All of us have seen those fear mongering headlines about how artificial intelligence is going to steal our jobs and how we should be very careful with biased AI algorithms. Bias means that the algorithm favors certain groups of people or otherwise guides decisions towards an unfair outcome. Bias can mean giving a raise only to white male employees, increasing criminal risk factors of certain ethnic groups and filling your news feed only with topics and point of views that you are currently consuming – instead of giving a broad, balanced view of the world and educating you.
Valohai and Microsoft cross lightsabers in the battle for artificial intelligence, through Microsoft’s global ScaleUp Program.
Just lately we’ve been playing around with IBM PowerAI in order to ensure our customers can leverage it in large-scale on-premise training. PowerAI in itself is IBM’s solution for deep learning consisting of software and hardware to help you quickly train deep learning models. Today we’re happy to announce that Valohai fully supports PowerAI and our customers can start using it!
If developers used to be the rock stars of the dotcom era, Data Scientists are quickly overtaking them as the new Whitesnake cover bands of the 2020s. Although both might be sporting the same hobo beards, Data Scientists are getting their work done with just sticks and stones as their tools while us Software Engineers have every tool in the universe.
Developing a machine learning model for a new project starts with certain common groundwork and exploration, to understand your data and figure out the approaches to try. A popular choice for this groundwork is Jupyter, an environment where you write Python code interactively. In Jupyter notebook's cells you can evaluate and revise and it is an attractive, visual choice (and many times the right choice) – for this step of data science work. Since Jupyter kernels, the processes backing a notebook’s execution, retain their internal state while the code is being edited and revised, they’re a highly interactive, fast-feedback environment.
Reproducibility and replicability are cornerstones of the scientific method. Every so often there’s a sensationalized news article about a new scientific study with astounding results (for instance, we’re looking forward to seeing what’s hot at ICML 2018 – we’re attending, come say hi!) – and it’s not uncommon in these cases that there’s no way for other fellow scientists to verify these results by themselves, be it due to missing or proprietary data, or faulty methodologies. This, naturally, casts shade over the entire study in question.
If machine learning is a team sport, like I so frequently hear, machine learning platforms must be the playing fields. And to up your machine learning game, you must have the proper environments to do it.
With the promise of relieving strain on the transport network in maritime cities using Artificial Intelligence and autonomous driving technology, Finnish software powerhouse Reaktor set to build a solution for future waterways. As part of the project, the Valohai platform empowered Reaktor to increase the speed of model development almost tenfold, making it possible to train the self-steering algorithm over night beating the initial training time of one week.
After spending two days at the AI Summit fair in London and having several conversations with people from different business backgrounds, I wanted to clarify why machine learning infrastructure is one of the biggest things to concentrate on when building production level machine learning models.