Thanks to generative AI, a whole ecosystem is emerging, including hardware vendors and application developers to help realize its commercial potential.
In late 2022 and early 2023, the tech pioneer will launch generative AI solutions that will wow astonishing investors, business owners and the general public, producing completely original and possibly human-made prose and imagery. provided the ability of the technology to generate.
And the reaction has been unprecedented.
ChatGPT, OpenAI’s generative AI language model that generates original material upon user request, attracted 1 million people in just five days. With the iPhone, it took Apple over two months to achieve the same level of adoption. He had to wait more than 10 months for Facebook to reach the same user base compared to Netflix.
My role as a data and analytics leader has given me the opportunity to be at the forefront of the benefits and risks of generative AI. Over the past few months, I’ve spent time evaluating several generative AI startups. Generative AI, in my opinion, is an effective tool that requires human oversight to use responsibly, but it is not a doomsday as detractors claim.
In the last three years, venture capital firms have spent over $1.7 billion on generative AI solutions, with the biggest funding going into AI-enabled pharmaceutical research and creating AI software.
Moreover, ChatGPT is not the only company using generative AI. Stable Diffusion, a Stability AI that can create visuals based on text descriptions, has earned over 30,000 stars on GitHub within 90 days of its debut. That’s 8x faster than any other package.
A brief introduction to generative AI
The field of machine learning, known as “generative AI,” uses algorithms to create new data such as images, text, and audio. It is similar to a virtual writer or artist producing creative writing and art. However, this is just a group of clever algorithms working behind the scenes, not actual artists and writers.
Transformer-based models and GANs (Generative Adversarial Networks) are the two most used generative AI models at the moment.
GANs excel at transforming text and images into visual and multimedia content. Transformer-based models, such as the Generative Pre-Trained (GPT) language model, can ingest data from the internet and create a variety of content such as press releases, white papers, website articles, and more.
Why should you care about generative AI?
I see, there are a lot of explanations. Here are the top three:
- You can create completely new data that doesn’t exist yet. Think of the myriad opportunities for research and experimentation.
- Enhance existing algorithms by generating training data for new neural networks or developing top-notch deep learning architectures.
- It’s basically a machine that builds better machines.
But that’s not all.
Gartner declares generative AI as one of the most disruptive and rapidly evolving technologies in its 2022 Emerging Technologies and Trends Impact Radar report.
And get this – they’ve made some pretty bold predictions about its future impact.
By 2025, generative AI is expected to generate 10% of all data (currently less than 1%) and 20% of all test data for consumer applications. Moreover, by 2025, it will be used in 50% of drug discovery and development projects.
And by 2027, a whopping 30% of manufacturers will be using it to improve their product development process.
Generative AI is making waves. So it’s pretty important, right?
Generative AI industry-specific applications of use
Business models that use generative AI can help companies not only automate routine tasks, but also increase their income. One of the most practical uses of generative AI is content production.
education
Leverage generative AI to provide your students with the most effective and customized education possible with personalized lesson plans. These plans are created by analyzing student data such as past performance, skill sets, and feedback on curriculum content. This ensures that each student, especially those with disabilities, receives an individualized experience designed for maximum success.
Logistics/transportation
Exploring historically unexplored areas is made possible by generative AI that accurately transforms satellite imagery into map views. For logistics and transportation businesses that want to break new ground, this could be very helpful.
travel industry
Systems for face detection and verification at airports can benefit from generative AI. Combining a full-face image of a passenger from images taken from different angles makes it easier to identify and identify travelers.
banking
Another area where generative AI has proven to be a useful tool is fraud detection. Using historical data, banks teach ML and AI algorithms to suggest risk criteria. By exposing the generative AI to previous fraudulent and non-fraudulent incidents, the system can be trained to prohibit or allow certain user actions depending on the likelihood of fraud. This makes fraud detection faster and more effective than humans alone.
It’s important to remember that automated tasks, whether it’s fraud detection or product creation, are typically low-level, tedious, and repetitive. Human intervention is still required to ensure the quality and safety of the final product. The true benefit of generative AI is doubling general productivity and efficiency.
The future of generative AI
The capabilities of generative AI are unknown to us. Over 30% of new medicines and materials are expected to be discovered by generative AI technologies by 2025, which will lead to significant cost savings for the healthcare sector. Generative AI is poised to become a powerful tool in financial forecasting and scenario building, with its ability to predict future market trends and investment probabilities to mitigate risk. It can also have a significant impact on the entertainment sector, allowing companies to improve visual effects, preserve and colorize movies, and age or rejuvenate performers’ faces.
Generative AI can analyze vast amounts of data and patterns, but it cannot replace human ingenuity, creativity, and common sense. Therefore, human oversight is essential for its creation and implementation. Businesses and decision makers need to approach generative AI with caution to solve ethical issues and ensure a future that expands the economic pie for the benefit of humanity.
This article was written by a member of the AIM Leaders Council. The AIM Leaders Council is an invitation-only forum for senior executives in the data science and analytics industry. Please complete this form to see if you are eligible for membership.
