You’re working on an R&D project. How do you find the best AI tools to help you succeed?

As the first step, I would recommend considering a sample case - a simpler version of your problem that you can solve and know the process and answer to. Now, think of it from the perspective of an AI tool. How would the tools you have in hand go about solving them and what kind of results do you think you will get . Will they match the results that you already have ? Next, jot down all the cases, constraints that may be encountered at every step of the way to obtaining the result.

?? Before Start to looking for the AI tools with clearly define your objectives question with which is the main problem that your R&D projects aims to address. ??To briefly review the existing literature up to date on your particular area of R& D project along with try to examine the foundation of the existing literature and make note of what is known method about the research topic?and?which approaches have been used in prior studies.

Finding AI tools that truly help with projects has been a challenge for me. Start by identifying a need. Then determine if AI will help by saving you time. If not then pass and wait for the right opportunity.

You may need to have experience with relevant keywords based on Search engine optimization (SEO), hence keywords must be chosen properly according to objectives of search. To proceed further, several AI tools provide different services. Hence, selection shall be done with careful observation of required work material or tasks which can be carried out using a particular AI. You will need to check the features of AI, if that matches your search. Also checkout AI which gives you instant results, so criteria of having efficiency & accuracy in AI must be observed.

Identifying the best AI tools for a research and development project involves assessing the project's specific needs, comparing tool capabilities, considering integration ease, and evaluating user reviews. Focus on tools with robust support, scalability, and a track record of success in similar R&D endeavours for optimal results.

Choosing the best AI tools for R&D project requires careful consideration of several factors. For example, you'll have to find the best AI tools for your protein prediction project, start by clearly defining your goal like what are you predicting (structure, function, etc.) and to what accuracy? Then, explore established methods like AlphaFold alongside emerging models like AlphaFold2, evaluating their performance, user-friendliness, and cost. Don't be afraid to combine tools or adapt them to your needs, and seek expert advice to navigate the rapidly evolving field. By carefully considering these steps, you'll land on the perfect AI allies to unlock the secrets of your protein!!"

Have three tools that have good predictions in your area of research. Know the limitations of each of the tools (what is their data source) and try to offset these limitations with each other.

It’s always important to test products or AI tools yourself because when it comes to choose the best AI it sometimes comes down to preferences of users.

Do you really need an AI to solve the problem? Many people uses AI to solve problems which already have well defined analytical solutions and well known physics.

Set your goal and expectations first. If you are ready to accept external ready-to-use tools, look for scientific publications and associated Github repositories. They usually show what they use to get their results or the procedure to reproduce them.

In a recent AI R&D project, we rigorously tested various transformer models, including Google Gemini Pro, OpenAI's ChatGPT 4, Claude, and LLama. Our comprehensive evaluations aimed to determine the most effective model for our specific needs. While ChatGPT 4 emerged as the superior option in general performance, we encountered certain edge cases where it faltered. This highlights a crucial aspect of R&D: the inevitability of anomalies. Such instances underscore the importance of thorough testing in AI development, ensuring the selected tools are robust and versatile enough to handle a range of scenarios. This was a clear example that revealed to me, that because there are so many AI tools today it is better to test out as much as possible.

Probar y evaluar herramientas de IA es como probarse diferentes pares de zapatos antes de una larga caminata; necesitas asegurarte de que se ajusten perfectamente a tus necesidades y sean cómodos para el viaje. Las pruebas gratuitas y demostraciones te permiten "caminar" con cada herramienta, sintiendo cómo se adapta a tu proyecto. Evalúa cada herramienta considerando criterios como la facilidad de uso y fiabilidad, que serían el ajuste y la comodidad del zapato; la escalabilidad y seguridad, equivalentes a la durabilidad para diferentes terrenos; y el soporte, similar a tener un excelente servicio posventa. Esta selección cuidadosa asegura que elijas las "botas" ideales para emprender la aventura de tu investigación.

Defining to what you compare AI tools delivery can be super complex since it can sometimes be put in parallels to human tasks that were never really benchmarked... You need to be smart and find the right colleagues that will be brave enough to compare themselves as "augmented researcher".

In addition to trying to use free trials, demos, etc., try to execute pilot projects to gain momentum and build internal support in your organisation. My favourite quote comes from Andrew Ng's article "Choosing Your First AI Project," in which Andrew said, "The purpose of your first 1-2 pilot projects is only partly to create value; more importantly, the success of these projects will help convince stakeholders to invest in building up your company’s AI capabilities."

With topics 1 and 2 completed, you can now begin defining AIs. Test across a wide range of task and project topic. Evaluate the results in an overview including the previously set goals.

Any tool can become redundant if not implemented with a clear objective and in defined process. For the tool to be effective a proper use case with 'pros' and 'cons' needs to be identified which later on helps in its effectiveness. Also, not just correct implementation but compilation of its working history is equally important in order to make sure that it continues to work as effectively as it was meant to be

Again, AI is a trend so our minds will be biased to believe that the AI is improving our productivity regardless of the KPI. So, we must study very well which KPI to be used in order to present the increase in productivity due to using AI.

Implement the selected AI tools into your R&D project and establish monitoring mechanisms. Track the performance of the implemented AI solutions regularly, ensuring they align with your project goals. Utilize metrics, feedback loops, and ongoing evaluation to optimize and enhance the effectiveness of the AI tools throughout the research and development process.

As with any interaction with search, but especially AI, one needs to be mindful that if you work for a global or even small company, inserting search requests that divulge company strategy or, worse, confidential projects, may be shared by the company operating the AI itself. Larger corporations use AI safely within their intranet [is that an old term now? :-)] without risking their proprietary information being used as part of an external AI training algorithm.

I would consider that any patent that came from use of an LLM might not be valid as it might contain information from other organisations involved in the trading that might make it impossible to receive IP protection

To find the best AI tools to help you succeed in an R&D project, the major step is Joining a research community that will provide valuable information and feedback. It can also help to enhance your research skills, for sharing your ideas, with other researchers and receive their opinion about your research thoughts. You need to collect the information, insights, and feedback from the AI tools and your R&D project through your mentor support. Also, research collaboration helps to learn from other researchers.

Picture data as characters in an adventurous yet purposeful with meaningful AI Tools can conclude the R&D project as a powerful revelation with efficient resource utilization.

The learning process in sustainability research is always ongoing. After using AI tools, critically analyze their impact on your project. Did they help reduce energy consumption or improve recycling rates? Share your findings with the sustainability research community to foster collective learning. Also, stay updated with the latest AI advancements to continuously refine your approach.

The practical experience now allows continuous learning in order to improve and learn in a broader spectrum to reach the goal more quickly. As already mentioned in point 1, all of this should always be documented and defined using the SMART approach.

Sin duda la mejora continua es la base para que la implantación sea un éxito. Aprender de los errores y la experiencia nos ayudará a realizar mejores implantaciones en el futuro. Identificar todo aquello que no funcionó será esencial para proponer cambios y realizar adaptaciones. Identificar aquello que sí que funcionó tanto si estaba planeado como si no, será de gran utilidad para repetirlo. Puede ser muy útil establecer algunos indicadores que una vez monitorizados nos ayuden a tomar mejores decisiones. Realizar análisis de riesgos también puede ser útil como parte de la mejora continua. Y finalmente compartir los resultados con el equipo multidisciplinar y analizarlos juntos puede ser una muy buena oportunidad de aprendizaje.

Considering recent trends of AI & ML, R&D should integrate AI technology such as AI-powered drug discovery platforms, manufacturing systems and many more. With the right ML platform, R&D can boost drug research, drug discovery, knowledge transfer, scale-up activities, clinical studies etc.

I consider most of the generic AI tools as of today (ChatGPT and the like) at high school level, they help you gather basic information, and organize it quickly and save your clerical time, but not good enough to help with actual R&D. I suspect, any sophisticated AI tools in development (capable of understanding scientific journals and answering questions) will be highly proprietary or prohibitively expensive ? AI seems good at answering social science related questions/issues that are subjective and perceptive, but it is no where close to answering scientific questions that have to be objective, specific, accurate, and true…. There is no deepfake science!

To find the best AI tools for an R&D project: 1. Define specific AI needs and goals of your project. 2. Research AI tools specialized in your project's domain. 3. Evaluate tools based on performance, scalability, and ease of use. 4. Read reviews and case studies, and seek recommendations from AI experts. 5. Test selected tools through pilot projects or trials to assess their effectiveness.

As a denim designer, it is crucial to capture the intricate details of the fashion products we are producing with the help of AI. General AI tools available out there have some limitations when it comes to achieving the details. Customization is important when seeking the desired results. In this case, Stable Diffusion provides many tools for tweaking AI generations, allowing us to tailor the output to match the specific requirements of our denim designs. So, it comes down to the power of customization and the data we are using or preparing for image generations to create a realistic and detailed representation of our denim products.

Obviously, R&D projects consist of two parts the R (research) + D (development). These are two distinctive stages that require different types of skills and support. In the R part, AI tools could increase productivity and efficiency by conducting academic or market research to identify the latest trends in the area of the research. In the D part, AI tools could support by brainstorming ideas and identifying suitable, practical frameworks to use.