You’re starting an AI career. What are the most important things to know?

The best way to get started With very little friction Is stick to your zone of genius & figure out how to apply AI to it For example - I have marketing and sales skills So I focus on finding ways to get AI to work in those realms I don't go and start learning a whole new industry That would create friction AI can be applied everywhere So start with what you know, and grow from there.

For starters, just as you don’t need to be an automotive technician to run an automobile company, you don’t need to know AI technicalities to be an AI leader. Yes, you do need a general understanding of AI tools/apps and their business implications, along with these key traits: ~ Visionary: Anticipate AI’s future; how it can solve problems and create opportunities. ~ Strategist: Chart the roadmap of your company’s AI projects to achieve KPIs. ~ Agile: Build an agile culture that responds fast to change, to stay competitive. ~ Innovator: Sustain a 24/7 innovative AI culture, to optimize stakeholder value. ~ Ethical: Commit to prioritize AI transparency, data security, and fairness.

Embrace the role of an AI MYTHBUSTER, separating hype from reality to guide the public, policymakers, and businesses towards a grounded understanding of AI's true capabilities and limits. This critical perspective is essential for fostering realistic expectations and informed discussions about AI's impact. Simultaneously, venture beyond traditional datasets to explore unconventional data sources, such as social media trends, satellite imagery, ancient texts, or patterns in nature. This approach not only challenges the status quo but also unlocks novel insights and innovations, paving the way for groundbreaking AI applications often overlooked. Combining these strategies, opens new research avenues that redefine what's possible with AI.

?? Begin your AI journey with a focus on both the foundational and the cutting-edge: delve into technical topics through project-based learning. ?? Familiarize yourself with essential tools and frameworks like TensorFlow and PyTorch, applying them in real-world scenarios. ?? Beyond technical skills, pay attention to AI Ethics, understanding the implications of bias, fairness, privacy, and security. ?? Engage with the AI community through forums and discussions to stay updated on latest trends and considerations.

Know the basics: Gain a solid foundation in core AI concepts like machine learning, algorithms, and data analysis. Learn about popular tools and frameworks like TensorFlow and Python. Choose your path: AI offers various specializations like computer vision, natural language processing, or robotics. Explore and pick a path that aligns with your interests and strengths. Build your portfolio: Showcase your skills through real-world projects. Participate in hackathons, competitions, or open-source communities to gain experience and build a portfolio that highlights your work. Network learn: Stay up-to-date on the latest advancements by connecting with other AI professionals and enthusiasts. Attend online/offline events, discussions.

Embarking on an AI career is like setting out on an exploration of the unknown, filled with both opportunities and challenges. A key aspect not covered earlier is "Mastering the Art of Inquiry." AI thrives on questions - the more precise and creative they are, the more effective the algorithms become. Therefore, honing your skill in asking relevant, innovative questions is crucial. This approach not only enriches your technical knowledge but also enhances your problem-solving capabilities. In AI, the ability to ask questions that challenge conventional wisdom can uncover hidden insights, unlock new possibilities, and lead to groundbreaking discoveries. Remember, in a world where AI is reshaping boundaries, the right question

The SPICE framework can be useful to guide your choice of a career path: ? Skills: Assess your unique competencies and soft skills. ?Passion: Reflect on AI domains that excite you. Whether it's intelligent systems, research, industry-specific problems, or policy making, find where your enthusiasm lies. ?Impact: Consider the type of contribution you want to make. Advance AI, solve societal challenges, drive innovation, or inspire future generations? ?Compatibility: Evaluate how your chosen path aligns with your experience and long-term career goals. ?Exploration: Actively explore various AI fields through internships, projects, and mentorship that aligns with your skills, passion, impact and compatibility.

Identifying Interests and Goals: Choosing your path involves identifying your interests, passions, and career goals within the broad field of AI. Reflect on which aspects of AI intrigue you the most, whether it's machine learning, computer vision, natural language processing, robotics, or another specialization. Assessing Skills and Strengths: Evaluate your existing skills, strengths, and experiences to determine which path aligns best with your abilities and interests. Consider your background in mathematics, programming, data analysis, and domain-specific knowledge to identify areas where you excel and where you may need further development.

Domain Knowledge Pick an industry that interests you (healthcare, finance, etc.), and build a portfolio of AI projects around that. Understanding the field will make you significantly more valuable as an AI practitioner.

There’s a lot to AI. In my own experience, find a niche - in my case AI as related to educations, and then explore it and share your journey with others. There will always be those further along your path, but your voice helps others along as well, so don’t let imposter syndrome stop you from sharing and contributing to the discussion.

Demonstrating Skills and Expertise: Your portfolio serves as tangible evidence of your skills, expertise, and proficiency in AI concepts, algorithms, and technologies. It provides concrete examples of your ability to apply theoretical knowledge to real-world problems and deliver meaningful results. Highlighting Projects and Achievements: Your portfolio allows you to highlight relevant projects, research, or academic work that demonstrate your capabilities in AI development and implementation. Include details about the objectives, methodologies, datasets, and outcomes of each project to provide context and showcase your contributions.

Reflecting on my journey in building an AI portfolio, I've encountered both challenges and rewards. A notable challenge was the strain on my MacBook during the training of my first AI model, a common hurdle for many beginners. However, this experience led me to discover the power of open-source APIs, which significantly simplify the project-building process. Participating in events like Hackathons has also been invaluable. These gatherings are not only enjoyable but provide a unique opportunity to gain hands-on experience and to bring real projects to life.

Building your portfolio showcases your skills and accomplishments to potential employers or clients. Start by working on real-world projects that align with your chosen path in AI. Develop projects that demonstrate your proficiency. Document your projects thoroughly, including problem statements, methodologies, and results. Consider contributing to open-source projects or participating in AI competitions to gain additional experience and exposure. Assemble your portfolio with care, highlighting your best work and providing context for each project. A strong portfolio not only showcases your technical abilities but also demonstrates your creativity, problem-solving skills, and passion for AI.

Expanding Opportunities: Networking allows you to connect with professionals, researchers, and industry experts in the field of AI. By attending conferences, workshops, meetups, and online forums, you can expand your network, learn about job opportunities, and connect with potential mentors or collaborators who can provide guidance and support. Accessing Resources: Networking provides access to valuable resources, including research papers, tutorials, online courses, and learning materials shared by peers and experts in the AI community. Engaging with your network allows you to stay informed about the latest advancements, trends, and best practices in AI, enabling you to continuously learn and improve your skills.

Starting a career in AI is like joining an ever-evolving dance of innovation, where staying in rhythm with the latest trends and technologies is as crucial as the steps you learn. It’s a field where collaboration amplifies creativity, and networking is not just connecting but co-creating, unlocking doors to new opportunities, insights, and invaluable feedback. Engage actively in the AI community through groups, events, forums, or podcasts, and embrace the mentorship and camaraderie of peers and influencers. In the dynamic world of AI, your growth is fueled by the shared journey of continuous learning and mutual inspiration.

Two phases 1/ Preparation phase - you learn all the theory behind it from school 2/ "Real" learning phase - you talk to the pioneers who are using AI to solve actual problems to see what they're doing It's far more important to talk to people who are actually applying AI to solve problems and what they're doing to achieve that than to study the math behind everything

Find some like-minded persons and start a business to build a product. Networks are key and having a united goal will push you to develop quickly.

Networking and learning are pivotal for your AI journey. Connect with peers on platforms like LinkedIn, attend industry events, and engage in discussions. Keep learning through online courses and stay updated on advancements. By networking and continuous learning, you'll expand your knowledge, build valuable relationships, and accelerate your growth in the AI industry.

Protecting Privacy and Data Security: As an AI practitioner, it's crucial to prioritize the protection of privacy and data security in all aspects of your work. Adhere to ethical guidelines, laws, and regulations governing the collection, use, and storage of data, ensuring that individuals' privacy rights are respected and safeguarded. Mitigating Bias and Fairness: Be vigilant in identifying and mitigating biases that may be present in AI algorithms, datasets, or decision-making processes. Strive to develop and deploy AI systems that are fair, transparent, and unbiased, considering the potential impact on diverse populations and marginalized communities.

One of the best ways to project your integrity is to be open and transparent about your use of AI in your work or job search. For instance, if you use a GenAI tool to help write your CV, say so! If you are pursing a career in AI, then using GenAI to help create your CV is not a weakness; it says that you are comfortable with GenAI tools and know how to use them to get good results. Likewise, if you use AI in a work task assigned to you, and it helps you get faster/better/cheaper results, then say so! Put forward the data on how much time or effort it saved, or on how much further your work was able to go as a result. If you are embarking on a career in AI, your use of AI should always be in the spotlight, not hidden in the shadows.

When considering AI ethics and governance, there are two key questions to keep in mind: How will AI impact people's lives at various levels (individual, business, market, and society)? How much decision-making power should we give AI, and under what conditions? AI's influence spans multiple scales, from making daily tasks easier for individuals to revolutionizing industries and enhancing public services. However, with great potential comes great responsibility. It's crucial to establish guidelines and frameworks for AI decision-making, ensuring transparency, accountability, and alignment with human values. This may involve AI providing information while humans make the final call, or allowing AI to make choices in well-defined situations.

?? Begin by educating yourself on the ethical dimensions of AI. This includes understanding data privacy, bias and fairness, accountability, and transparency. ?? Make ethical considerations a part of your AI project's lifecycle from the onset. This involves assessing potential biases in data sets, evaluating the impact of your AI system on various demographics, and designing with privacy and transparency in mind. ?? Advocate for ethical and responsible AI practices within your organization and the broader community.

Being ethical and responsible is crucial in your AI career. Consider the societal impact of your work and prioritize ethical considerations. Ensure fairness, transparency, and accountability in your AI models and algorithms. Respect privacy and data security, and mitigate biases in your datasets. Stay informed about ethical guidelines and regulations in AI, and advocate for responsible AI practices within your organization and the broader community. By upholding ethical standards, you contribute to building trust in AI and promote its positive impact on society.

AI is a competitive ever-growing field, ensure you have skills that are transferrable across innovations. Adaptability matters more than talent.

There's two sides to AI careers The technical side: - those who build the tools - and the infrastructures The implementation side: - those who actively implement AI into business processes (i.e. prompt engineers, consultants) - no technical knowledge required (although it does help to at least be aware how it all works)