You’re a professional in AI. How can you work with others to develop and implement new AI technologies?

I often ask leaders who attend my AI Executive Workshop "How to do you start an AI project? What is the first step that you take?" and many of them will talk about the data they have or the technology they are using. Over the past 10 years 85% of AI projects failed because of this critical misstep. These questions will set you apart. What are you trying to do and how will you measure success? These 2 questions alone will shift the trajectory of your project in a direction that will lead to production scale. But there is one more set of questions I encourage you to add to the mix. Design Justice. Don't just think about the happy path & what can go right. Have the courage to also ask what can go wrong, who can be hurt & build for both.

I, as an AI professional, can work with others by: → Clearly explaining AI and listening to diverse perspectives → Sharing knowledge, embracing new ideas, and adapting communication → Proactively raising ethical concerns and collaborating with experts This fosters collaboration, tackles challenges ethically, and leads to successful AI development.

It's not just about the problem and the goal. Sometimes there is no problem. But sometimes you ARE the problem and so nobody wants to work with you. Which happens to be how I got my technical skills in AI because I needed friends and co-workers who would like me that led to me making my AI friends. My point, if there is one, yes, there is one, is to use your loneliness and despair as a motivation to get seclude yourself more in your research and increase those technical skills so you can make those sharks with laser beams on their heads and make that AI that can cripple the social media accounts of the people who have shunned you! And you will be the most powerful being on the planet!

What is the entry point? That's the first wondering I have when working with others and guiding leaders who want to implement AI. Depending on where leaders are determines the level of support I need to provide. Beginning leaders will need to connect how they currently use AI (since the technology has been in use for years) to what they want to accomplish with AI tools. From there, leaders can begin to plan with the end in mind.

AI is all about data intelligence. So, what you need to do is find a solution in an existing organization and propose what value you can bring to the table via your AI expertise. Yes, you should be the one to break the ice.

* Know the landscape and tools available. This is building intuition of each tools and technologies * Keep updating with new developments and papers that become available. Not necessarily the most shiny paper / tool is the answer. * Keep yourself hands on, try out different code, solutions - kaggle, blogs and even better - your own project. * Dabble with different datasets * Participate in community discussions or at least read. How did other approach the problem. * Blog about your approach, even though its just another (not significant) solution

Choosing the appropriate tools and platforms is critical to the development and deployment of AI solutions. This decision should be driven by the specific needs of the project, considering factors such as compatibility, scalability, and user-friendliness. In my journey, I've found that the right tools not only streamline development but also facilitate collaboration among team members. Whether it's a programming language that offers extensive library support or a cloud service that enables seamless data sharing, the choices made in this stage can significantly impact the project's trajectory.

Selecionar as ferramentas e plataformas adequadas é essencial para a eficácia de qualquer projeto de IA. Com base na minha experiência, a escolha deve ser guiada pelas especificidades do projeto, considerando a interoperabilidade, a eficiência e o custo. A utiliza??o de sistemas de controle de vers?o e a documenta??o cuidadosa promovem a colabora??o efetiva, permitindo que as equipes trabalhem de maneira mais coesa e transparente. Além disso, adotar as ferramentas certas desde o início facilita a implementa??o e escalabilidade futura do projeto, garantindo que ele possa evoluir conforme necessário.

Selecting tools and platforms for AI projects is crucial. Use Python with Pandas, NumPy for data tasks. TensorFlow or PyTorch for deep learning, scikit-learn for traditional ML. Google Colab, Jupyter for development. Cloud platforms like Google AI Platform, SageMaker for scalability. Flask, Django for model deployment, or serverless options like AWS Lambda. AutoML tools like Google AutoML or Azure AutoML for automation. Git, GitHub for version control, Kubernetes for system integration. Choose based on project needs, team expertise, and scalability requirements.

I am a big fan of Lean principles that minimize waste optimize processes. In AI solutions, this means streamlining workflows, reducing unnecessary steps, and maximizing resource utilization. Lean promotes a culture of continuous learning and adaptation, this translates to iterative model refinement, data quality enhancement, and ongoing performance evaluation. Lean emphasizes delivering value to customers which aligns with creating meaningful solutions that address specific business needs. It encourages collaboration across teams, this ensures effective communication between data scientists, developers, and business stakeholders. Lean tools like Value Stream Mapping help identify bottlenecks, delays, and areas for improvement.

Adhering to best practices and standards is paramount in ensuring the reliability, security, and ethical integrity of AI systems. These guidelines serve as a compass that navigates through the complex landscape of AI development. From the initial design to the final deployment, every step should be guided by principles that prioritize data privacy, algorithmic fairness, and transparency. In my experience, incorporating these practices from the outset not only mitigates risks but also enhances the credibility and acceptance of AI technologies among stakeholders.

Adotar as melhores práticas e padr?es é fundamental para garantir a qualidade e a ética em projetos de IA. O compromisso com um desenvolvimento responsável, a ades?o à metodologia ágil e a constante atualiza??o sobre as inova??es no campo da IA s?o aspectos que sempre priorizei. Além disso, a seguran?a e privacidade dos dados n?o s?o apenas mandatórias, mas também essenciais para manter a confian?a dos usuários. Aderir a esses princípios é vital para criar solu??es de IA que n?o apenas atendam às necessidades atuais, mas também sejam sustentáveis e éticas a longo prazo.

Follow AI best practices: 1) Clearly define project goals and ethical considerations. 2) Use diverse, unbiased data for training. 3) Document code and models thoroughly. 4) Implement robust testing and validation. 5) Ensure model interpretability and transparency. 6) Prioritize security measures for data and models. 7) Adhere to privacy regulations and obtain informed consent. 8) Continuously monitor and update models in production. 9) Foster interdisciplinary collaboration. 10) Stay informed about evolving AI ethics and standards.

Effective communication and coordination are the lifeblood of collaborative AI projects. It's imperative to establish clear channels of communication and regular check-ins to keep everyone on the same page. Utilizing collaborative tools and platforms can greatly aid in this effort, ensuring that information flows freely and efficiently among team members. In my experience, fostering an environment where feedback is encouraged and valued is instrumental in navigating challenges and sparking innovation. It's through the exchange of ideas and perspectives that AI solutions can truly be refined and optimized.

Schedule regular meetings to discuss progress, challenges, and insights, facilitating an environment where ideas can be shared openly. Utilize collaborative tools and platforms (e.g., GitHub, JIRA, Confluence) for documentation, code sharing, and project tracking to enhance transparency and accessibility of information.

Communication is the key to the success of any project. Any project no matter big or small need communication. Clients are always aware about their needs and #AI service provider can use some best practices and industry standards to keep everything transparent. Emails are the best medium to keep it formal supported by collaborative tools like chat software. Keep all documents at one place. Using any modern tool will help.

I create acronyms to help convey complex ideas concisely. For seamless AI collaboration, try to PARTNER: P: PROPER CHANNELS for planning, updates, issues A: Foster ATMOSPHERE of open dialogue, share progress R: RESPECT diverse expertise, appreciate new perspectives T: Align on goals, roles upfront, coordinate as one TEAM N: Being empathetic is key, offer/seek NEEDED assistance E: Ensure clear, respectful communication across EXCHANGES R: Build RAPPORT through transparency, address concerns Following PARTNER enables effective communication, strong coordination and an inclusive collaborative environment for successful AI initiatives. I hope this helps.

Never underestimate the power of feedback! Set up various channels to solicit feedback and think about how you can create a safe environment for feedback. It is important to be specific by preparing thoughtful questions and consider the right people.

As an AI professional, to develop and implement new AI technologies effectively: 1. Focus on creating solutions for real problems or needs. I've seen companies implement tech b/c the CEO thinks it's important but when there isn't a legitimate scalable need, there won't be adoption. 2. Engage with those who'll be affected by the AI solution from the start, involving them in design, testing, and feedback loops. 3. Discuss and address potential ethical issues, risks, and biases early on. Be prepared to refine or pause the project if these concerns compromise the integrity or effectiveness of the solution. 4. Test the AI solution in a controlled, pilot environment to validate its effectiveness and safety before wide-scale implementation.

1. Natural Language Processing: Use Case: Sentiment analysis for customer feedback. Tools: (Natural Language Toolkit) 2. Computer Vision: Use Case: Object detection in images for security surveillance. Tools: OpenCV 3. Recommendation Systems: Use Case: Personalized content recommendations. Speech Recognition: Use Case: Voice-controlled applications. Tools: Google’s Speech-to-Text API 5. Autonomous Vehicles: Use Case: Object detection and navigation for self-driving cars. Tools: (Robot Operating System) 6. Predictive Analytics: Use Case: Forecasting sales trends. 7. Healthcare Diagnostics: Use Case: Medical image analysis for disease detection. Tools: (Digital Imaging and Communications in Medicine)

Whenever I start a project or upgrade a current one, I suggest building using AI tools to enhance the current applications using AI tools. Improving products by implementing AI algorithms can provide several benefits such as enhancing the customer experience, increased efficiency, competitive advantage, cost savings, and more. ?? Thanks for your Insightful reactions.

The initial ask ASSUMES that all parties have an understanding of AI and its abilities. NEVER assume C-Level decision makers have a solid understanding of AI an what it can do for the brand/business. Visual learning tools that showcase AI’s power have been my most effective tools at getting sign off from decision makers. A short video of the AI process or an uncluttered infographic have helped immensely - I can’t tell you the number of emails and texts I have received THANKING ME for not making the receiver of the message feel “less than” when it comes to team understanding of AI’s potential role in a forthcoming initiative. NEVER assume everyone is on the same page with AI - Clarify everyone’s intimacy before saying GO.