Machine Learning is about making predictions. This post will give an introduction to Machine Learning through a problem that most businesses face: predicting customer churn.
ML can help predict which of your customers are at risk for leaving in advance, and give you an edge by pre-empting with action.
Serverless architecture is making cloud deployment even easier by removing the need to design your own server-side systems. Integrated properly, this paradigm can get your applications out the door faster and free up company resources to build more.
In a nutshell, serverless, also called Functions as a Service (FaaS), is a further abstraction on what cloud computing platforms like AWS already do—making it easier than ever to get your applications up and running at scale. Serverless takes the power of a hosted cloud to a software level – it abstracts away the entire concept of the server. Instead, you just write functions. The provider takes care of how and where to run those functions, ensuring that you focus on code and not the hardware and systems that operationalize that code.
Machine Learning algorithms are being developed and improved at an incredible rate, but are not necessarily getting more accessible to the broader community. That’s why today Algorithmia is announcing DanKu, a new blockchain-based protocol for evaluating and purchasing ML models on a public blockchain such as Ethereum. DanKu enables anyone to get access to high quality, objectively measured machine learning models. At Algorithmia, we believe that widespread access to algorithms and deployment solutions is going to be a fundamental building block of a balanced future for AI, and DanKu is a step towards that vision.
The DanKu protocol utilizes blockchain technology via smart contracts. The contract allows anyone to post a data set, an evaluation function, and a monetary reward for anyone who can provide the best trained machine learning model for the data. Participants train deep neural networks to model the data, and submit their trained networks to the blockchain. The blockchain executes these neural network models to evaluate submissions, and ensure that payment goes to the best model.
The contract allows for the creation of a decentralized and trustless marketplace for exchanging ML models. This gives ML practitioners an opportunity to monetize their skills directly. It also allows any participant or organization to solicit machine learning models from all over the world. This will incentivize the creation of better machine learning models, and make AI more accessible to companies and software agents. Anyone with a dataset, including software agents can create DanKu contracts.
We’re also launching the first DanKu competition for a machine learning problem. Read More…
If you’re trying to create value by using Machine Learning, you need to be using the best hardware for the task. With CPUs, GPUs, ASICs, and TPUs, things can get kind of confusing.
While for most of computing history there was only one type of processor, the wide growth of Deep Learning has led to two new entrants into the field: GPUs and ASICs. This post will walk through the different types of compute chips, where they’re available, and which ones are the best to boost your performance.
Source: Frontiers in Psychology
You expect employees to have high levels of emotional intelligence when interacting with customers. Now, thanks to advances in Deep Learning, you’ll soon expect your software to do the same.
Research has shown that over 90% of our communication can be non-verbal, but technology has struggled to keep up, and traditional code is generally bad at understanding our intonations and intentions. But emotion recognition – also called Affective Computing – is becoming accessible to more types of developers. This post will walk through the ins-and-outs of determining emotion from data, and a few ways you can get some emotion recognition and running yourself.