We are about to wrap up 2022, a year that brought plenty of Machine Learning projects, events, education opportunities, and many groundbreaking Machine Learning applications developed by ML practitioners around the world. The challenges and business needs of our customers continue to fuel our passion to bring to life the robust and innovative Machine Learning solutions they deserve. In this blog post, we put together the highlights of 2022 covering Machine Learning's lasting impact on a vast number of industries and businesses, BigML's new additions and enhancements to our pioneering Machine Learning software platform, our live and virtual events, education initiative updates, and much more! None of the numbers listed above and the activities described on this blog post would be possible without our customers, partners, followers, and certified practitioners. That's why this blog post is dedicated to all of you.

Today, we see how computer programs, algorithms, and robots replace simple human activities, but there is the technology that is at the forefront of the spectrum: AI. The consequences of artificial intelligence have such an impact that they incite us to wonder if we are experiencing the beginning of a new era. According to Gartner, business use of AI has grown by 270% in the last four years, and slightly more than a third of organizations have implemented AI in some way, according to their specific needs. But is this also a reality within the legal sector? AI has found its way to support attorneys and clients alike, and there is a clear growing interest in technology.

Today, we see how computer programs, algorithms, and robots replace simple human activities, but there is the technology that is at the forefront of the spectrum: AI. The consequences of artificial intelligence have such an impact that they incite us to wonder if we are experiencing the beginning of a new era. According to Gartner, business use of AI has grown by 270% in the last four years, and slightly more than a third of organizations have implemented AI in some way, according to their specific needs. But is this also a reality within the legal sector? AI has found its way to support attorneys and clients alike, and there is a clear growing interest in technology.

Machine Learning (ML), the subset of artificial intelligence, is gaining fresh momentum. Powerful and affordable computational processing, growing volumes of huge data sets, affordable data storage options and the ability to automatically apply complex mathematical calculations to big data faster than before are the factors responsible for the resurging interest. ML is basically the idea of training machines to recognize patterns in data and apply them to particular problems. The iterative aspect of machine learning is important because when models are exposed to new data, they are able to independently adapt. They learn from previous computations and predictions to produce reliable, repeatable decisions and results with minimal or no human intervention.

Machine learning now powers a huge range of applications, from speech recognition systems to search engines, self-driving cars, and prison-sentencing systems. Many applications that were once designed and programmed by humans now combine human-written components with behaviors learned from data. This shift presents new challenges to computer science (CS) practitioners and educators. In this column, we consider how machine learning might change what we consider to be core CS knowledge and skills, and how this should impact the design of both machine learning courses and the broader CS university curriculum. Computing educators1,6 have historically considered the core of CS to be a collection of human-comprehensible abstractions in the form of data structures and algorithms.

Machine learning is not a new phenomenon. It has co-existed silently for years and has made noise only recently. Take for example the act of looking up things online, using a search engine. This is possible because of the underlying machine learning principles that are at work here. But, that does not mean that it is still the same at this point. What I want to say is that machine learning has improved since then, and is advancing at a swift pace.