Issue 113: CognitionX Data Science, AI and Machine Learning Briefing


Loom.ai are creating an AI-based avatar that can capture your personality. Cool or creepy? Tell us what you think in the forums. Elsewhere in the news, we’re looking at Gartner’s top ten strategic expected technology trends for 2017, 50 takeaway points
from last week’s NIPS conference, and ask whether DataRobot are set to revolutionise deployment of AI.

Best,

Tabitha UntilTheBotsTakeOver Goldstaub

P.S.

Come and join 200 other community members at our Christmas party today.

Business Impact of AI

Top 10 Strategic Technology Trends for 2017

Gartner’s top 10 trends on the future of the intelligent digital mesh. An intelligent digital mesh is emerging to support the future of digital business and its underlying technology platforms and IT practices. The mesh focuses on people and the Internet of Things (IoT) endpoints, as well as the information and services that these endpoints access. Capabilities such as blockchains, distributed ledgers and digital twins will bring the physical and digital realms ever closer to supporting digital business initiatives. AI and machine learning are the means of injecting intelligence into new and existing apps and things to form the intelligent digital mesh.

Research

50 things to take from NIPS 2016

During The Thirtieth Annual Conference on Neural Information Processing Systems, experts discussed applied machine learning, neural nets, chat bots, tidbits, factoids and more. Here the 50 things that you cannot miss.

Case study

Insights on Slack’s data wrangling

The Data Engineering team at Slack works to provide an ecosystem to help people in the company quickly and easily answer questions about usage, so they can make better and data informed decisions: “Based on a team’s activity within its first week, what is the probability that it will upgrade to a paid team?” or “What is the performance impact of the newest release of the desktop app?” Most of the biggest challenges on the data engineering team were not centred around writing code, but around understanding the discrepancies between the systems used, because those seemingly small differences can cause big headaches when it comes to interoperability.

Education

Basic Feature Engineering With Time Series Data in Python

Time Series data must be re-framed as a supervised learning dataset before we can start using machine learning algorithms. There is no concept of input and output features in time series. Instead, we must choose the variable to be predicted and use feature engineering to construct all of the inputs that will be used to make predictions for future time steps. In this tutorial, you will discover how to perform feature engineering on time series data with Python to model your time series problem with machine learning algorithms.

Tools of the trade

Top programming languages that will be most popular in 2017

Which is the most preferred programming language or the top programming languages to learn across the globe? How do we judge it and what should be the criteria? “By most preferred language, we do not mean Java is superior to C++ or Python is better than MATLAB. In this analysis, a prediction of the most preferred languages by users, and ones that are most in demand in the industry.”
C++, MATLAB, Java have always been technical institutes favourite, and most of the fresh graduates end up with great skills in these languages. But what if the best programming languages they know is not the immediate skills required by the industry?

Products we love

Anil Dash: The Future of Tech is (Weirdly) Human

Fog Creek’s latest creation, Gomix is a new tool in the app creation space. Its simple interface allows users to collaboratively edit code, similar to the way Google Docs allows multiple people to simultaneously edit a document. Here its CEO Anil Dash discusses Gomix and how it is meant to be an inclusive, fun, and rewarding community for creators of all skill levels.

Innovation

Loom.ai: the avatar that can capture your personality

Who are we and what makes us human? Loom.ai is aiming to answer by making it as easy as possible for users to build a realistic avatar of themselves while also crafting a system that can learn more about a user’s personality. The company’s avatar system allows users to upload a single photo of themselves and create a realistic avatar immediately. The Loom.ai team brings their visual effects expertise from LucasFilm and DreamWorks, where the founders worked on creating CGI models that captured the personalities of the actors and voice actors behind them. The company’s CTO Kiran Bhat said: “The key to building believable digital characters is to extract the perceptually salient features from a human face in 3D. The new suite of computational algorithms built by Loom.ai will democratise the process of building
believable 3D avatars for everyone, a process that was previously expensive and exclusive to Hollywood actors benefiting from a studio infrastructure.”

Podcast

AI in Stock Trading – Future Trends and Applications

In this podcast, CEO and Co-founder of Kavout Alex Lu, whose company offers AI trading applications for enterprises and individuals, discusses why AI and finance are made for each other. Lu speaks today about the kinds of patterns that traders now have access to in finance, and he gives examples of ways Kavout and other institutions are using AI in stock trading to build better and more personalised products and services.

Exciting opportunities

DataRobot to revolutionise data science

For the last four years, some of the best data scientists gathered to create DataRobot: the world’s most advanced enterprise machine learning platform that helps you solve business problem by finding the best predicted model for your data. Request a demo here.

I’ve been making some changes based on Feedback. Would love to hear from more of you. Please do click to share your thoughts!

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