How can you keep your AI project on the cutting edge?

The best way to follow trends in AI and keep yourself updated so as to keep your AI projects in tandem with the cutting edge technologies is to subscribe to AI focused newsletters, read scientific journals and papers , follow AI influencers on LinkedIn like Yann Le cunn Andrew Ng and Ian Goodfellow because they tend to publish the most recent advances in AI and ML , participate in AI conferences and workshops , join AI communities online , take online courses by following these tips you can ensure that you remain updated with the cutting edge technologies that are emerging rapidly and consequently update your projects accordingly or else you’ll lose base of the technology and your project will become antiquated

Take your ideas to the bazaar. Start by developing a strong point of view on how the market is going to move, and experiment with new tech and tools like crazy. Then, bring your ideas to your customers in the bazaar to iterate and refine them. The goal is to meet them where they are on their AI journeys. Remember, AI is an ecosystem. Whatever project you're delivering, remember there is a larger ecosystem around it. Your product/service should be compatible with the tools developers are using. And it should align with the impact that enterprises are trying to deliver.

There is so much more material than one person can consume, so scaling out is essential to keep up with AI. I recommend following top influencers on LinkedIn such as Andrew Ng, and Yann Le Cunn, as well as Medium blogs. Also attend conferences (or read summaries) when possible and build your network!

Appoint individuals within your AI project teams as "Ethical AI Champions." Their role is to ensure that the project adheres to ethical guidelines and principles. By proactively integrating ethics into the development process, you can mitigate potential issues and build trust with users and stakeholders.

Experiment with SotA for your PoC, but remember to consult your Legal dept and/or DPO before making it a product. Especially in sensitive areas like healthcare, it's crucial to consider the implications and risks of adopting the latest technologies too swiftly. Factors to consider: 1. Reliability: New NLP models might lack the transparency and or interpretability crucial in healthcare. 2. Privacy Risks: Cutting-edge models may require require external hosting. 3. Regulatory Hurdles: Latest AI advancements may not yet align with the existing healthcare regulations.

Acquiring knowledge about the AI tools and techniques is necessary but not sufficient as you have to get your hands dirty coding algorithms , exploring and experimenting with the various components and steps that comprise the whole model for instance if you know how diffusion models work but you’ve not experienced how it works practically by coding the diffusion algorithm and make it to convert text into images you can’t claim knowledge of stable diffusion unless you practice it and the same thing goes for every AI technology and these projects give you the opportunity to experiment and explore these new algorithms and keep your skills as sharp as your competitors in the labor market as Steven covey said sharpen the saw to remain effective

Integrate continuous learning into your AI systems. AI models that can adapt and learn over time are more valuable in dynamic environments. Implement algorithms and strategies that enable your AI to evolve as it interacts with users and encounters new data.

Diving hands-on into new tools and techniques is like unlocking doors to rooms filled with potential for your AI project. While the theory gives you the map, practical experimentation is the key that turns the lock. Each framework or model you engage with not only sharpens your skills but could pivot your project to a path of enhanced efficiency and innovation. However, the shiny allure of every new tool can be distracting. It's crucial to gauge the compatibility of a new tool with your project's needs before integrating it. This practical approach ensures that your project doesn't just ride the wave of trends, but surfs adeptly on waves that propel you forward, keeping you on the cutting edge.

Embracing continuous experimentation in AI ensures adaptability, drives innovation, and solidifies a project's competitive edge.

Staying on the cutting edge in AI is like being a digital explorer. Try various frameworks, libraries, platforms, APIs, algorithms, and data sources to unlock new possibilities and insights. It's like having a versatile toolkit, cheat codes, a cloud playground, takeout for AI, diverse recipes, and a library of knowledge. Experiment away and become the Sherlock Holmes of AI! ??????

Collaborating with universities is a strategic move that can yield substantial benefits for your AI project. Universities serve as hubs of research and innovation, often at the forefront of emerging technologies. They conduct experiments, develop new algorithms, and explore novel applications. Moreover, universities are home to bright, motivated students and faculty members with deep knowledge of AI. These individuals can make valuable additions to your project, contributing fresh perspectives and innovative ideas. When you collaborate with academic researchers and institutions, you tap into a vast pool of knowledge and expertise

To stay at the forefront of AI, go beyond individual collaborations and work on building an AI innovation ecosystem. Create an environment where AI practitioners, researchers, and businesses can collaborate seamlessly.

To keep your AI project cutting edge, collaborating with other AI practitioners is key. This fosters knowledge sharing, fresh insights, and the opportunity to leverage diverse expertise, ensuring your project stays innovative and up-to-date. ????

Another awesome way to keep your AI project up-to-date is to collaborate with other AI practitioners. They can offer different perspectives, skills, and feedback that can help you improve your project. You can do this by joining online communities, forums, groups, and platforms that are relevant to your interests and domain. You can also participate in open source projects, competitions, challenges, and initiatives that can help you learn from others and contribute to the common good. By collaborating with other AI practitioners, you can leverage collective intelligence, foster innovation, and create impact. So don't hesitate to reach out and connect with others in the field. Together, you can achieve great things!

Collaborating with other AI practitioners is a powerful way to stay on the cutting edge. Engaging in partnerships, knowledge sharing, and cross-disciplinary projects fosters fresh insights, best practices, and access to diverse expertise, helping your AI project remain innovative and informed by a broader community of practitioners.

Optimization of AI systems is an ongoing process. It commences with problem identification, Proper data quality, and preprocessing methods chosen, alongside defining comprehensive system requirements for data processing and training. Select the most optimized model, balancing accuracy and production costs. Feedback loops help to know the performance. For minimal system usage, quantized models are employed, compromising a bit on accuracy but leading to substantial cost savings in the long run, especially when running on limited resources like CPUs or fewer GPU cores. Its usage involves dynamic scaling based on demand, and optimizing costs. Strategies are devised to alleviate pressure on the AI systems, leading to reduced maintenance costs.

Automation is key to making your AI practice more optimized. There are many mundane steps in the journey to make a useful model out of a heap of data. Many of those steps can be automated, and then some of them need to share their results in an automated manner. Your innovation is not nifty whiz-bang code, but rather incredible insight into unseen correlations in the data that can be processed by your model and its whiz-bang code. Your factory should be pushing new insights to you regularly so that you can adjust the learning parameters and further refine the model for other data markets.

Continually optimizing your AI project lifecycle, from ideation to deployment, is a vital approach to staying at the cutting edge. By consistently applying best practices, standards, and methodologies, you can efficiently manage your project. Moreover, utilizing tools and techniques like version control, testing, debugging, documentation, and deployment in an iterative manner not only saves time and resources but also ensures the delivery of high-quality results.

Incorporate best practices and standards like Agile or DevOps, enabling effective project management. Implement tools such as Git for version control, automated testing, and continuous integration/continuous deployment (CI/CD) pipelines. By streamlining your workflow, like creating clear documentation, debugging efficiently, and automating deployment, you can save resources and ensure high-quality results. For example, adopting Git and CI/CD can lead to faster, more reliable model updates, enhancing your project's cutting-edge capabilities. Focus on MLOps or LLMOps process to optimize ML or LLM Pipeline.

To keep your AI project on the cutting edge, optimize your AI project lifecycle through continuous learning, experimentation, ethical AI practices, and monitoring. Embrace open source contributions, cloud services, interdisciplinary collaboration, and hardware acceleration. Stay agile, analyze competitors, and protect innovations with patents. Compliance with regulations and a long-term vision are key for success.

BE A CONTINUOUS LEARNER. Artificial Intelligence is growing exponentially, what you know today is the base for your knowledge in 5 years time, but if you stop educating yourself you might loose the upcoming trends and innovations. TIME Find yourself 1 hour every week, a continuous hour, and spend it by testing and trying new solutions. FOCUS Find a specific subject you love particularly or the subject that you have been working on for years. Study deep this subject, do not keep it too wide or you might not become a specialist in the subject but just having a general grasp on too many topics without mastering none of those. COLLABORATE AND SHARE Share your ideas, your results with the community and receive their feedback to improve

The computational methods for AI haven't changed much, yet the computational platforms have improved considerably. The most relevant changes to learn are in your data market; how are the applications changing and what kind of new data telemetry is available. Your AI practice is about its application in a data market. Staying at the cutting edge means keeping your AI relevant in the tsunami of new data being collected in these markets.

To write in Plain English, consider the target audience and their needs. Organize the text logically, with important information first. Use short, direct sentences and simple language. Avoid jargon and legal terms and use the active voice. Do not change the meaning of the text or remove important information.

With the dizzying pace of AI development, it's easy to let trying to keep up take huge swaths of your time. It's definitely helpful to join a Discord or Slack devoted to #AI learning. It's also important to try new tools - but no more than one or two a day to determine whether one is more helpful than platforms you are already using. It's also helpful to determine which tools are actually worth paying for. Most AI platforms offer a free trial and those are generally enough to determine if the platform is a keeper. Multiple newsletters do an excellent job of providing news and tools. The ones I read include Ben's Bites, Prompts Daily, Superhuman and AI Tools Report.

Continuous learning and improvement are key to staying on the cutting edge in AI. Encourage team members to upskill, attend training, and invest in their professional development. A culture of learning fosters adaptability, ensuring your AI project remains at the forefront of technological advancements.

Totally agree with @Jon Shen, in industry there's very little need for a project to be "cutting edge". Research and Applications deployed in Products can often have divergent incentives. A lot of methodologies introduced in research can prove to be either too expensive or too slow to be applicable in real life applications. A great example of this is a lot of the multi-step prompting techniques like Graphs/Tree of Thought that have shown to enhance the performance of LLMs but are just too costly in real life to serve a large scale of customers. There's also a cost of engineering/employee time and resources that goes into asking them to explore areas of research, without any guarantee of a return on investment.

You should always ask the question up-front: does your project *need* to be cutting edge? There are some initiatives where the answer is 'yes', for example, in cases where incorrect answers might result in significant harms to life. If the answer is 'no', it may be preferable to use a simpler approach (perhaps even a deterministic algorithm) to deliver value faster and have a better understanding of what your model is doing.

Be rigorous in your conceptual analysis and task decomposition. It's more of a philosophical skill than a technical one. No matter how powerful the AI tools you use, if you don't have clearly defined concepts and processes to apply AI to, then your results suffer.

A more constructive perspective might be to consider 'fit-for-purpose' rather than striving to be 'cutting edge'. Usually, the primary goal of an AI project is not to be at the pinnacle of technology but to optimise value, while maintaining a balance with cost and time.

Having thought-out AI strategy is the key. I often see companies jumping in projects without getting stakeholders on board. Strategy, alignment and change management is key.