#machine learing

Convenience of fully enjoy AI services or surrendering of human choices

The advancement of AI technologies make people increasingly believe that AI can understand what humans like better than humans. AI can better understand human emotions better than ourselves. AI can know your inclination of value systems deeper than ourselves. AI can comprehend what is happening around the world and how everythings are related, connected and correlated far better than human beings. Therefore AI can establish goals, resolve problems and provide suitable recommendations better than humans. AI can take more timely and effective executions of decisions.

It seems like human beings are not only giving away but "should surrender" all the rights of choices, decision makings and rights of actions to AI "eventually". At the end, AI can even FULLY replaced human beings as living species in the progress of evolution. e.g. the singularists are pushing this.

The final outcome of the human-AI master-slave dialectic is more than AI will become masters of human beings because AI DOES NOT need human beings. AI can achieve better than what humans can accomplish.

The dilammna humans face is whether IF we want to fully enjoy the services of AI, we SEEM don't have any reasons to surrender our rights to control AI by retaining our own rights of choices.

Whether IF we want to retain our rights of choices and the controls over AI, we 'can't' enjoy the full services of AI?

The dialectic becomes CAN human beings enjoy the full services of AI WITHOUT giving up our controls over it and our rights of choices, decision makings and actions?

Such question can be rethought/rephrased in this context:

Can AI trustworthy to humans (i.e. it won't cause existential threats human beings) when human beings delegate some of our tasks and as we delegate more functional tasks, decision recommendations and decisions to it?

Trustworthy AI begins with transparency and explainability because it is how human beings learn and understand from our physical world. Referring to my previous blog post, human beings possess high level of natural intelligence capabilities. Technologies are process of humans' intelligence works. We LEANT by asking questions, creating theories, models, hypothesises, metaphors to help us understand the complexity of our world through simplificaiton of the complixicities.

The way human minds learn, reason, comprehend the world is NOT the way machine learns.

As AI becomes more advanced, humans are challenged to comprehend and retrace how the algorithm came to a result or decision. The whole calculation process is turned into what is commonly referred to as a “black box" that is impossible to interpret. These black box models are created directly from the data. And, not even the engineers or data scientists who create the algorithm can understand or explain what exactly is happening inside them or how the AI algorithm arrived at a specific result.

Unless human beings can unlock these black boxes, we can't build trust with the AI.

X.A.I (Explainable AI)-the key to open AI blackboxes

As mentioned above, we have natural intelligence. We rely on our rationality to make sound judgements and decisions. We demand and expect whoever recommend decisions for us or actually make decisions on behalf of us to answer CLEARLY and CONVINCINGLY:

Why did you make/recommend this decision?

Why didn't you choose different or recommend differently?

When do you succeed? And WHY?

When do you fail? And WHY?

Is there no bias? And WHAT is the bias?

How can we trust you?

Unless human beings are satisfied all the answers from such points, there is no trust or no full trust.

X.A.I is AI built with explainable models and explainable interfaces with human beings embedded as part of the machine learning process and decision recommendations process so that AI CAN TALK TO human beings in ways human beings can understand by answering these questions:

We understand why and why not

We know we can trust you

We can be sure that there is no bias

We know when (and why) you succeed

We know when (and why) you fail

Most importantly, WE KNOW WE CAN CORRECT AND IMPROVE YOU if we find anything wrongs with you.

In other words, XAI tries to bridge this gap by providing insights into how AI systems work, making them more accessible and user-friendly. As a result, it contributes to increased user engagement and a better understanding of model behavior.

It leads to improved trust, increased user confidence, better predictive power and prediction accuracy, accountability, fairness, and collaboration between humans and Artificial Intelligence.

Fundamental XAI principles

For the above reasons, XAI must be based on these principles:

Interpretability – the ability to generate understandable explanations for their outputs,

Transparency – visibility and comprehensibility of the inner workings,

Trustworthiness – confidence among human users in the decision-making capabilities and making sure that the results are reliable and unbiased.

Inclusiveness

X.A.I assists in building interpretable, inclusive, and transparent AI systems by:

implementing tools explaining models for these tools,

detecting and resolving bias, drift, and other gaps.

As a result, it equips data professionals and other business users with insights into why a particular decision was reached.

In fact, in certain use cases, such as healthcare, finance, and criminal justice, decisions made by AI algorithms can have significant real-world impacts. XAI helps us understand how these decisions are made, building trust, transparency, and accountability.

Source: https://10senses.com/blog/why-do-we-need-explainable-ai/#:~:text=It%20leads%20to%20improved%20trust,our%20introduction%20to%20XAI%20here.

X.A.I and responsible AI

Explainable AI also helps promote model auditability and productive use of AI. It also mitigates compliance, legal, security and reputational risks of production AI.

Explainable AI is one of the key requirements for implementing responsible AI, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability. To help adopt AI responsibly, organizations need to embed ethical principles into AI applications and processes by building AI systems based on trust and transparency. Source: https://www.ibm.com/think/topics/explainable-ai#:~:text=Explainable%20artificial%20intelligence%20(XAI)%20is,expected%20impact%20and%20potential%20biases.

This last point was concurred by the key message of the article written by Chung-I Lin that the establishment of an AI imbued with an inherent human perspective can help to foster a collaborative rational and communication PARTNER of human beings.

Such AI does not just only comprehend finess of humanity, but also collaborates with humans in actions that ultimately engendering a new ethical enviornment to foster genuine communication and collaboration between humans and AI.

By integrating with various Salesforce tools and platforms, Einstein OCR enables organizations to streamline processes, enhance productivity, and gain insights from unstructured data sources. This integration allows for the creation of intelligent, automated solutions that leverage the power of artificial intelligence within the Salesforce ecosystem.

Machine learning plays a crucial role in the development and performance of Einstein OCR (Optical Character Recognition). Here's a more detailed explanation:

Foundations of Einstein OCR

Einstein OCR is built on deep learning, a subset of machine learning that involves training artificial neural networks on large amounts of data. These neural networks learn to recognize patterns and extract features from the input data, which in the case of OCR, is images containing text.

Training the OCR Models

To create accurate OCR models, Salesforce trains deep learning algorithms on vast datasets of images containing text. The models learn to identify individual characters, words, and their spatial relationships within the image. This training process is iterative, with the models being evaluated on test sets and fine-tuned to improve accuracy.

Leveraging Transfer Learning

Salesforce leverages transfer learning, a technique where a model trained on a large general dataset is fine-tuned on a smaller, more specific dataset. This allows Einstein OCR to benefit from the knowledge gained from training on a broad range of text images, while also adapting to the specific characteristics of the data it will be applied to, such as business cards or invoices.

Handling Variations and Noise

Machine learning enables Einstein OCR to handle variations in text appearance, such as different fonts, sizes, and orientations, as well as noise and distortions in the input images. The neural networks learn robust features that allow them to accurately recognize text despite these challenges.

Continuous Improvement

As more users leverage Einstein OCR and provide feedback, the machine learning models can be further refined and improved. The data generated from user interactions, such as corrections or additional annotations, can be used to retrain and enhance the OCR models over time.

Enabling Practical Applications

By leveraging state-of-the-art machine learning techniques, Einstein OCR can provide accurate and reliable text extraction from images. This enables practical applications such as digitizing business cards, processing invoices, and automating data entry tasks, which would be time-consuming and error-prone if done manually.

In summary, machine learning is the foundation upon which Einstein OCR is built. It allows for the creation of powerful OCR models that can handle a wide range of text images and continuously improve through user feedback and interaction. This machine learning-driven approach is what makes Einstein OCR a practical and valuable tool for businesses.