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- 2016 has been a prolific year for Machine Learning/AI companies in all fronts.
- In the post I have tried to capture some notable funding rounds and acquisitions of Machine Learning (ML) startups that took place last year.
- Deep Learning (DL) companies that can help their clients run more successful marketing campaigns are highly valued.
2016 has been a prolific year for Machine Learning/AI companies in all fronts. In this post I have tried to capture some notable funding rounds and acquisition…
Continue reading “Notable Fundraising Highlights of Machine Learning Startups in 2016”
- com: Banks find big innovation payoff in hackathons ]
- With the Wells Fargo fake accounts scandal fresh on your mind, it’s hard to feel sorry for banks these days.
- Payment services like PayPal and investment services like Wealthfront are competing for the clients that banks used to take for granted.
- ‘s a look at what three banks – Capital One, RBS and TD Bank – have learned from adopting AI.
- Artificial intelligence may prove to be a key part of the solution for banks seeking to remain competitive.
How Capital One, RBS and TD Bank use artificial intelligence to boost customer service and revenue.
Continue reading “Banks turn to AI for improved customer service, competitive edge”
- In the short clip embedded above, Shea explains why the firm invests in knowledge management, artificial intelligence, machine learning, and augmented reality.
- 80 percent of the law firms surveyed see digital strategy as critical
- Law firms must respond to the changing demands of consumers just as companies do in other industries.
- The legal industry has a reputation for being slow to change and behind the curve on adopting new technologies.
- The firms that adopt and change and innovate will be the haves and the have-nots in the industry.
The CIO for a $2 Billion law firm explains how his organization responds to changing customer expectations with the most modern technology available.
Continue reading “#CXOTALK Reinventing the legal industry with AI, machine learning, and augmented reality”
- We build a model that builds both word and document topics, makes them interpreable, makes topics over clients, times, and documents, and makes them supervised topics.
- lda2vec also yields topics over clients.
- lda2vec the topics can be ‘supervised’ and forced to predict another target.
- It’s research software, and we’ve tried to make it simple to modify lda2vec and to play around with your own custom topic models.
- LDA on the other hand is quite interpretable by humans, but doesn’t model local word relationships like word2vec.
Read the full article, click here.
@andradeandrey: “Very interesting code, lda2vec tools for interpreting natural language #machinelearning #NLP”
Contribute to lda2vec development by creating an account on GitHub.