If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
We have all been working hard to become such people known as Data Scientists (or whatever anyone wants to call it). AI is becoming more mainstream, especially in the last 2 years. The fact that computers/robots will learn after being built and will surpass a human's intelligence is terrifying. Computers can definitely think faster than us and compute faster than us. Being able to compute larger volumes of data does not necessarily mean smarter.
In this presentation, Kathrin Melcher, who works as a data scientist at KNIME, will give an overview of KNIME Software, including the open-source tool KNIME Analytics Platform for creating data science applications and services and also the different deployment options you have when using KNIME Server. While the structure is often similar--data collection, data transformation, model training, deployment--each project required its own special trick, whether this was a change in perspective or a particular technique to deal with the special case and business questions. You'll learn about demand prediction in energy, anomaly detection in IoT, risk assessment in finance, the most common applications in customer intelligence, social media analysis, topic detection, sentiment analysis, fraud detection, bots, recommendation engines, and more. Join us to learn what's possible in data science. She holds a Master's Degree in Mathematics from the University of Konstanz, Germany.
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.
This paper presents a study of semi-supervised learning with large convolutional networks. We propose a pipeline, based on a teacher/student paradigm, that leverages a large collection of unlabelled images (up to 1 billion)... Our main goal is to improve the performance for a given target architecture, like ResNet-50 or ResNext. We provide an extensive analysis of the success factors of our approach, which leads us to formulate some recommendations to produce high-accuracy models for image classification with semi-supervised learning. As a result, our approach brings important gains to standard architectures for image, video and fine-grained classification. For instance, by leveraging one billion unlabelled images, our learned vanilla ResNet-50 achieves 81.2% top-1 accuracy on the ImageNet benchmark.
Build prototypes on small data sets to gain momentum, support and experience with AI in your organization. Remember to train and involve everyone from the C-suite to frontline employees in the transformation. It takes 2-3 years to transform a large company into an AI company, but initial results should be evident within 6-12 months. This is the experience of former Google Brain Founder and Lead, Andrew Ng. He is a man who knows his AI, having previously also served as Chief Scientist at Baidu, and currently working as the Founder of Landing AI and as Adjunct Professor at Stanford University.
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.
Welcome to the first O'Reilly Radar column. We plan to use this column to cover topics related to the themes that have our attention these days: AI/ML; Next Economy and Future of the Firm; Next Architecture; and tech-driven innovation and disruption. We'll also venture outside of O'Reilly's core focus on technology practitioners to include how technology fits into the modern economy, and offer the kind of information that can provide guidance and confidence to technology leaders facing this brave new world. The Radar team uses a combination of input from our wide-ranging social network, our own experience as practitioners, and data analysis (particularly from analyzing aggregate search and usage data on the O'Reilly online learning platform) to contextualize trends around technology adoption and to consider the impact of those trends. Put another way, we use our intuition and social network to vet our math, and we use math to vet our intuition and what our community tells us.