Trends in Tech: Software 2.0

“This trend makes possible the rapid scaling and diffusion of new data-rich, AI-driven applications,” according to McKinsey.???

Get ready for Software 2.0, where neural networks and machine learning write code and create new software. In simple terms, the transition helps the software creation industry more powerful and replaces complicated software and coding procedures by making them more standardized and automated.

Figure 1: Machine Learning by Markus Winkler on Unsplash

What is Software 2.0?

We're all familiar with Software 1.0 - it's commonly used in traditional software development and written in widely known languages like Python, C++, and others. It involves providing clear instructions to the computer, which are written by the programmer. Each line of code helps the programmer pinpoint a particular aspect of the program with a desired behaviour.?

In contrast, Software 2.0 is written much more abstractly and ambiguous, using uncongenial language such as the neural network weights. Most of the steps in this code-writing process involve no human interference because neural networks have far more capability in writing comprehensive codes than human beings as they have millions of lines of code ready to handle countless changing variables.?

Coding manually can often be a challenging task for humans. To address this issue, Software 2.0 is employed to explicitly determine the desired program's behaviour goals, generate an initial code structure, and then identify and resolve bugs by comparing them with existing online solutions.

This approach is increasingly gaining popularity in practical applications as a standard practice. It involves transforming Software 1.0 components into a process of organizing, expanding, refining, and cleansing labeled datasets. Additionally, 2.0 programmers (or data labelers) are responsible for modifying and advancing the neural networks, while 1.0 programmers maintain and improve the code infrastructure, analytics, visualizations, and labeling interfaces.

“As Andrej Karpathy said, Software 1.0 is eating the world, and now AI (Software 2.0) is eating software.” - Andrej Karpathy

Implementations of Software 2.0

Let’s examine some examples of this growing trend. Over the last few years, there have been improvements in these areas as the challenging task of addressing a complex problem by writing explicit code is being replaced by Software 2.0.

Speech Recognition?

The technology consisted of many preprocessing, gaussian mixture models, and hidden Markov models, but today everything can be done through neural networks.?

The Emergence of New Roles in Software 2.0

With the emergence of machine learning models and Software 2.0, there’s been a rising number of new responsibilities. Jobs like data scientists, data engineers, and ML engineers are just a few of the occupations that will be going under changes. It is worth pointing out that these roles are a hybrid of software engineering, software operations, statistics, data management, and machine learning.

These changes changed the software development paradigm, it is vital to notice a resource problem faced in the transition to Software 2.0: lack of skilled workers, trouble finding the correct use cases, and difficulty locating data. In reality, the transition creates new job titles that include ‘data labelers,’ ‘data curator’, or ‘data enablers.’?

AI Assistants?

Traditionally, an AI assistant aids developers by determining what kind of function they are trying to build and filling the rest in for them based on their style using high-level predictive analysis. In sum, the machine writes the code automatically; then, the code will be processed and approved by the developers.

Another benefit of an AI assistant could help with is test-drive development. Usually, a human can write 100 lines code a day, or roughly 25,000 lines per year, while also tackling the responsibility of ensuring the tests pass. Instead of doing both jobs - writing the tests and making the tests pass - they would have the machine does the latter. Therefore, developers would have less time to implement code and more time for learning and cracking business problems.

Benefits of Software 2.0

Content Moderation

Every day, artificial intelligence (AI) is utilized to detect and screen out harmful materials like explicit images, videos, text, and audio content from user-generated streams. This technology assists advertisers in identifying off-brand and low-quality content, as well as detecting profanity, hate speech, and inappropriate text within both text posts and images. Through AI, such content can be promptly identified and removed.

Predictive Maintenance?

Airlines, manufacturers, and businesses are using computer vision technology to save inspection and maintenance costs and increase the lifespan of capital assets. Equipment surveillance, maintenance planning, asset scheduling, and asset efficiency are well positioned to benefit tremendously from Software 2.0.?

Overall, Software 2.0 transforms an algorithm difficult to create into a more straightforward day-to-day process; it is a more intuitive way to code.?

Limitations of Software 2.0

Diversity and Bias

Machine Learning and AI risks will need to be addressed through cross-functional teams. These teams consist of experts from different fields such as security, privacy, compliance, ethics, design, and domain experts - with people from different ethnic and cultural backgrounds. This is done to prevent socio-cultural groups from rejecting each other ideas.?

Explainability

The integration of machine learning in Software 2.0 raises questions about how it makes decisions. Providing explanations for the behaviour of the software becomes challenging, especially in situations where explainability is highly valued. Although Software 2.0 automates tasks that were traditionally done by humans, it is crucial to offer justifications for the choices made by these systems.

Accuracy

Creating a machine learning system does not guarantee 100% accuracy. While these systems may make fewer errors compared to humans, it is undeniable that humans are generally more tolerant and forgiving when it comes to human mistakes.?

Conclusion

Ultimately, will machines replace software engineers entirely? Some evidence shows that Software 2.0 will do 90% of human jobs; however, this reality is still a long journey to come true. Neural networks aren’t the main concern, but these AI tools must be redesigned to work with other solutions.??

Currently, many aspects of software development are still used to work well with deep learning, while some won’t. In sum, Software 2.0 potential will shine in any situation where data is abundant and affordable and the algorithm is difficult to design.

Software 2.0 is a driving change in how humans interact with computers, and new technologies and development methodologies will be required to manage this process. Companies must ensure that they are hiring the right person to keep up with technological trends. If your business is having trouble catching up with the current technology trend, book a free consultation with us at Rofi Labs, and let’s talk about the next steps together today!