What are the best ways to get involved in AI research?

Start by exploring relevant academic programs or online courses in artificial intelligence, machine learning, and healthcare applications. Stay updated with the latest research papers, journals, and conferences in the field. Join online forums and communities like Kaggle, AI-related Meetups, or LinkedIn groups focused on AI in healthcare. Collaborate with researchers and domain experts to gain practical experience and insights. Build a portfolio of AI projects demonstrating your expertise and contribute to open-source AI initiatives. Lastly, don't forget to network with professionals in the AI research community to seek mentorship and potential opportunities.

Getting involved in AI research can be a rewarding and intellectually stimulating endeavor. Here are some steps you can take to get started: ? Educational Background ? Acquire Relevant Knowledge ? Online Courses and Certifications ? Participate in AI Competitions ? Read Research Papers ? Contribute to Open Source Projects ? Attend Conferences and Workshops ? Pursue Advanced Degrees ? Collaborate with Researchers ? Start Your Own Projects ? Build a Portfolio ? Apply for Internships or Research Positions Remember, the field of AI research is vast and continually evolving. Persistence, continuous learning, and active participation in the community are key to a successful journey in AI research.

#Payitforward - here are some useful tips for all novices: Tip1 Foundation for Advanced Learning: Think of the fundamentals as the foundation of your knowledge. Before moving on to more complicated topics, be sure you understand them completely. Advantage: A good foundation in the fundamentals gives a solid framework for understanding and handling more complicated issues in AI. Tip2: Instead of memorizing formulas, try to understand the concepts behind them. Tip3 - Applying Theory into Practice:?Engage in hands-on projects early?to put academic knowledge into practice. Tip4 - Celebrate small victories as you learn fundamental concepts. Tip5 - Embrace Failure, and recognize that each algorithm has strengths and errors.

Research in many ways is an advent into uncharted territory. Knowledge will be your best friend in a foreign environment. If the foundation is strong, the mind can undertake a journey of discovery. So yes, if you are interested in research, spend some time and get the basics right.

It all starts with learning the basics, as this will give you a better grip on the topic & also your understanding will be clear too. We do sometime skip basics thinking we know it, but I strongly recommend to start from basics & then slowly advance it, as remember it's can't be done overnight, hence having some patience & paying attention to trusted sources will help a lot

As we navigate the ever-changing AI landscape, it's critical to stay up to date on the most recent innovations and market developments. Consider contributing to GitHub open-source projects. It's an excellent way to stay informed while also giving back to the community. Enroll in online classes whenever possible. Courses on platforms like Coursera keep our abilities sharp and up to date.

Vouch for people whose posts give a clarity in layman terms if you're a beginner. Outreach the right set of people instead of randomly following a generic source. Tutorials,self research and finding a good mentor to guide you and share the progress will help. Seeking help and customizing the learning process will give you the results.

Following trends has been helpful for my creativity in the AI space. My AI collaboration partner follows different trends than I do, so when we discuss what's new out there inevitably the conversation turns to: *Who do these trends help or hinder? *What new problems could arise from this trend or application? *What loopholes or biases could this trend expose? *Will this trend potentially impact regulatory or compliance practices down the road?

Make sure that the conference you want to go to will really teach you useful things. Some just want to sell you stuff. But others help you learn new AI skills and meet experts in the field. Before you spend money and time on a conference, ask questions like: - Will it have technical talks or just basic overviews? - Can I network with researchers and engineers there? - Will they have hands-on workshops to teach real skills? Or only PowerPoint slides? - Is the ticket price worth what I'll learn and who I'll meet? Some free webinars and courses I recommend are those from DeepLearning.AI.

'Follow the trends' might sound abstract and hard to implement, especially if you are just beginning your journey. To simplify: subscribe to some newsletters, join communities and spend time perusing AI-related articles. Remember, it will take time but the outcome will be worth your while.

There are a lot of open-source data science and machine learning libraries that accept contributions on GitHub. Brose some open issues on their repositories based on your interests and skills. They are great ways to get involved in open-source and apply your AI knowledge.

Creating notebooks on kaggle is cool and all but applying AI to real life problems and actually finding a solution should be the goal. Don't start big, you are not going to solve any NP hard problems on day one, start small, look around you and question what daily life based task can I automate using AI that'll make life easier and will be impactful. Your own daily life tasks and surroundings are the best place to begin.

2 tips: - Focus on a AI-powered technology that is ready for prime time today, such as LLMs, and avoid nascent applications unless you are already a pro. - Identify a clear business problem that is small enough for you to chew, but big enough for people to notice if you solve it. In marketing, for instance, content marketing is a low hanging fruit. Brands want to generate personalised content at scale, but are constrained by capacity and LLMs can help with that as they are ready for production when properly aligned and prompted.

Once you have identified a problem, then your next steps should be, - To formulate a clear hypothesis or research question based on the problem you're addressing. The hypothesis should be specific and testable, setting the direction for your research. - Study existing literature to understand different models and approaches people have already tried for that specific problem. - Identify your novel approach and ideas, these could be in model configuration or in data preparation and dimensions - Identify the right framework, which can provide rapid development and training envionment both locally and in cloud. - Data collection, cleanup and pre-processing - Finally program your solution. Keep versioning of the data and code both

Exploring with a real use case is crucial to make your learning real. Try to do things that haven't been done before - pushing the barrier of the possible. Paradoxically, now AI can help you to brainstorm on what use cases could be of interest or explore what already exists. Develop a MVP, make it public and let people test it. HuggingFace is a great place to start with.

Sharing your projects and results can be one of the best ways to learn. By sharing your results, you expose them to the community and receive diverse feedback and insights that can help you evolve and learn more about the field of AI. The field of AI is very broad and evolving very quickly, so sharing your results helps other people learn about your experience and at the same time helps you learn about other people's views on your work.

As its a famous saying that when you share your knowledge it increases and you also learn new things, same applies here to, so do share the research with your network, so that you can get other prospective also

One thing I found useful in AI research is providing right prompts which would help in enriching model. Different prompts would help is deriving different output and help the system to derive different perspectives.

Engaging in AI research involves networking and collaboration. It's crucial to seek mentorship for guidance and insight into AI challenges. Pursuing grants, fellowships, or internships also provides practical experience and helps advance one's AI research career. Collectively, these steps offer a well-rounded approach for anyone aspiring to delve into AI research.

If you're a university student, talk to professors and PhD students in your department. Tell them about your interests and ask if they need help on any projects. Offer to volunteer time assisting with literature reviews, data collection, coding, etc. The key is being proactive in getting involved, talking to as many people as possible, and seeking out mentors and peers to learn from and work alongside.

Prepare for dead ends, unexpected hurdles, & countless revisions. I faced numerous setbacks while analyzing vast literary texts. However, each failed experiment yielded valuable insights that refined my approach & ultimately led to the successful development of a sentiment analysis model, proving that the journey holds as much value as the destination Share your research through publications, conference presentations, & open-source repositories. I actively publish my research in peer-reviewed journals & contribute to open-source NLP projects. This not only strengthens the broader research community but also attracts potential collaborators & grants me the satisfaction of being part of a shared journey of discovery.

Se você tiver tempo e interesse, acho legal se juntar a um grupo de pesquisa de alguma universidade, como bolsista ou voluntário. Dessa forma poderá pesquisar, desenvolver e publicar projetos de IA com uma equipe e aprender bastante.

Dive in AI research by, - Researching and developing algorithms - Testing different models - Writing code - Debugging code - Developing AI strategy - Testing machine learning models - Staying up to date on current AI technologies and developments -Collaborating with fellow team members, like data scientists, software engineers, and product managers

Contribute to open source projects on Github. You don't have to rewrite the code and solve complex bugs on day, you can just start with writing documentation, formatting the code and making readable, adding comments. You can also write medium articles to show your findings, explain your code, employ already available models to automate daily life tasks. And at the end, be consistent. Give at least 1 hour per day and you'll see your growth after 1 month.

Partner up. If you want to go far, it's best to join forces with someone bringing in a complementary skill set. If you are a marketing expert but know nothing about coding, consider partnering with an experienced coder to develop research and pilot projects together.