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After a successful 2020 and disrupting several industries, can AI still bring new exciting surprises for us?
TECHNOLOGIES IN ARTIFICIAL INTELLIGENCE-Artificial Intelligence or popularly known as AI, has been the main driver of bringing disruption to today’s tech world. While its applications like machine learning, neural network, deep learning have already earned huge recognition with their wide-ranging applications and use cases, AI is still in a nascent stage. This means, new developments are simultaneously taking place in this discipline, which can soon transform the AI industry and lead to new possibilities. So, some of the AI technologies today may become obsolete in the next ten years, and others may pave the way to even better versions of themselves. Let us have a look at some of the promising AI technologies of tomorrow.
Generative AI
Recent advances in AI have allowed many companies to develop algorithms and tools to generate artificial 3D and 2D images automatically. These algorithms essentially form Generative AI, which enables machines to use things like text, audio files, and images to create content. The MIT Technology review described generative AI as one of the most promising advances in the world of AI in the past decade. It is poised for the next generation of apps for auto programming, content development, visual arts, and other creative, design, and engineering activities. For instance, NVIDIA has developed a software that can generate new photorealistic faces starting from few pictures of real people. A generative AI-enabled campaign by Malaria Must Die featured David Beckham speaking in 9 different languages to generate awareness for the cause.
It can also be used to provide better customer service, facilitate and speed up check-ins, enable performance monitoring, seamless connectivity, and quality control, and help find new networking opportunities. It also helps in film preservation and colorizations.
Generative AI can also help in healthcare by rendering prosthetic limbs, organic molecules, and other items from scratch when actuated through 3D printing, CRISPR, and other technologies. It can also enable early identification of potential malignancy to more effective treatment plans. For instance, in the case of diabetic retinopathy, generative AI not only offers a pattern-based hypothesis but can also construe the scan and generate content, which can help to inform the physician’s next steps. Even IBM is using this technology for researching on antimicrobial peptide (AMP) to find drugs for COVID-19.
Generative AI also leverages neural networks by exploiting the generative adversarial networks (GANs). GANs share similar functionalities and applications like generative AI, but it is also notorious for being misused to create deepfakes for cybercrimes. GANs are also used in research areas for projecting astronomical simulations, interpreting large data sets and much more.
