieving Turing completeness can occur by accident when constructing any adequately complex system. However, our goal is not solely to create computational systems; we aspire to develop intelligent ones too. Although we know they are capable of performing computations, can they acquire knowledge and learn?
Cellular Automata That Learn
Distill.pub, an interactive machine learning journal, has recently introduced a new research topic called “differentiable self-organizing systems.” The research thread currently contains only two publications, one highlighting graphical sprites that are capable of self-generation and self-repair, while the other focuses on the self-classification of MNIST digits. Both articles provide an interactive display of visualizations and code, which would be beneficial for machine learning and cellular automata enthusiasts.
The MNIST article demonstrates that cells will eventually agree on a classification for a given digit through repeated application of CA rules. Applying CA rules to an image for n updates is akin to passing the same image through an n-layer convolutional network. It is, therefore, unsurprising that CA can solve a traditional conv-net demo problem. Additionally, CA rules may be used as convolutions, allowing us to leverage the significant software, hardware, and systems progress made in deep learning.
Although CA has abilities beyond simulating physics, the method of CA computation does not suit conventional serial computation on Von Neumann style architectures. In order to achieve desirable performance, high-throughput parallelism is necessary. Fortunately, instead of creating new accelerators from the beginning, we can employ many of the same hardware and software tools utilized to accelerate deep learning to obtain comparable speed enhancements for cellular automata universes.
Must Go Faster: Accelerating Cellular Automata
Remember that Von Neumann designed his 29-state CA using a pen and paper, whereas Conway came up with the 2-state GOL by tinkering with stones and a grid on a Go board. Although it would likely be effortless to find fresh regulations that fulfill the growth-like features Conway aimed for in Life by employing a computational search, Neumann and Conway’s use of basic instruments in their research on cellular automata serves as a pleasant reminder that Moore’s law is not accountable for all progress made.
Neural networks and other CA systems are not ideal for being implemented on standard computers that are designed with the Von Neumann architecture. These computers operate by executing instructions in a sequential manner and prioritize low latency compared to parallelism, despite recent advancements like multiple cores and cache memory. Conversely, a cellular automaton universe operates inherently in parallel and often to a massive degree. The reason is that every cell has to conduct the same computations solely based on its local surroundings.
Several projects for specialized cellular automata machines (CAMs) were initiated due to the effectiveness of CA systems, similar to the development of several neural coprocessors and dedicated accelerators owing to the current emphasis on deep learning.
BONUS: Frequently Asked Questions about AI / ML
The utilization of AI and ML is expanding across various industries, including healthcare, business, and analytics, resulting in a potential transformative impact on numerous practices and functions. These technologies are revolutionizing language models, generative AI, enterprise, and the automobile industry. The implementation of AI applications is on the rise, and global advancements are already underway.
The online courses offered by Emeritus are tailored to meet your educational and professional needs in the fields of AI and ML. By getting a grip on the key concepts and principles of AI and ML, as well as their application in decision-making, product design, automation, and business and healthcare strategies, you will be able to broaden and deepen your expertise and skills.
AI and ML technology bring us closer than ever towards the future. To make it sustainable and safe for everyone, having a thorough proficiency and expertise with AI and ML technology is crucial in the preparation process.
1. Is Artificial Intelligence and Machine Learning a Good Career?
Despite being prominent in numerous fields today, AI and ML are still an enormous industry that is gaining momentum. According to the We Forum, there is an anticipation that by 2025, the duration spent by humans and machines on current duties will be equivalent, while 97 million new positions will likely arise, as the distribution of responsibilities among humans, machines, and algorithms continues to expand.
The AI and ML field is projected to require frequent role adjustments and adaptions, resulting in a diverse array of employment possibilities that extend beyond mere job duties to encompass proficiency levels. Among the present-day job openings in this industry are positions such as data analyst, business intelligence developer, ML engineer, and data scientist.
Learn More About Starting a Career in AI / ML:
2. What Do You Need to Launch a Career in AI and ML?
- A bachelor’s degree in mathematics, computer science, or a related field will provide a stable foundation for a career in AI and ML.
- A master’s or doctoral degree might further help in administrative and supervisory roles.
- A basic understanding of mathematics, physics, robotics, and popular coding languages can elevate your skills further.
- As AI and ML continue to change and develop, so will the knowledge around them. To keep up with the transformations in the industry, Emeritus offers an array of courses that will suit your career requirements and upgrade your knowledge with the latest advancements in the industry. Emeritus online courses are created to ensure continuity in your knowledge so that you do not fall behind in a rapidly changing industry.
3. What is the Difference Between Artificial Intelligence and Machine Learning?
Despite their close relationship, AI and ML are distinct concepts and cannot be used interchangeably. AI empowers machines to emulate human behavior, whereas machine learning enables machines to automatically learn from existing data without explicit programming.
Discover additional information about the comparison between Artificial Intelligence and Machine Learning.
4. What is the Difference Between Machine Learning and Deep Learning?
Machine learning is a larger category compared to deep learning, which is a subcategory. The development of deep learning is an advancement of machine learning. Furthermore, deep learning is included in artificial intelligence as a subset, while machine learning is a larger category within the field. The evolution of machine learning is due to the advancements in artificial intelligence.
Expand your knowledge on the differences between Machine Learning and Deep Learning.
5. What are the 4 Types of Machine Learning
There exist Supervised Learning, Unsupervised Learning, Semi-Supervised Learning, and Reinforced Learning as the four categories of machine learning. Acquire further knowledge on the four types of machine learning and typical use cases associated with each category.
6. Why Is an Online Course the Best Option for Working Professionals Now?
Having a variety of options is the main advantage of taking an online course. Emeritus provides an array of courses that tackle different aspects of the industry and are developed in collaboration with universities worldwide. This enables you to get exposed to diverse professional settings. You can opt for a course that is not related to your job in the industry or one that complements your current position and obligations. Flexible course options allow you to manage your schedule more effectively and be productive while learning online. As a response to the pandemic, online courses are adjusting and have the added benefit of enabling you to keep up with your regular duties while improving your skills in your area of expertise.
7. What Does an Online Course at Emeritus Look Like?
Our team of experts develops each AI/ML online course with specific learning outcomes in mind, using a backward design methodology that caters to learners of all ages. This approach allows us to create interactive and personalized learning experiences that combine various assessments, practical activities, and skill applications. We collaborate with renowned faculties and universities worldwide to curate industry-relevant course curriculum that prepares learners for prevalent challenges and future trends. Our courses feature a balanced blend of practical and theoretical concepts, assignments, exams, and capstone projects, along with networking opportunities and insights from industry leaders. Our ultimate goal is to provide learners with a comprehensive and standardized learning experience that adheres to Quality Matters’ stringent quality standards for digital teaching and learning environments, ensuring every learner invests in quality education.
8. Why Should You Take an Emeritus Course?
The completion rate for online courses at Emeritus is more than 80 percent, a clear indicator of how effective and interesting our learning modules are. The instructor-led approach also ensures that each learner completes and succeeds in their learning journey. With curricula from top universities, you learn about global perspectives in responding to transitions in the industry.
9. What Are the Next Steps to Joining an AI/ML Course?
Examine the course descriptions for additional information on the courses available, length, criteria, benefits, and learning accomplishments. You may also communicate with the program faculty and advisors to guarantee that the course you’ve chosen is appropriate for you. Once you’ve found the course that meets your educational objectives, enroll and start learning! After you’ve successfully finished the course, you’ll get a digital certificate that will elevate your CV and distinguish you to your current and future bosses. It will also keep you up-to-date and current in this ever-changing world.