Artificial Intelligence is big. And getting bigger. Enterprises that have experience with machine learning are looking to graduate to Artificial Intelligence based technologies.
Enterprises that have yet to build a machine learning expertise are scrambling to understand and devise a machine learning and AI strategy. In the midst of the hype, confusion, paranoia and the risk of left behind, the slew of open source contribution announcements from companies like Google, Facebook, Baidu, Microsoft (through projects such as Tensorflow, BigSur, Torch, SciKit, Caffe, CNTK, DMTK, Deeplearning4j, H2O, Mahout, MLLib, NuPIC, OpenNN etc.) offer an obvious approach to getting started with AI & ML especially for enterprises outside the technology industry.
Find the project, download, install…should be easy. But it is not as easy as it seems.
The current Open Source model is outdated and inadequate for sharing of software in a world run by AI-enabled or AI-influenced systems; where users could potentially interact with thousands of AI engines in the course of a single day.
It is not enough for the pioneers of AI and ML to share their code. The industry and the world needs a new open source model where AI and ML trained engines themselves are open sourced along with the data, features and real world performance details.
AI and ML enabled and influenced systems are different from other software built using open source components. Software built using open source components is still deterministic in nature i..e the software is designed and written to perform exactly the same way each time each time it is executed. AI & ML systems especially artificially intelligent systems are not guaranteed to exhibit deterministic behavior. These systems will change their behavior as the system learns and adapts to new situations, new environments and new users. In essence, the creator of the system stands to lose control of the AI as soon as the AI is deployed in the real world. Yes, of course, creators can build in checks and balances in the learning framework. However, even within the constraints baked in the AI, there is a huge spectrum of interpretation. At the same time, the bigger challenge that faces a world encompassed in AI is the conflict borne out of the human baked in constraints.
AI & ML systems especially artificially intelligent systems are not guaranteed to exhibit deterministic behavior. These systems will change their behavior as the system learns and adapts to new situations, new environments and new users. In essence, the creator of the system stands to lose control of the AI as soon as the AI is deployed in the real world. Yes, of course, creators can build in checks and balances in the learning framework. However, even within the constraints baked in the AI, there is a huge spectrum of interpretation. At the same time, the bigger challenge that faces a world encompassed in AI is the conflict borne out of the human baked in constraints.
Yes, of course, creators can build in checks and balances in the learning framework. However, even within the constraints baked in the AI, there is a huge spectrum of interpretation. At the same time, the bigger challenge that faces a world encompassed in AI is the conflict borne out of the human baked in constraints.
Consider the recent report of Mercedes chairman von Hugo being quoted as saying that Mercedes self-driving cars would choose to protect the lives of their passengers over lives of pedestrians. Even though the company later clarified that von Hugo was misquoted, this exposes the fundamental question of how capitalism will influence the constraints baked into AI.
If the purpose of an enterprises is to drive profits, how soon would it be before products and services start hitting a market that depicts the AI based experience as a valued added, differentiating experience and asks the buyer to pay a premium for this technology?
In this situation, the users that are willing and able to pay for the differentiated experience will gain an undue advantage over other users. Because enterprises will try and recoup their investments into AI, this technology will be limited to those that can afford the technology. This will lead to constraints and behavior baked into the AI that effectively benefits, protects or gives preference to the paying users.
Another concern is the legal and policy question of who is responsible for malfunctioning or suboptimal behavior of AI & ML enabled products.
