Microsoft AI (Artificial Intelligence) is here! With more AI available within the PowerPlatform and Dynamics 365 we decided to take a better look at it. We asked, what is Microsoft AI, how can it help the housing sector, how do you learn about it and what are the disadvantages?
What is Microsoft AI?
Microsoft AI refers to the suite of artificial intelligence (AI) technologies and services offered by Microsoft. These technologies and services are designed to help organisations and individuals develop and deploy AI-powered solutions.
One of the key components of Microsoft AI is the Azure Cognitive Services, a collection of pre-built APIs for natural language processing, computer vision, and speech recognition. These APIs allow developers to easily integrate AI capabilities into their applications without the need for extensive knowledge of machine learning or data science.
Another important part of Microsoft AI is the Microsoft Bot Framework, a set of tools and services for building conversational AI-powered bots. These bots can be integrated into a variety of platforms, including web, mobile, and messaging apps, to provide a natural and personalised experience for users.
In addition to these services, Microsoft also offers a range of tools and resources for developing and deploying AI solutions, including the Microsoft Machine Learning platform, which provides a cloud-based environment for training, deploying, and managing machine learning models, and the Microsoft AI School, which offers a range of tutorials and learning resources to help developers and data scientists get started with AI.
Microsoft also offers a number of pre-trained models and solutions, such as the Microsoft Translator, which can translate text between different languages and the Microsoft Custom Vision, which can be used to train custom image and object recognition models.
How do I learn about Microsoft AI?
There are several ways to learn about Microsoft AI and its capabilities:
- Microsoft AI website: The Microsoft AI website provides an overview of the company’s AI offerings, including Azure Cognitive Services, the Microsoft Bot Framework, and other tools and resources.
- Microsoft AI documentation: Microsoft provides detailed documentation and tutorials on its AI services and tools, including Azure Cognitive Services, the Microsoft Bot Framework, and the Microsoft Machine Learning platform. These resources can help you understand how to use these services and tools to develop and deploy AI-powered solutions.
- Microsoft AI blog: The Microsoft AI blog features articles and updates on the latest developments in Microsoft’s AI offerings, as well as tips and best practices for using these services.
- Microsoft AI School: The Microsoft AI School offers a range of tutorials and learning resources to help developers and data scientists get started with AI. It covers a variety of topics, including machine learning, natural language processing, and computer vision.
- Microsoft Learn: Microsoft Learn is a platform that offers interactive tutorials, hands-on learning paths, and other resources to help you learn new skills and technologies. It offers a variety of AI-related modules, including Azure Cognitive Services and the Microsoft Bot Framework.
How could Housing Associations use it?
We are scratching the surface of whats possible with AI and the list below is just a small selection of ways Housing associations could use AI to improve their operations and better serve customers, including:
- Predictive maintenance: AI-powered predictive maintenance systems can analyse data from building systems such as heating, ventilation, and air conditioning (HVAC) to identify potential issues before they become critical. This can help housing associations to proactively schedule maintenance and repairs, reducing downtime and costs.
- Resident/customer engagement: AI-powered chatbots and virtual assistants can help housing associations to engage with tenants more effectively. These systems can be used to provide information, answer questions, and even complete tasks such as submitting maintenance requests.
- Fraud detection: AI-powered fraud detection systems can analyse data from various sources, such as financial transactions, to identify potential fraudulent activity. Preventing financial losses and protect tenants from fraudulent behaviour.
- Resident screening: AI-powered screening systems can analyse data from various sources, such as credit reports, to evaluate a tenant’s risk profile. This can help housing associations to make more informed decisions when approving or denying tenant applications and ensure the correct support is in place.
- Energy management: AI-powered energy management systems can analyse data from building systems and usage patterns to identify opportunities to improve energy efficiency and reduce costs.
- Smart home management: AI-powered smart home management systems can help housing associations to monitor and manage the use of smart home devices by tenants. This can help housing associations to identify patterns of usage and identify potential issues, such as security threats or energy waste.
It’s important to note that while AI can bring many benefits to housing associations, it’s important to consider the ethical implications of using these technologies, such as ensuring data privacy and security, and avoiding bias.
What are the first steps in implementing AI?
Implementing AI can be a complex process, but there are several key steps that organisations can take to get started:
- Define the problem: clearly define the problem or opportunity that you want to address. This will help to ensure that your AI solution is focused and effective.
- Assess your data: Before you can develop an AI solution, you need to assess the quality and availability of your data. This will help you understand what data you need to collect, and how you can use it to train your AI models.
- Choose the right AI technology: There are many different AI technologies available, so it’s important to choose the one that is best suited to your problem and data. This may involve working with AI experts or vendors to evaluate different options.
- Develop a prototype: Once you have a clear understanding of the problem, data and technology, you can start developing a prototype of your AI solution. This will allow you to test your approach and make any necessary adjustments before moving on to full development.
- Test and evaluate: Before deploying your AI solution, it’s important to test and evaluate it to ensure that it is accurate and reliable. This may involve using a small set of data to train the model and then testing the model with a separate set of data.
- Deploy and monitor: Once your AI solution has been developed, tested, and evaluated, you can deploy it to your organisation. You should also set up a system to monitor the performance of your AI solution over time and make adjustments as needed.
- Continuously Learn: Implementing AI is an iterative process, where you learn and improve the solution. You should continuously monitor the performance of your AI models, and retrain them if necessary.
- Keep Ethics in mind: As AI is being implemented, it’s important to consider the ethical implications of the technology, such as data privacy, bias, and transparency.
What are the disadvantages?
Microsoft AI, like any other technology, has some potential disadvantages that are worth considering:
- Dependence on a single platform: Microsoft AI is primarily built on the Azure platform, which means that organisations who want to use it need to have an Azure subscription. This can limit flexibility and increase costs for those that have already invested in other cloud platforms or on-premises infrastructure.
- Limited integration with other platforms: Microsoft AI services can be integrated with other platforms, but it may require additional development and technical expertise to do so.
- Bias in AI models: Like any AI model, Microsoft AI models can be trained on biased data, which can result in biased outputs. This is especially concerning in applications where decisions are made based on the model’s predictions, like in healthcare, finance or law enforcement.
- Complexity of AI solutions: AI solutions can be complex and may require a significant investment in terms of time, resources and expertise to implement, this can make it hard for small and medium organisations to adopt them.
- Risk of vendor lock-in: Microsoft AI services are proprietary and may not be easily transferable to other platforms or vendors, which can make it difficult for organisations to switch to other solutions in the future.
- Limited control over data: As Microsoft AI services are cloud-based, organisations may have limited control over the data used to train the models, and may not have the same level of control over data security and privacy as with on-premises solutions.
- Ethical considerations: As with any AI solution, it’s important to consider the ethical implications of the technology, such as data privacy, bias, and transparency.
Implementing AI is a process that requires a clear understanding of the problem, the right data, and the right technology. It’s also important to test and evaluate your solution before deploying it, and continuously monitor and improve it over time.