For years, the world wanted “real” things in their products – like real milk, real cheese, real juice, and real bacon. But then the world changed – now people want more artificial things – artificial meat, artificial furs and, of course, artificial intelligence, also known as AI technology or simply AI. From virtual assistants like Apple’s Siri®, Amazon’s Alexa®, and Microsoft’s Cortana®, to the use of AI in home appliances to help consumers make the perfect dinner, to IBM’s Watson® to help develop new drugs, consumers and businesses alike seem to have an insatiable appetite for more products and services that have “AI Inside.” For businesses, the use of AI is rapidly becoming not just a competitive advantage, but a must have business process. For consumers, AI brings the hope of an easier and more comfortable life.
The growth of AI should come as no surprise. Science and technology have made AI products that perform activities that traditionally would be performed by humans using their own intelligence more accessible and affordable to the masses while simultaneously advancing the technology by leaps and bounds towards general purpose artificial intelligence systems that rival human intelligence itself. AI is most definitely not only here to stay, but will continue to be a larger and larger part of business operations.
Many businesses still believe, however, that contracting for the development or use of AI products and services should be the same as contracting for the development or use of traditional software products and services. After all, some AI products are built solely in software, although hardware-based architectures that accelerate many of the basic operations used in AI are becoming more common – why isn’t contracting for the development or use of AI products and services the same as contracting for any other types of software products and services? While we will save the complex answer for later discussion, the simple answer is that the characteristics and design methodology of AI technology is very different from that of conventional software and, as a result, the legal issues when contracting for AI goods and services are fundamentally different than the issues that arise when contracting for traditional software. A failure to contract for AI technology differently than contracting for conventional software often results in the parties (both vendor and user) attempting to make unreasonable demands and either over- or under- valuing the different types of data and the significant use of know-how by both parties necessary to develop AI goods and services. As a result, contract negotiations for AI often fail. In this series of articles we explore what those differences are, how and why they arise, and some ideas on how to tackle them.
The terms “artificial intelligence” or “AI” are often used as generic terms for a type of technology referred to as “machine learning.” Machine learning is a technology that aims to “teach” a computer to do various tasks by finding particular rules in certain types of data and making inferences or predictions regarding other data based on those derived rules. This involves developing these rules inductively based on actual observed events (the data) instead of using the deductive approach commonly used for conventional software. Deep learning is a subcategory of machine learning that attempts to draw more accurate inferences through the use of a neural network. A neural network attempts to imitate the processing of a human brain through the use of many small processors with significant interconnection. Although this type of learning was traditionally implemented in graphic processors (GPU’s) due to the similar characteristics in GPUs of many processing units with high connectivity, more recently processors specialized for artificial intelligence have and are being developed.
Since the beginning of computers, the development of conventional software has been through the use of a deductive process, usually through a “waterfall” development model. For a software developer, the task is relatively simple: first, the software specifications are defined in detail based on known rules (algorithms), and then the software is designed, implemented, tested and gradually refined until all of the desired functions have been implemented and the rules have been met. A typical conventional software development process is shown below. In general, going backwards in the flow is relatively rare other than some refinement to the design or implementation as a result of bugs discovered through testing.
However, the development of AI technology, and specifically machine learning, is based on a fundamentally different concept. Machine learning is a type of training for finding the particular rules given both input and output data (or events) and later making inferences and predictions regarding user data based on these derived rules. This is accomplished through the use of inductive development methods using actual observed input and output data, generally without any understanding of any rules or algorithms that resulted in the output given the input. The goals are analyzed, an initial AI architecture and initial parameters are tried, the results tested, and the process is repeated until the desired results are achieved. This often involves significant trial and error to understand the dataset and to adjust the structure and parameters of the AI processing elements until the desired results are achieved. A typical development process for AI technology is shown below.
Conventional Software Development
AI Technology Development Process
The inductive nature of machine learning creates certain problems for contracting that distinguish it from contracting for conventional software. However, there is one similarity – the mere use of AI that has already been developed has different concerns, and therefore demands a different contract approach, than AI being developed specifically for a party by the vendor. However, that is where the similarities generally stop and the intrinsic differences between conventional software and AI technology begin to create challenges in contracting. Some of the major challenges are as follows:
We will delve further into the contractual impacts of these challenges in later installments of this series.
Contracting for the development and use of AI technology must be different than contracting for the development and use of conventional software. This is largely a result of the inductive nature of AI technology development as contrasted to the deductive process generally used by conventional software. Furthermore, the quality of the AI model depends largely on the training data used – as the old saying goes, garbage in, garbage out. Users should be aware of these differences while contracting and be prepared to adjust their contracting practices away from the practices used when contracting for traditional software services to something … different. In our upcoming installments, we will describe the key components necessary for both the development and use of AI technology, the IP implications of each of these components from both the user’s and the vendor’s perspective, and describe contract approaches that can be deployed to reach a reasonable agreement between the parties.