Heralded as one of the most exciting breakthroughs in the field of machine learning in recent years, generative AI is a blanket term used to describe any type of artificial intelligence that uses unsupervised learning algorithms to go about optimising any number of highly complex tasks.
Note the keyword in the above statement: unsupervised. The stumbling block of early AI iterations, the bias obtained from non-pure learning methods has seemingly been solved or at least dramatically reduced. As Google, Meta, Amazon, and Microsoft are all racing at breakneck speed to develop practical generative AI models, as the clubhouse might suggest, the usage of this breakthrough has been cost-prohibitive to many.
Co-founders CEO Dr. Yichuan Zhang, Dr. Jinli Hu (Chief Research Scientist), and Dr. Hanchen Xiong (CTO) have created a platform in Boltzbit that allows data scientists to dramatically speed up the prototyping of various neural network frameworks, down to minutes from days, all without the need to write a single line of code, or, introduce any potential human errors by manually tuning training parameters.
Ok. Now that that’s out of the way, allow me to take off my glasses and put my pocket protector away.
Here’s what’s what: generative AI is the next generation of our future overlords, and with Boltzbit, businesses can now get their hands on the same goodies that only the moneybags have been able to afford prior.
At present, the ‘smart-AI’ platform can leverage unlabeled data, e.g. images, natural language data, time-series data, and any other number of unstructured data sets. Practical applications you say? Why I’m glad you asked!
How about a global pandemic that needs drug and vaccine candidates yesterday? Generative AI, Boltzbit.
Keeping the air breathable, the oceans wet, and the Sun’s radiation at bay? Carbon accounting, Boltzbit.
Smarter search and recommendation engines? Boltzbit.
Potential customer service representatives that never need a break, do not sleep, and can demonstrate human-like qualities when engaging with customers? Boltzbit.
“Current generative neural networks are designed for data synthesis that is often highly limited in practice. This approach is also inefficient and counter-productive for training generative A.I.,” commented CEO Dr. Yichuan Zhang. “We’ve changed this with a breakthrough in our unique dynamic generative neural networks that learn many generative tasks shared by multiple real-world use cases altogether, making our generative A.I. vastly more efficient and effective.”
Boltzbit’s £1.6 million seed funding round was led by Speedinvest, with participation from IQ Capital, and will be used to bolster the team numbers, scale up the platform's offering, and open an office in Berlin.
“One of the tricky parts in A.I. is to develop models and algorithms that can understand and analyse troves of data, and generative models are one of the most exciting approaches to achieve this goal. But deep generative models are prone to making poor predictions based on limited data distribution,” says Rick Hao, partner at Speedinvest. “Boltzbit has developed unique techniques to overcome these challenges, and a low code platform to allow enterprises to easily train and deploy deep generative models. We are very excited to support Yichuan and the team to build the next generation of A.I. products.”