For those who are involved in AI and Machine Learning, may have noticed a trend recently. While new announcements in algorithmic advances like GPT-2 from OpenAI, EvaNet from Google and considerably more that I can't list here, have all garnered interest, industry at large seems to feel something is missing. That missing feeling is the dirty P word - productionization. This is troubling, not just for the practitioners but for the field as a whole. Before we go further in diagnosing the cause and identify remedies, let's put a definition on what productionization is (Disclaimer: this is my perspective, feel free to add a comment).

I've never really thought about how important personalized medical care is until I was diagnosed with breast cancer myself - it gets real personal real quick. October is Breast Cancer Awareness Month and also my anniversary of being diagnosed and now cancer free! So, I felt it appropriate to focus on AI developments in early detection. I am healthy, active and do not have the gene for breast cancer. Matter of fact, on the human medical doctor prediction scale it was a 3% chance my abnormal mammogram would come back anything but precancerous tissue.

Only a few years ago, using artificial intelligence in journalism was cutting edge, but nowadays it is quickly entering the workflow of a growing number of news organizations. The knowledge around machine learning methods is becoming more accessible as the cost of AI projects has gone down since many models are readily available online and can be implemented -- even by small newsrooms. Using these models, our team of data scientists took on, at first sight, an enormous challenge: analyzing and deriving insights from a decade's worth of WSJ articles. By using well-established language analysis methods like Doc2Vec, we were able to quickly reveal what the Journal has been reporting on, pinpoint gaps in coverage, and identify topics that were of particular value to our readers. This approach enabled us to translate a large set of documents (WSJ news articles) into a list of representative numbers, known as "vectors."

In 2011, scientists made one of the most important discoveries in the history of AI development. They found that graphics processing units (GPUs) are far better at simulating biological learning than central processing units (CPUs). In retrospect, it seems obvious. Human brains are much more like GPUs than CPUs. Both brains and GPUs rely on parallel processing that simulates and predicts real world physics.

Considering the public awareness of artificial intelligence and the speed new breathtaking progress is taking place, it seems to be just a matter of time when AI will surpass the human intelligence level. And yes, the headlines AI is writing are stunning! While the victory of the IBM chess computer Deep Blue over the former chess world champion Garry Kasparov in 1996 (and again in 1997) was something like the eighth wonder of the world, the victory of Googles AlphaGo over Lee Sedol in 2016 in the strategy board game "Go" was seen as predictable for many. AI development has undergone a vast acceleration during the last decade. Assuming a stable growth rate of AI development: Is AI supposed to surpass the human intelligence level over the next few years?

Transformers are currently beating the state of the art on different NLP tasks. Something I noticed is that in all of the papers, the models are massive with maybe 20 layers and 100s of millions of parameters. Of course, using larger models is a general trend in NLP but it begs the question if small transformers are any good. I recently had to train a sequence to sequence model from scratch and I was unable to get better results with a transformer than with LSTMs. I am wondering if someone here has had similar experiences or knows of any papers on this topic.

Understanding which AI technologies to use to advance a project can be challenging given the rapid growth and evolution of the science. This article outlines the differences between machine learning and Deep learning, and how to determine when to apply each one. In both machine learning and Deep learning, engineers use software tools, such as MATLAB, to enable computers to identify trends and characteristics in data by learning from an example data set. In the case of machine learning, training data is used to build a model that the computer can use to classify test data, and ultimately real-world data. Traditionally, an important step in this workflow is the development of features – additional metrics derived from the raw data – which help the model be more accurate.

Last March, Chinese researchers announced an ingenious and potentially devastating attack against one of America's most prized technological assets--a Tesla electric car. The team, from the security lab of the Chinese tech giant Tencent, demonstrated several ways to fool the AI algorithms on Tesla's car. By subtly altering the data fed to the car's sensors, the researchers were able to bamboozle and bewilder the artificial intelligence that runs the vehicle. In one case, a TV screen contained a hidden pattern that tricked the windshield wipers into activating. In another, lane markings on the road were ever-so-slightly modified to confuse the autonomous driving system so that it drove over them and into the lane for oncoming traffic.

Manufacturing and production systems have a lot of catching up to do with the world of software. The manufacturing ecosystem has seen a lot of upgrade and innovation but it still lags in terms of software application. With the onslaught of artificial intelligence, new opportunities have opened for the sector to leverage new technology and improve productivity. In the recent decade, deep learning is driving most of the innovation in AI. Deep learning systems have found applications in a variety of fields such as healthcare, aviation, agriculture and many others.

Summary: As a profession we do a pretty poor job of agreeing on good naming conventions for really important parts of our professional lives. "Machine Learning" is just the most recent case in point. It's had a perfectly good definition for a very long time, but now the Deep learning folks are trying to hijack the term. Let's make up our minds. As a profession we do a pretty poor job of agreeing on good naming conventions for really important parts of our professional lives.