How to Become an AI Technical Lead (AI Algorithms, Your Ally)
If you’ve ever envisioned leading cutting-edge AI projects or pondered on what it takes to become an AI Technical Lead, you’ve arrived at the right destination.
In this comprehensive guide, we’ll delve into the SPECIFIC steps required to catalyze your journey towards becoming an AI Technical Lead. We’ll discuss:
- The skills you need to master.
- The educational background that can bolster your path.
- How to secure a position as an AI Technical Lead.
So, whether you’re an AI enthusiast just starting out, or a tech-proficient individual aiming to elevate your career, keep reading.
We’re about to decode the roadmap to becoming an AI Technical Lead.
Let’s embark on this journey!
Steps to Become an AI Technical Lead
Step 1: Obtain a Strong Educational Foundation in Computer Science or a Related Field
Starting a career as an AI Technical Lead requires a strong educational foundation in Computer Science, Mathematics, Data Science or related fields.
Most AI technical leads hold a bachelor’s degree in these areas, and many have also pursued further studies, like a master’s degree or PhD.
These degrees offer the mathematical and programming skills necessary for the role.
During the course of these programs, you’ll learn about various areas of computer science such as algorithms, data structures, computer networks, operating systems, and software engineering.
You’ll also gain exposure to various programming languages like Python, Java or C++.
It’s also beneficial to take courses specifically focused on artificial intelligence and machine learning.
These specialized courses will provide you with a solid understanding of AI principles, machine learning algorithms, neural networks, and more.
Such a strong foundational understanding of AI principles is crucial for an AI Technical Lead, who will be responsible for overseeing AI projects, mentoring teams and making strategic decisions about AI implementation.
Remember that the AI field is ever-evolving, so continuous learning is a key part of this career.
Be prepared to stay updated with the latest advancements and technologies in the field through ongoing education and self-learning.
As AI expands into various industries and applications, a robust and versatile educational foundation will prove invaluable.
Step 2: Specialize in Artificial Intelligence and Machine Learning
As a continuation to your bachelor’s degree, it is important to specialize in the field of Artificial Intelligence and Machine Learning.
This could be done through a master’s program, or even a series of intensive courses or bootcamps that focus on AI and Machine Learning.
During this specialization, you’ll get a deeper understanding of various AI algorithms, neural networks, deep learning, statistics, and probability.
This is also the time to get hands-on experience by working on projects that involve real-world applications of AI.
This might include projects in natural language processing, predictive modeling, or even AI-driven product development.
It’s also advantageous to learn various AI and Machine Learning tools and languages such as Python, TensorFlow, Keras, PyTorch, and Scikit-learn.
These will be essential in building and implementing AI models.
While specializing, make sure to stay updated with the latest AI trends and technologies, as the field is constantly evolving.
This can be achieved by participating in relevant webinars, conferences, and online forums.
After your specialization, consider gaining a certification from recognized institutions or platforms.
This not only validates your skills and knowledge but also enhances your credibility in the job market.
Remember, becoming an AI Technical Lead is not only about having the technical knowledge but also about understanding the application of technology to solve complex business problems.
Hence, develop your problem-solving skills and work on projects that showcase your ability to use AI in practical business scenarios.
Step 3: Gain Mastery Over Programming Languages and AI Frameworks
To become an AI Technical Lead, one of the most critical steps involves mastering the main programming languages and Artificial Intelligence (AI) frameworks used in the field.
The most common programming languages for AI include Python, Java, R, and C++.
Python, in particular, is a favorite because of its simplicity and the numerous libraries it provides for AI and Machine Learning (ML).
You should also learn about AI frameworks like TensorFlow, PyTorch, Keras, and scikit-learn.
These AI libraries and frameworks are essential tools for developing AI models and applications, as they provide a simplified way to work with complex algorithms.
During this step, focus on learning the ins and outs of these languages, and get plenty of hands-on experience by working on various AI projects.
This will not only strengthen your programming skills but will also familiarize you with the practical challenges and solutions in AI development.
Additionally, learning about cloud platforms like AWS, Google Cloud, or Azure is beneficial as they offer services that support AI development.
This step is crucial for an AI technical lead because you will be expected to spearhead the development and execution of AI projects, and deep technical knowledge is vital for such a role.
Remember that this learning process is continuous, given the rapid evolution of technology.
Always staying updated about the latest AI trends, tools, and best practices is a fundamental part of your role as an AI Technical Lead.
Step 4: Develop a Strong Understanding of Data Science and Analytics
As an AI Technical Lead, a deep understanding of data science and analytics is key.
You should be well versed in programming languages commonly used in data science, such as Python, R, or SQL.
Knowledge about machine learning algorithms, data visualization, statistical modeling, and data wrangling is crucial as well.
Apart from the technical knowledge, you should also have the ability to interpret data and extract useful insights from it.
This involves not only looking at numbers and figures but also understanding the context and the implications of the data you’re working with.
There are numerous ways to develop your understanding of data science and analytics.
If you’re still in school, consider taking classes or minoring in these areas.
If you’re already in the workforce, look into online courses or certificate programs that can help you deepen your knowledge.
Remember, the field of data science is constantly evolving, and staying up-to-date with the latest developments is a must.
Attend workshops and conferences, join professional networks, and read industry publications to keep your skills sharp.
Lastly, practical experience is invaluable.
Try to get involved in projects or research that allows you to apply what you’ve learned.
This real-world experience will not only cement your understanding but also provide you with tangible proof of your skills, which can be incredibly beneficial when seeking a role as an AI Technical Lead.
Step 5: Accumulate Specialized AI Project Experience
As you continue to grow in your career and further your education, it’s crucial to gain specialized AI project experience.
Working on practical projects that involve AI technologies will not only improve your understanding and skills but also make you a more attractive candidate for an AI Technical Lead role.
Consider taking on projects that allow you to apply AI in various fields such as healthcare, finance, retail, and more.
This could involve designing and implementing machine learning models, developing AI algorithms, or working on data mining techniques.
It’s also beneficial to work on projects that require you to build and lead a team, as this is a key aspect of the AI Technical Lead role.
Joining open-source projects or contributing to AI research can also help you acquire specialized experience.
Additionally, participating in AI challenges or hackathons could provide you with the opportunity to solve real-world problems and learn from other AI professionals.
Keep in mind that the more diverse your project experience, the more prepared you’ll be to handle the wide range of challenges that can arise in AI projects.
Always document your work and achievements in these projects as they can serve as evidence of your hands-on experience when applying for jobs or promotions.
Step 6: Cultivate Leadership and Management Skills
As an AI Technical Lead, you will be responsible for leading a team of AI engineers and developers, guiding them in their tasks, and overseeing the design and implementation of AI projects.
Hence, beyond having a strong technical skill set, you also need to cultivate leadership and management skills to effectively guide your team towards the project’s goals.
You can enhance these skills by taking up leadership and management courses.
There are many online platforms, such as Coursera and edX, which offer such courses.
The topics usually covered in these courses include effective team communication, conflict resolution, project management, strategic planning, and time management.
You should also aim to gain practical experience.
This can be achieved by taking the lead on small projects or tasks in your current job, volunteering for leadership roles in community or industry groups, or even leading teams in non-work-related environments, such as clubs or sports teams.
It’s also important to continuously learn and stay updated with the latest AI technology trends.
This would not only assist you in guiding your team on the technical front but would also help you in making strategic decisions that align with the changing technology landscape.
Remember, a good leader is also a good listener.
You should be approachable and open to feedback from your team members.
Encourage collaboration and innovation within your team and cultivate an environment that promotes learning and growth.
Finally, remember that leadership is not only about leading others, it’s also about personal growth.
So, always be introspective, take feedback positively, and strive to improve as a leader.
Step 7: Engage with AI Research and Stay Current with Trends
As an AI Technical Lead, it is crucial to remain at the forefront of AI research, breakthroughs, and trends.
This includes reading publications and attending webinars or conferences related to artificial intelligence.
You should take the time to understand new techniques, algorithms, and tools that are being developed and how they can be applied to your current work.
Engaging with the AI research community can be done by subscribing to relevant AI research journals, attending AI-focused conferences and events, participating in online AI communities, and even contributing to open-source AI projects.
These activities not only keep you updated with the latest advancements but also give you the opportunity to network with other professionals and researchers in the field.
Keeping track of emerging trends in AI is critical for the AI Technical Lead role.
As the field of AI is rapidly evolving, staying ahead of the curve allows you to identify potential opportunities for your company and strategically direct your team in developing innovative AI solutions.
Additionally, staying current with AI trends allows you to provide mentorship to your team members and help them grow in their roles.
You could also organize internal workshops and training sessions to share your knowledge and encourage continuous learning within your team.
Remember, the key to succeeding as an AI Technical Lead is not just possessing in-depth technical knowledge, but also having the ability to adapt and evolve with the changing technology landscape.
Step 8: Build a Portfolio Highlighting AI Projects and Leadership Roles
As an AI Technical Lead, it’s crucial to have a diverse portfolio that showcases your experience in leading and executing AI projects.
This portfolio should highlight the projects you’ve handled, the AI models you’ve built, and the results you’ve achieved.
Start by selecting the projects that best demonstrate your skills and knowledge in AI.
This could include projects from your professional experience, personal projects, or projects from your academic coursework.
It’s important to choose projects that showcase your expertise in various AI techniques such as machine learning, deep learning, natural language processing, or computer vision.
In addition to the technical aspects, the portfolio should also highlight your leadership skills.
Describe the teams you’ve led, the challenges you’ve overcome, and the strategies you’ve used to successfully deliver the projects.
This could involve detailing your role in managing the project timeline, coordinating with other team members, and making critical decisions to ensure the project’s success.
Remember, the portfolio should not just list your roles and responsibilities.
It should tell a story about your growth as an AI professional and leader.
Use metrics and tangible results to show the impact of your work.
This could involve highlighting improvements in accuracy, efficiency, or other key performance indicators (KPIs) that resulted from your AI projects.
Finally, keep your portfolio updated.
As you take on new roles and tackle new projects, add these to your portfolio.
This will show potential employers that you are continually learning, growing, and taking on new challenges in the field of AI.
Step 9: Network with Professionals in the AI Community
Building a strong professional network within the AI community is an essential step in becoming an AI Technical Lead.
Attending industry conferences, participating in online forums and being active in AI communities on platforms such as LinkedIn or GitHub can help you create connections with other professionals in the field.
Participating in discussions about AI trends, issues, and advancements can increase your knowledge and give you a wider perspective of the industry.
You can also join professional AI organizations or groups where you can meet and interact with AI specialists, data scientists, and researchers.
These connections can offer valuable advice, potential job opportunities or collaborations, and insider information on the latest technologies and developments in AI.
Additionally, consider presenting at conferences or writing for AI-focused publications to further establish your credibility and expertise in the field.
This can also make you more visible to potential employers and partners.
Networking is an ongoing process and it’s important to maintain these relationships even after you’ve landed your desired role.
The AI field is fast-paced and continually evolving, and a strong professional network can provide the support and insights you need to stay ahead.
Step 10: Apply for AI Technical Lead Positions and Demonstrate Your Expertise
As you begin to apply for AI Technical Lead roles, it’s essential to showcase the breadth and depth of your technical knowledge, leadership abilities, and project management skills.
A key aspect of this step is tailoring your resume and cover letter to highlight your experience in AI, machine learning, data science, software development, and team leadership.
Emphasize projects where you have successfully led teams to develop and implement AI solutions, improved processes, or solved complex technical problems.
In addition to your resume and cover letter, a portfolio of your work can be a powerful tool to demonstrate your expertise.
Your portfolio should include detailed descriptions of AI projects you have worked on, including the challenges you faced, the solutions you implemented, and the results achieved.
If possible, include metrics to quantify your success.
Before and during the interview process, take the time to refresh your understanding of the latest AI trends and technologies.
Be prepared to discuss how you have used these in your projects and how you would apply them in the role you’re applying for.
This can include deep learning frameworks, natural language processing, computer vision, and robotics.
Lastly, make sure to convey your ability to manage and mentor a team of AI professionals.
Display your leadership style, your approach to problem-solving, and how you foster a collaborative and innovative work environment.
Remember, the role of AI Technical Lead is not just about technical expertise, it’s also about effective leadership and management.
AI Technical Lead Roles and Responsibilities
AI Technical Leads oversee the development and implementation of AI models and systems within an organization.
They are responsible for the management of AI projects, from conceptualization to deployment, and ensure that the AI solutions meet the organization’s needs and objectives.
They have the following roles and responsibilities:
AI Project Management
- Oversee the design and implementation of AI models and systems.
- Ensure the completion of AI projects within the given timeline and budget.
- Coordinate with various teams to implement AI solutions.
AI System Development
- Design, code, test, and maintain AI models and systems.
- Use AI programming languages and development frameworks.
- Implement machine learning algorithms and statistical models.
Problem Solving
- Analyze complex technical problems related to AI and propose solutions.
- Debug and troubleshoot issues in AI systems and models.
- Optimize AI algorithms for performance.
AI System Architecture
- Participate in AI architectural design discussions.
- Collaborate with system architects to define AI architecture.
- Ensure the scalability and efficiency of AI systems.
AI System Testing and Quality Assurance
- Develop testing strategies for AI models and systems.
- Ensure AI systems meet quality and security standards.
- Perform automated and manual testing of AI systems.
Data Management
- Manage and analyze data sets used for AI.
- Ensure data quality, integrity, and security.
- Understand and use data mining techniques.
Collaboration
- Work closely with cross-functional teams (data scientists, engineers, product managers).
- Communicate with stakeholders to understand their AI needs.
Documentation
- Create technical documentation for AI models, systems, and APIs.
- Maintain documentation and keep it up-to-date.
Technical Research
- Research and evaluate new AI tools, libraries, and frameworks.
- Stay updated with the latest AI technologies and industry trends.
Mentoring and Knowledge Sharing
- Mentor junior AI engineers and data scientists.
- Conduct training sessions to improve team’s AI knowledge and skills.
Communication
- Communicate AI technical information effectively to non-technical stakeholders.
- Document and communicate project progress and issues.
Continuous Learning
- Maintain and improve AI technical skills and knowledge.
- Attend conferences, workshops, and training programs related to AI.
What Does an AI Technical Lead Do?
An AI Technical Lead typically works for companies in industries such as technology, healthcare, finance, and others.
They may also serve as members of AI firms or consultancies.
AI Technical Leads are responsible for guiding and overseeing the development of AI systems and solutions.
They work in collaboration with data scientists, machine learning engineers, and other technical team members to design, implement, and deploy AI models and algorithms.
They play a crucial role in defining the AI strategy of an organization, working closely with the management and stakeholders to align AI initiatives with business objectives.
An AI Technical Lead also helps in maintaining and enhancing existing AI systems and models.
This often involves monitoring system performance, analyzing results, identifying improvements, and implementing changes to optimize performance and accuracy.
Moreover, they often participate in research and development activities to explore new AI technologies and methodologies.
They are expected to stay updated with the latest advancements in the field of AI and machine learning, and incorporate relevant ones into their organization’s practices.
The AI Technical Lead is also responsible for ensuring adherence to AI ethics and privacy considerations when designing and implementing AI solutions.
In certain cases, they may be involved in client interaction, helping to understand client needs, explaining AI capabilities, and ensuring that the AI solutions meet or exceed client expectations.
Essential AI Technical Lead Skills
- Deep Learning: Understanding and applying deep learning models is critical for an AI Technical Lead. This includes designing, implementing, and fine-tuning neural networks.
- Machine Learning: Proficiency in various machine learning algorithms and their applications, including supervised, unsupervised, and reinforcement learning, is a must.
- Programming Languages: Mastery in Python, R, and/or Java is necessary for implementing AI solutions. Familiarity with libraries like TensorFlow, Keras, or PyTorch is also crucial.
- Problem-Solving: AI Technical Leads must identify complex issues, analyze them, and develop innovative AI/ML solutions. They must excel in critical thinking and troubleshooting.
- Data Science: Good understanding of data science principles, including statistics and probability, is essential. This skill is vital for making data-driven decisions and interpreting analytical results.
- Communication: The ability to effectively communicate technical details to both technical and non-technical stakeholders is essential. This also includes listening to and understanding the needs of various teams.
- AI Software Development: Knowledge of the entire AI software development lifecycle is necessary. This includes requirement gathering, design, development, testing, and deployment of AI solutions.
- Project Management: AI Technical Leads often manage complex projects involving multiple teams. Skills in task prioritization, time management, and familiarity with Agile methodologies are valuable.
- Research: Keeping up-to-date with the latest AI trends and technologies is important. Research skills can help find innovative solutions and stay ahead in the fast-paced AI field.
- Leadership: As a lead, you must be able to guide, mentor, and inspire your team. Strong leadership skills are crucial for the successful execution of AI projects.
- Big Data Technologies: Familiarity with big data technologies like Hadoop and Spark can be beneficial. These technologies are often used for processing and analyzing large datasets in AI.
- Computer Vision: Knowledge of computer vision techniques and libraries, such as OpenCV, can be a plus. These skills are often required in AI projects involving image or video analysis.
- Natural Language Processing: Experience with NLP techniques is often required, especially when dealing with AI projects involving speech recognition, text analysis, or chatbot development.
- Cloud Platforms: Experience with AI services on cloud platforms like AWS, Azure, or Google Cloud Platform can be beneficial. These platforms offer various tools that can accelerate the development and deployment of AI solutions.
- Model Deployment: Understanding how to deploy, scale, and maintain machine learning models in a production environment is crucial. This also includes knowledge of MLOps practices.
AI Technical Lead Career Path Progression
The Foundation: Junior AI Engineer
Your journey typically begins as a Junior AI Engineer.
This role involves learning and gaining hands-on experience in AI tools and techniques.
Your responsibilities may include assisting in designing AI models, developing algorithms, and maintaining AI systems.
Here are some tips for success in this role:
- Never Stop Learning: Stay up-to-date with the latest AI technologies and tools.
- Seek Guidance: Don’t hesitate to ask questions and seek guidance from senior colleagues.
- Hands-on Practice: Work on projects that allow you to apply AI algorithms and models.
The Ascent: AI Engineer
As you gain experience and confidence, you’ll transition into the role of an AI Engineer.
You’ll start working on more complex projects and models, participate in AI strategy discussions, and become an essential part of the AI team.
Here’s how to thrive in this stage:
- Problem Solving: Hone your problem-solving skills by working on complex AI models and algorithms.
- Collaboration: Work closely with data scientists, machine learning engineers, and other stakeholders.
- Code Quality: Focus on writing clean, maintainable code that adheres to best AI practices.
Reaching New Heights: Senior AI Engineer
The next stage in your career path is the Senior AI Engineer.
At this stage, you’re known for your proficiency and leadership within the team.
You may mentor junior engineers, guide AI strategy, and drive AI projects to success.
To excel as a Senior AI Engineer:
- Mentorship: Share your knowledge and help junior engineers grow.
- Strategic Thinking: Consider the bigger picture and help shape the AI strategy.
- Leadership: Lead by example and inspire others with your work ethic and problem-solving abilities.
Beyond the Horizon: AI Lead and Beyond
As your career progresses, you may choose to specialize in a particular area, such as becoming an AI Lead, AI Manager, or even an AI Architect.
Each of these roles involves greater responsibilities, leadership, and strategic decision-making.
Here’s what to focus on:
- Technical Leadership: Drive technical initiatives and shape the AI direction of your projects and teams.
- Management Skills: If you transition into management, develop strong leadership and communication skills to guide your team effectively.
- Innovation: Continue to innovate and stay at the forefront of AI trends.
Pinnacle of Success: AI Director or VP of AI
You may reach roles like AI Director or VP of AI at the highest levels of the AI career ladder.
Here, you’ll be responsible for shaping the overall AI strategy of the organization, making critical decisions, and managing larger AI teams.
AI Technical Lead Salary
Entry-Level AI Technical Lead
- Median Salary: $100,000 – $130,000 per year
- Entry-level AI technical leads typically have 2-4 years of experience in AI or machine learning and may hold master’s or doctorate degrees in computer science, data science, or related fields. Their role primarily involves handling AI projects and implementing machine learning models.
Mid-Level AI Technical Lead
- Median Salary: $130,000 – $170,000 per year
- Mid-level AI technical leads have 4-7 years of experience and often take on more complex responsibilities in designing AI applications and overseeing the development and maintenance of AI systems.
Senior AI Technical Lead
- Median Salary: $170,000 – $220,000 per year
- Senior AI technical leads possess 7+ years of experience and are responsible for leading AI projects, making critical decisions on AI strategies, and mentoring junior AI professionals.
Principal AI Technical Lead
- Median Salary: $220,000 – $280,000+ per year
- Principal AI technical leads have extensive experience and deep technical expertise in AI and machine learning. They are often responsible for setting the AI strategy of the company, leading AI development teams and making critical decisions on the implementation of AI technologies.
AI Technical Lead Work Environment
AI Technical Leads often work within the technology departments of diverse sectors such as IT companies, healthcare providers, financial institutions, and research organizations.
This role often operates within an office environment, but with advancements in technology, many AI Technical Leads can also work remotely from various locations.
The work schedule for an AI Technical Lead can be demanding, often requiring them to be available beyond regular working hours due to the global nature of technology projects.
This role may involve collaboration with international teams, which may require flexibility in working hours due to differing time zones.
In their daily duties, AI Technical Leads are often tasked with overseeing a team of data scientists, programmers, and developers.
They are involved in strategic decision-making processes, designing, implementing, and maintaining AI models, and ensuring the successful execution of AI projects.
As their career progresses, an AI Technical Lead may move into senior management roles, such as Director of AI or Chief Technology Officer, or they may choose to open their own AI consultancy or technology firm.
FAQs About Becoming an AI Technical Lead
What qualifications do I need to become an AI Technical Lead?
To become an AI Technical Lead, you would typically need a strong background in computer science, machine learning, or a related field.
A bachelor’s or master’s degree in these areas can provide you with the necessary theoretical knowledge.
Proficiency in programming languages such as Python, Java or C++ is essential.
Familiarity with AI frameworks like TensorFlow or PyTorch, understanding of algorithms and data structures, and experience with cloud platforms are also important.
On top of these, leadership skills and experience in managing technical teams or projects are crucial as this role involves leading and coordinating AI development efforts.
How long does it take to become an AI Technical Lead?
The time it takes to become an AI Technical Lead varies depending on your educational path and the amount of practical experience you gain.
If you’re starting with a bachelor’s degree in a related field, it typically takes four years, followed by several years of work experience to develop the necessary skills and knowledge in AI and leadership.
If you already have a relevant degree or experience, you might be able to move into this role faster.
Gaining practical experience through internships, personal projects, or contributing to AI-related projects can help you advance your career.
Is it possible to become an AI Technical Lead without a formal degree?
While a formal degree in computer science or a related field is often preferred, it is possible to become an AI Technical Lead without one.
Many companies value practical experience and skills over formal education.
You can acquire the necessary AI knowledge and skills through online courses, self-study, and practical experience.
Demonstrating leadership skills and gaining experience in managing technical projects or teams can also boost your prospects.
However, a formal degree might give you a competitive edge and could be essential for some organizations or roles.
Is being an AI Technical Lead a stressful job?
Being an AI Technical Lead can be stressful at times due to the challenges of managing teams, meeting deadlines, and staying up-to-date with rapidly evolving AI technologies.
However, the level of stress can vary depending on the organization, the nature of the projects, and your personal coping strategies.
Many AI Technical Leads find the job intellectually stimulating and rewarding, which can offset the stress.
Many tech companies also take steps to ensure a healthy work-life balance for their employees.
What are the prospects for AI Technical Leads in the future?
The prospects for AI Technical Leads are very promising in the future.
As AI continues to transform various industries, there is a growing demand for experienced professionals who can lead AI projects.
This trend is likely to continue, providing numerous opportunities for AI Technical Leads in diverse fields such as healthcare, finance, retail, and more.
Conclusion
And there you have it.
Embarking on the journey to become an AI Technical Lead is not a simple task, but the rewards are immeasurable.
Equipped with the essential skills, appropriate education, and unyielding determination, you’re well on your path to making a substantial influence in the field of artificial intelligence.
Remember, although the route may be arduous, the potential is boundless. Your creative solutions could be the catalyst for the next major innovation that revolutionizes how we live, work, and communicate.
So, take that first step. Immerse yourself in advanced learning. Network with industry professionals. And above all, never stop exploring the realms of AI.
Because the world is waiting for what you can innovate.
And if you’re seeking personalized guidance on initiating or progressing your career in AI, explore our AI-powered Career Path Advisor.
This complimentary tool is engineered to provide bespoke advice and resources to help you effectively navigate your career in artificial intelligence.
Recession-Resistant: Careers That Can Weather Any Economic Storm
Behind the Danger: What It’s Really Like to Work in High-Risk Jobs
Tomorrow’s Jobs Today: AI and the Future of Work
Low Pay, High Dedication: Unveiling the Least Rewarding Jobs in America
