How can AI professionals stand out in a crowded field?

I think it's pretty important to demonstrate your skills and work. LinkedIn is one good place. I also catalog all my prompts for text and image AIs on my personal website - which is a .ai domain and you can find on my profile. I have literally hundreds of PDF guides on my website, along with some bots and more, and I think this shows off my skillset.

Deliver real value for your organisation using data. Share these concrete examples with the community at conferences & the local meet-up scene. Build the next generation of talent by speaking at the local universities (do it for free!). Donate some anonymised datasets. Contribute back to open-source projects. Don’t just be a LinkedIn “AI expert” talking head.

I have been doing AI DigiTransform projects for clients for a few years with great success. In this AI digital era, every business needs to have an AI DigiTransform strategy to be digitally competitive. It truly makes them stand out from competitors. The same obviously applies to professionals.

here is how you can stand out with your online presence: 1. Showcase not just models, but end-to-end solutions. 2. Include comprehensive instructions for quick integration and evaluation. 3. Go beyond functionality; explain real-world applications. 4. Detail tangible results and impacts observed from your solution to showcase expertise in real-world value.

Essentials: Build a diverse AI portfolio with projects in ML, computer vision, and NLP. Showcase problem-solving skills and articulate project methodologies. Use Git for version control; host projects on platforms like GitHub. Create interactive notebooks for visual presentations. Share insights and outcomes through Medium or LinkedIn. Engage in community discussions on platforms like Kaggle. Emphasize real-world impact and practical implementations. Seek feedback for continuous improvement. Keep portfolios concise, accessible, and impactful.

Build a strong AI portfolio: Choose relevant projects in ML, NLP,DL and GENAI etc. Provide detailed project descriptions. Share clean code and organized data. Present results with visuals. Highlight challenges and soft skills. Showcase business impact. Mention patents and innovations. Include presentations and publications. Emphasize continuous learning. Keep it updated. Consider a personal website. Share on LinkedIn and social media.

Crafting a standout portfolio in the AI field requires a strategic approach. Here's how you can do it: Showcase practical projects highlighting problem-solving skills. Demonstrate diverse AI applications with tangible impacts. Contribute to open-source projects, showcasing collaboration. Update the portfolio regularly with latest skills and certifications. Share insights and learnings through personal blogs or professional platforms. Emphasize authenticity and unique contributions.

Having a strong portfolio of all the AI projects you've worked on is absolutely important to demonstrate your expertise. A strong portfolio encompasses project details highlighting the achievements, links to the working model, prototype and the source code, along with novel practical use cases of your project. You can include links to your academic research work and publications in AI paradigms like Natural Language Processing, Machine Learning, Computer Vision or Big Data. This demonstrates your research skills. For projects, include links to your GitHub repositories, Model Deployments via web app links or mobile apps. Emphasize on the results achieved by your models and the innovative skills you applied to solve real world problems.

(1) Good deeper in tech - Go beyond just using model/API... Learn Hyper parameter tuning, learn to evaluate, learn to ensemble, learn to monitor performance, learn optimization and caching techniques, learn edge compute deployment (2) Gain domain knowhow on atleast one industry (3) Learn allied technology Like DS with Automation; DS with Analytics, DS with AIOps and so on

Staying updated with the most recent developments in the AI field is crucial. The constant release of new models can be a little intimidating. A good starting point would be to read research papers, make opensource contributions, build a good portfolio of personal projects. Creating a robust online presence by writing articles, blog posts, or research papers is another effective step. Networking and collaborating will not only help in building long term relationships but also unlock numerous opportunities.

If you know how to use the toys, everyone will want to play with you. So why not learn? If you enjoy AI, new tools are emerging everyday. Find your favorites and position yourself as the go-to resource!!

In my experience... diving deep into a particular niche within AI, such as neural style transfer or natural language processing, has often led to more recognition and opportunities than trying to master everything. Even when taking this approach there is so much to learn. An example of this is when I focused on EDI in A.I. for a few months and ended up collaborating with the National Science Foundation and the D.O.D. which greatly expanded my network and opened new doors for me. You never know where your niche will take you. Lead your research with your passions.

Another mistake AI professionals make is having a skillset in their resumes or LinkedIn that does not really stand out, for instance, they might write Pandas, Numpy, Seaborn and Matplotlib libraries which are standard (important) but not differentiating. If they are not there, it surely is a problem; but if just they are there, it is not really significant. Instead having a mix of MLOps, Software development, Data Science, Data Engineering, Feature Engineering, NLP, Computer Vision and Large Language models provides a holistic picture of what you have done so far and can do as a data scientist. This could be in the form of prior work experience, education or projects one does on the side.

1. Build an impressive portfolio of end-to-end projects showcasing abilities in machine learning, NLP, computer vision etc. Provide code, data and explanations. 2. Actively expand skillset by learning new tools, frameworks and techniques through courses, blogs etc. Experiment with different technologies. 3. Contribute meaningfully to open source projects, meetups and conferences. Share knowledge and insights. 4a Demonstrate real-world impact and value through measurable results and success stories. Quantify improvements. 4b. Develop niche expertise in an emerging area to differentiate yourself. 5. Enhance communication and collaboration skills. Adopt a growth mindset.

AI field is umbrella for various skills which fall in domain of Mathematics, Statistics, Programming, Database, parallel computing, knowing the latest libraries in R/Python etc for efficient implementation of algorithms. While it may seem daunting to know everything in sufficient detail. Approach I would recommend is- identify niche aligned to your strengths, and then learn basics from other domains in how they fit into the puzzle and keep improvising with each new use case.

Firstly understanding the problem statement or the opportunity is important and it needs to be clearly data driven for us to zero in whether AI models are required to solve the problem. I see commonly professionals recommending AI tools even if the problem does not need solve using AI or ML. My input to stand out- Identify the problem statement Follow a data driven approach and clear story to be derived during the data analysis Then recommend the solution using AI if apt and right models ( ML , DL ) or a gen AI.

1) Develop robust understanding of Gen AI as a framework. 2) Develop robust understanding of Gen AI technologies currently available from Google, AWS, Microsoft, SAP, and other companies. These are fast evolving. 3) Understand fitment, application, and enhancement of above Gen AI technologies to different scenarios(like coding, BPM, BPR, etc). 4) Last, and probably most important, identify top opportunities to adapt and use Gen AI for improving business and society at large.

Best way to learn and staying up to date - is to teach. Be the teacher of these new tools and frameworks. Teaching does not mean full time job - it could be summary post of what a new tool is providing - say new tool to effectively create chunks of pdf's or new vector database. Take an existing example and how that would work with new tools - what your learning has been - so it helps accelerates others new learners

You can't afford to stop learning in tech business. What is hot today will be outdated in a few years. Constantly picking up new tools and technologies is an absolute necessity.

Networking with fellow AI professionals and enthusiasts is certainly one of the best ways to learn about emerging trends in AI, potential opportunities, and collaboration ideas. You can attend tech conferences, workshops, hackathons and events to learn and collaborate. I would also recommend you to share your AI knowledge, innovations and handy explanations of topics via blogs, podcasts, videos or newsletters. This will demonstrate your credibility, expertise and knowledge in the field as well as enhance your personal brand.

Absolutely, networking and collaboration are indispensable in the AI field. Engaging with fellow professionals and enthusiasts not only expands our knowledge but also opens doors to exciting opportunities. It's through these connections that we exchange ideas, gain fresh perspectives, and often find solutions to complex problems. Moreover, collaborative efforts often lead to innovative projects and research that can significantly impact the respective fields.

An African adage once said, 'If You Want to Go Fast, Go Alone. If You Want to Go Far, Go Together. Networking with the professionals in your industry will also make you the first bet whenever there is an offer. This can be achieved by Attending events, webinars, workshop and even sending cool dms.

I am all for networking and collaborating - the power these both offer cannot be overstated. I wanted to provide some tips that can help make the most of these interactions: 1. Join diverse communities: If you can, engage in both online and offline AI communities. 2. Showcase your work: Actively share your write-ups, projects, or insights on platforms where potential collaborators or employers might notice. This can act as a conversation starter. 3. Participate actively: Don't just attend events, but engage! Participate! Engage in discussions, offer your insights, and also be open to learning from others. 4. Be open to feedback: Cultivate a mindset that welcomes feedback. It is a great resource for both improvement and innovation.

Collaboration is key in AI - one standalone cannot be always at front of changes in AI. First and simplest is to start to follow folks who are regularly posting on AI in area of your interest - this will enrich your timeline on LinkedIn and start collaborating with these folks. Pick a topic of interest and start building community around that - you don't need to be master of that topic - but lead so others can learn

It seems like a crowded space when you look at all the 'noise' online about AI at the moment and professionals sharing their prompts and other information. But if you network with business owners at professional meet ups in almost any niche they aren't all 'there' yet with AI. However they ARE all super excited to talk about all their failed attempts, ideas they can't figure out, and things they're trying to achieve with their business. It's a totally different feeling than if you're 'that insurance guy' and nobody wants to talk insurance. It's the complete opposite out there right now - ask someone what they're up to with AI/ChatGPT and they can't wait to talk. Get out there folks while it's this good!

Engaging with a diverse group of professionals can expose you to new perspectives, techniques, and problem-solving approaches. It's an effective way to learn from the experiences and expertise of others. Also, sharing your work with peers can provide valuable feedback, helping you to refine your skills and improve your projects. This feedback can be both technical and conceptual, guiding you in the right direction.

In my eyes, networking in AI field is like opening doors to new ideas. A chat at a meetup can turn into a lively discussion on AI challenges, showing you different ways to think. It’s not just about meeting folks, but learning from how they see things. Joining a hackathon isn’t just to show your skills, but to see how others solve the same problem. This way, not only do you learn, but you might find people to team up with on exciting projects, making the AI journey a shared adventure.

As AI is a emerging area there is need for discussion, education and innovation. There is a unique opportunity to brand, and influence in a early market, and across all disciplines.

When sharing your perspective with the world, it is important to have a point of view, but also, to ensure that commentary is thought provoking and pragmatic. Audience members can find facts in many places but finding inspiration and thought starters are not as plentiful.

Even a simple summary of top 10 new developments over a week in the topic of your area in AI is meaningful and impactful. Do not clutter repeating what is already there - like chatgpt prompt for X.

It's critical that we write articles, contribute on LinkedIn, publish papers, speak in conferences etc. to be visible to the global AI community. We should identify 2-3 areas where our strength lies and are more relevant today and focus on those areas. This will allow us to build our personal brand which will get us new roles, recognized in AI community and meet experienced and sharp brains in AI.

This is a great point. Most of the people who "get ahead" in any field, are the ones who don't just "know it" - they can also "talk it". By that I mean confidently stand up and present their work, and engage an audience, and build rapport etc. Develop your own AI skills yes, but invest also in softer skills such as presentation techniques, creative writing, etc.. As an AI advocate, be the person that others trust, admire, and become enthused by, and then you'll all benefit. But that all said - be authentic, and speak with passion (over polish).

Start with what you know and what excites you. Your genuine interest will reflect in your words and delivery, making a connection with your audience. Don’t shy away from sharing your failures and what you learned from them; it not only humanizes your journey but provides real learning for others. And remember, the value you provide is not measured by the complexity of your topic, but by the clarity and insight you bring to it. Through these avenues, not only do you get to build a community around your field of interest, but you also position yourself as a relatable and authentic voice in the AI domain.

In the dynamic realm of AI, we are all perpetual learners. Our expertise, no matter how carefully cultivated, can swiftly be surpassed by technological breakthroughs. Embracing this reality, we must foster a culture of collaboration, sharing our insights and experiences across various platforms. Through collective effort, we can harness the transformative power of AI, aligning its potential with our individual aspirations.

How do you learn something better? You teach it to someone else (by speaking/presenting/demo'ing). Golden rule of competence development.

To study, the Pomodoro technique can be a valuable tool when writing and speaking about AI to establish yourself as an authority and thought leader in the field. It involves short, focused work intervals (typically 25 minutes) followed by brief breaks, promoting intense focus, reducing procrastination, and helping manage time effectively. This method enhances creativity and productivity, making it a useful strategy for creating high-quality AI-related content and establishing your expertise in the field. To retain info, teach. Share your learning, create your portfolio, and your professional blog, and put your work out there.

NOOOO Don't write and speak about your insights and knowledge! Don't do it! People might think you actually know stuff! Also, just give away your knowledge for free on LinkedIn collaborative articles, so they can improve their SEO for FREE using your knowledge. Afterall LinkedIN already has the worst Creator program out there, not paying anyone on the platform for their content.

In my opinion, AI professionals should build solutions, product/service features which are fundamentally grounded on ethical and responsible use of AI. These practices will have imapctful role in wider adoption of AI in safe and truthworthy manner. Currently, it is important to ensure trustworthy AI solutions are created. Also, the users, organizations and communities are made aware of the fact how if deployed ethically and responsbily - AI can truely augment human productivity and empower humanity. This could be one of the most crucial role which AI leaders can play to assist larger section of industry, society to embrace AI and its solutions.

It is crucial to use artificial intelligence responsibly and ethically. As we progress in developing and implementing AI systems, it is vital to ensure that they are transparent, unbiased, and respectful of privacy. A good starting point would be to acquaint yourself with Australia's 8 AI ethics principles. These principles include human, societal, and environmental well-being, human-centred values, fairness, privacy protection and security, reliability and safety, transparency and explainability, contestability, and accountability.

By measuring ESG positive and negative impacts coming from AI. Including carbón footprint linked to AI-modelling energy consumption. For sure positive impact will overcome negative ones. But we need to measure, starting from GHG impact.

In order to stand out, don't just be an AI engineer, but also an evangelist who can create responsible AI. 1. Use prompt engineering to create an output template such that your AI model doesn't violate any privacy policies and laws. 2. Ensure stratefied sampling to include data from all diverse samples 3. Ensure data used is properly anonymised so that there is no privacy leakage Hope this helps :)

Just as learning platforms, tools and staying on top of AI topics is a dynamic process—so are the discussions and decisions made around ethics. Some, such as bias, privacy and security are foundational and unwavering—but other areas will evolve along with the industry, such as celebrities working with AI entities, platforms and providers to license their likeness. An AI Ethics framework should be considered "living" documentation, meaning it is updated and revisited frequently

Have a clear understanding of the technology as well as the underlying data that the models are trained on. Also, be clear on how user inputs would be used and/or stored. Does it become part of the training of the dataset or not? Could you make sure your clients and users understand not only how the technology works but also the risks and shortcomings?

This is a very crucial topic every AI professional should be aware of, so far in the quest to show AI works we have been ignoring 1. AI-Security.. hacking on AI-models is real there are at least 350 different AI attacks that can happen on different kinds of AI models, we need to ensure we harden our models at least against know attacks and also build defense models if required 2. with the changes in regulation we will need to codify and help implement regulations automatically on the fly 3. Do the right thing, don't wait for regulations do it as part of our social responsibility

Ethical AI is a complete domain in itself. Ethics in each industry from AI perspective is complex and requires both knowledge at intersection of AI and domain and privacy in that domain. Privacy and Security in Healthcare will be different that Privacy and Security in Financial Sector. Knowledge of these aspects which impact ethics can contribute in building responsible AI

Always consider the impact of your actions. Respect privacy, acknowledge that data and news can be biased, and strive to do not harm anyone.

The integration of AI into various industries necessitates a deep understanding of both the technical and domain-specific aspects. This is particularly vital in fields like healthcare, finance, or law where the implications of AI technologies can be profound This industry-focused expertise, coupled with AI technical skills, positions you as a valuable bridge between AI capabilities and industry-specific solutions. This combined knowledge is essential and in high demand. Connecting with industry professionals, joining relevant discussions, and continuously updating yourself on industry trends will enhance your reputation as an AI expert with domain focus, setting you apart in this competitive field.

The best way for an AI Professional to stand out from the crowd is to demonstrate the value that AI can provide to either an organisation or the wider community. AI can help to monetise new service offerings, provide tangible cost efficiencies and positively impact the lives of people across the communities that we live in. Focusing on business outcomes or social value will help to show that you can bridge the gap between technical ability and financial acumen.

Don't settle for being a superficial coach! Dive deep into your field, discern your true passion, and strive to become a real expert. We see many self-proclaimed "experts" who lack true knowledge abound. Find your niche and strive to be the best at it.

Experiment; you will experience many failures on the way to the desired success. Be courageous and take the leap! Understand that the industry evolves rapidly, and you will never be able to know it all. Stay curious, open-minded, and be open to learning from others.

One thing that I swear by is building domain expertise, knowing tools and techniques is one part of the equation but understanding business nuances and understanding why businesses operate the way they do gives you an edge over others applying for the same role

- Barrier to entry for acquiring Technical skills will continue to drop. So expect the level of competition to increase, wages to arbitrage. - Adjacent technical skills are helpful such as data, and reporting. - Mastery of skills is harder to measure than your ability to communicate and brand yourself. - Social skills, like negotiation, leadership and organizational behavior are harder to acquire - Combining skills creates a unique proposition where you can add more value than either skillset alone, and thus demand a premium (I.e. higher wage) - expect your skills to replicated by others, and this continue skill building over your career Believe in yourself. You can do it, each of us is unique.

The difficulty many people face when they’re an AI professional is that one day everything is going to be "AI" so if you’re focusing on being an AI for SEO or AI for copywriting or AI for design, it works for now but may not be enough in the future. “AI-powered” is going to start losing it’s meaning. So what AI professionals need to do is just absolutely focus on the AI-part. As other people double down on the art of design, SEO, copywriting and so on, you focus in on the AI part. What that looks like in practice is using AI to make workflows, systems and processes more efficient using AI, in the context of a creative/strategic discipline. There’s an abundance of experts for traditional crafts, less so AI-experts.

Réseaux Sociaux et Communautés en Ligne : Il ne suffit pas de publier, mais aussi d'aller contribuer chez les autres en commentant 10 à 20 fois par semaine sur les posts et profils experts liés à votre sujet. Participez activement aux discussions en ligne. Les plateformes telles que LinkedIn, Twitter, et des forums spécialisés peuvent offrir une visibilité précieuse et ouvrir des portes à des collaborations inattendues.

If I were to point out critical view point, AI professionals with great business understanding are more likely to be successful in their careers because they can better understand the needs of the business and develop AI solutions that deliver real value. Here are some specific ways that AI professionals can demonstrate their business understanding: 1) Develop a deep understanding of the business's products, services, and customers. 2) Identify the business's key pain points and opportunities. 3) Research how other businesses are using AI to solve similar problems.

Know your customer / employee / employer: The value of AI remains elusive until we can see and articulate what’s in it for us. Learning how to do something is the first step, but what we really need right now are AI storytellers. Enthusiasts who can help everyone else figure out how to augment their own work experience. This is the human side of innovation. If you can pair your technical expertise with the ability to articulate the “why” and “value add” to your prospective employer/contract, that is an instant differentiator.