AI and Blockchain are two highly evolving new technologies that will undoubtedly drive the future of transformation across industries
One of the essential things in the AI industry is the growing importance of data, whether it is the mobility or banking or finance or customer-service sector. Data is essential to make algorithms more efficient and intelligent. New methodologies are surfacing in the market, but the data remains a crucial part when building a consumer-ready algorithm. The quality of an algorithm depends on the data it is trained upon. If your data is biased towards a particular entity, groups, religions, races, and other such socio-economic factors, then the algorithm comes with inherent bias. Though there are many ways bias can impact an algorithm, the bias in data is a fundamental issue. This is one of the biggest challenges in the AI industry today.
As the digital connectivity foot-print increases, there is an increase in the value of data as well. We are moving in the direction of a data-sharing economy where data will be traded in exchange for financial value. Often, people do not know the implications of giving consent to various applications concerning their data. This is one of the reasons we need better consent management systems, and Blockchain technology seems to be the answer. Blockchains offers transparency and establishes trust between different parties, so while sharing data, there is a trust that your data is being used as per your consent.
In the process of building an AI algorithm, clean and structured datasets are needed. There are many service providers in the industry today who offer data collection, annotation, and labeling services, and enterprises are still struggling with issues like trust, transparency, quality, and bias. Unbiased is addressing the problems above with its Data Marketplace solution, which makes the data collection and annotation services completely transparent and uses Telos Blockchain Proofs in real-time.
The recent announcement of European Commission AI Whitepaper refers to the similar issues of bias, trust, and transparency in AI. We create solutions that help AI Engineers, Data Scientists, and Enterprises to source data in a transparent way, which in-turn promotes them to build ethical AI applications.
Impact on Enterprises
As the innovation and adoption of AI increases across various industries, the issues become more prominent, and also regulations will be enforced. The direction already seems to be moving towards rules in different parts of the world, and this has a significant impact on enterprises as it adds new costs and challenges.
There are solutions that enterprises can use, to try to avoid new challenges and also improve the end-user perspective by showing the transparent blockchain proofs of data sourcing and validation.
The potential of using blockchain can be huge, for example, in the customer service industry, if you are building conversational AI algorithms, then you need speech datasets for different languages. The traditional voice-over services are expensive for satisfying the dataset requirements. Enterprises can use solution and crowdsource speech datasets with real-time blockchain proofs which establishes trust in the dataset. Another example could be to identify localized pollution patterns from crowdsourced data collection using sensors from connected cars and creating value for data generated by cars. The potential of data is huge and Blockchain-based solutions make it trustworthy and valuable.
We see a lot of potential in Mobility, HealthCare, and Customer-Service sectors. The competition of Self-Driving vehicles and Connected solutions opens up a lot of possibilities. The customer service sector can also derive a lot of value by using AI and Blockchain solutions. Another domain we think that has a lot of potential using these solutions is smart cities and sustainability initiatives.
Affect the media and marketing platforms
The media and marketing platforms today heavily rely on algorithms and analytics; they are using Natural Language Processing(NLP) to improve their outreach. Bias and diversity have much more impact on NLP algorithms as the textual data is context-oriented. Blockchain, in general, can help media and marketing platforms in many areas like copyrights, authenticity proofs, transparent polls, etc.