Do you love language and technology? Do you want to help computers understand human language better? Then a career in Natural Language Processing (NLP) might be perfect for you! NLP is a field of computer science that focuses on the interaction between computers and human language. It involves teaching computers to understand, interpret, and generate human language. NLP is a rapidly growing field, with many exciting opportunities for those interested in language, technology, and innovation. As an NLP professional, you could work on developing software that can understand and respond to human language, such as chatbots or virtual assistants. You could also work on improving machine translation, speech recognition, and sentiment analysis. The possibilities are endless! Typical duties in NLP include analyzing and processing large amounts of language data, developing algorithms and models to improve language processing, and working with other experts in related fields such as artificial intelligence and machine learning. There are also many areas of specialization within NLP, including speech recognition, machine translation, and sentiment analysis. To get started in NLP, you'll need a strong background in computer science, mathematics, and linguistics. Many universities offer undergraduate programs and majors in computer science, linguistics, or a combination of the two. Some popular undergraduate programs for NLP include Computational Linguistics, Natural Language Processing, and Computer Science with a focus on Artificial Intelligence. Helpful personal attributes for a career in NLP include a love of language and technology, strong problem-solving skills, and the ability to work well in a team. NLP professionals often work in interdisciplinary teams, so the ability to communicate effectively with experts from other fields is also important. Job prospects in NLP are excellent, with many exciting opportunities in both the public and private sectors. Some notable employers in the field include Google, Amazon, Apple, and Microsoft. NLP professionals are in high demand, and the field is expected to continue growing in the coming years. So if you're interested in language, technology, and innovation, a career in Natural Language Processing might be perfect for you!

Language models, the algorithms that power text prediction, voice assistants, and chatbots, have revolutionized the way we interact with technology. But what happens when they reproduce the biases that humans exhibit? Language models can perpetuate biases and stereotypes about gender, race, and culture. So, how can we mitigate bias in language models? First, it’s essential to understand how bias is embedded in the data used to train these models. Second, we need to examine the algorithms and processes that produce biased outputs. Finally, we must develop strategies that enable us to monitor and correct biased language models. One example of bias in language models comes from facial recognition technology. Joy Buolamwini, a computer scientist at MIT, discovered that facial recognition algorithms had trouble identifying dark-skinned faces, including hers. This discovery led her to create the Algorithmic Justice League, an organization that advocates for the ethical use of technology. Researchers have found that training language models on more diverse datasets can reduce bias. For example, Emily M. Bender, a professor of linguistics at the University of Washington, has argued that language models should be trained on data that reflects the diversity of human language. Another strategy for mitigating bias is to develop guardrails that monitor the outputs of language models. The goal of these guardrails is to flag potentially biased content and provide suggestions for alternative, unbiased language. Overall, detecting and mitigating bias in language models is a complex and ongoing challenge. As the use of language models continues to expand, it’s important to stay informed about these issues and engage in ongoing research and development.

Language models can perpetuate societal biases, but MIT researchers have trained a logic-aware model that significantly mitigates harmful stereotypes. With 350 million parameters, this model outperformed larger models on logic-language understanding tasks and had higher iCAT scores, indicating fewer stereotypes. The researchers propose this fair, smaller, and locally deployable model as a solution to challenges faced by current language models, such as fairness, computational resources, and privacy. While language models may still have a long way to go in achieving neutrality, this research offers a promising step forward.

Are you fascinated by the idea of machines learning from data and making predictions on their own? Look no further than the exciting field of Machine Learning! Machine Learning is a subfield of Artificial Intelligence that focuses on developing algorithms and models that enable machines to learn from data and make predictions or decisions without explicit programming. It has revolutionized the way we interact with technology, from personalized recommendations on Netflix to self-driving cars. Some of the most interesting and meaningful aspects of Machine Learning include its applications in healthcare, finance, and environmental sustainability. For example, Machine Learning algorithms can analyze medical data to diagnose diseases or predict patient outcomes, while also identifying patterns in financial data to detect fraud or predict stock prices. Innovations in Machine Learning are happening all the time, with exciting research being done in areas such as deep learning, reinforcement learning, and natural language processing. Some of the most inspiring academic discourse in the field comes from renowned figures like Andrew Ng, Yoshua Bengio, and Geoffrey Hinton, who have made significant contributions to the development and advancement of Machine Learning. At the undergraduate level, typical majors and modules in Machine Learning include courses in statistics, computer science, and mathematics. Students can specialize further in areas such as computer vision, natural language processing, or robotics. Examples of real-life applications of Machine Learning include image recognition in social media, language translation in Google Translate, and predictive maintenance in manufacturing. A degree in Machine Learning can lead to a range of exciting and lucrative career opportunities. Graduates can work in fields such as data science, machine learning engineering, or artificial intelligence research. Key industries for prospective future employment include tech, finance, healthcare, and automotive. Notable employers in these industries include Google, Amazon, Microsoft, and Tesla. To succeed in Machine Learning, students should have a strong foundation in mathematics and computer science, as well as an interest in data analysis and problem-solving. Good communication skills are also important, as Machine Learning often involves working in interdisciplinary teams. In conclusion, studying Machine Learning at the undergraduate level is an exciting and rewarding choice for those interested in the intersection of technology and data. With its endless possibilities and real-world applications, Machine Learning is a field that is sure to continue making waves in the years to come.

Artificial Intelligence (AI) is one of the most rapidly growing and exciting fields in technology today. AI has been transforming the way we live, work, and interact with the world, and it offers many promising opportunities for those interested in technology and innovation. As an AI specialist, you'll be working with complex algorithms, large datasets, and advanced technologies to create intelligent systems that can reason, learn, and adapt. This field has a wide range of applications, from creating self-driving cars and robots to analyzing big data, improving healthcare, and enhancing cybersecurity. In the world of AI, you could work as a data scientist, machine learning engineer, AI research scientist, or natural language processing expert, among other positions. For example, if you're interested in developing intelligent machines, you could work in robotics or autonomous vehicle development. Alternatively, if you're interested in improving healthcare, you could work on developing AI systems for drug discovery or disease diagnosis. To get started in this field, you'll typically need a bachelor's degree in computer science, engineering, mathematics, or a related field. Some popular undergraduate programs include computer science, artificial intelligence, and data science. In addition to technical skills, successful AI professionals should possess strong problem-solving skills, creativity, and an ability to work in a team environment. Strong communication skills are also essential, as you may be working with cross-functional teams. The job prospects in AI are highly promising. According to the Bureau of Labor Statistics, the job outlook for computer and information research scientists, which includes AI specialists, is expected to grow by 19% from 2020 to 2030, much faster than the average for all occupations. There are many notable employers in the AI industry, including IBM, Google, Amazon, Microsoft, and Facebook. Additionally, there are opportunities for AI specialists in various industries, including healthcare, finance, and government.

Have you ever wondered how machines can learn and make decisions on their own? If you're fascinated by the idea of artificial intelligence and want to be at the forefront of technological advancement, then a career in Neural Networks might be perfect for you! Neural Networks is a field of study that involves designing and developing computer systems that can learn and adapt on their own, similar to the way the human brain works. It's a rapidly growing field with endless possibilities, and it's changing the way we live and work. As a Neural Network expert, you'll have the opportunity to work on exciting projects like self-driving cars, voice recognition systems, and even medical diagnosis tools. Imagine being able to create a computer system that can diagnose diseases with the same accuracy as a human doctor! Typical duties in this field include designing and developing algorithms, testing and evaluating systems, and implementing machine learning models. There are also various areas of specializations within the field, including deep learning, natural language processing, and computer vision. To become a Neural Network expert, you'll need a strong foundation in mathematics, computer science, and statistics. Popular undergraduate programs and majors include Computer Science, Mathematics, and Electrical Engineering. Helpful personal attributes for this field include strong analytical skills, problem-solving abilities, and creativity. You'll need to be able to think outside the box and come up with innovative solutions to complex problems. Job prospects in Neural Networks are excellent, with a projected growth rate of 22% between 2019 and 2029. There are many potential employers in both the public and private sectors, including tech giants like Google, Microsoft, and Amazon. Other notable employers include healthcare organizations, financial institutions, and government agencies. In conclusion, a career in Neural Networks is an exciting and rewarding field that offers endless opportunities for growth and innovation. If you're passionate about technology and want to make a difference in the world, then this might just be the perfect career for you!

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.

Are you fascinated by technology and the limitless possibilities it holds? Do you have a passion for problem-solving and a desire to make a real impact on the world? Look no further than the exciting field of Artificial Intelligence (AI). AI is the study of intelligent agents, which are computer programs that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. From self-driving cars to virtual personal assistants, AI is transforming the way we live and work. At the forefront of AI research are innovators like Andrew Ng, who co-founded Google Brain and Coursera, and Yoshua Bengio, who won the prestigious Turing Award for his work in deep learning. These academic giants have paved the way for groundbreaking developments in AI, such as natural language processing and computer vision. As an undergraduate in AI, you'll delve into a range of fascinating modules, such as machine learning, robotics, and data mining. You'll also have the opportunity to specialize in areas such as computer vision, natural language processing, and game AI. Imagine creating a robot that can navigate a maze or designing a system that can translate languages in real-time. But what about the future? With a degree in AI, you'll be well-equipped for a range of exciting careers, including data scientist, machine learning engineer, and AI researcher. You could work for tech giants like Google, Facebook, and Amazon, or for cutting-edge startups like OpenAI and DeepMind. And it's not just tech companies that need AI experts - industries like healthcare, finance, and transportation are all looking for talented AI professionals. To succeed in AI, you'll need a strong foundation in mathematics, computer science, and programming. But equally important are skills like critical thinking, problem-solving, and creativity. If you're passionate about technology and driven to make a difference, a degree in AI could be the perfect fit for you.

Are you interested in finding out how Netflix recommends your next binge-worthy series or how Amazon knows what products to suggest for you to buy? If so, then studying Data Science might be perfect for you! Data Science is an exciting field that combines statistical analysis, computer science, and domain knowledge to extract insights and knowledge from complex and large data sets. It is the foundation of many cutting-edge technologies, from self-driving cars to personalized medicine. One exciting aspect of Data Science is its real-world applications. Data Scientists play a crucial role in solving a variety of problems, from predicting hurricanes to optimizing supply chains for major retailers. They also work on designing recommendation engines and fraud detection algorithms, as well as developing models to analyze data from social media and other sources. In the academic world, Data Science is a rapidly evolving field, with new innovations and academic discourse constantly emerging. For example, researchers are exploring ways to use machine learning algorithms to identify and predict disease outbreaks, as well as developing techniques to ensure that AI systems are fair and unbiased. At the undergraduate level, typical majors and modules include Statistics, Programming, Data Mining, and Machine Learning. Students may also have the opportunity to specialize in areas such as Natural Language Processing, Computer Vision, and Deep Learning. Real-life examples of Data Science in action can include using machine learning algorithms to analyze MRI scans to detect early-stage cancer, or developing predictive models to identify which customers are most likely to leave a telecommunications provider. Upon graduation, Data Science majors can expect to have a wide range of career options available to them. Many work in technology companies, but opportunities also exist in healthcare, finance, and government. Employers such as Facebook, Google, and IBM all hire Data Scientists, as do public sector organizations like the Centers for Disease Control and Prevention. To succeed in Data Science, students should have strong analytical skills, an aptitude for mathematics and statistics, and an interest in computer programming. In addition, they should be curious and possess a willingness to learn and explore new ways of working with data.

Discover how large language models like ChatGPT are shaping the way we write and reinforcing existing hierarchies in language use. Learn about the impact of AI technology on linguistic diversity and the ways in which it perpetuates dominant modes of writing, potentially sidelining less common ones. Explore how we can use writing as a tool to resist oppression and create a more equitable future.

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.

Are you a problem solver with a passion for numbers and technology? If so, you might want to consider a career in data science! With the ever-increasing amount of data generated every day, the need for professionals who can gather, analyze, and interpret this data has never been greater. As a data scientist, you will work with large and complex datasets to derive meaningful insights and make informed business decisions. Data science is a multidisciplinary field that incorporates aspects of computer science, statistics, and mathematics, among others. It involves using advanced analytical and machine learning techniques to identify patterns and trends in data, and to develop predictive models that can be used to make data-driven decisions. Data science is a crucial aspect of many industries, including finance, healthcare, marketing, and technology. For example, Netflix uses data science to recommend movies and TV shows to viewers, while Amazon uses it to personalize product recommendations for customers. In healthcare, data scientists analyze medical data to improve patient outcomes and predict disease outbreaks. As a data scientist, you might be responsible for tasks such as data cleaning and preprocessing, exploratory data analysis, feature engineering, and building and deploying machine learning models. You may also specialize in areas such as natural language processing, computer vision, or deep learning. To become a data scientist, you will typically need a bachelor's degree in a related field such as computer science, statistics, mathematics, or engineering. Popular undergraduate programs include Computer Science, Statistics, Applied Mathematics, and Data Science. Many data scientists also pursue advanced degrees, such as a Master's or PhD in Data Science. In addition to technical skills, data scientists should also possess strong communication and problem-solving skills, as well as an ability to work collaboratively in a team environment. Some helpful personal attributes might include curiosity, attention to detail, and a passion for learning. The job outlook for data scientists is extremely positive, with the demand for skilled professionals expected to continue to grow in the coming years. Notable potential employers in this field include tech giants such as Google, Facebook, and Amazon, as well as a variety of other companies across industries such as healthcare, finance, and retail.

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?

Are you interested in creating cutting-edge software that changes people's lives? Do you enjoy working in a fast-paced, collaborative environment where innovation is encouraged and rewarded? If so, a career in software development may be right for you. Software developers are the creative minds behind computer programs. They use their expertise in programming languages, algorithms, and database management to design, develop, and test software applications for a variety of industries, from finance and healthcare to gaming and entertainment. They work closely with teams of software engineers, designers, and project managers to create software solutions that solve real-world problems and meet the needs of businesses and consumers alike. One of the most appealing aspects of a career in software development is the opportunity to work on projects that have a real impact on people's lives. For example, software developers at Google have worked on products such as Google Maps, which has revolutionized the way we navigate the world, and Google Translate, which makes it possible for people around the globe to communicate with each other in their own languages. Typical duties of a software developer include analyzing user needs and designing software solutions to meet those needs, writing and testing code, and collaborating with other team members to ensure that the software is delivered on time and to the highest quality standards. As you gain experience in the field, you may also have the opportunity to specialize in areas such as mobile app development, web development, or artificial intelligence. To become a software developer, you'll typically need a bachelor's degree in computer science, software engineering, or a related field. Popular undergraduate programs and majors include computer science, computer engineering, and information technology. In addition to a solid academic background, helpful personal attributes for a career in software development include strong problem-solving skills, a passion for learning, and excellent communication skills. The job prospects for software developers are excellent, with the Bureau of Labor Statistics projecting a 22% growth rate in employment from 2019 to 2029. This growth is driven by the increasing demand for software in virtually every industry, from healthcare and finance to education and entertainment. Some of the top employers in the field include companies such as Microsoft, Amazon, and IBM, as well as startups and small businesses around the world.

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!

If you love words, language, and communication, you may want to consider studying lexicography at university undergraduate level. Lexicography is the field of study that focuses on the creation, compilation, and use of dictionaries and other reference works. It is an exciting and dynamic field that is constantly evolving to meet the changing needs of society. Lexicography is a fascinating field that combines linguistics, computer science, and library science. It involves the analysis and description of words and their meanings, as well as the creation of dictionaries and other reference works. Lexicographers work to understand how words are used in different contexts and cultures, and they use this information to create comprehensive and accurate reference works. One interesting aspect of lexicography is that it is a truly global field. Lexicographers work with words and languages from all over the world, and they must be sensitive to the cultural nuances that affect the meanings and usage of words. For example, the word "sushi" has different connotations in Japan than it does in the United States. Lexicography is also an important field for innovation and research. Lexicographers are constantly developing new techniques and tools for analyzing and describing language. For example, computational lexicography uses computer programs to analyze large amounts of language data and extract useful information. At the undergraduate level, students can expect to take courses in linguistic analysis, lexicography theory, and computer programming. They may also have the opportunity to specialize in a particular area, such as corpus linguistics or historical lexicography. A degree in lexicography can lead to a variety of exciting career paths. Many lexicographers work for publishing companies, creating dictionaries and other reference works for a wide audience. Other potential employers include libraries, government agencies, and educational institutions. For example, Merriam-Webster, Oxford University Press, and the Library of Congress are all notable employers in the field. To succeed in lexicography, students should have a strong interest in language and communication. They should be detail-oriented and able to work independently, as well as collaboratively with other professionals in the field. Excellent writing and research skills are also important.

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.

Are you fascinated by the idea of machines that can think and learn like humans? Do you want to be at the forefront of technological innovation? Then studying Artificial Intelligence & Machine Learning might be your calling! Artificial Intelligence & Machine Learning is a field of study that focuses on creating intelligent machines that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. It involves a combination of computer science, mathematics, and statistics. One of the most exciting aspects of this field is the potential for real-life applications. For example, self-driving cars, virtual personal assistants like Siri and Alexa, and facial recognition technology are all examples of AI and machine learning in action. The field of AI & Machine Learning is constantly evolving, with exciting research and innovations happening all the time. Some notable academic figures in the field include Geoffrey Hinton, who developed deep learning algorithms, and Andrew Ng, who co-founded Google Brain and Coursera. At the undergraduate level, typical majors and modules might include programming, data analysis, and machine learning algorithms. There are also many potential areas of further specialisation, such as natural language processing, computer vision, and robotics. If you pursue a degree in AI & Machine Learning, you'll be well-equipped for a range of exciting future jobs and roles. Some key industries for prospective employment include healthcare, finance, and transportation. Companies like Google, Amazon, and Microsoft are all actively hiring for AI and machine learning roles. To succeed in this field, you'll need a strong foundation in math and computer science, as well as an interest in problem-solving and a willingness to keep up with the latest developments in the field. If you're interested in creating cutting-edge technology that has the potential to change the world, then studying Artificial Intelligence & Machine Learning might be the perfect fit for you.

Are you curious about Artificial Intelligence (AI) but intimidated by the mathematical complexity behind it? Artificial Intelligence for Humans is a book series that breaks down AI algorithms into digestible chunks, using simple language and actual numeric calculations. In the first book, you'll learn foundational algorithms such as clustering, linear regression and hill climbing, which are not only fundamental for the rest of the series but also useful on their own. This book requires only basic college algebra or computer programming knowledge, making it accessible to anyone interested in AI. Recommended for students and professionals interested in Artificial Intelligence, Computer Science, Mathematics, and Engineering, as well as anyone curious about how AI algorithms work. This book series is especially relevant for those without an extensive mathematical background, as it uses simple language and actual numeric calculations to explain complex concepts. The first book teaches foundational algorithms such as clustering, linear regression and hill climbing, which are not only useful in AI but also in other fields that involve data analysis and prediction. Additionally, the programming examples provided in various languages make this book series practical for those who want to apply AI algorithms in their own projects.