Looking for some neat looking imagery for your next blog post, but feel like Unsplash is played out? Then this article is for you!
There is no doubt that machine learning AIs have impressed us with their superhuman performance in the last few years. Computer programs such as Alphago, Alphazero, and Openai Five have shown that machines can dominate us humans in Go, Chess and Dota2. While these advances in technology are making a lot of people excited, they are also making some people very scared. If machines can beat us mano a mano in some of our most complex games, what is stopping them from dominating us in real life? In an interview with BBC, Stephen Hawkins said that "The development of full artificial intelligence could spell the end of the human race," and during a presentation for MIT Elon Musk called AI our "biggest existential threat." Surely, when Elon Musk and Stephen Hawkins are calling out the dangers of AI, the rest of us should be afraid. Or should we?
Vi snakker jul, julekaker og maskinlæring!
Helping you pick the top team for Boxing Day!
When knitting met machine learning, an untold love story.
Becoming “data-driven” has been a stated objective for many organizations over the past few years. However, according to Harvard Business review most companies are failing to achieve this.
The most important part of any data science project is the data. It does not matter how fancy your algorithm is if your data has low quality or does not capture the relationships you are interested in. In the end it all comes down to the simple truth: “garbage in, garbage out”. Data cleaning refers to a variety of methods for improving data quality. It can be a time consuming and challenging process, but the reward for properly cleaning your data can be great.
Recently, machine learning research has increasingly been focused on general learning algorithms where the same algorithm can perform a huge variety of tasks. The ultimate goal by many reinforcement learning researchers is to create machines that can learn to solve any general task, just like humans! Yesterday's article introduced you to the concept of reinforcement learning, and today we're going to take a brief look at some of the coolest projects and greatest breakthroughs in the field of self-learning machines.
From the moment they’re born, animals learn by interacting with their surrounding environment. The basic question in the field of reinforcement learning is: can machines do the same?
When I decided to write a blog post called "How to talk like a machine", I discovered that the question of how to write a simple blog post had a lot of deep implications. The follow-up question was "How can I make my blog post read like a machine-made blog post?". It turns out that there is a difference between writing a single paragraph of prose and a long essay in a natural language. There are a few things you need to be aware of when you write a machine-made blog post.
The modern consumer is blessed with an endless supply of products and services that address their needs. In such a competitive environment, companies need to understand the driving forces shaping consumers perception of its products and services. How else can Burger King entice modern consumers into buying their milkshake from them instead of McDonalds? Or how can Nike convince the occasional jogger that their shoes are better than a pair of sneakers with the three tilted and iconic Adidas-stripes imprinted on the side?
Exploring NLP libraries for Norwegian
When it comes down to it, machine learning is, as probably all of you know – statistics on steroids. That’s why todays post is dedicated to one of the fundamental tests we often use in statistics and we perhaps should use even more often.
High dimensional data can be a pain sometimes, at least when it comes to visualization and exploration. Today we will introduce one common technique that can potentially ease the pain a bit.
Some things in life are certain - for instance, we tend to take for granted that the sun will rise every day. Similarly, everyone knows that each Christmas, Canadian-Italian singer-songwriter Michael Bublé will emerge from his secret hideaway and top Christmas playlists yet again.
Usually we let neural networks adapt their parameters to data, for instance images. Ever wondered what happens if we instead adapt images to match the network? Style transfer applies the style of one image onto another, and it's a crowd-pleaser.
If you have paid some attention to the artificial intelligence community the latest years, you’re bound to have heard of neural networks. These kinds of computational algorithms have pushed the boundaries of machine learning in pretty much every sub field there is. But what are they really?
Is it straight forward to collect unbiased data for the average spending on Christmas gifts in Oslo? Did Nicolas Cage cause swimming pool drownings in US in 1999 to 2009? And how can your understanding of Bayesian probability help to prevent your doctor from erroneously removing your breasts? Read on to find out.
How can we use methods from machine learning, along with traditional principles from service design, to get a better understanding of our customers and their needs?
Why is it important to make beautiful graphs? Isn't it most important what the numbers show?
Machine learning algorithms are helping us predict the future by learning patterns from past observations. How does this effect the way we assess our models?
First of all, this is not a Git article. This article will give a short introduction to a set of trees almost as powerful as Christmas trees: decision trees.
Up until now we have learnt what machine learning is and looked at some initial examples. But before we move on with more details of how to do machine learning we must first talk about why. In this blog post we‘ll present a high-level overview of practical application before diving into a real-life example from our work at the Center for Service Innovation (CSI) in Norway. The basic concept? It‘s all about business value!
There are many different types of machine learning models. Linear models are some of the simplest, but also some of the most widely used. In this post we’ll explain what a linear model is and why linear models are so popular.
Welcome! Welcome to our Christmas calendar for machine learning and data science, one of 12 (!) calendars prepared by Bekk this year.