You’re starting an AI career. What are the most common mistakes to avoid?

Purpose is intrinsic to a successful AI career. You can't just say you want to be in AI. You can't just say you want to invest in AI. You should pick a domain and develop your "why" behind why you want to be in AI. The space is too big to just be so general about it. #ai #artificialintelligence #aicareers #aijobs

Think of knowing your goals in AI like planning a road trip. You need to know where you're going and why you're going there. If you're interested in making computers see like humans, you might focus on computer vision and learn about stuff like images and deep learning. But if you're more into teaching computers to understand and talk like us, you might dive into natural language processing and learn about language and text. Knowing what you want to achieve helps you pick the best routes (courses, projects, mentors) to get there!

I've seen many budding AI practitioners make the mistake of spreading themselves too thin by trying to learn everything at once. In reality, AI is an expansive field, and it's crucial to pick a direction early on. Understanding your goals involves introspection about what fascinates you the most. Is it the potential of autonomous vehicles? Or LLMs? Once you've identified interest, align your learning accordingly. This doesn't mean you shouldn't have a broad base of knowledge, but specialization will make you stand out in the field. Remember, your goals can evolve over time. As you grow in career, so too will your understanding of what areas you're truly passionate about. Be open to redefining your goals as your knowledge and exp expand.

I think the following 3 would be some of the top mistakes to avoid: - Believing that the current SOTA (or state of the art/best at the moment) is as good as it gets. Many people even in AI make this mistake and end up underestimating development and progress and thus become unprepared and struggle to adapt/change. - Neglecting to get a general overview/understanding of multiple facets of AI. For example, there are too many engineers who focus purely on the technical aspects and neglect the philosophy of what they are building. Ok this car image recognition system is great, but why did we build it, and what are the potential benefits or dangers. - Not getting help from others. The field is so vast that no single person can master it all.

Common mistakes to avoid when starting an AI career include neglecting to build a solid foundation in mathematics and programming, relying too heavily on pre-built libraries without understanding underlying concepts, overlooking the importance of data quality and preprocessing, and failing to stay updated on the latest advancements in the field. Additionally, avoid overfitting models and neglecting ethical considerations in AI development.

Many people underestimate the importance of mastering the fundamentals before diving into advanced fields like AI. However, a strong foundation in math, statistics, and computer science empowers you to unlock the true potential of AI and create solutions that push the boundaries of imagination.

Think of learning the fundamentals of AI like building a sturdy house. You need a strong foundation, right? Well, for AI, that means understanding math, stats, and computer stuff. It's like knowing how to add and subtract (linear algebra), figuring out rates of change (calculus), and understanding chances (probability). You also need to know how to use tools (programming languages) to build stuff. You don't have to be a genius in all of them, but knowing the basics helps you understand how AI works and lets you be creative with it!

For a start in AI, you'll need proficiency in programming languages like Python and/or R, a strong grasp of mathematics and statistics, and in-depth knowledge of machine learning and deep learning techniques. Familiarity with data science tools such as pandas and NumPy, as well as AI frameworks like TensorFlow and PyTorch, is crucial. Additionally, understanding the domain you're working in, honing problem-solving skills, effective communication, and a commitment to continuous learning are all vital components for success in the field.

Most common mistake is rushing into the deep end without building a solid foundation. Picture this: You wouldn't build a house without a solid foundation. Similarly, in the world of AI, it's essential to grasp the fundamentals. Dive into mathematics, statistics, and computer science to build a robust base. Concepts like linear algebra, calculus, and probability theory may seem daunting, but they are the bricks and mortar of your AI knowledge.

Skipping fundamental concepts in mathematics, statistics, and computer science can hinder your understanding and progress in AI. Invest time in mastering foundational knowledge such as linear algebra, calculus, probability, algorithms, and data structures.

Think of practicing and experimenting with AI like cooking or playing a sport. You can read recipes or watch tutorials, but you won't really learn until you start chopping veggies or kicking a ball around. Similarly, with AI, you gotta dive in and play with real data and problems. Use platforms like Kaggle or Google Colab and tools like TensorFlow or PyTorch to try out different ideas and see what works. Don't worry if you mess up – that's how you learn! Just keep practicing and experimenting, and you'll get better and better!

Stepping into the AI arena? It's more than just theory. Practice and hands-on learning play the leading roles. Think of Google Colab, TensorFlow, PyTorch as your training grounds. You can go as simple as using OpenAI Assistants in the Playground. They're stocked with real-world scenarios, ripe for you to explore. Sure, there'll be stumbles. But every stumble is a step towards mastery. Embrace it. Learn from it. Grow with it. Remember, the most common mistake is that we want to over use AI. This will soon be a commodity that's found everywhere, use it where you see it's full potential. Don't get lost in the solution, always solve problems.

You could choose people based on country of origin from different parts of the world. So say choose 10 countries and have one person from each. What you have is a global AI innovation group all in one room at one time, not too large, not too small. Say mix the people up month to month and have different participants, or have the same individuals meet monthly. Write about the collective intelligence and experiences of the group or podcast on it, only further increasing engagement and learning. #ai #artificialintelligence

Theory is essential, but its true power is unlocked through practical application. Work on real projects, contribute to open-source initiatives, or dive into Kaggle competitions. Bridging the gap between theory and application is the key to becoming a proficient AI practitioner.

Embarking on my AI journey, I intertwined AI with growth marketing, learning through hands-on application rather than just theory. At AirGarage, optimizing a $20,000 PPC budget with AI tools led to a 30% conversion rate boost. My work as a Fractional Growth Marketer, automating operations, highlighted the importance of applying AI to real-world problems. These experiences taught me the value of practical application and innovation in AI, significantly impacting revenue growth and operational efficiency. Embrace AI not just as a tool, but as a catalyst for transformation in your field.

Definitely seek mentorship, but realize that mentors can only do so much. What is really important is seeking and fostering collaboration in this space. You can do that by doing things like 1. Podcasting 2. Blogging 3. Hosting Open AI Innovation Rooms/Events #aiinnovation #aiknowledge #ailearning #aieducation

Feedback is invaluable for growth. Seek feedback from peers, experts, and mentors who can provide constructive criticism and guidance. They can help you identify areas for improvement, share insights, and offer valuable advice based on their experience. Mentorship can accelerate your learning and help you avoid common mistakes.

Platforms like online forums, blogs, podcasts, and social media groups offer accessible avenues for connecting with AI enthusiasts and experts, while local meetups, workshops, and conferences provide opportunities for face-to-face interaction and mentorship. By actively seeking feedback and mentorship, I not only gain insights into best practices but also learn from others' experiences, enabling me to avoid common pitfalls and accelerate my growth as an AI practitioner.

Trying to do AI alone is like trying to dance solo at a group party – not the best idea! AI works better as a team sport. So, team up with friends, seek feedback, join AI groups, and work on real projects. Teamwork helps make stronger and smarter solutions. It's like making a pizza together – more toppings, more fun! Embrace the power of collective intelligence :)

First, grab a solid idea and put it down in a potential AI-based project. Once you define the business purpose of the AI implementation, then seek feedback among colleagues. Different departments and not technical stakeholders shall be able to understand the project, and see the clear benefits. if this is not the case, then check the effective usefulness of your project. With regards to the viability, seek support of more specialised people to understand the potential implications of the AI implementation within the company you work for.

Why not start an AI learninig club or book club. Couple of names ideas for names: "No Limits Through Words" "AI Wrote The Book" "Better Than AI" "AlgoREADINGS" "All We Need Is Love ... and AI" Every one reads one AI book a month. And then shares what they learned at the end of the month. The books get passed around because everyone will have learned something different from each book. The human version of "reinforcement learning" #ai #artificialintelligence #aibooks #ailearning #aiknowledge #aieducation

From my perspective, one thing has remained consistent: "change". The field is perpetually in motion, with new algorithms, tools, and use-cases emerging regularly. Therefore, ongoing learning is not just a suggestion; it's a necessity. But in my opinion, continual learning goes beyond just consuming new information. It involves active engagement with the material. Participate in discussions, write blogs, deliver talks or webinars. Explain new concepts to others; there's no better way to solidify your understanding of a topic than by teaching it. I'll advice, always remember to apply what you learn. Theory is important, but application cements knowledge. This not only solidifies your understanding but also keeps your skillset relevant AI!

AI is developing at breakneck speed. You do not need to try and learn all of the literally thousands of AI platforms and apps vying for your attention. You do not need an advanced degree in math and you do not need to know Python to use AI effectively. What you do need is a solid understanding of the fundamentals of AI -- which you can learn in free courses from Microsoft, Google and other tech companies. Learning AI is a lot like learning to dance. You need to know the basics before you can do the fancy steps. There are literally no books that are up to date on AI because AI changes constantly. But there are multiple excellent newsletters, like Ben's Bites and Neuron and more that will help you learn the basics and keep up.

Here are some key points to consider: Adaptability: Embrace change and remain adaptable. The ability to pivot into new roles or industries can make you invaluable as the job market evolves. Continuous Learning: Engage in lifelong learning. Expanding your skill set to include knowledge of AI and automation technologies can open doors to new opportunities. Innovation: Use your experience to innovate. Your accumulated knowledge and skills can provide unique insights into how AI can be applied in your field or in new areas. Cross-disciplinary Skills: Combine your expertise with AI knowledge. For example, writers can explore roles in AI-driven content creation, leveraging their creativity in new ways.

AI is quickly evolving and to stay ahead, we must embrace ongoing learning and maintain the desire to change with it. Remember change happens whether you are or not. This is a time to keep gaining knowledge to stay at pace as must as possible.

It is important to come to AI with an open mind. Classroom education will help a little bit, but knowing how the fundamentals could apply in real world situations will help more. For me, I wanted to gain real world experience in AI first before having a whole bunch of classroom education. The classroom education means more if I had room to back it up with experience. This approach has worked well for me. #ai #artificialintelligence #ailearning #aiknowledge #aieducation

Never ignore theory!!, While it's tempting to jump straight into coding and building models, it's crucial to understand the theory behind AI algorithms. This can solve many problems for you in long run. Also, avoid working in isolation. AI is a collaborative field. Working with others can help you learn new things, get different perspectives, and produce better results. Lastly, AI is not just about tools and libraries. I understand that tools and libraries are important, they are constantly changing. Focus on understanding the fundamental concepts that underpin these tools instead. This will make it easier for you to adapt to new tools and technologies in the future.

Embracing a culture of continuous learning and collaboration fosters personal and professional development, propelling me toward success in the ever-evolving landscape of AI.