We introduce **EvoGraph**, a framework that enables software systems to evolve their own source code, build pipelines, documentation, and tickets. EvoGraph represents every artefact in a typed directed graph, applies learned mutation operators driven by specialized small language models (SLMs), and selects survivors with a multi-objective fitness. On three benchmarks, EvoGraph fixes 83% of known security vulnerabilities, translates COBOL to Java with 93% functional equivalence (test verified), and maintains documentation freshness within two minutes. Experiments show a 40% latency reduction and a sevenfold drop in feature lead time compared with strong baselines. We extend our approach to **evoGraph**, leveraging language-specific SLMs for modernizing .NET, Lisp, CGI, ColdFusion, legacy Python, and C codebases, achieving 82-96% semantic equivalence across languages while reducing computational costs by 90% compared to large language models. EvoGraph's design responds to empirical failure modes in legacy modernization, such as implicit contracts, performance preservation, and integration evolution. Our results suggest a practical path toward Software 3.0, where systems adapt continuously yet remain under measurable control.

A central question in the discussion of large language models (LLMs) concerns the extent to which they memorize their training data versus how they generalize to new tasks and settings. Most practitioners seem to (at least informally) believe that LLMs do some degree of both: they clearly memorize parts of the training data--for example, they are often able to reproduce large portions of training data verbatim [Carlini et al., 2023]--but they also seem to learn from this data, allowing them to generalize to new settings. The precise extent to which they do one or the other has massive implications for the practical and legal aspects of such models [Cooper et al., 2023]. Do LLMs truly produce new content, or do they only remix their training data? When dealing with humans, we distinguish plagiarizing content from learning from it, but how should this extend to LLMs?

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! As technology progresses, business leaders understand the need to adopt enterprise solutions leveraging Artificial Intelligence (AI). However, there's understandable hesitancy due to implications around the ethics of this technology -- is AI inherently biased, racist, or sexist? And what impact could this have on my business?

This article is sponsored by IBM. It is a new concept in which creators can apply passion and creativity to make money, instead of simply relying on likes and views. It focuses on bringing more life and meaning to the traditional media landscape in a way that empowers creative people worldwide to bring out the best in themselves, entirely driven by their passion. According to EMarketer, the creator economy is defined as follow: "We define creators as people or entities that develop original content for digital properties, and who consider creating that content to be either their full-time or parti-time career or livelihood. Of course, there is some overlap between many of these groups; for instance, celebrities can also be creators and vice versa. What's more, few successful influencers today are purely sales-oriented, and many of them are also creators, developing digital or, at times, physical products."

Earlier this year, we reported that more than 80% of insurance leaders think that AI technologies will drive better customer engagement and create better employee experiences. But of course, realizing those benefits in any sector is often easier said than done. That's why how to take advantage of the latest innovations in AI and implement new technology successfully were key topics at last week's CDAO Insurance Executive Think Tanks. "Technology for technology's sake does not really take us to a happy place, unless we are able to bring the people along," noted Prashant Natarajan, Director of Data Science and Analytics at employee benefits provider Unum. "That means [dealing with] their apprehensions but also getting them excited, because both of those things exist in equal measure whenever you roll out the subject of technology."

Traditional colonial power seeks unilateral power and domination over colonised people. It declares control of the social, economic, and political sphere by reordering and reinventing the social order in a manner that benefits it. In the age of algorithms, this control and domination occurs not through brute physical force but rather through invisible and nuanced mechanisms such as control of digital ecosystems and infrastructure. Common to both traditional and algorithmic colonialism is the desire to dominate, monitor, and influence the social, political, and cultural discourse through the control of core communication and infrastructure mediums. While traditional colonialism is often spearheaded by political and government forces, digital colonialism is driven by corporate tech monopolies--both of which are in search of wealth accumulation. The line between these forces is fuzzy as they intermesh and depend on one another. Political, economic, and ideological domination in the age of AI takes the form of "technological innovation", "state-of-the-art algorithms", and "AI solutions" to social problems. Algorithmic colonialism, driven by profit maximisation at any cost, assumes that the human soul, behaviour, and action is raw material free for the taking.

I am sure some of you have seen the image below. You probably have also heard headlines like "Tech companies stoped selling Facial Recognition because the models are inherently biased". "Police offices stop using Predictive Policing because the model is inherently biased" and etc… These headlines can easily trigger the feeling of scared, angry, and confused about new AI technologies. But, what does "inherently biased" mean…? Let's unpack this a bit.

"Why? - Because I am your mother, that's why." - My mom/your mom/everyone's mom. Artificial Intelligence is growing in sophistication, autonomy, and market reach offering transformational opportunities for businesses and their customers. AI relies on the collection and smart processing of personal information to function. However, the privacy scandals of social media and recent breaches of consumer data have eroded consumer confidence: not only around data usage but the implications of its omnipotence. We live in an age of maximum customer empowerment and, as a result, maximum business anxiety.