Experience tells us that the main challenge facing start-ups and large companies involved in Artificial Intelligence (AI) and Machine Learning (ML) is that they traditionally develop products slowly. Since scientific models need considerable fine-tuning in terms of functional outputs, and research models require considerable resources and time to be deployed, most AI-ML projects never see the light of day.
At a recent GLOBIS seminar, Dr. David Malkin, Director of AI Architecture at Cogent Labs, explained that one of the root causes to why a company might fail in developing an AI or ML product at all, let alone rapidly, is a conflict in culture between scientists and engineers.
Scientists and engineers play a crucial role in virtually all of the world’s major innovations. Oddly enough, each group thinks very differently and finds it difficult to work together. Dr. Malkin, however, says that his company has found a solution to this complicated problem: eliminate gaps in culture and promote collaboration.
Dr. Malkin states, “AI scientists view their software as an exoskeleton.AI engineers view their software as a robot.”
The approach of an AI scientist can be summed up as:
-Seek accuracy and/or performance match domain
-Create new algorithms and code samples to validate them
-May be embedded in product teams
The approach of an ML engineer can be summed up as:
-Ensure model metrics match product metrics
-Manage code and data inventories
-Track model performances during product lifetime
Scientists prioritise maximizing knowledge through isolated conceptual models that engineers find extremely difficult to quickly convert into real products. From the engineers’ perspective, they need to take into account many other factors in addition to their feasibility in being produced and providing value to clients. Hence, many companies that implement AI-ML systems have serious difficulties in monetizing their AI solutions.
The Solution – Building ML Products Faster
Now that the conflict is clear, the key is to eliminate the gap in perspectives between the characteristic cultures of scientists and engineers. This can be done in two ways:
1. Collaborative Approach by Cross-Functional Product Teams.
The problem lies in the fact that even the best AI scientists available, are faced with serious difficulties in communicating outside of their strictly regulated scientific environment and in defining metrics that align with the product’s needs. They tend to build parallel prototypes, recreating production models to iterate and systematically value accuracy improvement over maintainability/scalability concerns, and because of this, a culture of collaboration needs to be created between scientists and engineers as well the sales team and clients, involving all in the products development cycle. This involves taking on scientists who understand software engineering, equipping them with the tools to iterate ideas with the collaboration of engineers, thereby rewarding maintainability, stability, complexity, and reduction.
The function of ML engineers is to professionalize the model design with the idea of developing a viable product from the beginning. They need to understand scientific models and their limitations. They then need to be able to improve them and work with researchers in order to understand new concepts and research ideas.
There should be a common understanding, from both scientists and engineers, that the model itself is a small part of an AI-ML system.
2. Recreate Production for Experimentation.
Dr. Malkin summarises it thus: “[The] traditional split of infrastructure between production, where the infra is well defined, scalable, and ML teams, where the infra is ad hoc, customized, is slowing innovation in AI products”.
– Build your production system to be clone-able for experimentation
– Set up integration testing for model updates
– Build pipelines so that training is part of production
In this way, Cogent Labs has designed a cooperative culture that has allowed it to create a general AI-ML system for the automation of business processes, or “Business Smartization,” with solutions such as processing manually written, spoken or even unstructured information, and Big Data with its Time-Series Forecasting solution, which also incorporates information sources and external networks.
This AI-ML system is growing to be a general AI system in terms of scalability, providing increasing productivity through the implementation of extended “Business Smartization” in AI-driven companies. Something similar occurred in the era before Business Digitalization in terms of the need to transform data into information, except that now the capacity exists to convert this massive, unconnected, unstructured information, coming from various sources into automatic decisions AI-driven with exponential results.
Further supporting the efficacy of their collaboration concept, in just three years Cogent Labs has managed to launch three products based on AI Machine Learning, acquiring a notable portfolio of clients around the world including Nomura, Daiwa Securities, SoftBank and Canon in a co-creation model in which products are developed and perfected together with the clients. They have also managed to raise JPY 1.472 billion in capital (US$13 million), which enables them to continue developing their general AI Machine Learning, currently with 45 employees. It will be exciting to see what new ideas they come up with next.
Cover photo by Andy Kelly