For decades, scientists have tried to use artificial intelligence techniques and algorithms to provide computers with human-like knowledge and behavior. Although more sophisticated than traditional programming, the techniques used have focused primarily on manually growing and improving the system's knowledge base, which has always been limited. Limited domain knowledge has proven to be a poor substitute for human expertise; i.e. AI systems are only as good as their programming (done manually by a human)
The new approach is to build systems that learn from themselves, becoming experts that model and abstract rules from the data they are fed. These systems are improving in their precision, adapting to the unknown and expanding their capabilities beyond the original programming. Traditional techniques of natural language processing (NLP), rule-based reasoning, and knowledge representation are being augmented with machine learning—especially deep learning—to improve AI (see Figure 1). Preliminary results are promising: we are seeing new apps emerge with a certain “intelligence” in a wide variety of domains.
This wave of artificial intelligence will impact the work of software developers, so it is important to be prepared. In both the software development life cycle and in the application development developers must understand what these technologies are and how they can apply them.
Impact on the development cycle
Below we list some of the possibilities of artificial intelligence applied to software development:
Implementing software code in order to execute a business idea is a very hectic task, despite the improvements there have been in this area thanks to agile methods and business analysis practices. AI results in better code generators which will enrich requirements models and test cases with more sophisticated text recognition.
Improve the precision of estimates.The audit of software projects continues to be a complex and asymmetrical task, requiring the involvement of experts with extensive knowledge of relevant subject matter.Let’s think of it as an accounting software development services that analyzes historical data from past company projects to find statistics and relationships, and uses predictive analytics and business rules to provide accurate estimates of time and effort.
Make fault and solution tracing faster. When a system fails in production, teams spend a lot of time and effort re-creating these failures in order to identify and fix them, and often the team that did the development no longer has AI available.
Automate decisions about what to build and test next. A.I. This analysis of usage behavior can also be used to automate test scripts.
Impact on applications
A new generation of applications is coming to our computers, phones and devices that can talk, listen, feel, reason, think and act. The list of companies building AI-enriched applications is growing rapidly.
Here are some capabilities in next-generation applications made possible by AI:
Natural interaction with humans. Throughout the history of computing we have had to interact with computers through unnatural interfaces: punched cards, keyboards, mice, forms of capture, etc. AI is enabling computers to see and hear their users, in addition to responding via voice in natural language.
Expert systems. Coding domain-specific policies and business rules through traditional programming languages is a complex and effort-intensive activity. Artificial intelligence gives the option of building expert systems focused on a specific domain that can support novices in an activity or help managers in their decision making. Although expert systems are nothing new, until now we will begin to see them become popular, in addition to being enriched through deep learning.
Imitate typical human capabilities. A Brazilian mining company was looking to automate its inventory process and to do so it required being able to correctly identify a large number of train cars. He initially considered putting RFID tags on each car. However, he realized that all the cars are already visually identified with signs, so it was a better solution to use optical character recognition.
Software that learns by itself. Deep learning combined with big data is one of the technologies that will cause the greatest disruption in the applications we build. It will be very interesting to see what applications we will be building soon when unsupervised learning is available to everyone.
AI enables new types of applications
Thanks to artificial intelligence, we will gradually build unprecedented types of applications. Companies need to develop imagination and expertise to build these AI-enabled applications. Companies will adopt AI gradually. Forrester Research envisions this process as having 3 stages:
Make existing apps more “conversational” and fluid. Initially, AI experiments focus on adding “cool” things with a limited scope to improve user experience and interest.
Improve understanding, reasoning and decision making. Through an appropriate combination of data and ontologies enriched with machine learning algorithms, applications will have the ability to reason and deduce information.
Build apps that are more than just apps. Traditional desktop or web applications will gradually give way to bots and intelligent agents. Developers will no longer focus on programming them but on training them.
Conclusion
Custom Software development company need to develop capabilities in the field of artificial intelligence. Artificial intelligence will not replace the work of developers, but will enrich it. Creating AI-enriched applications as well as improving development processes through AI will require new skills.
I recommend avoiding the notion of the data scientist as someone super gifted with business domain knowledge, mathematical, analytical, programming and infrastructure management skills. It is more realistic to have people specialized in artificial intelligence (mathematicians) who collaborate with people specialized in data engineering (programming and infrastructure management).
The software development process is a candidate to be improved through artificial intelligence. However, for this to happen we require that the processes be defined and implemented. Mature organizations already have this and will be the first to reap these benefits, which in turn will allow them to build better software with less effort.
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