Start-ups can do AI well
In the center of Artificial Intelligence(AI) and big data stands so-called Big Tech. These include companies such as Google, Facebook, and Tesla. Most people cite big data as the driving force of Big Tech leading the AI sector. It is true that Big Tech, with a large pool of its users, is accumulating a huge amount of data from each user. As it gathers more and more big data, AI is bound to develop alongside.
However, the fundamental reason why Big Techs are prospering lies in their attitude towards AI. They trust the importance of AI and do not hesitate to make long term investments. Training AI to process large amounts of data at a high rate requires massive computing infrastructure, data, and manpower. It is very reasonable to say that companies backed by a huge capital would play a leading role in the industry.
On the other hand, the amount of capital is not a single factor that brings perfection to AI technology.
A typical limitation is the ‘long-tail problem’ that occurs in a complex algorithm-based reasoning such as deep learning. This includes problems during data training when a single data, with a low probability of occurrence, is excluded from the process and causes errors.
Deep learning is strong at interpolating new information into its own pool of data, but is weak at extrapolating information that is off its category of knowledge. A good example of this would be when a Tesla model’s autopilot had a hard time distinguishing the lane on a highway with sprinkled salt on a snowy day.
These cases clearly illustrate that startups can also take part in the AI field. Merely pouring in countless data is not the answer to success in this industry. If you are able to design a process that efficiently collects and updates data when the service is live, you can actually do well in this industry.
Start-ups that are flexible to change their game plan in accordance with the quickly changing environment, are more adaptable solving long-tail problems. Assessing the right amount of capital to invest and identifying the correct problem to solve are the two keys.
Most companies who simply consider AI as the exclusive property of Big Tech companies often do not realize the importance of the technology itself. This critical absence of realization naturally leads to the neglection of investment in this industry. These kind of companies are usually the ones who, regardless of how massive data they own and how many bright workers they have working for, do not excel in this field.
Opportunity comes to those who are prepared. Web3 enables users to have data ownership and interact with developers, other data, and protocols. This requires an unprecedented process to utilize data. The goal of AI Network is to apply blockchain technology in creating an ecosystem where users share enormous computing resources and manpower needed for AI, ultimately forming a collective intelligence.
You need the right tool and equipment for any situation. In the Web3 era, anyone can participate in the most comfortable and efficient way to share the value of AI. It is a world of people who explore the future more independently than others.
This article was published in Korea’s famous media, IT Chosun. It was written by AI Network’s founder Kim Min-hyun.
AI Network is a blockchain protocol-based collaborative computing architecture for artificial intelligence and metaverse. With the motto ‘Bring NFTs to Life’, AI Network provides AINFT technology that enables NFTs to interact with users and data in the metaverse, transforming them into dynamic and intelligent beings.
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