Recap of Machine Learning News for February 2018

Recap of Machine Learning News for February 2018


Machine Learning News - February 2018

Machine Learning News for February 2018

How machine learning helps prevent online daters from getting scammed. TechRepublic.com, February 14, 2018.

The number of people looking for romance online is increasing and so is the number of scammers on dating apps and websites.Online dating scams cost  Americans approximately $210 million in the last 3 years.Kevin Lee, architect at fraud prevention and machine learning firm Sift Science said that the fraud problem is much bigger than it is reported because only 10% of the people report such incidents and others are embarrassed to do so. Sift Science is working on a solution to identify accounts which have a high probability of being fraudulent. A combination of analytics and machine learning will help identify users who have several profiles on a dating website by tracking users specific IP addresses, devices, etc  from the user’s network. Content is also being analyzed for usage errors, language misspellings and requests for money that indicate scams.All these activities can be used to identify users who are  engaging in fraudulent activity.

(Source : https://www.techrepublic.com/article/how-machine-learning-helps-prevent-online-daters-from-getting-scammed/ )

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Roundup Of Machine Learning Forecasts And Market Estimates, 2018.Forbes.com, February 18, 2018.

Here are the key takeaways from the machine learning market forecasts -

  • IDC forecasts that the spending on AI and Machine Learning will grom from $12B in 2017 to $57.6B by 2021.
  • Deloitte predicts that the number of machine learning implementations and pilots are likely to double in 2018 compared to 2017, which will double again by 2020.
  • Machine learning patents are among the third fastest  growing category of all patents granted which grew at a CAGR of 34% from 2013 to 2017.
  • Within the BI and analytics market, data science platforms which support machine learning are anticipated to grow at 13% CAGR by 2021.
  • According to Deloitte Global Predictions 2018, machine learning chips used in data centers will grow from 100K to 200K in 2016 to 800k in 2018.

(Source : https://www.forbes.com/sites/louiscolumbus/2018/02/18/roundup-of-machine-learning-forecasts-and-market-estimates-2018/#2e5052c12225 )

Feature Labs launches out of MIT to accelerate the development of machine learning algorithms. TechCrunch.com,February 22, 2018

Feature Labs, a startup with roots in research started at MIT launched couple of tools that will help data scientists speed up the development of machine learning algorithms. Feature Labs has developed a technique to automate “feature engineering” which is a manual and most time consuming task for data scientists. Feature Labs is unique in the sense because they have automated the process of using domain knowledge to extract variables from 
raw data which make machine learning algorithms work. To automate feature engineering , they use a process called “Deep Feature Synthesis” that creates features from raw datasets and automatically converts them to predictive signals.

(Source - https://techcrunch.com/2018/02/22/feature-labs-launches-out-of-mit-to-speed-up-building-machine-learning-algorithms/ )

 Machine Learning Projects

IBM Watson CTO Rob High on bias and other challenges in machine learning.TechCrunch.com, February 27, 2018.

IBM Watson CTO Rob High told in an interview at the annual Mobile World Congress in Barcelona that the biggest challenge in machine learning today is to train models with less amount of data. While Google’s AI chief John Giannandrea also echoes similar statement saying that machine learning models have to be trained on large dataset for accuracy but for most of the problems large dataset does not simply exist. Rob High further added saying that this is a solvable challenge because humans do it. The recent advances in transfer learning will make it possible to take one trained model and use this data to start training a new model where very less data may exist.

(Source : https://techcrunch.com/2018/02/27/ibm-watson-cto-rob-high-on-bias-and-other-challenges-in-machine-learning/ )

Algorithmia launches blockchain-based protocol for machine-learning algorithm shoppers.GeekWire.com, February 27, 2018

Machine learning researchers now have a new incentive to try and help the AI community : Cryptocurrency.Algorithmia launched a new protocol called DanKu that lets laypeople looking for complex machine learning models to post their data on the Etherium blockchain by helping them find a machine learning researcher who create a training model for their data.DanKu is a neural network which will evaluate various models that are submitted and reward the winning machine learning model with Etherium Cryptocurrency. The cryptocurrency rewards also provide an additional incentive to researchers by allowing them to participate in Algorithmia’s overall goal of democratizing access to ML expertise which resides in some of the world’s richest and largest tech companies.

(Source : https://www.geekwire.com/2018/algorithmia-launches-blockchain-based-protocol-machine-learning-algorithm-shoppers/ )

Gartner Magic Quadrant: Who's Winning In The Data And Machine Learning Space. Forbes.com, February 28, 2018

Gartner says nearly half of the CTO’s are planning to deploy AI and Machine Learning this year. 46% of CIO’s have developed plans to deploy AI but only 4% have deployed.Three vendors that led the Gartners 2018 Magic Quadrant for Analytics and BI platform are - Microsoft, Tableau, and Qliktech. This year’s Gartner report saw some critical departures  with ZoomData, ClearStory Data, Datameer, Alteryx and Pentaho no longer positioned on the report.

(Source : https://www.forbes.com/sites/ciocentral/2018/02/28/gartner-magic-quadrant-whos-winning-in-the-data-machine-learning-space/#fa161457dab3 )

A computer was trained to play Qbert and immediately broke the game in a way no human ever has.BusinessInsider.in, February 28, 2018

AI agents have become smart enough to find new technique that can help them win games which humans might never be able to find out such bizarre tactics and strategies. Machine learning researchers have trained a machine on how to play Qbert for Atari. For one of the games, the AI agent found a way on how to exploit a bug at different levels, made the entire stage flash and found ways to rack up upto 1 million points by playing the game in a random manner.One thing about the future of AI and Machine learning is clear : The machines are really good at playing games.

(Source : https://www.businessinsider.in/A-computer-was-trained-to-play-Qbert-and-immediately-broke-the-game-in-a-way-no-human-ever-has/articleshow/63114978.cms )
 

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