The future of social investments – and it's not about blockchain

<p class = "canvas-atom canvas-text Mb (1.0em) Mb (0) – sm Mt (0.8em) – sm" type = "text" content = "Big Data is Machine Learning they have already changed the way we receive information. Advertisers no longer rely on billboards or television to reach us. Recent events regarding Facebook, Twitter and Google have thrown a very public light on how those mammoths used their users' data to serve commercial and political purposes to pay third parties. By simply reading the user profile and applying the tools of Machine Learning, they can, more easily than ever, identify the needs and needs of the user, with an almost appalling precision. "Data-reactid =" 23 "> Big Data and Machine Learning have already changed the way we receive information Advertisers no longer rely on billboards or on TV to reach us The recent events on Facebook, Twitter and Google have thrown a very public light on the way in which those mammoths used their users' data to serve the commercials for political purposes to pay to third parties.Eachly by reading the user profile and applying the tools of Machine Learning, they can, more easily than ever, identify the needs and needs of the user, with almost scary precision.

<p class = "canvas-atom canvas-text Mb (1.0em) Mb (0) – sm Mt (0.8em) – sm" type = "text" content = "The pervasiveness with which Artificial intelligence (AI) has entered our daily life is really impressive. And if we could also apply it to trading and investment? "Data-reactid =" 24 "> The pervasiveness with which Artificial Intelligence (AI) entered our daily life is truly impressive, and what if we could also apply it to trading and investing?

In reality, this is already possible. Some hedge funds and quantitative funds have already developed artificial intelligence-oriented trading bots. But how can we make it easily available for everyone?

In general, we would like to automate the following investment process:

1. Generation of ideas
2. Validation of ideas
3. Commercial execution
4. Evaluate steps 1 to 3 to improve iteratively. This is a very logical place for AI to help us improve our trade prejudices.

Most of these processes could be automated with AI robots. We could implement bots that can identify price variations, volumes or even related news from reliable print sources. Automation with bots will allow investors to expand their trading universe without spending more time in front of a computer to find and analyze the information that an AI robot would be able to process more accurately and tirelessly. Sometimes we also need automation to ensure that we are not guided by our emotions, especially when we closely monitor a position for long periods of time.

<p class = "canvas-atom canvas-text Mb (1.0em) Mb (0) – sm Mt (0.8em) – sm" type = "text" content = "Read also: These three blockchain startups are building on artificial intelligence to change our daily lives"data-reactid =" 33 ">Read also: These 3 blockchain startups are building on artificial intelligence to change our daily lives

I believe that artificial intelligence robots would be able to play an important role here to help our investors rely on the automation and scalability of these robots to improve their trade. It's almost like creating a commercial team without hiring a team.

<h2 class = "canvas-text canvas-text Mb (1.0em) Mb (0) – sm Mt (0.8em) – sm" type = "text" content = "Next steps"data-reactid =" 35 ">Next steps

For this to happen, there are still many challenges that the AI ​​will have to face before becoming commodified and easily used by everyone.

The first challenge is to guarantee the perfect understanding of the user's request by the AI. To be used for trading, artificial intelligence robots should understand 100% certainty of the merchant's requests. This 100% AI robot is still far from being reached. Even Siri is still struggling when you ask for the weather or the nearest gas station. Imagine allowing Siri to give trading orders to your broker … Today I would still be afraid.

The second major obstacle for a good investment robot lies in the quality of the data and the method of data collection. As for the former, the current data available to the general public are limited and with minimum controls to ensure validity. If the data source is not good or if the data sets are corrupted, it is not possible for the AI ​​bot to manage a successful investment strategy. In short: "garbage, trash".

On this last issue, we repeat the data collection processes over and over again between different data providers, hedge funds, individual traders and large institutions. Would not it be advantageous for everyone to have a single data collection and a validation structure to share with all market participants? I appeal to all market participants to be "green" about how we collect and store our financial information. Currently, we are all doing it in a highly unsustainable and environmentally friendly way.

One of the main reasons invoked by market participants to manage data collection individually is the ability to differentiate itself from the competition, obtaining a "better" data set. Of course, but I think that focusing on raw information is the wrong battle. The focal point should increase the ability to gather knowledge from the data. This would be similar to the commodification of computer power with cloud computing, so why not even the data? After all, data is simply oil in the race car's engine. A good driver is still needed to make sure you can reach the finish line.

<h2 class = "canvas-text canvas-text Mb (1.0em) Mb (0) – sm Mt (0.8em) – sm" type = "text" content = "New interfaces"data-reactid =" 41 ">New interfaces

The third obstacle lies in the currently existing user interfaces. We see the chatbots successfully used in managing relationships with customers or as virtual assistants, but not yet in the area of ​​finance. Why? First, we need a way to transform the dependency of our financial sector from obsolete user interfaces such as spreadsheets, checklists and card-reading skills to identify signals.

The story continues