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Building AI is much more expensive than you think: InstAI finds a solution to manufacturing AI
One in three companies is using or considering AI to automate IT processes, according to IBM Global AI Adoption Index. The AI adoption rate comes to 35% now, which is still climbing. To catch up with this AI craze, companies are spending a lot to build their AI features, as much as millions of dollars.
“A successful AI project will cost 15 times more than you think,” according to a Forbes article. The author calculates the costs including AI infrastructure, AI access via API, data protection for every customer, and human control of the AI experience, which adds up to 15 times the cost of an AI expert. To solve this problem, InstAI empowers businesses to build AI models in a cost-effective manner.
Targeting AI vision recognition systems, Keyu Pi, the founder of InstAI, having experienced the challenges of data collection firsthand, took it upon himself to revolutionize the process. He created an embedded vision system development tool. By harnessing generative AI technology, InstAI can produce lifelike synthetic data for training purposes. This groundbreaking approach eliminates the need for traditional data collection. A staggering 75% reduction in model development time and a 20% decrease in development costs.
For industries and researchers grappling with ethical dilemmas, like sourcing images involving children, the collection and trading of such sensitive data present both moral and legal impediments. Enter InstAI, which can ingeniously produce synthetic data of comparable quality, eliminating the need for real-world data collection in these sensitive domains.
By curating high-quality datasets, creating varied data from existing sets, and producing entirely new synthetic data, the toolbox equips businesses with a formidable arsenal for their AI endeavors. One of the most captivating features of InstAI's new system is its ability to generate diverse outputs from minimal inputs. Imagine providing the system with a single image of a cat and receiving, in return, a rich array of images featuring the cat in assorted poses and against myriad backgrounds.
With barriers in data collection and model training being significant hurdles for many businesses venturing into AI, InstAI's innovative solution promises a smoother and more efficient route to AI implementation.
Experienced the difficulty of training autonomous driving, Pike Yu hands-on to solve the problem
From industrial inspections to autonomous driving, integrating AI vision recognition systems is becoming a trend. However, it is very time-consuming to collect all the needed images for data training, and the quality of images can’t be guaranteed.
“Simply collecting imagery from sunny summer days and snowy winter scenes can easily take over half a year,” said Pi, who had three-year experience in a company that manufactures automotive information and safety systems. Pi had a rich tenure as the COO of an autonomous driving vision company, granting him firsthand experience of the manifold challenges automotive manufacturers face when developing AI vision models.
For instance, to train an AI for autonomous driving, data from various times and conditions are essential. The AI needs to have access to a diverse range of road conditions and images to make reliable judgments for any given scenario. However, as Pi highlighted, the more rare and unexpected the situation, the harder it becomes to obtain the required data. Plus, there's the seasonal aspect to consider. Well aware of the intricacies of data collection, Pi decided to found InstAI to transform AI data solutions.
Currently, the company collaborates primarily on a project basis with OEM and ODM manufacturers. They have secured partnerships with six manufacturers to develop embedded AI vision system models. Moreover, these collaborations span various sectors, including home care, which has specific image requirements. In line with their growth, InstAI is also in the phase of raising seed round funding.
Managing a US-Taiwan hybrid team: Culture is the key
Headquartered in the United States, InstAI has a hybrid team in the U.S. and Taiwan. In today's globalized business landscape, managing a cross-cultural team is both a challenge and an opportunity. For this startup, embracing cultural diversity is a strategic move.
They believe that the continuous emphasis on cultural understanding should become a habit, as culture is arguably the most effective tool for reducing management costs.
Too much micro-management can result in an overly mechanized workforce, robbing the team of their initiative and creativity. It's crucial to equip the team with the tools and cultural acumen to make judgments and decisions independently.
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