Bigdata, datascientist, machinelearning, ai & much more…
AI News Friday, June 15
- 2018 European conference on Process Safety and Big Data
- Updating the Definition of ‘Data Scientist’ as Machine Learning Evolves – AI Trends
DataScientist, MachineLearning, AI
- New AI Tech Can Mimic Any Voice
- Air Liquide will be holding a third edition conference with the theme of Augmenting Process Safety Performance through Big Data and Digitalization, to be held at the DECHEMA House in Frankfurt am Main, Germany from 14 th -15 th November 2018.
- The conference is jointly sponsored by the Centre for Chemical Process Safety (CCPS) and the European Process Safety Centre (EPSC).
- Air Liquide is aware that those who have attended these events have felt that Big Data, in relation to Process Safety, is an emerging area that must be kept abreast of as it is an important evolving subject and can deliver exceptional value to the companys key stakeholders.
- This event will serve as a common platform that brings together Air Liquides key stakeholders industry, academia, governmental regulatory agencies and labour organisations to lead the way in improving industrial process safety and achieving process safety excellence.
- Air Liquide invites both Process Safety and Big Data practitioners to submit abstracts by 23rdJuly, 2018, for this important conference.
@IainLJBrown: 2018 European conference on Process Safety and Big DataRead more here: https://t.co/e7pLtTPP9P#BigData… https://t.co/LTASkwyFez
- However, the requirement today is that data scientists develop anunderstanding of the problem the algorithm was meant to solve, so interviews with subject matter experts focused on that particular problem are essential.
- Now, data scientists can work on neural networks that span a range of broad knowledge areas, from predicting the mortality of African butterflies to deciding when and where to publish advertising for seniors.
- For example, to predict which applicants will default on their loans, the data scientist must know to ask questions such as: – – These are some of the many questions to ask on this topic, and there is long lists of questions for every machine learning process.
- A data scientist who only wants to create algorithms without talking in depth with those involved in the phenomenon being explored will have a limited ability to create effective algorithms.
- A well-trained, inquisitive data scientist will also seek out related data online via search, crawler, and API to pinpoint the most relevant predictive knowledge on top of computational knowledge, experience, and judgment matters for the definition of the response variable, the separation of the database, the certification of past data…
@ipfconline1: Updating the Definition of #DataScientist as #MachineLearning Evolves https://t.co/H9b2YQz9W9 v/ @aiworldexpo#AI… https://t.co/u9dqQNTkgP
- Montreal-based start-up Lyrebird is looking to change that with an artificially intelligent system that learns to mimic a persons voice by analyzing speech recordings and the corresponding text transcripts as well as identifying the relationships between them.
- Introduced last week, Lyrebirds speech synthesis can generate thousands of sentences per secondsignificantly faster than existing methodsand mimic just about any voice, an advancement that raises ethical questions about how the technology might be used and misused.
- Switching to a different voicesuch as having Alexa sound like a manrequires a new audio file containing every possible word the device might need to communicate with users.
- Lyrebirds system can learn the pronunciations of characters, phonemes and words in any voice by listening to hours of spoken audio.
- Last year Google-owned company DeepMind revealed its own speech-synthesis system, called WaveNet, which learns from listening to hours of raw audio to generate sound waves similar to a human voice.
@ipfconline1: New Artificial Intelligence Tech Can Mimic Any Voice!https://t.co/HXgUDYII7y [by @BaharGholipour v/ @sciam]#AI… https://t.co/5J7DmIDdSQ