In the midst of the Fourth Industrial Revolution, as we increasingly integrate Generative AI into our daily lives, we face a critical paradox: Can we expect a machine, inherently void of morals or consciousness, to be responsible if we, the architects and users, sometimes falter in our own responsibilities?
Generative AI systems, like OpenAI’s GPT series or the imaginative Midjourney, have not just demonstrated capabilities to create text or images but have also exemplified the power to inspire, innovate, and occasionally intimidate. Trained on vast troves of data, they’re a mirror, reflecting the collective knowledge, biases, and intentions of humanity.
Before we delve deep, let’s set the context:
Real-world Scenario: In 2020, generative models birthed ‘deepfake‘ technologies, a double-edged sword capable of creating realistic yet entirely synthetic media. While artists found new avenues for creativity, malicious actors found ways to spread misinformation, impacting political landscapes and individual lives.
“A tool is but an extension of one’s hand, an AI is an extension of one’s mind. Both amplify intent; neither possess their own.”
Generative AI
Typical Usage
Potential Misuse
GPT-4
Content Creation, Customer Support
Spreading misinformation
MidJourney
Image Generation
Creating misleading imagery
To visualize the evolution and potential implications of Generative AI, consider this simple flowchart:
This blog will uncover the mechanics of Generative AI, examine the landscape of human responsibilities, and ascertain whether there’s a ceiling to how responsible an AI can truly be. But remember, every tool, even AI, requires judicious and mindful human use. The question isn’t just about what AI can do, but more crucially, what we do with AI.
In the age of digital transformation, where every piece of information is becoming rapidly accessible and organized, business cards remain one of the few tangible pieces of professional information exchange. While their physical form offers a personal touch, extracting information from them in a quick and efficient manner poses a unique challenge. To address this I have thought to write my approach for business card text extraction in the best possible manner.
Deep Dive into the Mechanisms, Challenges, and Innovations of Business Card Text Extraction
In the powerful combination of Natural Language Processing (NLP) and Optical Character Recognition (OCR), NLP enables machines to understand and respond to human language. On the other side, OCR technology converts different types of documents, including scanned paper documents, PDF files, or images taken by a digital camera, into editable and searchable data.
In this blog, we will delve into an innovative method that combines the strengths of both NLP and OCR, specifically the renowned Tesseract-OCR tool, to extract and categorize information from business cards. From identifying specific phone numbers such as office, fax, or mobile numbers to precisely extracting detailed address components like city, state, and country, this technique has shown great potential in revolutionizing the way we process business cards. Join us as we unravel the intricacies of this method and explore its future implications.
Extraction Step
Description
Example
Optical Character Recognition (OCR)
Conversion of images of typed, handwritten, or printed text into machine-encoded text.
Cognitive Robotics, a harmonious blend of AI, Machine Learning, and robotics, signifies the dawn of a new age in numerous industries. By infusing robotic systems with capabilities such as understanding, learning, and autonomous decision-making, cognitive robotics sets the stage for an extraordinary level of supply chain automation.
Existing automation technologies have already improved supply chain efficiency, minimizing labor costs, lead time, and error rates while enhancing productivity. With cognitive robotics, the industry is on the brink of a new era, wherein robots are not merely task performers but cognitive entities capable of decision-making.
The use of artificial intelligence and Machine Learning (AIML) systems can be an effective method for automating disaster response, but they need to be properly trained to interpret disasters for them to be useful.
Guest talk at the Indian Institute of Technology, Guwahati zoom-live session on the launch of the 2022 batch of AIML certification, addressed the questions like;
How AIML is helping this world to be a safer planet for a living?
How big is this disaster problem?
How humans have become intelligent over years by using Artificial Intelligence and Machine Learning to handle Disasters?
How the wildfire in technologically advanced countries is getting handled or maturing to get ready to handle?
