How can you optimize AI for today's challenges?

Also consider using AI as a Service (AIaaS) in order to achieve much more, reliably, at a modest cost from a partnership vendor.

Choosing the right use case is obviously important, but also the quick wins as well, building it and experimenting is also important which drives the learning curve.

Choosing the right problem to solve is crucial in optimizing AI. It's important to remember that AI is not a one-size-fits-all solution. By considering factors such as business value, data availability, technical complexity, and ethical implications, we can better align our AI projects with our goals and stakeholders. Using frameworks like the AI Canvas can help in defining and scoping our problems effectively. Let's leverage AI responsibly to tackle today's challenges and unlock its full potential!

You need to apply agile methods to optimize AI and adapt to changing needs and feedback. Agile methods will help you iterate faster, deliver value sooner, and reduce risks and costs. You can use frameworks, such as Scrum or Kanban, to organize your AI team and workflow.

We tend to move too quickly out of defining the problem into immediate answers, with or without AI on our side. Spending an incredible amount of time understanding the problem from every angle you can is important to creating a real answer that will solve that problem. Sounds intuitive, but you can’t imagine how many people speed through this process just to get to a mediocre answer or product.

It goes without saying that you need quality clean data for any type of AI application. And it is far better to have more than enough data to enable you to use the most appropriate high quality data.

It is mission critical to ensure the data you use is relevant, accurate and as bias-free as possible. To achieve this, it is important to obtain data from reliable sources and also perform a verification process. Additionally, if you are using machine learning algorithms or artificial intelligence-based systems to analyze the data further, then you must ensure that the results of these analyses are not inherently biased. By taking these steps, you can be sure that your use of data will be trustworthy and beneficial.

To maximize AI performance, prioritize high-quality, diverse data and adhere to robust data governance practices, including security and privacy measures. Employ data cleaning, validation, and augmentation tools to maintain data integrity and minimize biases.

You need to apply agile methods to optimize AI and adapt to changing needs and feedback. Agile methods will help you iterate faster, deliver value sooner, and reduce risks and costs. You can use frameworks, such as Scrum or Kanban, to organize your AI team and workflow

Human and expert oversight is an important factor in using quality data. Only with qualitative validation ensure proper training of AI models

It goes without saying, for any kind of project, that it should have flexible roadmap which is pursued in an iterative manner with numerous milestones and suitable KPIs.

Traditional project methodologies can struggle to keep pace with the dynamic demands and swift shifts of AI. Adopting agile methods, like Scrum or Kanban, can empower teams to swiftly adjust to unforeseen challenges, whether they're data inconsistencies, algorithmic changes, or stakeholder feedback. Regular review cycles ensure that AI projects remain aligned with ever-changing objectives. While tools like Jira or Trello can streamline this agile transition, the pivotal shift is in approach: recognizing that in the world of AI, adaptability isn't just an asset—it's a requirement.

Agile is an approach to project management that centers around incremental and iterative steps to completing projects. The incremental parts of a project are carried out in short-term development cycles. The approach prioritizes quick delivery, adapting to change, and collaboration rather than top-down management and following a set plan. In the Agile process, there is continuous feedback, allowing team members to adjust to challenges as they arise and stakeholders an opportunity to communicate consistently. Though originally created for software development, the Agile approach is now widely used in executing many different types of projects and in running organizations.

To successfully navigate the world of AI, remember that it's not a one-and-done project but an ongoing journey of learning and improvement. Embracing agile methods like Scrum or Kanban can be a game-changer. These frameworks empower your AI team to iterate quickly, deliver value faster, and minimize risks and costs. And don't forget handy tools like Jira or Trello to keep your tasks organized and progress on track. So, stay agile and stay ahead in the AI game!

Agile is not a silver bullet. It is a risk management strategy. It does allow for fast pivots. It is a process strategy nothing more.

Testing and evaluating data in AI involves several steps, including: Data preparation Splitting the data Choosing evaluation metrics Training the model. Evaluating the mode. Iterating and improving.

Test and evaluate repeatedly in order to refine results and even to find unthought of solutions. By cross-fertlizing various ML test runs, you can unearth breakthrough value propositions and use cases.

Testing and evaluation play a vital role in fine-tuning AI models to ensure they deliver the best results. It's like taking your car for a test drive to make sure it runs smoothly. But, it's not just about regular conditions; we also need to test how AI behaves in extreme situations, like handling unusual data or facing adversarial challenges. Imagine your car needs to perform well not only on the highway but also in off-road conditions. To make this process easier, we have tools like TensorFlow, PyTorch, MLflow, and DVC: Your trusty GPS helping you navigate and keep track of your AI experiments.

Models also need to be thoroughly tested on biases and output fallacies in exceptional queries. Evaluation has to be on functionality as well as qualitative performance of the output.

In certain respects, the workflow tools for simultaneously training and assessing the effectiveness of models have yet to materialize as commercially available products. The training processes I've observed often resemble parallel agile loops, akin to stations on an assembly line. Within this workflow, there's a perpetual loop dedicated to data quality assessment, another focused on refining model architectures, an ongoing tuning loop, and a relentless adversarial loop. Occasionally, all four loops converge, and a comprehensive run is executed. This particular workflow design remains relatively novel, and it's foreseeable that someone will eventually develop a tool to automate and streamline this entire process.

You need to apply an XAI framework to help you explain the model operations and outcomes. You also need to include AI ethics principles at the early stages of the design. Once a model has been built and trained it will be difficult to find where the defects are or where the bias come from. Also, remember that big complex models are not necessarily better than simpler smaller models. So keep an eye on the model size and don't let it over run unnecessary.

Explainable AI is growing in importance as business begins to make greater inroads to integration with automated technologies. As such, it is essential to understand how the technology works and the implications for using it in an operational environment. Explainable AI provides clear insight into the decisions that are being made by a system, enabling organizations to better understand why certain outcomes are occurring and how they can adjust their processes for improved results.

To make AI work effectively and gain people's trust, you've got to be crystal clear about how your models operate and why they make the choices they do. But remember, it's equally important to openly discuss their limitations and potential uncertainties. Tools like LIME or SHAP can help you break down the nitty-gritty of your models, while platforms like Dash or Streamlit make it easy to present that information in an understandable way.

As indicated about, repeat the ML experiements to learn and imporve and to find new value-added models that cen be highly beneficial in related situations.

Developing small AI models that address specific tasks, serving as inputs for a larger model, can prove beneficial for maintaining scalability in your workflow. Updating these smaller models over time is more practical than working with a large model on every occasion.

Fairness AI systems have the potential to perpetuate the bias in the dataset they have been trained on. Therefore, it is critical to consider fairness at every step of AI development. That way, we ensure the AI algorithms do not reproduce racial, gender or other form of societal biases.

I recently came across the Microsoft Responsible AI Impact Assessment Template that is the product of a multi-year effort at Microsoft to define a process for assessing the impact an AI system may have on people, organizations, and society. It contains a comprehensive set of questions to understand the AI system, the intended use, the adverse impact, the data requirements and finally a summary of the impact. I do have to point out that it is a tad bit long.

A few other things to consider when evaluating and understanding how AI can be optimized include human-AI interactions, data privacy and security, decision-making processes, and legal obligations. Additionally, it’s important to consider how AI can be used across different sectors such as healthcare, finance, retail, logistics and transportation. To ensure successful implementation of AI solutions in these industries, organizations need to have a strong focus on understanding the nuances of each sector and having the right personnel in place to support it. Taking all of this into consideration, enterprises can ensure that they are building and deploying AI applications that add value and meet their desired objectives.

Environmental impact of Generative AI products: 1. Electricity consumption for training humungous language models is beyond the roof, for example, training a 13B parameter LLM on 400B text tokens on 200 GPUs for 7 days costs ~$150,000. 2. Water consumption for cooling, researchers at the University of California, Riverside estimated the environmental impact of ChatGPT-like service, and say it gulps up 500 milliliters of water (close to what’s in a 16-ounce water bottle) every time you ask it a series of between 5 to 50 prompts or questions. Given the benefits of AI/ GenAI, its demand is only bound to go up, and so will its side effects on our planet. How can we reduce or neutralize the side effects of AI on our planet?