For many, artificial intelligence is something incredible, something that might soon replace humans in many job positions and even change the whole traditional business processes. To some extent, it is actually true. Empowering machines or software with intelligence requires loads of scientific and technical efforts, which can be made by exceptional companies, equipped with quality data sets. As we already mentioned once before, developing an AI startup is far from easy. Some experts even argue against the viability of artificial intelligence startups because of the huge budgets and development resources that large tech companies apply, which might be often inaccessible for startuppers. Surprisingly, it turns out most of the currently operating AI-powered applications were designed by startup companies. How?
As it is known, designing AI systems requires extremely relevant data sets used for data training or machine learning. This is often the main challenge for developers – to find quality data. Many startups have found the way out: they use existing open APIs for cloud business apps like Gmail, Google Analytics, Salesforce and LinkedIn as a basis for their products. For example, American startup X.ai applied Gmail email context to design an AI-powered scheduling assistant, which schedules meetings for the user. On the whole, public APIs for cloud applications provide startuppers, engaged in developing AI apps, with the following advantages:
- rich semantics
This allows creating more intelligent artificial intelligence systems thanks to properly structured data and a lot of metadata, used to build complicated hierarchical data sets for the neural network, performing deep learning.
- narrow and therefore highly relevant data sets, which shorten training periods, making market entrance happen faster
Big data that AI systems are built on should be trained as quickly as possible. Domain-specific data of the cloud business apps encourages untrained sets to identify AI relations between inputs and logically needed outcomes in a short period of time.
In contrast, big AI platforms, built by large companies like IBM and applied to develop complicated AI systems, are not designed to get integrated with open APIs. IBM Watson requires loads of manual data management and training, all of which cost pretty much. So here are the problems large companies are faced with too many data, longer training periods, lack of scalability. This makes their products not really demanded in the market, compared to numerous Ai-powered apps, produced by startup companies.
All this leads to the following conclusions. Large AI platforms, like IBM Watson, will be for sure valuable in developing specific solutions, like diagnosing patients or decision-making business software. However, most of the tech developers are very likely to be engaged in innovative startups, building AI-powered applications because of their ease of deployment.
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