Technology products have changed the way we live, eat, interact, and commute. We shop through Amazon instead of going to physical stores. We order Uber or Lyft ride before leaving our home instead of trying our luck to get a taxi on the street. We use GrubHub to order delivery online. We make new friends through Tinder. We use Airbnb to find a place to stay when traveling. We search on Google when we have questions to ask. What changes our lives the most is the way we . People often open their social apps like Facebook, Twitter, or WeChat first thing in the morning before getting out of beds. We often see people on their phones during social events. It is amazing to learn that more than 2.3 billions of people are using Facebook on a daily basis in 2019. Yet, Facebook former vice president of user growth, Chamath Palihapitiya, said that he does not use Facebook nor his children are allowed. Facebook’s LIKE function have been manipulating and controlling people’s psychological weaknesses to seek for quick rewards. interact with one another The short-term dopamine-driven feedback loops that we created are destroying how society works. — Chamath Palihapitiya These technology products can be realized thanks to and many business models exist today relies on . Amazon trains its AI algorithms to make personal recommendations based on purchase and browse history to increase sales. Uber uses AI models to estimate arrival time and determine ride fare base on the current condition of the traffic. Google tunes its AI algorithms to determine the page rankings and earn money through its advertising systems. Without AI models, none of these would even exist. However, these technology giants exist today and they are tweaking the algorithms in favor of them. big data AI algorithms What Tech Giants can do to Their AI Algorithms are way beyond Your Imagination Human relies on others for our own decisions. We often read product reviews or watch video reviews before making a purchase. A psychologist at American Institute for Behavioral Research and Technology in California named Robert Epstein conducted an on 661 Americans who are not familiar with Australian politics to vote for the two candidates in the Australian election based on search engine results. The study found that the ratio of positive to negative suggestions in the autocomplete can change the preference of undecided voters by nearly 80%. experiment The above scenario happened in an experiment, but imagine if it happens in real life, and . In the 2016 US presidential election, Google was of altering the candidate’s autocomplete suggestion for its search engine in favor of Hillary Clinton. When you typed “Hillary Clinton is” in the Google search engine, the phrase “Hillary Clinton is winning” would appear in the autocomplete suggestion. However, in Yahoo and Bing search engines, the results of the autocomplete suggestions were “Hillary Clinton is a liar” and “Hillary Clinton is a criminal”. It is to believe that the tech giant was tinkering with its search suggestion algorithm to cast Clinton in a positive right. it actually did accused Public figures as controversial as Trump also suffered from Google’s autocomplete algorithm. Trump that Google where “96 percent of Google search results for the word “Trump” were articles from left-leaning sites.” believed buried conservative news in search results The Power of Manipulating Human Attention and Sentiment Our moods are also affected by the mood of others just like how we depend on others to make decisions. Have you ever experience this? When you open your social Apps like Facebook or Twitter and read a post from one of your friends, it either brightens up or ruins your day. Facebook’s data scientists believe human sentiments are on social networks. The researchers 689,003 Facebook users and then split them into two groups with one group removing one of the users’ negative emotion posts and the other removing positive emotion posts. The study showed that when the user sees less emotionally active post from friends, the number of positive posts by the user decreases and the number of negative posts increases; vice versa. contagious selected It is unbelievable to imagine that with a minor tweak of an algorithm can make such a huge difference to our sentiments. Yet, what is more striking is that the experiment conducted in the paper tampered the News Feed meaning but you just did not know. without user’s knowledge or consent you may be one of the guinea pigs Whether We should trust Centralized Entities with the Creation of AIs that will shape Our Future Big companies remain threats to . Facebook is only showing users the posts they agree with while Google is altering search results which increase polarization and harm democracy. A fundamental change to business models can reduce the risk of centralized AIs. AI democratization The democratization of Artificial Intelligence is crucial to the future development of mankind. The enormous potential of AI means that it can either increase the inequalities of society or free us from suffering. The current AI research tends to lead to concentrations of power and money, which means that the future development of AI will only be more centralized and may even harm humanity. In order to avoid such an event to happen, AI needs to be much easier to use, more reliable, and more intuitive than it is today. Blockchain may be the answer. Blockchain’s decentralized nature supports transactions directly between disparate parties without third parties (i.e. Facebook and Google). It is ultimately a means for individuals to interact directly and to govern themselves in a more secure and decentralized manner. Blockchain technology provides a unique token economy, also known as tokenomics model. A token economy is a form of behavior modification designed to increase desirable behavior and decrease undesirable behavior with the use of tokens. Individuals receive tokens immediately after displaying desirable behavior. The tokens are collected and later exchanged for a meaningful object or privilege. Infographic by blockchainhub Each blockchain platform has its own unique tokenomics model. was designed so that a steady stream of tokens can enter the network through block rewards. After a block has been successfully validated by a miner, tokens are distributed as block reward. The number of tokens rewarded for each validated block halves overtime. Bitcoin has a maximum supply of 21,000,000 BTC. tokens, similar to Bitcoin, are continuously distributed as block rewards but with no max supply. Bitcoin Ethereum There are several projects working on democratizing AI to foster the world of open source AIs. aims to create a democratically governed network of AI agents where users can access AI algorithms through SDK and API using SingularityNet token, AGI. is leveraging the blockchain technology to achieve Artificial Intelligence democratization where everyone can easily gain access to the AI model and use in smart contracts and DApps. Cortex uses an incentive mechanism to reward AI model contributors where the contributor gets paid when the model is called by a smart contract (similar to Amazon API Gateway where you get paid per the amount of calls). With tokenomics model design, we can communicate with one another without a central entity but also gets the goodies and benefits of AI that bring to our lives. SingularityNet Cortex Conclusions Like many technologies, AI algorithm is a double-edged sword. The question is, Let us not forget that Facebook allowed its data scientists to manipulate user experiences and collect data without users’ explicit consents. Blockchain technology provides a mean for users to have sayings on what is right and what is wrong. The decentralized nature of blockchain provides a powerful solution to democratize AI algorithms and actually serve the user’s interest. This is just the beginning of an era, it still requires an enormous amount of hard work to realize AI democracy and only time will tell where blockchain will lead us to the future development of AI. is it really enough for technology giants to have the right to make rules by relying on the suspicion of not doing evil?
