Magenta's AI Jam: Making Music with TensorFlow Models


Find more information and to download links on the Magenta blog: https://goo.gl/yoXbgf Members of the Google Brain team have been exploring how machine learning can be used as a creative tool through a project called Magenta. In this video, members of the team give an overview of how they turned an interface that allows researchers to interactively evaluate their music generation models into a fun and powerful creative tool for musicians called "AI Jam". This interface won the Best Demo award at the 2017 Conference on Neural Information Processing Systems (NIPS) and is freely available on Magenta's GitHub site.

The MachineLabs Blog Our road ahead to private beta


I teach machine learning labs at a local school and with a tool like this my lab setup time could be cut down to a fraction of what they are now and the students could focus on the core material. Let's take a closer look at our recent progress, brand new features and our road ahead. Executions play the most important role at MachineLabs. Having these configurations in a config file as part of the file tree ensures that they work across different clients (app, CLI) to our service.

The Guerrilla Guide to Machine Learning with Julia


With learning machine learning -- much as with machine learning itself -- there is no free lunch. With that in mind, here is a bare bones take on learning machine learning with Julia, a complete course for the quick study hacker with no time (or patience) to spare. For a more detailed introduction to the language, watch this introductory video from David Higgins which better explains Julia from the perspective of a Python developer. Another interesting video is Josh Day's discussion of the OnlineStats.jl For a more advanced take on Big Data analytics using Julia, watch this video by Ehsan Totoni, covering the use of a particular framework called the High Performance Analytics Toolkit (HPAT), which aims for efficient large-scale analytics in the Julia ecosystem.

An Overview of Multi-Task Learning in Deep Neural Networks


Finally, we can motivate multi-task learning from a machine learning point of view: We can view multi-task learning as a form of inductive transfer. In the context of Deep Learning, multi-task learning is typically done with either hard or soft parameter sharing of hidden layers. The constraints used for soft parameter sharing in deep neural networks have been greatly inspired by regularization techniques for MTL that have been developed for other models, which we will soon discuss. Learning just task \(A\) bears the risk of overfitting to task \(A\), while learning \(A\) and \(B\) jointly enables the model to obtain a better representation \(F\) through averaging the noise patterns.

How artificial intelligence is revolutionising enterprise software in 2017


This study isn't representative of global AI, data engineering and machine learning (ML) adoption trends. It does, however, provide a glimpse into the current and future direction of AI, data engineering, and machine learning. The following graphic provides an overview of company readiness for machine learning and AI projects. Venture Scanner believes that machine learning applications and machine learning platforms are two relatively early stage markets that stand to have some of the greatest market disruptions.

What Is Machine Learning and Why Does It Matter?


Every day we hear buzzwords like artificial intelligence and machine learning at some point or another in the news cycle, typically associated with smartphones, smart speakers, drones, and so on. In an interview with Wired UK, Nidhi Chappell, head of machine learning at Intel, explained that "AI is basically the intelligence – how we make machines intelligent, while machine learning is the implementation of the compute methods that support it. The computer's algorithm learns how to classify the pictures based not on tags, but based on patterns it observes; once the algorithm correctly identifies the set of pictures, it is ready to take on new data and can start identifying pictures it hasn't seen before based on its learnings. Beyond supervised ML, the intelligence can be broken down further into unsupervised machine learning" and reinforcement learning.

How Artificial Intelligence Can Help Mankind Solve Global Problems


Current research projects show that artificial intelligence (AI) can help us solving global problems for the greater good of mankind. For instance, a partnership between artificial intelligence company IBM Watson Health and Barrow Neurological Institute has led to a recent discovery in the disease Amyotrophic Lateral Sclerosis (ALS). Research has shown that one of the main factors behind students' dropping out is a lack of support, therefore a highly supportive robot specially designed for teaching can improve the learning process. Tech giants such as Google has enormous data centers requiring a massive amount of energy in order to run the servers as well as keeping them cool.

Artificial Intelligence for the Enterprise: A Primer on AI Use in the Enteprise


The world of Artificial Intelligence (AI) is growing at an unprecedented rate. This report provides a broad look at how enterprises leverage AI in meaningful ways. This report includes data from Gigaom's recent AI survey, insights from our recent AI Conference, and personal experience working with corporate enterprises on their AI journey.

What I learned from I/O 2017 – Margaret Maynard-Reid – Medium


We heard a lot about AI and machine learning, from TensorFlow, Cloud TPU to Auto ML, and how Google is democratizing AI . Living room watch time is growing. Josh Gordan shared with us his favorite open-source machine learning models in Open Source TensorFlow Models. Past, Present and Future of AI / Machine Learning was presented by Alphabet's top AI experts Daphne Koller, Diane Greene, Fei-Fei Li, Fernada Viegas and Francoise Beaufays.

Artificial Intelligence Will Create a Paradigm Shift Within the Next Decade


Today, enterprise software is largely at the "power steering" phase. Today, enterprise software is largely at the "power steering" phase, where workflow-based software helps you "steer" more easily. Over the next decade, I believe enterprise software will get to level 4/5, where software will be self driving, and we'll see a paradigm shift in the coming years when we move from a mindset of machines are assisting humans to humans are assisting machines. Salesforce has been a largely workflow driven solution to push sales reps to input their activities (so they get paid) and thus allow sales managers to view activities of their direct report and manage more efficiently.