Learn some of the methods and challenges of making AI systems more robust and reliable in various domains, such as testing, training, monitoring, securing, explaining, and learning.

In my experience, transfer learning is one of the most effective ways to make AI systems more robust. With just a few thousand samples, we can improve Models' reliability, robustness, and efficiency. After fine-tuning, relativity smaller models can produce quite impressive results. This is definately a space to watch.

How do you make AI systems more robust and reliable?

Artificial intelligence (AI) systems are becoming more prevalent and powerful in various domains, such as healthcare, education, entertainment, and security. However, to ensure that these systems are trustworthy and beneficial, they need to be robust and reliable. This means that they can handle uncertainty, errors, adversarial attacks, and changing environments, while producing consistent and accurate results. In this article, you will learn some of the methods and challenges of making AI systems more robust and reliable.

1 Test and validate

One of the essential steps of developing any AI system is to test and validate its performance and behavior. This involves using different types of data sets, metrics, and scenarios to evaluate how well the system meets the desired objectives and specifications. Testing and validation can help identify and correct errors, bugs, biases, and limitations in the system, as well as measure its robustness and reliability. However, testing and validating AI systems can be challenging, as they often involve complex and dynamic interactions, hidden variables, and non-linear outcomes.

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Felix Wong

Cybersecurity Infrastructure Engineer @ NUS | Digital Forensics Trainer | Cloud, Network, and Security
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Testing and validation can help make AI systems more robust and reliable in several ways: Identifying and addressing vulnerabilities: we can identify potential vulnerabilities and address them before they become problems in the real world. Preventing overfitting: A common problem in machine learning where the model performs well on the training data but poorly on new, unseen data. Improving generalizability: Improve the system's ability to generalize from the data it was trained on to new, unseen data. Ensuring consistent performance: Ensure that the AI system performs consistently under different conditions and scenarios, thereby increasing its reliability.

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Bhuwan Bhatt

Machine Learning | Generative AI | Neural Networks, MLOps, LLMs, Technical Writer | AWS
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Data is a fuel for machine learning systems. I think it is best to start with data. If the data is corrupted, a thorough testing and validation won't help us.

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2 Train and optimize

Another important aspect of making AI systems more robust and reliable is to train and optimize them using appropriate and diverse data and algorithms. Training and optimization can help improve the system's accuracy, efficiency, and generalization, as well as reduce its overfitting, underfitting, and variance. However, training and optimizing AI systems can also pose some difficulties, such as finding the optimal balance between complexity and simplicity, choosing the right hyperparameters and loss functions, and avoiding local minima and saddle points.

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Ronnie Sheer

Senior AI Engineer | Top AI Voice 2024 | LinkedIn Learning Instructor
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In my experience, transfer learning is one of the most effective ways to make AI systems more robust. With just a few thousand samples, we can improve Models' reliability, robustness, and efficiency. After fine-tuning, relativity smaller models can produce quite impressive results. This is definately a space to watch.

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Jon Shen

IAPA Top 10 Analytics Leader | Data Science Actuary | Enabling organisations to extract value from data
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When training your model, it is critical to understand the business problem you are trying to solve. Working backwards from there, you can determine the appropriate loss function to optimise, and relevant evaluation metrics to ensure your model is achieving the desired outcome.

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Ganes Kesari

2X Founder & Chief Decision Scientist | TEDx Speaker | Contributor to MIT SMR & Forbes | B-School Adjunct Prof. | C-Suite Advisor
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Training an AI system is quite like mentoring a new employee. You feed them information, correct their mistakes, and shape their decision-making capabilities. Optimizing an AI is an extension of this. It's refining their skills based on performance feedback. One key model engineering consideration is simplicity vs accuracy. As you scale up model complexity to improve accuracy, assess how much of a compromise you're making on model simplicity and future model maintenance efforts.

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3 Monitor and update

A third key factor of making AI systems more robust and reliable is to monitor and update them regularly and continuously. This involves collecting and analyzing feedback, metrics, and logs from the system's operation and performance, as well as from the users and stakeholders. Monitoring and updating can help detect and resolve issues, anomalies, and deviations in the system, as well as adapt it to changing needs, preferences, and contexts. However, monitoring and updating AI systems can also require some trade-offs, such as balancing between stability and flexibility, transparency and privacy, and human oversight and autonomy.

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Lisa Thee
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All models are a good as the data they are trained on. it is critical to monitor and adjust to accurately reflect what you are attempting to predict. For example, if you don’t continue to train your models for slang words that teens use on social media, you will miss many signals as that language evolves quickly and the model requires new examples to keep the accuracy level consistent over time.

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Bhuwan Bhatt

Machine Learning | Generative AI | Neural Networks, MLOps, LLMs, Technical Writer | AWS
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It is essential to update the model at regular intervals. Else, shifts in labels or input data can hamper the performance of the model.

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4 Secure and defend

A fourth crucial element of making AI systems more robust and reliable is to secure and defend them from potential threats and attacks. This involves applying various techniques and tools to protect the system's data, code, and infrastructure from unauthorized access, modification, or deletion, as well as from malicious manipulation, exploitation, or sabotage. Securing and defending can help prevent and mitigate the risks of data breaches, identity theft, fraud, vandalism, and cyberattacks. However, securing and defending AI systems can also entail some challenges, such as coping with evolving and sophisticated attackers, ensuring compliance and accountability, and maintaining ethical and social standards.

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5 Explain and verify

A fifth vital component of making AI systems more robust and reliable is to explain and verify their logic, reasoning, and decisions. This involves using methods and models to make the system's outputs, inputs, and processes more understandable, interpretable, and traceable for the users and stakeholders. Explaining and verifying can help increase the system's trustworthiness, credibility, and accountability, as well as enable its validation, correction, and improvement. However, explaining and verifying AI systems can also present some obstacles, such as dealing with uncertainty, complexity, and ambiguity, respecting the system's autonomy and creativity, and aligning with the human values and expectations.

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Ganes Kesari

2X Founder & Chief Decision Scientist | TEDx Speaker | Contributor to MIT SMR & Forbes | B-School Adjunct Prof. | C-Suite Advisor
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Transparency is at the heart of any trustworthy AI system. In my interactions with business leaders, the most common apprehension I've encountered is the "black box" nature of AI. By visualizing and explaining model decisions, we instill confidence and foster an environment where AI and human decisions complement each other.

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6 Learn and improve

A sixth and final ingredient of making AI systems more robust and reliable is to learn and improve from their own experiences, feedback, and outcomes. This involves using mechanisms and strategies to enable the system to self-correct, self-optimize, and self-adapt to new situations, challenges, and opportunities. Learning and improving can help enhance the system's intelligence, efficiency, and effectiveness, as well as its robustness and reliability. However, learning and improving AI systems can also raise some concerns, such as ensuring safety and reliability, avoiding unintended consequences and side effects, and preserving human dignity and control.

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Bhuwan Bhatt

Machine Learning | Generative AI | Neural Networks, MLOps, LLMs, Technical Writer | AWS
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It is better to merge the content of this section with "Monitor and update". These two sections are closely related. .

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How do you make AI systems more robust and reliable?

1

2

3

4

5

6

7

1 Test and validate

2 Train and optimize

3 Monitor and update

4 Secure and defend

5 Explain and verify

6 Learn and improve

7 Here’s what else to consider

Artificial Intelligence

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How do you make AI systems more robust and reliable?

1

2

3

4

5

6

7

1 Test and validate

2 Train and optimize

3 Monitor and update

4 Secure and defend

5 Explain and verify

6 Learn and improve

7 Here’s what else to consider

Artificial Intelligence

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