When discussing AI, many focus on the incredible suite of functionalities that the technology can bring to the table to make our lives easier, such as the capabilities in personal assistants and self-driving cars. However, in order for these functions to take place and provide the most utility to human users, the AI behind it needs to be built to learn and execute its functionalities in a way that aligns with human end user success metrics and standards. This is where the concept of AI alignment comes into play. AI alignment is the study and practice of building out AI utility functions to be in line with our own. This practice requires the designer to establish a detailed point system that assigns points based on the positive or negative utility that the human end users realizes based on the outcome of specific actions. If there is too little detail, then negative outcomes can come about.
A simplistic example of AI misalignment can be seen in Disney’s Fantasia, where Mickey Mouse brings to life a broom and orders it fill up a cauldron. There was not sufficient detail inputted in aligning the broom to this task and it ends up flooding the room. The utility function in this case can be summarized as “If cauldron is full = 1 point, If cauldron is empty = 0 points”. Now, if we were to apply AI alignment principals to this situation, the function would include more details to align the intelligent agent’s values with that of the end user such as “If room floods = -10 points, If someone dies in the pursuit or result of this task = -1,000 points, If task can be completed in 10 minutes = +0.2 points, etc”. By adding additional nuance, the AI is able to complete the task as intended by the end user without leaving room for unintended consequences.
What are some other examples of proper or improper AI alignment in technology today? How can integrative thinking be applied to AI alignment? How do differing cultures impact deriving end user utility?
In 2012, the Raspberry Pi Foundation developed a credit card sized, Linux operated computer that cost between $25 – $35 and was meant to be a cheap tool to teach computer literacy for educational institutions. However, it expanded out of it’s initial target market and was soon adopted by techies as a means to build web servers, routers, arcade machines, and other projects. By 2014 the Raspberry Pi Foundation sold over 4.5 million units as well as disrupted the personal computing (PC) industry. The reason for it’s massive popularity is rooted in the fact that there is a segment of overshot customers in the personal computing market. In 2010, the average price of a desktop personal computer was between $500 – $600 and brought with it a huge portfolio of specs and features. However, there were customers that were looking for a stripped down product to work on small projects such as data collection and server monitoring, where the Raspberry Pi fit in nicely. The Raspberry Pi Foundation continues to innovate in the low end segment releasing new iterations of the Raspberry Pi, some with more functionality and some with less but all fall in a price range between $5 and $35.
Do you think that incumbent PC producers should feel threatened by the Raspberry Pi? What do you think the next strategic step is for the Raspberry Pi foundation?
Most every industry has a complex supply chain working behind the scenes to gather components, assemble them into products, and ship them to stores to sell to consumers. These supply chains are often complex with many moving parts and, in more cases than not, relying on third parties to carry out one or more of the tasks along the supply chain, such as manufacturing or fulfillment. It is imperative to apply systems thinking to supply chains because when all parts of the supply chain act independently there are a variety of issues that arise. One major issue is the bullwhip effect which is when supply chain members use independent forecasting methods that project incoming demand upwards to maximize potential profit. Once these forecasts make it down the supply chain they compound and produce a massive false demand resulting in excess inventory for supply chain members upstream. By applying systems thinking to the supply chain, members can map out how certain actions will affect other members of the supply chain and reduce bullwhip effect volatility. What other risks arise when supply chain members act independently? Can these risks be mitigated or avoided by applying systems thinking?