This chart comes from Reddit. Since many data scientists occasionnally have to make a presentation at a conference (or when presenting their doctoral thesis), I thought you would relate to this chart. . Article: What is Data Science? Article: What is Data Science?

It may take several years or even decades, but hackers won't necessarily always be human. Artificial intelligence -- a technology that also promises to revolutionize cybersecurity -- could one day become the go-to hacking tool. Organizers of the Cyber Grand Challenge, a contest sponsored by the U.S. defense agency DARPA, gave a glimpse of the power of AI during their August event. Seven supercomputers battled each other to show that machines can indeed find and patch software vulnerabilities. Theoretically, the technology can be used to perfect any coding, ridding it of exploitable flaws.

This is the continuation of my mini-series on sentiment analysis of movie reviews, which originally appeared on recurrentnull.wordpress.com. Last time, we had a look at how well classical bag-of-words models worked for classification of the Stanford collection of IMDB reviews. As it turned out, the "winner" was Logistic Regression, using both unigrams and bigrams for classification. The best classification accuracy obtained was .89 So, bag-of-words models may be surprisingly successful, but they are limited in what they can do.

I'd love to see us advance these new ideas, whether its memory, reinforcement learning, or transfer learning, unsupervised learning. Deep learning has certainly been successful, but it's only a very approximate simulation of what goes on in the brain. All of these areas of research will expand the capabilities of this tool called deep learning dramatically. Deep learning has given us an algorithm that can finally allow robots to learn for themselves, from high-level goals, and through iteration discover for itself. Nvidia's CEO says his hardware will revolutionize robotics and that his chips can learn from Google's AlphaGo.

Hospitals in Paris are trialling Big Data and machine learning systems designed to forecast admission rates – leading to more efficient deployment of resources and better patient outcomes. The result was the first contribution to an open source framework of code designed to carry out the analysis over a scalable, distributed framework. Machine learning is employed to determine which algorithms provide the best indicator of future trends, when they are fed data from the past. The core of the analytics work involves using time series analysis techniques – looking for ways in which patterns in the data can be used to predict the admission rates at different times. This code is already being put to use in several other projects involving healthcare and finance.

Warc's'Toolkit 2017', in association with Deloitte Digital, indicates that more than half of global CMOs said they expected the impact of Artificial Intelligence (AI) on marketing and communications to be greater than the impact of social media. Machine learning is already being applied to programmatic trading and we have seen brands such as Aviva in the UK improve its media efficiencies as a result. The report, produced in association with Deloitte Digital, brings together the best of Warc's content over the past year – the latest ideas, research and examples. The result is a guide to current thinking in each area, and the implications for marketers. Warc's'Toolkit 2017', in association with Deloitte Digital, indicates that more than half of global CMOs said they expected the impact of Artificial Intelligence (AI) on marketing and communications to be greater than the impact of social media.

Evernote has reversed proposed changes to its privacy policy that would allow employees to read user notes to help train machine learning algorithms. The changes to the privacy policy were to come into effect on Jan. 23. The company plans to revise in the coming months its existing privacy policy to reinforce that customers' data remains private by default, and "confirm the trust they have placed in Evernote is well founded." By late Thursday, however, O'Neill was taking measures to fix the crisis caused by the proposed policy change. "We announced a change to our privacy policy that made it seem like we didn't care about the privacy of our customers or their notes. This was not our intent, and our customers let us know that we messed up, in no uncertain terms. Evernote has reversed proposed changes to its privacy policy that would allow employees to read user notes to help train machine learning algorithms. The changes to the privacy policy were to come into effect on Jan. 23. The company plans to revise in the coming months its existing privacy policy to reinforce that customers' data remains private by default, and "confirm the trust they have placed in Evernote is well founded." By late Thursday, however, O'Neill was taking measures to fix the crisis caused by the proposed policy change. "We announced a change to our privacy policy that made it seem like we didn't care about the privacy of our customers or their notes.

Microsoft has released a set of 100,000 questions and answers that artificial intelligence researchers can use in their quest to create systems that can read and answer questions as well as a human. The MS MARCO dataset is available for free to any researcher who wants to download it and use it for non-commercial applications. Majumder said the ability for systems to answer complex questions could augment human abilities by helping people get information more efficiently. These datasets can be used to teach artificial intelligence systems to recognize questions and formulate answers and, eventually, to create systems that can come up with their own answers based on unique questions they haven't seen before. MS MARCO is modeled on similar training sets that were created to help spur breakthroughs in other areas of machine learning and artificial intelligence.

A couple weeks back, I spent a couple of days in San Francisco for the IBM Watson Developers Conference and our nation's largest AI business conference: AI World Conference and Expo. I met a handful of the very brightest -- and most prominent -- members of the AI community: Qualcomm Ventures Partner, Patrick Eggen; NVIDIA VP & GM, Jim McHugh; PwC Global & US Consulting Analytics Leader, Paul Blasé; IBM Fellow and IBM Watson VP and CTO, Rob High; Baidu Senior Researcher and Head of Systems, Gregory Diamos; Artificial Solutions CEO Lawrence Flynn and CMO Andy Peart; Steve Ardire, Advisor to Software Startups to name a few. They blew my mind with what they had to share! I consolidated key highlights from my chats -- and general sessions -- into 10 things you need to know about the enterprise AI world. To address the need, NVIDIA built the DGX-1 supercomputer which packs the horsepower of 250 servers.

Recently we wrote about the development of AI and neural nets beyond the second generation Convolutional and Recurrent Neural Nets (CNNs / RNNs) which have come on so strong and dominate the current conversation about deep learning. Our research shows that the next generation of neural nets is most likely to be led by Spiking Neural Nets (SNNs) that are a return to the'strong' AI tradition and closely mimic actual brain function. Unlike CNNs that fire signals to every one of their deep layer connections every time, SNNs are modeled after the fact that in the brain neurons do not constantly communicate with one another. Rather they communicate in spikes of signals or more correctly short trains of spiking signals. As each spike in the train arrives at a neuron it raises the potential of that neuron until finally a spike arrives that tips it over its potential threshold and it in turn fires, propelling the signal onward.