How can I learn about AI so I can grow professionally?

I've started using it as a way to check my work to make it easier to embed into my workflows - way easier to learn as you start using it!!! For example, was using Facebook Ads Manager the other day to run a campaign for a Shake Shack x Trolls collaboration (an exercise in one of my classes at Oxford), coming up with the various audience segments to target. I tried it on my own, then used ChatGPT to "check my work" and learn what the gaps were in my own expertise/knowledge. You can read about how to use AI, but using it and finding ways to build it into your routine is a whole new thing! If you're looking for tools/tips, I recommend The Rundown newsletter :)

Easiest way to increase own motivation towards AI is to learn the use cases of AI in your own industry. It might still be a long path for some people, so my simplest recommendation is to watch movies featuring AI. We all know the classics and common ones such as Terminator, Matrix, A.I... If you want a bit more technical (but definitely not boring) movie suggestions, I'd highly recommend Transcendence (Johnny Depp) and Ex-Machina. After this broad motivation, you can hunt for examples and use cases of AI in your own industry. Also Bill Gates says 'I choose a lazy person to do a hard job. Because a lazy person will find an easy way to do it.' It helps a lot to push yourself to learn about AI and in order to delegate some tasks to AI.

Early this year, Sundar Pichai, CEO of Google, said that AI is more transformative than electricity or fire. For me, the main motivation is related to the development of our own ability to stay up-to-date and ready to move with a world that is transforming in an incredibly fast pace. Of course, this change can be perceived in an optimistic way or in a not-so-positive way. Anyway, it is sufficient to make anyone motivated to learn and embrace AI. Everyone can do that by incorporating it into their professional workflows and also by addressing their hobbies and preferences. For example, I love cooking, and I used chefgpt.xyz to generate new recipes with AI. This not only allowed me to have fun but also incorporated AI into my own life.

AI will help in Future Projects in India in terms of support for the proper execution of the projects, Safety implementation, quality and feedback. AI collaboration with BIM technology will be more successful as BIM is being implemented in all Indian Project Industries so the future will be AI and BIM

The best way to start learning AI is to start talking to people in the AI field. Engaging with individuals actively involved in the field provides valuable insights into their daily work routines, the challenges they face, and the skills required to thrive in this domain. It can be as simple as reaching out to them through LinkedIn and requesting a Coffee Chat. Taking online courses is also a great way to understand the basic concepts of AI, then reading Research articles and starting to practice implementing AI projects, and Kaggle, a renowned data science and machine learning competition platform, can be an excellent starting point.

Just pick a chatbot like: - ChatGPT - Claude - Bard (I recommend ChatGPT) Then follow LinkedIn Creators like: - Ruben Hassid - Max Rascher - Heather Murray - Audrey Chia - Matthew Lakajev - (or me lol) and so many others. And keep your eyes peeled for latest updates like OpenAI's DevDay keynote last week.

We live in a golden age for learning, where much of the world's knowledge is at our fingertips —?but I'm often surprised by how many people do not use the free tools at hand. So for a start: - start with a Google search (e.g. "best ways to learn about AI") and read every word on the first 10 linked sites - use those 10 linked sites to identify people and organizations to follow, and educational opportunities to learn more - follow key tech and AI figures on Twitter In a nutshell: search, follow, watch, and read, read, read. And as you hear about AI tools, sign up for them (many have free accounts) and try them out. Use them for your work and for fun. If you're a coder, code. Do this and you're already ahead of most people.

I recommend following leading tech companies for the latest in AI developments. They're often at the forefront of integrating AI into new products and services and will be talking about it. Also, leverage your network - tech leaders, and CIOs especially, are another invaluable resource. I personally lean on Lenovo CIO, @Art Hu! Don't overlook online publications like @Technology Magazine and @MIT Technology Review which are not only informative but also thought-provoking. Finally, experiment with Gen AI tools yourself. You'll gain a much deeper understanding and appreciation of their potential. Stay curious and keep exploring!

You should have a healthy mix between learning about AI (newsletters, videos, online courses etc) to experimenting with AI hands on. There's no replacement to rolling up your sleeves and getting a taste of the various products out there. Start from the big ones like ChatGPT, Bard or Dall-e and find more niche products that may suit your needs.

Diving into AI as a newcomer can feel overwhelming given the range of resources, but fear not—there are plenty of accessible entry points. Start learning with online courses from platforms like Coursera and LinkedIn Learning. These platforms offer beginner-friendly introductions to AI and machine learning, breaking down complex concepts into digestible modules. Consider popular courses such as Andrew Ng's "Machine Learning" and "AI For Everyone" on Coursera for a solid foundation. Practical experience is also crucial –?start playing around with tools like Chat GPT, Bard, Midjourney and others. Don't be afraid to experiment!

Slightly biased - but can’t recommend PartyRock highly enough. It’s the way I built most of my intuition and ideas about generative AI (and now available to everyone, for free: PartyRock.aws).

Disagree -- the idea of LLMs and GenAI is to democratize the ability to use AI. Anyone can access Bard or ChatGPT (or at @Walmart you can use My Assistant) to start messing around. I started by having the LLM write a bedtime story for a 4 year old. The best way to learn is to try it. Find a problem and see if you can solve it with the AI. It probably won't work the first time but keep trying! Also, you can search on X or other places to find ideas on what to try.

We jumped right in! Here's a quick guide from our experience diving into AI at Taskade: ?? Find Your AI Drive: Our push was enhancing Taskade for our users. What’s yours? Solving problems, tech fascination? That’s your starting point. ?? Real AI Applications: We wove AI into Taskade’s fabric. For you, explore AI in your favorite fields. See its real-world impact and get inspired. ?? Learn by Doing: If you’re tech-savvy, try adding AI elements to your projects. New to this? Start with beginner tools. Hands-on is the way to go. AI’s a journey of discovery and innovation. Be curious, experiment, and embrace the new – that's our mantra at Taskade! ???

Artificial Intelligence is really a valuable tool for learning knowledge, practice new skills, or even acquire new job skills. You can find there a patient and tireless teacher, to receive feedback, enhance your problem-solving practices, or improve your communication skils. Generative AI has the potential to support you in achieving your goals. It can provide you with the information and insights you need, to help you succeed in your mission challenges and career evolution. It cannot replace experience, however it can enhance it, or ease its acquisition.

Here's a 30-day roadmap basis my experience: Days 1-3: Learn Python basics Days 4-5: Dive into functions & modules for practical coding. Day 6-7: Work on simple projects, like data parsing scripts. Days 8-10: Study basic statistics and probability. Days 11-12: Learn about algorithms and data structures, focusing on those relevant to AI. Days 13-14: Begin linear algebra, covering vectors and operations. Days 15-17: Explore machine learning concepts. Days 18-19: Get to grips with libraries like scikit-learn for implementing ML models. Days 20-21: Build and evaluate simple models using datasets. Days 22-24: Understand neural networkslike TensorFlow. Days 25-26: Learn about CNNs and RNNs. Days 27-30: Apply all skills on a live project.

I would say be selective about what trends you follow. Since AI is in fast and wide development, it outpaces the regulations and legislation. Select few key AI experts/voices and follow their suit about AI's new features, releases, limitations, legal and GDPR updates.

AI is a rapidly evolving field, so it's important to stay updated with the latest research and developments. Follow AI news, read research papers, and participate in online forums and communities to connect with fellow enthusiasts and professionals.

It's understandable to feel overwhelmed by the dizzying pace of AI advancement. We're experiencing a technology growing faster than any in history, amplified by hyper-connectivity. Each day brings new capabilities, discoveries and innovations. Hype and fear run rampant on social media, obscuring a clear view. But take heart—while the AI landscape shifts rapidly, the fundamentals will remain stable. Focus on understanding key concepts like machine learning, neural networks and data training. With that base, you can better contextualize the daily changes. No one grasps it all, so be selective in consuming news. Seek trusted sources, diverse opinions, and thoughtful leaders. The basics equip you to navigate an ever-changing AI landscape.

To follow the trends of what is happening in AI, I suggest The Batch newsletter by deep learning.ai , TechCrunch, MIT Technology Review, and Wired.

* Discover the benefits trying yourself. Bing Chat and Bing Image Creator are incredibly easy and universal ways to start. * Experiment with other free Generative AI tools like ChatGPT. * Read about Generative AI - follow the sources and experts opinions on Linkedin. * Understand AI deeper - there's life aside ChatGPT. Do the effort to explore foundational models and how machine / deep learning works. * Engage in discussion groups and communities to keep on trying and learning. * Broaden the discussion further from tech innovation: business, social, ethical, legal, regulatory and human perspectives are key parts of the conversation too. My 5 cents :)

The real comprehension of AI dawned on me when I started applying what I had learned. I initiated small projects, faced numerous challenges, but with each hurdle, my understanding deepened. At SensViz, we encourage our team to continuously apply their knowledge in real-world scenarios. It's the practical application that unveils the nuances of AI, making you more adept and confident. Every project you undertake, no matter how small, contributes to your grasp of AI. So, don't hesitate to get your hands dirty; it's all part of the learning curve.

A mentor can provide you with guidance, support, and feedback as you learn about AI. A good mentor should be someone who has experience in the field of AI and who is willing to share their knowledge and expertise with you. Here is a list of 10 AI experts at LinkedIn who can be your mentor: 1. Andrew Ng 2. Ian Goodfellow 3. Fei-Fei Li 4. Geoffrey Hinton 5. Yann LeCun 6. Yoshua Bengio 7. Demis Hassabis 8. David Silver 9. Pieter Abbeel 10. Russ Salakhutdinov

The best approach is hands-on experimentation: ?? Explore Broadly: Test various AI applications to discover their capabilities firsthand. ? Practical Usage: Implement AI in real-life scenarios to gauge what delivers value. ? Learn from Failure: Identify what doesn't work to refine your AI strategy. Don't just read about AI—use it, test it, and understand it through experience. #AIExperimentation #TechHandsOn #InnovationLearning

I started applying my AI knowledge by developing a small project to automate part of the software deployment process. Using machine learning, I created a tool that predicts potential deployment failures based on historical data. I suggest to start with small, personal projects in your area of expertise. These projects allow you to apply what you've learned in a practical setting, helping you understand the real-world implications of AI concepts.

Dive into AI by integrating it into your work or personal projects. Tackle real-world problems, build your own models, and iterate based on peer feedback. Contribute to open-source AI or share your insights with the community. Learn by doing, improve continuously, and make an impact.

I think it requires being open-minded and curious. I recall when I first started my career, the IT department always assumed when I had an issue it was 'user error'. Later in my career, I learned, the help desk/IT was good at trying things until they found a solution. They'd push buttons—until they resolved the issue—now that's what I do as a remote worker. I've applied this to my learning of GenAI——be curious and push some buttons.

I mentored a doctor exploring AI in medicine. He began with basic AI courses, fitting in study sessions post-shift. His interest deepened through a course on AI's impact in healthcare, particularly in enhancing patient care. Entering a medical image analysis contest, his model excelled, allowing him to establish connections at a healthcare AI conference. He stayed informed on AI developments by consistently reading industry journals and participating in professional forums. He learned about data privacy to apply AI responsibly in his practice. His initiatives contributed to the creation of an AI tool for patient triage, improving efficiency at his hospital. His path motivated his colleagues to investigate the synergy of AI with healthcare.

Roadmap to learn AI: 1. Understand the basics: Learn linear algebra, calculus, and probability. 2. Learn programming: Focus on Python for its AI libraries. 3. Familiarize with machine learning: Study algorithms, regression, classification, and clustering. 4. Dive into deep learning: Learn about neural networks and frameworks like TensorFlow or PyTorch. 5. Explore computer vision and NLP: Study image recognition, object detection, text classification. 6. Work on projects: Gain practical experience through hands-on projects. 7. Stay updated: Follow research papers, blogs, and attend conferences. 8. Specialize in an area: Choose a specific AI subdomain to focus on. 9. Continue learning: Stay curious, experiment, and keep up with advancements

To learn AI as a beginner, it is essential to master the fundamentals of the subject. All the advancements & innovations in AI are based on the core concepts. If you have a solid foundation, you can learn and create anything on top of it. For instance, you can start with the AI 900 - Azure AI Fundamentals. It covers the basic topics very effectively along with the exposure to Microsoft Azure. There are also many YouTube Channels, including mine, that offer hundreds of free learning videos. Once you finish, try to share what you learn with others, such as your peers, friends, etc. It is the best way to validate your learning. Start applying your learning to your projects. Get your hands dirty by diving in. Most importantly, 'Start' learning.

For now, there is a lot of untapped value and productivity for everyone in just exploiting the consumer friendly AI tools that have flooded the space. You do not have to be an AI expert, or understand how it all works, to benefit from it. Take the first step in curiosity. As you do this, be sensitive to and aware of how what you share is being used. When it comes to your privacy and security, you are your best defense and protection.