Here's how you can spot and prioritize innovative ideas in AI innovation.

Any idea that is not more than a prompt, multi model chaining, or a toolchain is not going to lost longer. They run the risk of being made obsolete with new model updates. The ideas or products that can sustain have below qualities: - uses rich intellectual property dataset which are not public - still produces value even when AI component is removed - use of AI in the product amplifies products value - Owns the AI or has a fine tuned version of it

Finding gaps, inefficiencies, or areas that are ready for change in current systems or processes is a necessary step in determining a problem or the necessity for an AI project. 1. Examine Current Systems or Processes for Gaps or Inefficiencies Make a Comprehensive Analysis: • Process Mapping: Record existing workflows to comprehend each stage that is required. Seek out manual chores that are laborious or prone to mistakes. • Measures of Performance: Determine which areas of performance fall short of expectations by analyzing key performance indicators (KPIs). These could be production bottlenecks, error rates, or customer response times. • Stakeholder Feedback: Learn about the problems that users, consumers, and staff are facing.

Elon Musk encourages potential entrepreneurs to have many idea based brainstorming meetings. Well, there is actually a proven science behind it. You see, when we discuss a potential concept and try to materialize it, we go through group meetings. People involved in those meetings are capable enough to bring new concepts and dimensions that you could have missed if it was done only by you. This is why the step of ideation is so important especially for technology based projects like AI👍

Ideas are not standalone and are usually in clusters. When you come up with a new idea, build the cluster of ideas around that. That helps navigate existing ideas, collaborating and expand the original idea itself

I feel that the recent deep fake attack which lead to a huge loss for the corporation is an example of the importance of AI in cyberspace. Hence cyber for AI is also an emerging field.

💡 When delving into AI innovations, focusing on market need is pivotal for success: - Prioritize solutions addressing current challenges or fostering growth. - Seek out unmet needs that could revolutionize user experiences. - Conduct thorough market research and trend analysis to anticipate demand. - Innovation often lies in unveiling needs users weren't aware of initially. Let's engage: What strategies have you found effective in identifying market needs for AI solutions? Share your insights!

Sometimes, before you can validate a market fit, the market needs to catch up and learn what the capabilities of AI technology are, and what is pure hype. An educated and mature market produces long lasting benefits and a better ROI. On the other hand, if there is a clear problem and AI addresses it well, don't look back!

In my MBA in NMIMS, I have learned that prioritizing AI innovations requires a thorough understanding of market demand. Make sure there is a market for the technology that is being created. Concentrate on concepts that solve urgent problems or open doors in fast-growing industries. The most inventive AI solutions are frequently those that, although not yet recognized by users, turn out to be vital after being implemented. Potential demand for novel AI applications can be estimated with the use of trend analysis and market research. You can create artificial intelligence (AI) solutions that are not only ground-breaking but also extremely valuable and extensively used by recognizing and ranking breakthroughs that address market needs.

Begin by understanding the current and future demands in various industries. Conduct thorough market research to identify gaps and pain points that AI could address. Align your brainstorming sessions with these insights to generate targeted, relevant ideas. Evaluate each idea based on its potential to solve significant problems or meet unfulfilled needs. Consider market size, growth potential, and competitive landscape. Develop prototypes to test market viability and gather feedback from potential users and industry experts. Use this feedback to refine and validate the ideas. Form dedicated teams to champion the most promising ideas, ensuring they have the resources and market insight needed for successful implementation.

Prioritizing AI innovations with technical feasibility involves a systematic approach to ensure practical implementation and success. Perform feasibility assessment. Start by evaluating each idea’s complexity and compatibility with existing infrastructure. Develop a clear roadmap with achievable milestones and allocate resources strategically to address potential technical challenges. Focus on innovations that align with current technological capabilities. Continuously review and iterate based on real-world technological progress, ensuring that the ideas remain aligned with practical capabilities and strategic objectives.

I'll provide a counter voice here: feasibility is overrated! What I mean by this is not to let yourself be constrained by limitations that exist right now, look at the industry's trajectory and determine whether your limitation will go away. When LLMs first started to become popular, context windows were small and you could've dismissed ideas that processed large chunks of data or over engineered workarounds but now we have models with context windows of two million tokens - all in the space of a few years.

Focus on ideas that align with available data, systems compatibility, and required computational resources. Involve technical experts to evaluate the viability of these ideas. Develop prototypes to test feasibility and identify potential technical hurdles early on. Gather feedback from engineers and data scientists to refine the concepts. Form specialized teams to oversee the development of technically viable ideas, ensuring they have the necessary resources and technology. Encourage continuous technical learning and share advancements across teams. By focusing on technical feasibility, you ensure your AI innovations are both visionary and practically implementable, ready for successful deployment.

Spotting viable AI innovations demands a visionary approach. While some ideas dazzle with their revolutionary potential, practicality is key. Prioritize concepts with clear paths from imagination to implementation, steering clear of technological dead ends. Seek ideas that dance on the cusp of possibility, poised to transform the landscape of AI. It's about not just dreaming big, but also bringing those dreams to life with ingenuity and determination.

One time at work, I encountered a proposal for an AI system to predict stock market trends with unprecedented accuracy. While the idea was exciting, I had to assess its technical feasibility. Determining if an AI concept is technically possible with current or near-future technology is crucial. Some ideas might sound revolutionary but could be years away from practical implementation due to technological constraints. One thing I’ve found helpful is prioritizing ideas that have a clear roadmap to development and deployment. This ensures the innovation can move from concept to reality without insurmountable technical hurdles.

AI is a game changer when looking at its subset, deep learning can be use in all fields with the appropriate data, but do prefer to use machine learning as we do meet a factor called black box With DL we do not get a any understanding of the function used in the black box but where as in ML we do understand how the function performs its operation and distinguish between different data point Scalability is to meet up with a lot of factors one being as discussed above. Tho its scalable is it the best practice?

‽ Vamos inverter a perspectiva: e se a escalabilidade real residisse em "teach AI to forget"? ‽ Pense em algoritmos que, assim como o cérebro humano, aprendem a descartar informações irrelevantes, otimizando seu processamento e tornando-se mais ágeis. ‽ Essa "escalabilidade pela "amnésia" estratégica" poderia revolucionar a eficiência da IA, permitindo que ela se concentre no que realmente importa, sem se perder em um mar de dados. –Exemplo: Em vez de armazenar históricos completos de navegação para oferecer anúncios, a IA poderia identificar padrões de consumo e, com o tempo, "esquecer" informações individuais, focando apenas nas tendências relevantes para cada usuário. #ai #ia

Begin by evaluating whether the idea can be expanded effectively across different markets or applications. Consider the flexibility of the AI solution to adapt to various environments and its ability to handle increased data volumes and user loads. Prototype the idea to test its performance at a smaller scale, and then analyze how it can be optimized for larger deployments. Assess the required infrastructure and resources needed for scaling, including cloud services and computational power. Choose ideas that demonstrate robust performance and can be efficiently scaled without significant loss of quality. By focusing on scalability potential, you ensure your AI innovations can grow and succeed in broader markets.

AI innovations that don't just keep up with the crowd but lead the charge. Scalability isn't just a feature; it's a superpower, propelling ideas into realms of endless possibility. Imagine AI solutions that grow and adapt effortlessly, like living organisms, thriving on data abundance and user influx. It's not just about handling the load—it's about embracing it, transforming challenges into opportunities for boundless expansion and impact.

One time at work, our team developed an AI system for customer support. Initially, it handled queries efficiently, but as user numbers grew, performance issues arose. Ensuring an AI innovation has scalability potential is critical. The idea should be able to grow and adapt as more data or users are introduced. Scalable AI solutions are designed with expansion in mind, allowing them to maintain efficiency and effectiveness even as they evolve. One thing I’ve found helpful is to consider whether the AI concept can handle increased loads and complexity without significant performance degradation. This foresight ensures that your AI innovation can make a lasting impact, sustaining growth and user demands.

Adopting established Frameworks like NIST AI RMF provides a comprehensive risk management framework for AI systems, emphasizing trustworthiness, fairness, and transparency. ISO 42001 sets standards for AI management systems, ensuring that AI operations adhere to international best practices. Both frameworks help organizations comply with regulatory requirements and industry standards. Integrating these frameworks ensures that ethical considerations are embedded into AI development and deployment. They provide structured approaches to uphold ethical standards and societal values.

When prioritizing AI innovation ideas with ethical considerations, it is crucial to establish a framework that balances innovation with responsibility. Begin by assessing the potential societal impact of each AI idea, prioritizing those that offer significant benefits while minimizing risks. Access business value and the risks. Look for any low hanging fruits that bring in high value with minimal effort and risks, that could be easily prioritized and get buy in for implementation. Ensure transparency and fairness by involving diverse stakeholders in the decision-making process. Integrate ethical guidelines and compliance checks into the development lifecycle to prevent biases and misuse.

Customers expect solutions that treat them equitably. To ensure fairness, businesses must prioritize diverse and representative datasets, actively seeking out biases and taking corrective measures to eliminate them. This starts with acknowledging that datasets are not inherently neutral. Auditing for potential biases stemming from collection methods, representation gaps, or historical societal prejudices is essential, as is investing in data cleaning and augmentation strategies. Rigorous testing and validation under diverse conditions and with different user groups are essential to uncover any unintended discriminatory outcomes.

Evaluate AI ideas using: - Ethical Alignment: Adherence to ethical guidelines. - Social Impact: Positive societal impact. - Feasibility: Technical and economic viability. - Transparency: Explainability and transparency. - Regulatory Compliance: Adherence to laws and regulations. - Stakeholder Engagement: Involvement of diverse stakeholders. Ethical AI innovation must always aim for positive societal impact and transparency while adapting to new ethical challenges.

Ever heard the saying 'With great innovation comes great responsibility'? Well, when it comes to AI innovation, it's not just about chasing the latest trends; it's about navigating the ethical landscape with finesse. Spotting and prioritizing innovative ideas in AI isn't just about technical prowess; it's about ensuring those ideas align with ethical principles. Take a moment to weigh the ethical considerations on your innovation scales and ask yourself: -> Does this idea promote transparency and accountability? -> Does it uphold privacy and data security? -> Is it inclusive and equitable?

This is difficult to identify especially at early stage of idea. What makes investment worthy is if you can validate the idea by speaking to few users - and asking them will they be willing to pay for the solution. If you have initial interest - where potential buyers are willing to pay for solution - that is proof enough of being investment worthy at early idea stage.

Focus on ideas that offer clear ROI and align with strategic goals. For instance, in one project, we plugged a LLM into a customer success platform because it will have significant time savings and improve customer satisfaction. Evaluate potential impact, scalability, and feasibility. Use pilot tests to validate ideas before full-scale implementation. By identifying investment-worthy projects, you can allocate resources effectively and drive meaningful innovation in your AI initiatives.

‱ Esqueça o ROI tradicional por um momento. Pense, qual a capacidade da ideia de impactar positivamente a humanidade a longo prazo, mesmo que isso signifique sacrificar ganhos imediatos? ‱ Assim, em vez de desenvolver IA para automatizar trabalhos existentes, você pode priorizar projetos que explorem o potencial da IA na resolução de problemas globais complexos, como a crise climática ou a desigualdade social, mesmo que a viabilidade comercial seja desafiadora. ‱ Embora desafiadoras, certas ideias podem ter um impacto positivo duradouro no mundo e ainda assim propiciar o retorno financeiro esperado. #ai #ia

For me, any investment, AI or otherwise, can only really be proven to be 'worth it' by demonstrating return on investment. That return could be financial, with an increase in revenue and profit, or operational, with an increase in efficiency of processes or an improvement in the quality of services delivered, or something else. As long as it can be measured, my strategy for spotting and prioritising innovative ideas is to focus on ROI delivered as a direct result of the application or implementation of the technology.

Approach: Determine the financial viability of the idea. Assess the return on investment and the interest it might generate among investors. Evaluation: Analyze the cost-benefit ratio and potential profitability. Create a compelling value proposition for investors, highlighting the market potential, competitive advantage, and projected financial outcomes.

If you are looking to prioritize a product in AI, avoid trying to fill a minor gap the big LLMs are missing today but should have as part of their offering. It is great to fill a gap to enhance your overall offering but don’t think for a minute a pretty wrapper or rag is sustainable IP. These things are easy to do and will not help with your IP moat (uniqueness). Keep your eye on the likely roadmap for the big players … they have $billions and can afford to offer things for free

There are 3 areas ripe for innovative AI solutions and for increasing need for skill development: (1) Radical Empathy: Uncover people's core needs worth solving. It requires deep understanding of people, their most meaningful challenges, their intrinsic motivations, psychology, and foundational core needs. (2) Creative Technologist: Architect many niche AI solutions together to solve a challenge. The outputs from one would become an input into another. It requires broad knowledge of many tools and solutions and creativity to weave them together in novel ways. (3) Tool Builder: Technical specialists focused on building solutions for narrow niche use cases. It requires expertise in collecting data, training new AI models, and the use case

Value, viability and usability. Like any other product, your AI project/product should demonstrate substantial value that users are willing to pay for, it's viable for the business to build with clear ROI and it's usable as close to frictionless as possible.

Here's the scoop: when it comes to spotting and prioritizing innovative ideas in AI, it's like hunting for diamonds in a sea of cubic zirconia. First off, look for ideas that tackle real-world problems with elegant simplicity. Lastly, don't forget the practicality factor. Innovation isn't just about flashy tech; it's about feasibility and scalability. So, if an idea not only dazzles but also has a roadmap for implementation, well, that's the golden ticket right there.

Another important aspect is the innovation's adaptability to changing regulatory landscapes. AI technologies are often subject to evolving regulations, and staying compliant can be a moving target. Building flexibility into the AI solution to accommodate future legal and regulatory requirements can prevent costly overhauls down the line. Additionally, fostering a culture of continuous learning and improvement within the development team can drive sustained innovation and adaptation in response to new challenges and opportunities.