Did you know that the origin of the word 'algorithm' dates back to the late 17th century? It comes from the name of a Persian mathematician, Muhammad ibn Musa al-Khwarizmi, who was born around 780 AD. His innovative contributions to mathematics, astronomy, geography, and cartography revolutionized the field. He wrote an influential book called “On the calculation with Hindu Numerals" which introduced Hindu-Arabic numerals to the West, the system we use today. His books showed how complex problems could be broken down into simpler parts and solved, which eventually gave birth to the computer age. Today, algorithms are everywhere, helping us get from A to B, driving internet searches, making recommendations of things for us to buy, watch or share, and even predicting how we vote or who we fall in love with. Learning about algorithms can give you a better understanding of how the world around you works, and it can even inspire you to create something new that could change the world.

Did you know that ancient civilizations had a sophisticated understanding of math? The Babylonians, for example, knew about Pythagorean triples - a concept that we attribute to the Greeks. They used this knowledge for surveying land accurately, which was essential for people who owned private property. Plimpton 322, a clay tablet discovered in modern-day Iraq, is a testament to the Babylonians' understanding of mathematics. By delving into ancient math concepts, you can broaden your perspective and gain a deeper understanding of the world. Not only does this improve your critical thinking and problem-solving skills, but it also allows you to appreciate the rich history of human achievement. So, grab a book on ancient math, start reading, and be prepared to be amazed!

Maps are a fundamental tool for navigation and education, but did you know that every map humans have ever made is wrong? That's because it's impossible to make a 100% accurate flat map of a spherical planet. Despite this, maps are still widely used today, including the Mercator projection, which distorts the size of land masses and bodies of water. However, maps are not just about navigation - they also shape our perception of the world. Simple changes in map design can completely shift our point of view. Learning about the different types of maps and their limitations can expand your understanding of geography and the world around you. By exploring this topic through reading, reflection, and self-directed projects, you can become an expert in cartography and gain a deeper appreciation for the power of maps.

Throughout history, many women have made significant contributions to society, often overcoming immense challenges to accomplish extraordinary feats. Ada Lovelace, Zora Neale Hurston, Nadia Comaneci, Beryl Markham, and Sonia Sotomayor are just a few examples of women who blazed trails in various fields. Lovelace, the first computer programmer, Hurston, an influential novelist and folklorist, Comaneci, the first athlete to receive a perfect 10 in an Olympic event, Markham, the first person to fly solo across the Atlantic from the east to the west, and Sotomayor, the first Hispanic to be appointed to the US Supreme Court. By exploring the lives of these remarkable women, students can learn about diverse fields of study, gain inspiration and develop important skills like critical thinking, creativity, and leadership, and be motivated to make their own mark on the world.

Are you curious about how to identify if a text is written by an AI language model or a human? Researchers at Stanford University have developed a tool called DetectGPT that can accurately distinguish between human- and LLM-generated text. The tool could benefit teachers, journalists, and citizens who need to know when they are reading model-generated text. By calculating how much a language model "likes" a piece of text, DetectGPT provides a reliable, actionable prediction as to whether a text was machine-generated. Discover the latest developments in LLM research and its implications for society.

The World Wide Web is an integral part of our daily lives, but do you know what it really is? It's not the same as the internet, which is simply a way for computers to share information. The World Wide Web is like a virtual city, where we communicate with each other in web languages, with browsers acting as our translators. What makes the Web so special is that it's organized like our brains, with interconnected thoughts and ideas, thanks to hyperlinks. By exploring the World Wide Web, you can learn more about web languages like HTML and JavaScript, and gain valuable skills in communication, research, and problem-solving. Plus, you'll be part of a global community that connects minds across all boundaries. So why not dive in and explore this fascinating virtual city?

Ever wonder how computers turn a blurry image into a crisp one? New research from MIT and UC Berkeley reveals how neural networks can de-blur fuzzy images with a "generative" model algorithm. But how accurate are the results? The researchers developed a way to represent uncertainty in a way that is meaningful for non-experts, offering a range of images with precise bounds and probabilistic guarantees. This milestone not only has implications for image restoration, but also for fields such as medical imaging and law enforcement.

The study published in PLOS ONE reveals the discovery of Roman gold coins that were previously thought to be fake for over 150 years. The coins, which are now scientifically proven to be authentic, shed light on the long-lost emperor Sponsian and his reign. The coins were found in an isolated gold mining outpost in Roman Dacia, providing insight into the area's untold story.

Understanding the science behind climate change is crucial for high school students. Eunice Foote, a women's rights activist, discovered the greenhouse effect in 1856, but her work was buried for 150 years. Guy Stewart Callendar and Charles Keeling continued their research in the 1930s and 1950s, respectively, and proved that human activity was causing global warming. Keeling's Mauna Loa project is still the world's benchmark site for measuring CO2. By learning about the history of climate science, students can appreciate the importance of perseverance in scientific research and the impact of human activity on the environment. Understanding climate science can also help students make informed decisions about their own carbon footprint and advocate for change in their communities.

Lovecraftian horror is a unique type of terror that responds to the rapid scientific progress of our era, using scientific elements to lend eerie plausibility. Rather than recognizable monsters or graphic violence, the terror in Lovecraft's fiction lies in what's not directly portrayed, but left instead to the dark depths of our imagination. Lovecraft's protagonists often stumble onto hints of the existence of the Great Old Ones, beings that exist beyond our conceptions of reality, whose true forms are as inscrutable as their motives. Even indirect glimpses of them are enough to drive them insane. The Cthulhu Mythos, named after Lovecraft's infamous blend of dragon and octopus, is a shared universe adopted by a wide variety of authors, often reimagining them from diverse perspectives that transcend the author's prejudices. Learning about Lovecraftian horror will inspire you to independently explore academic topics through reading, reflection, writing, and self-directed projects.

Are you curious about the possibility of asteroids hitting Earth in the next 1,000 years? Well, according to a new study published in The Astronomical Journal and reported by MIT Technology Review, there's good news - none of them are expected to hit us! The study, led by Oscar Fuentes-Muñoz from the University of Colorado, Boulder, used a new method to model when asteroids are expected to come near Earth in their orbit and pushed those estimates up to 1,000 years into the future. Read on to discover more about the study's findings and what this means for our planet's future.

Cornell researchers have made a breakthrough in fault-tolerant quantum computing by constructing a model with non-Abelian anyons, exotic particles that can protect bits of quantum information by storing them non-locally. This discovery opens up new opportunities for quantum computation, and the researchers have even provided specific instructions for executing the experiment on devices available today. Collaborating with Google Quantum AI, they have successfully proved the theory with an experiment, making non-Abelian anyons a reality. This breakthrough could pave the way for a new era in quantum computing.

Medieval texts, ice core, and tree ring data reveal how volcanic eruptions triggered the Little Ice Age. Researchers from the University of Cambridge and the University of Geneva examined hundreds of annals and chronicles from across Europe and the Middle East, in search of references to total lunar eclipses and their coloration. They found that the darkest lunar eclipses all occurred within a year or so of major volcanic eruptions, leading to cooling and other climate anomalies. Find out how the monks' records of the brightness and colour of the eclipsed moon helped narrow down when the eruptions must have happened.

Are you interested in artificial intelligence, data science, and solving complex problems using cutting-edge technology? If so, a career in machine learning might be just the path for you. Machine learning is an exciting and rapidly growing field that allows computers to learn and make decisions based on data, without being explicitly programmed. As a machine learning engineer or scientist, you can use your skills to tackle real-world problems and create innovative solutions. In this field, you could work on developing new algorithms to analyze vast amounts of data, build and train predictive models, and design intelligent systems that can learn and adapt on their own. For example, you might work on creating a chatbot that can answer customer queries, or on designing self-driving cars that can safely navigate roads and make real-time decisions. Typical duties in machine learning can vary based on your area of specialization, which could include natural language processing, computer vision, deep learning, or reinforcement learning, among others. You might work in research and development, or in a practical setting, helping to implement machine learning solutions in businesses, healthcare, finance, or other industries. Other related fields in this area include data science, artificial intelligence, and computer programming. To prepare for a career in machine learning, you will typically need a strong background in math, statistics, and computer science. You might pursue a degree in a relevant field such as computer science, electrical engineering, or applied math. Some popular undergraduate programs include a Bachelor's in Computer Science, a Bachelor's in Mathematics, or a Bachelor's in Data Science. You might also pursue a Master's or PhD in Machine Learning or a related field, to gain specialized expertise. Helpful personal attributes for a machine learning career include a strong analytical mindset, excellent problem-solving skills, attention to detail, and the ability to work independently and as part of a team. You should also be curious, creative, and have a passion for learning, as this field is constantly evolving. The job prospects for machine learning professionals are excellent, with a strong demand for these skills across many industries. Major tech companies like Google, Amazon, and Microsoft are among the top employers in this field, along with many startups and other private and public sector organizations. The long-term outlook for machine learning is very promising, as the technology is expected to continue to advance and play an increasingly important role in our lives.

MIT researchers have found a way to reduce the time and cost of training large machine learning models by leveraging smaller models. This technique could help researchers make advancements faster with less expense and reduce carbon emissions. MIT's Yoon Kim and his team's method saves about 50% of the computational cost required to train a large model, compared to methods that train a new model from scratch. The research will be presented at the International Conference on Learning Representations.

In today's digital age, we're surrounded by algorithms that shape our daily lives in ways we may not even realize. From social media algorithms that decide what content we see to predictive policing algorithms that influence law enforcement decisions, algorithmic culture is ubiquitous and powerful. So, what is algorithmic culture, and how does it shape our lives and perceptions? At its core, algorithmic culture refers to the way algorithms and the data they process have become embedded in contemporary culture. According to Lev Manovich, a leading academic in the field, algorithmic culture is "a new way of producing and representing knowledge based on data analysis, and a new form of power." In other words, algorithms are not just tools but are also shaping the way we understand and interact with the world around us. One example of algorithmic culture in action is the use of predictive algorithms in the criminal justice system. Proponents argue that these algorithms can help prevent crime by identifying high-risk individuals before they offend. However, critics argue that these algorithms reinforce existing racial biases and lead to unfair treatment of certain groups. Another example is the use of recommendation algorithms on social media platforms. These algorithms decide what content we see based on our past behavior and interests, creating a "filter bubble" that can limit our exposure to diverse viewpoints. Despite its potential pitfalls, algorithmic culture also offers new opportunities for creativity and innovation. For example, computer-generated art is a growing field that harnesses the power of algorithms to produce unique and compelling works. As we navigate our increasingly algorithmic world, it's important to understand the ways in which these tools shape our lives and perceptions. By engaging with academic research and exploring new ideas, we can become more informed and empowered citizens in the digital age.

Are you fascinated by the possibilities of artificial intelligence and machine learning? Do you have a passion for problem-solving and a natural curiosity about the world around you? If so, a career as an AI/ML Engineer might be the perfect fit for you! As an AI/ML Engineer, you'll be at the forefront of one of the most exciting and rapidly growing fields in technology today. You'll work with cutting-edge algorithms and tools to develop intelligent systems that can learn, reason, and make decisions on their own. From self-driving cars to personalized healthcare, the possibilities are endless. Your typical duties as an AI/ML Engineer might include designing and implementing machine learning models, analyzing data to identify patterns and trends, and collaborating with other engineers and data scientists to develop innovative solutions to complex problems. You might specialize in areas like natural language processing, computer vision, or robotics, or work in related fields like data science or software engineering. To prepare for a career in AI/ML engineering, you'll need a strong background in computer science, mathematics, and statistics. Popular undergraduate programs and majors include computer science, mathematics, statistics, and electrical engineering. Helpful personal attributes include a strong work ethic, attention to detail, and a willingness to learn and adapt to new technologies and methodologies. Job prospects for AI/ML Engineers are excellent, with strong demand from both public and private sector employers around the world. Some notable and attractive potential employers include tech giants like Google, Amazon, and Microsoft, as well as cutting-edge startups and research institutions. And with the continued growth of AI and machine learning, the longer-term outlook for this field is very promising indeed. So if you're looking for a career that combines cutting-edge technology, intellectual challenge, and the potential to make a real impact on the world, consider a career as an AI/ML Engineer. The possibilities are endless!

Have you ever thought about why we use the number system we do? Did you know that there are other systems out there, like base 12, that could make math easier and more enjoyable? In a dozenal world, numbers are represented using 12 symbols instead of 10, which makes it easier to divide numbers into whole numbers. This makes it easier to use currency and measure things like fractions. Plus, some times tables work more nicely in base 12. It might seem confusing to switch to a new system, but civilizations have switched between bases throughout history. By exploring different number systems, you can expand your understanding of math and challenge yourself to think outside the box. Who knows, maybe you'll discover a new way of thinking about numbers that will make math more enjoyable for you!

Neural networks are computer systems designed to operate similarly to the human brain. These networks have revolutionized the field of computer science and have transformed the way we process and analyze data. The study of neural networks is a fascinating and exciting area of research, with many appealing and meaningful aspects. One of the most interesting aspects of neural networks is the way they can learn from data. For example, facial recognition technology uses neural networks to learn and recognize faces. This has transformed security systems and made our lives easier. Similarly, self-driving cars use neural networks to process data and make decisions on the road. There are many famous academics in the field of neural networks, including Geoffrey Hinton, Yann LeCun, and Yoshua Bengio, who won the 2018 Turing Award for their work on deep learning. Their research has led to innovations in natural language processing, image recognition, and speech recognition, among others. At the undergraduate level, students can study neural networks as part of a computer science or electrical engineering major. Students will learn about the principles of neural networks and how they are applied in various fields. They can specialize further in machine learning, data science, or artificial intelligence. There are many potential jobs and roles that students can pursue after studying neural networks, including data analyst, software engineer, and machine learning engineer. Top companies that work with neural networks include Google, Facebook, Amazon, and Tesla, to name just a few. To succeed in the field of neural networks, students should have a strong foundation in mathematics and computer science. They should also have an interest in machine learning, data science, and artificial intelligence.

The video clip discusses the importance of curiosity and exploration in academia. It highlights the stories of Richard Feynman, Eratosthenes, and Armand Fizeau, who made significant contributions to science by asking simple questions and investigating them further. Feynman's father's explanation of inertia sparked his interest in physics, leading him to earn degrees at MIT and Princeton, solve the Challenger disaster, and win the Nobel Prize in Physics. Eratosthenes calculated the Earth's circumference by measuring the shadows cast by the sun, and Fizeau confirmed Galileo's experiments on the speed of light. These stories demonstrate the value of asking questions and pursuing knowledge, even if the answers are not immediately clear. By exploring academic topics through reading, reflection, writing, and self-directed projects, students can develop their critical thinking skills and gain a deeper understanding of the world around them.