This post is by Anton Buchner, a senior consultant with TrinityP3. Anton is one of Australia's leaders in data-driven marketing. Helping navigate through the bells, whistles and hype to identify genuine marketing value when it comes to technology, digital activity, and the resulting data footprint. And the Artificial Intelligence (AI) space is no exception. Over the past few years we've seen the rise and rise of AI discussion and solutions in marketing. I have spent the past month talking to a wide variety of industry thought leaders and experts in the AI space – from business, agency, and tech vendor perspectives. With the aim of identifying how Australian marketers are using AI solutions to enhance and anticipate consumer interaction. In this post, I would like to share some of their experiences and learnings to date. However, before we jump in, as I'm sure most of you know, AI dates back decades. Let's take a quick look back at how AI emerged.

Researchers from all over the world contribute to this repository as a prelude to the peer review process for publication in traditional journals. We hope to save you some time by picking out articles that represent the most promise for the typical data scientist. The articles listed below represent a fraction of all articles appearing on the preprint server. They are listed in no particular order with a link to each paper along with a brief overview. Especially relevant articles are marked with a "thumbs up" icon.

How does Text Analysis work?

Here's what AI can learn from biology. In building the world's first airplane at the dawn of the 20th century, the Wright Brothers took inspiration from the "insightful" movements of birds. They observed and reverse-engineered aspects of the wing in nature, which in turn helped them make important discoveries about aerodynamics and propulsion. Similarly, to build machines that think, why not seek inspiration from the three pounds of matter that operates between our ears? Geoffrey Hinton, a pioneer of artificial intelligence and winner of the Turing Award, seemed to agree: "I have always been convinced that the only way to get artificial intelligence to work is to do the computation in a way similar to the human brain."

The research lab OpenAI has released the full version of a text-generating AI system that experts warned could be used for malicious purposes. The institute originally announced the system, GPT-2, in February this year, but withheld the full version of the program out of fear it would be used to spread fake news, spam, and disinformation. Since then it's released smaller, less complex versions of GPT-@ and studied their reception. Others also replicated the work. In a blog post this week, OpenAI now says it's seen "no strong evidence of misuse" and has released the model in full. GPT-2 is part of a new breed of text-generation systems that have impressed experts with their ability to generate coherent text from minimal prompts.

The world right now is seeing a global AI revolution across all industry. And one of the driving factor of this AI revolution is Deep Learning. Thanks to giants like Google and Facebook, Deep Learning now has become a popular term and people might think that it is a recent discovery. But you might be surprise to know that history of deep learning dates back to 1940s. Indeed, deep learning has not appeared overnight, rather it has evolved slowly and gradually over seven decades.

The Global AI bootcamp is a free one-day event organized by local communities all over the world that are passionate about Artificial Intelligence on the Microsoft stack. The event takes place on the 14th of December on venues all over the world and as well as in Colombo supported by Microsoft. The event is the perfect balance between quality content, awesome lectures, getting your hands dirty and learn & share with other community members. Dive in and learn how to implement intelligence into your solutions with the Microsoft AI platform, including pre-trained AI services like Cognitive Services and Bot Framework, as well as deep learning tools like Azure Machine Learning, Visual Studio Code Tools for AI, and Cognitive Toolkit. During this AI bootcamp you will get inspired through sessions and get your hands dirty during the workshops.

In a new report on Science Advances, Hui Kwon Kim and interdisciplinary researchers at the departments of Pharmacology, Electrical and Computer Engineering, Medical Sciences, Nanomedicine and Bioinformatics in the Republic of Korea, evaluated the activities of SpCas9; a bacterial RNA-guided Cas9 endonuclease variant (a bacterial enzyme that cuts DNA for genome editing) from Streptococcus pyogenes. They used a high-throughput approach with 12,832 target sequences based on a human cell library to build a deep learning model and predict the activity of SpCas9. The data contained oligonucleotides (nucleotides or building blocks) containing target sequence pairs and a corresponding guide sequence to encode single-guide RNA (sgRNA), which can direct the Cas9 protein to bind and cleave a specific DNA sequence for genome editing. They implemented deep learning-based training on the large dataset of SpCas9-induced indel (insertion or deletion) frequencies to develop an SpCas9 activity predicting model named DeepSpCas9 now available online. When the team tested the software against independently generated datasets, the results showed high generalization performance, i.e. the model could properly adapt to new, previously unseen data.

As debates around AI's value continue, the risk of an AI winter is real. We need to level set what is real and what is imagined so that the next press release you see describing some amazing breakthrough is properly contextualized. Unquestionably, the latest spike of interest in AI technology using machine learning and the neuron-inspired deep learning is behind incredible advancements in many software categories. Achievements such as language translation, image and scene recognition and conversational UIs that were once the stuff of sci-fi dreams are now a reality. Even as software using AI-labeled techniques continues to yield tremendous improvements in most software categories, both academics and skeptical observers have observed that such algorithms fall far short of what can be reasonably considered intelligent.