How do you apply new AI techniques?

Artificial intelligence (AI) is transforming every industry and sector, from healthcare to entertainment. But how do you apply new AI techniques to your own projects and challenges? In this article, we will explore some steps and tips to help you get started with AI, whether you are a beginner or an expert.

Key takeaways from this article

Define your AI goals:

Start by identifying a clear, SMART goal for your AI project. This ensures you have a specific, measurable outcome to aim for and guides your subsequent steps effectively.### *Gather quality data:Collecting high-quality and relevant data is crucial for training your AI model. Use existing datasets or generate new ones to ensure your model performs accurately and reliably.

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1 Choose your goal

The first step to apply new AI techniques is to define your goal. What problem do you want to solve, or what value do you want to create, with AI? Your goal should be specific, measurable, achievable, relevant, and time-bound (SMART). For example, you might want to use AI to automate a customer service chatbot, or to improve the accuracy of a medical diagnosis.

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Bharanidhar Raju Tarimana

President at GTS Facility Management Services Private Ltd
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There are many ways to apply new AI techniques in the facility management industry. *Predictive maintenance:*AI can be used to analyze data from sensors and equipment to predict when maintenance is needed before a breakdown occurs. *Energy optimization:*AI can be used to optimize energy usage in buildings by monitoring and adjusting HVAC systems, lighting, and other energy-consuming devices. *Space utilization:*AI can be used to optimize space utilization in buildings by tracking occupancy levels and adjusting layouts as needed. *Intelligent asset management:* The quality and quantity of data available is essential for the success of any AI application. *Model development and training and many other features which shall contribute.

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Imoh Etuk

Senior Technical Architect at KPMG MBS || AWS Community Builder || MCT || Microsoft Azure MVP
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These are the ways I apply the new AI techniques: * I take an innovative approach to AI by stayingÂ up-to-date with the most recent research, thoroughly vetting new methodologies, and tailoring them to the unique requirements of my projects. * When applying new AI techniques, I prioritize a phased approach, starting with a comprehensive understanding of the problem, experimenting with diverse models, and fine-tuning for optimal performance. In summary, I apply new AI techniques by promoting collaboration among specialists and stakeholders. I take a staged approach, concentrating on knowledge and responsible deployment while also emphasizing ethics and openness.

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2 Find your data

The next step is to find the data that you need to train and test your AI model. Data is the fuel of AI, and the quality and quantity of your data will affect the performance of your model. Depending on your goal, you might need structured or unstructured data, labeled or unlabeled data, or synthetic or real data. You can use existing data sources, such as public datasets or APIs, or collect your own data, such as through surveys or sensors.

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Adriano Amalfi

Head of Innovation Lab at CDP Cassa Depositi e Prestiti
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If you have already reached a good maturity with data management and AI in general you can consider generating synthetic data to fine tune or train new AI models

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3 Select your technique

The third step is to select the AI technique that best suits your goal and data. There are many types of AI techniques, such as machine learning, deep learning, natural language processing, computer vision, or reinforcement learning. Each technique has its own advantages and limitations, and requires different skills and tools. You should research and compare different techniques, and consider factors such as complexity, scalability, interpretability, and ethics.

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4 Build your model

The fourth step is to build your AI model using the data and technique that you have chosen. This involves writing code, using frameworks and libraries, and applying algorithms and methods. You should follow the best practices of AI development, such as splitting your data into training, validation, and testing sets, choosing appropriate metrics and evaluation methods, and optimizing your model parameters and hyperparameters.

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5 Deploy your model

The fifth step is to deploy your AI model to your target environment, such as a web app, a mobile app, or a cloud service. This involves packaging your model, testing its functionality and reliability, and integrating it with other systems and platforms. You should also monitor and update your model regularly, and ensure its security and compliance.

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6 Learn and improve

The final step is to learn from your AI model and improve it over time. You should collect feedback and data from your users and stakeholders, and analyze the results and outcomes of your model. You should also keep track of the latest trends and innovations in AI, and explore new techniques and opportunities to enhance your model. You should always strive to improve your AI skills and knowledge, and apply new AI techniques to new challenges.

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7 Hereâ€™s what else to consider

This is a space to share examples, stories, or insights that donâ€™t fit into any of the previous sections. What else would you like to add?

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Adriano Amalfi

Head of Innovation Lab at CDP Cassa Depositi e Prestiti
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Governance and ethics in artificial intelligence are topics gaining increasing attention, both in academia and within the industry. This isn't merely a matter of regulatory compliance but also one of social responsibility and long-term sustainability of AI projects. Here are some key points that warrant attention: Transparency in AI algorithms enhances trust and ensures organizational accountability. Regular audits ensure AI practices align with legal and ethical standards, especially vital in sectors like healthcare and finance. Unchecked AI algorithms can perpetuate biases. Implementing bias-check mechanisms is essential. Secure data encryption and restricted access are non-negotiables, particularly with sensitive data.

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How do you apply new AI techniques?

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1 Choose your goal

2 Find your data

3 Select your technique

4 Build your model

5 Deploy your model

6 Learn and improve

7 Hereâ€™s what else to consider

Information Technology

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Directory

How do you apply new AI techniques?

1

2

3

4

5

6

7

1 Choose your goal

2 Find your data

3 Select your technique

4 Build your model

5 Deploy your model

6 Learn and improve

7 Hereâ€™s what else to consider

Information Technology

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