Blockchain mania grips Amazon, Facebook’s Python library MUSE, Taplytics AI-powered Experience Cloud, and LinkedIn’s 2017 U.S. Emerging Jobs Report among today’s top stories around machine learning, artificial intelligence and data science news.
Amazon joins Blockchain bandwagon
Amazon Web Services announces AWS Blockchain Partners Portal
Amazon Web Services is investing in blockchain though its partner ecosystem. You can visit the AWS Blockchain Partners Portal here.
The portal supports customers’ integration of blockchain solutions with systems built on AWS. “We invite you to check out current blockchains and review reference architecture, deployment strategies, and development tools on our new portal page,” Amazon said in a statement. “We will be launching an AWS Blockchain Competency in 2018, and you can sign up for our Blockchain for AWS Partners mailing list to learn more.”
The following Blockchain Partner Solutions are available now as one-click deploy: Sawtooth Supply Chain, Sawtooth 1.0, R3 Corda, PokitDok, and Blockapps Strato. “Expect to see Samsung SDS, Tibco, Quorum, and Virtusa solutions in 2018 along with reference architecture from our partners,” Amazon said, adding that the products will have native AWS integrations to allow plug-and-play access to the portfolio of services.
Ahead of Christmas, Facebook announces MUSE!
MUSE: A Python library for multilingual unsupervised or supervised word embeddings
Facebook has just open-sourced MUSE. It is a Python library for multilingual word embeddings, whose goal is to provide the community with:
- state-of-the-art multilingual word embeddings based on fastText
- large-scale high-quality bilingual dictionaries for training and evaluation
“We include two methods, one supervised that uses a bilingual dictionary or identical character strings, and one unsupervised that does not use any parallel data (read about Word Translation without Parallel Data for more details),” Facebook said in its official release.
MUSE is available on CPU or GPU, in Python 2 or 3. “Faiss is optional for GPU users – though Faiss-GPU will greatly speed up nearest neighbor search – and highly recommended for CPU users. Faiss can be installed using conda install faiss-cpu -c pytorch or conda install faiss-gpu -c pytorch,” Facebook commented on the dependencies. For more details please visit the Github page.
Redefining marketing cloud with the power of Artificial Intelligence
Optimization startup Taplytics integrates “intelligent” Experience Cloud, expands beyond mobile apps
Taplytics has announced ‘intelligent’ Experience Cloud—a set of experimentation, messaging, engagement and analytics tools that unify cross-channel optimization for brands. In a way, the Taplytics Experience Cloud is a scenario where artificial intelligence sits at the center of an integrated portfolio of experimentation, engagement and analytics solutions to help brands create holistic, data-driven experiences. As part of the new Experience Cloud, Taplytics is also announcing the release of its Visual Web Experimentation Engine and Dexter, Taplytics’ new AI Smart Assistant. Dexter surfaces smart, contextual recommendations on key areas of opportunity within a user’s journey to take the guesswork out experimentation and personalization.
“The traditional marketing cloud concept falls short of connecting with an individual – it gets companies in the habit of viewing people as numbers. The Taplytics Experience Cloud changes the way that brands create experiences that are personalized, relevant and ultimately engaging,” commented Ashley Lewis, VP, Dollar Shave Club.
Yes. Machine Learning is the most sought after job category.
LinkedIn analyzes emerging job categories since 2012 and founds machine learning, data science related jobs growing the fastest
LinkedIn has released data on the jobs that have been experiencing the most growth in numbers over the past 5 years. As expected, tech and data oriented jobs are among the fastest growing categories.
The role of machine learning engineer is at the top, the job category growing 10-fold between 2012 and 2017. This is followed by data scientist, multiplying by a factor of seven during this same time. There are also six times as many big data developers, as well full-stack engineers.
LinkedIn’s top-10 leading categories are as follows:
- Machine learning engineer (9.8x as many jobholders as in 2012)
- Data scientist (6.5x)
- Sales development represenative (5.7x)
- Customer suvcess manaer (5.6x)
- Big Data developer (5.5x)
- Full stack engineer (5.5x)
- Utility developer (5.1x)
- Director of data science (4.9x)
- Brand partner (4.5x)
- Full-stack developer (4.5x)
You can find the complete data here on LinkedIn’s 2017 U.S. Emerging Jobs Report.