Despite its monumental role in advancing technology, academia is often ignorant of industrial achievements. By the end of my PhD I realised that there is a myriad of great auxiliary tools, overlooked…

The one thing I tell most people upon meeting me is, I play chess. Long before I got into the data science career field I have played chess. It has always been a pastime to me. The reason I mention it so consistently is because those who play chess have a very analytical way of thinking. It is a natural adaption from the game itself, depending on your consistency of play.

DeepMind co-founder Mustafa Suleyman has departed Google after an eight-year stint at the company. Suleyman co-founded AI giant DeepMind alongside Demis Hassabis and Shane Legg in 2010 before it was acquired by Google in 2014 for $500 million. DeepMind has become somewhat of an AI darling and has repeatedly made headlines for creating neural networks that have beat human capabilities in a range of games. DeepMind's AlphaGo even beat Go world champion Lee Sedol in a five-game match. He left for Google in 2019 and was most recently the company's vice president of AI product management and policy.

If you can recognize a dog by sight, then you can probably recognize a dog when it is described to you in words. Deep neural networks have become very good at identifying objects in photos and conversing in natural language, but not at the same time: there are AI models that excel at one or the other, but not both. Part of the problem is that these models learn different skills using different techniques. This is a major obstacle for the development of more general-purpose AI, machines that can multi-task and adapt. It also means that advances in deep learning for one skill often do not transfer to others.

Nvidia has rolled out the latest version of its AI Enterprise suite for GPU-accelerated workloads, adding integration for VMware's vSphere with Tanzu to enable organisations to run workloads in both containers and inside virtual machines. Available now, Nvidia AI Enterprise 1.1 is an updated release of the suite that GPUzilla delivered last year in collaboration with VMware. It is essentially a collection of enterprise-grade AI tools and frameworks certified and supported by Nvidia to help organisations develop and operate a range of AI applications. That's so long as those organisations are running VMware, of course, which a great many enterprises still use in order to manage virtual machines across their environment, but many also do not. However, as noted by Gary Chen, research director for Software Defined Compute at IDC, deploying AI workloads is a complex task requiring orchestration across many layers of infrastructure.

Imagine a team of physicians using a neural network to detect cancer in mammogram images. Even if this machine-learning model seems to be performing well, it might be focusing on image features that are accidentally correlated with tumors, like a watermark or timestamp, rather than actual signs of tumors. To test these models, researchers use "feature-attribution methods," techniques that are supposed to tell them which parts of the image are the most important for the neural network's prediction. But what if the attribution method misses features that are important to the model? Since the researchers don't know which features are important to begin with, they have no way of knowing that their evaluation method isn't effective.

Being at the intersection of image processing, pattern recognition, and computer vision, we develop automated tools using machine learning to uncover hidden patterns in data. This data-driven approach is used to identify biomarkers of cerebrovascular diseases.

This year has been full of a lot of great models. In this article, my hope is to highlight 10 of the most noteworthy models. I have been regularly reviewing papers and explaining them over this year and I think I have quite a few good mentions. Disclaimer: There might be other good models not mentioned here and I am not claiming to be the ultimate expert when it comes to evaluating the quality of machine learning models! Also, note that this list isn't ordered!

In 9 hours, Google's AlphaZero went from only knowing the rules of chess to beating the best models in the world. Chess has been studied by humans for over 1000 years, yet a reinforcement learning model was able to further our knowledge of the game in a negligible amount of time, using no prior knowledge aside from the game rules. No other machine learning field allows for such progress in this problem. Today, similar models by Google are being used in a wide variety of fields like predicting and detecting early signs of life-changing illnesses, improving text-to-speech systems, and more. Machine learning can be divided into 3 main paradigms.

Despite widespread interest in machine learning (ML), relatively few projects leave the proof-of-concept phase and enter production. In fact, in a 2020 report, Capgemini found that roughly 85% of all ML projects grind to a halt across Capgemini's client organizations--despite successful preliminary models and ample support from executive leaders. Further, the study found, only half of the world's leading AI-powered enterprises successfully roll out artificial intelligence projects, including ML models, and this number drops substantially among organizations without dedicated ML teams. In recent years, AI solutions have attracted the interest of executive leadership across industries. Machine-learning models, perhaps the leading subset of AI, have particularly interested enterprises racing to digitize in the modern market because of their ability to automatically "learn" and update.