This is a particularly exciting time in the computing industry, with exciting progress happening in both technology and software engineering to harness that technology. Large Language Models (LLMs) are at the forefront, doubling their capabilities every seven months, which raises questions about the future of software engineering. The industry is already quickly adjusting to the latest innovations. Big things are happening, too, such as the rise of Apache Airflow and the emergence of biocomputing technologies that are all capturing the headlines.
Amidst this fast-paced landscape, one constant remains—Python is the most popular programming language by far. At the same time, AI informs much of the discussion on software quality and productivity. First, consider the success rate for AI-driven tasks. It’s possible to achieve success right now, but the success rate for the most difficult problems is only about 50%. So much of what’s happening stylistically is more about trends than the times. Within several years, LLMs might perform tasks that currently require a month of human labor, with transformative effects across the sector.
The Rise of Large Language Models
In the past few years, Large Language Models (LLMs) like GPT have taken the world by storm. Their ability to learn and process vast amounts of data has doubled every seven months, leading to increased speculation about their future applications in software engineering. Even with their explosive growth, experts are finding it increasingly difficult to agree on a reliable way to evaluate their performance.
The confusion about what LLMs hold in store goes even further. It’s affecting the future of software engineering. As these models increase in capability, they create more significant questions regarding their incorporation into current workflows. What roles will human engineers be left to play in this new world? Now the industry has to continue innovating to meet this new paradigm while tackling the complex issues it brings with it.
Beyond this, LLMs are being applied in experimental and creative ways, such as virtual trials of experimental drugs on impaired neural cultures. This application highlights the creative ways that LLMs can be used. It demonstrates the effects in wide domains outside of formal software engineering.
Apache Airflow’s Remarkable Comeback
Apache Airflow, an open-source workflow orchestration software, has seen a stunning turnaround from the brink of death. Airflow was first released in order to efficiently orchestrate complex workflows. Since then it has ballooned, gaining 35-40 million downloads every month and more than 3,000 active contributors from around the world.
The subsequent release of Airflow 2.0 in late 2020 was a critical inflection point for the platform. It brought a number of features on board that made it downright user-friendly and powerful. Building on this momentum, Airflow 3.0 released with a fully modular architecture, ready to operate anywhere end-to-end. It’s this flexibility that draws such a varied user base. It serves to further solidify Airflow’s position as a foundational tool on the modern software engineering landscape.
Apache Airflow is booming, and for good reason. It’s an inspiring example of how open-source projects can pivot and persevere in an age of rapid technological change. Its success story is an inspiration for other similar projects that might like to know how to overcome the same challenges.
Innovations in Computing Technologies
The innovations in computing technologies are coming at an unbelievable rate. Cortical Labs recently issued an exciting press release! They’ve rolled out a biocomputer that operates on 800,000 active living human neurons, all of them incorporated on a silicon chip. At $35,000 per unit, this high-performance biocomputer has some special properties such as learning, adaptation, and live response to stimuli in real time.
This biomedical innovation captures the fascinating merger of biology and technology and presents exciting potential for future use. Applications for biocomputing run the gamut, from hyper-accelerated data processing to venturing into uncharted territory with AI.
Alongside biocomputing, reversible computing is picking up steam outside of academia as startup Vaire Computing brings commercial applications to market. Their prototype chip orthogonally captures energy in the process of performing arithmetic operations. This new kind of chip has the potential to improve energy-efficiency up to 4,000 times compared to standard chips.
These advances are just two examples of the extraordinary evolution underway in the computing industry. They show that sustainability and efficiency are absolutely essential to any future that involves more technology development.