competition


The Kaggle data science community is competing to improve airport security with AI

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With the help of the Kaggle data science community, the Department of Homeland Security (DHS) is hosting an online competition to build machine learning-powered tools that can augment agents, ideally making the entire system simultaneously more accurate and efficient. Kaggle, acquired by Google earlier this year, regularly hosts online competitions where data scientists compete for money by developing novel approaches to complex machine learning problems. The TSA is making its data set of images available to competitors so they can train on images of people carrying weapons. To mitigate this, the TSA put special effort into creating the data set of images that will ultimately be used to train the detectors.


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#artificialintelligence

With the help of the Kaggle data science community, the Department of Homeland Security (DHS) is hosting an online competition to build machine learning-powered tools that can augment agents, ideally making the entire system simultaneously more accurate and efficient. Kaggle, acquired by Google earlier this year, regularly hosts online competitions where data scientists compete for money by developing novel approaches to complex machine learning problems. The TSA is making its data set of images available to competitors so they can train on images of people carrying weapons. Thankfully, Google, Facebook and others are heavily investing in lighter versions of machine learning frameworks, optimized to run locally, at the edge (without internet).


The tips and tricks I used to succeed on Kaggle

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I learned machine learning through competing in Kaggle competitions. In my first ever Kaggle competition, the Photo Quality Prediction competition, I ended up in 50th place, and had no idea what the top competitors had done differently from me. What changed the result from the Photo Quality competition to the Algorithmic Trading competition was learning and persistence. Because feature engineering is very problem-specific domain knowledge helps a lot.


AI Is Going to Change the 80/20 Rule

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As machine learning and AI algorithmic innovation transform analytics, I'm betting that next-generation algorithms will supercharge Pareto's empirically provocative paradigm. Third, as data become more granular and algorithms process complex patterns in smarter ways, Pareto portfolio management has changed. Where individual process owners, product managers, and sales teams once emphasized optimizing their own core Paretos, they now poke, probe, and play with other people's Paretos. Instead of emphasizing Pareto insights around customer satisfaction, complaints, or service, they discovered several sales and marketing Pareto data sets emphasizing upselling: the 20% of customers who accounted for 80% of new services purchased; the 25% of customers responsible for 75% of the new lines or data plans.


Deep learning & XgBoost : Winning it hands down !

@machinelearnbot

A one-liner R code running a deep learning algorithm with 3 hidden layers each having 1024,1024,2048 neurons respectively, the non-linear differentiable activation function being rectifier with dropout; achieved an error rate of 0.83 % on the test data! If are not feeling lazy, you gotta do some hyper parameter tuning. Train the model on the tuned hyper parameters that gave the best accuracy as per the cross validation. Wait, You could even try Optunity to optimize the hyper parameter tuning and achieve even better results with xgboost model.


3 ways AI is already impacting ecommerce

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Algorithmic technology and AI can be incredibly helpful tools to grow sales and optimize various aspects of ecommerce operation, from pricing to demand planning. AI solves this problem by repricing merchandise using complex learning algorithms that continuously assess the market dynamics and changes in competitive environment. They can identify key factors that affect the velocity of orders, and monitor the factors' impact to accurately model velocity and inventory requirements. Logistics used to be the core competency of retail; today, algorithms constantly crunch data, predict market trends, and respond to market changes in real time.


A discussion about AI's conflicts and challenges

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"The competition for talent at the moment is absolutely ferocious," agrees Professor Andrew Blake, whose computer vision PhD was obtained in 1983, but who is now, among other things, a scientific advisor to UK-based autonomous vehicle software startup, FiveAI, which is aiming to trial driverless cars on London's roads in 2019. Blake founded Microsoft's computer vision group, and was managing director of Microsoft Research, Cambridge, where he was involved in the development of the Kinect sensor -- which was something of an augur for computer vision's rising star (even if Kinect itself did not achieve the kind of consumer success Microsoft might have hoped). "I was recently trying to find someone to come and consult for a big company -- the big company wants to know about AI, and it wants to find a consultant," he tells TechCrunch. Returning to the question of tech giants dominating AI research he points out that many of these companies are making public toolkits available, such as Google, Amazon and Microsoft have done, to help drive activity across a wider AI ecosystem.


AI's Place in Marketing : It's a Competition Or a Collaboration?

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What marketers need to understand is that AI can relieve them of all of the mundane manual tasks, provide them with amazing insights, and allow them to spend time on the creative aspects of their jobs. It will also personalize that content based upon segmentation of target audiences. This information led them to make the decision to purchase the American version of "House of Cards" for $100 million. In the future, consumers will be fed only what AI shows will be of interest to them – whether that is the news they get, the TV shows that come on, the social media content they find in their feeds (this is already the case, especially on Facebook), and the push marketing and notifications they receive on their devices.


Medical Image Analysis with Deep Learning , Part 3

@machinelearnbot

In the next few articles we will discuss difference between DICOMand NIFTI formats for medical imaging, expand our learning further and discuss how to use deep learning for 2D lung segmentation analysis. In this article we will discuss Keras and use two examples one showing how to use keras for simple predictive analysis tasks and other doing a image analysis. From the Keras website -- Keras is a deep learning library for Theanos and Tensor flow. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them.


The impact of AI on jobs is larger than you think

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In March this year, PWC released a report saying that 10 million UK jobs are at risk of being replaced by AI within 15 years. Insurance companies are already dinosaurs and while we will still need insurance, we don't need our current insurance companies. Those expensive on-site skilled jobs are gone forever, replaced by massive automation and AI from mining operations to plant operations to administration. Australia, as a home of the corporate oligopoly, suffers the associated elitism, complacency, lack of innovation and resistance to change which are characteristics of all oligopolies.