#GenerativeAI and the road ahead..

My curiosity to learn about Generative AI or Generative Artificial Intelligence started a few weeks back at a weekend outbound with some friends and their families. It all started with a conversation during a Sunday morning walk with my friends. I was talking to a friend about where I work and how intriguing CAST Imaging is as a Technology for being the defacto "MRI for Software" and my friend,?who works as a Radiologist in a leading Healthcare chain, were exchanging notes about how Artificial Intelligence, Machine Learning, and Analytics are changing our lives. A rather intriguing conversation that set me thinking...?

The next week, there was an interesting exchange in a social media group of ex-colleagues on #chatgpt which the world was talking about. A friend took the lead and wrote an interesting post on #chatgpt. Much said about #Chatgpt and its threat on Google Search, I was keen to understand the genesis - what powers #Chatgpt and that's how I dawned upon my search.

Here's a summary of Generative AI and its applicability in our lives as I understand

Generative AI?refers to?unsupervised?and?semi-supervised machine learning?algorithms that enable computers to use existing content like text, audio and video files, images, and even code to create new possible content.

In Radiology, for instance, referring to inputs from my friend, Generative AI algorithms can help improve the accuracy and efficiency of using machine learning in combination with medical imaging techniques, such as CT and MRI scans. Machine learning models can automatically identify abnormalities in images and alert doctors to potential issues. She spoke to me about how humans are carrying out complex surgeries through AI/ML-trained robots and using the power of #generativeai to understand patterns and take healthcare to the next level.

X-rays or CT scans can be converted to photo-realistic images with the help of sketches-to-photo translation using GANs (Generative Adversarial Networks). In this way, dangerous diseases like cancer can be diagnosed in their initial stage due to a better quality of images.

Generative AI is rapidly becoming a reality. Global AI investment?surged?from $12.75 million in 2015 to $93.5 billion in 2021, and the market is projected to reach?$422.37 billion?by 2028.

Leading generative AI tools are DeepMind’s Alpha Code (GoogleLab), OpenAI's ChatGPT, GPT-3.5, DALL-E, MidJourney, Jasper, and Stable Diffusion, which are large language models and image generators.

Generative AI is of two models :

• Generative Adversarial Networks or GANs?— technologies that can create visual and multimedia artifacts from both imagery and textual input data. Used extensively in Healthcare apart from other applications as stated above.
• Transformer-based models?— technologies such as Generative Pre-Trained (GPT) language models (like #chatgpt) that can use information gathered on the Internet to create textual content from website articles to press releases to whitepapers.

Apart from Healthcare, #generativeAI has its applicability across varied applications including the following -

In?logistics and transportation, which highly rely on location services, generative AI may be used to accurately convert satellite images to map views, enabling the exploration of yet uninvestigated locations.

In the?travel industry,?generative AI can provide a big help for face identification and verification systems at airports by creating a full-face picture of a passenger from photos previously taken from different angles and vice versa.

In?marketing, generative AI can help with client segmentation by learning from the available data to predict the response of a target group to advertisements and marketing campaigns.

So, how does it impact us? And why this buzz on #GenerativeAI?

Simply, let's look at it this way. Human beings have always been good at data analysis. Machines are of course better. But machines need to be fed with data or inputs to make intelligent analyses. The edge Human beings have over machines is that they can "THINK" and machines can't, by themselves, unless they are "FED" with intelligent data. Human Beings on the other hand are creative and we exhibit that in so many fields, developing new code, new products, and R&D...

Up until recently, machines had no chance of competing with humans at creative work—Now they are starting to get good at creativity. . This new category is called “Generative AI,” meaning the machine is generating something new rather than analyzing something that already exists.?Generative AI is the move to make machines more creative and intelligent. As generative models become commonplace, they would deliver human or even superhuman capabilities, challenging us!

Machines would adapt as we train or adapt them. Even for #ChatGpt, we sometimes come across peculiar responses as the platform continues to learn, and like any other ML, it would get more relevant or accurate over time. 2023 would see Machines take on more of what Human Beings are capable of doing...it remains to be seen how we leverage #generativeai and use it for enhancing business productivity.

References :

1. https://www.altexsoft.com/blog/generative-ai/
2. https://venturebeat.com/ai/the-hidden-danger-of-chatgpt-and-generative-ai-the-ai-beat/

and insights through conversations!