Is AI going to be a jobs killer? How many jobs will be lost to AI? What jobs will AI create?
What are the key stages in a data science project? A recipe for a successful data science project flow. The naive view of a data science project’s stages When planning a data science project, it’s easy to think of it as a simple exercise in a little bit of data cleaning, some data science work, and a deployment stage at the end of the project. In fact, many data scientists and non-data scientists might expect this kind of idealised data science project flow.
What is unsupervised learning? When we think about acquiring a skill or learning a new subject, most of us see that process involving a teacher passing their knowledge on to us. If you’re teaching a child how to distinguish between different fruits for example, you might show them various images, identifying one as an apple, another as a pear and so on, so that when the child sees these fruits in real life, they can recognise which is which themselves, but initially via the labels you provided. This is known as supervised learning, and is one way in which Artificial Intelligence uses Machine Learning to predict particular outputs, having used data points with known outcomes. However, this is not the only way we, or computers for that matter, learn. Let us introduce to you Unsupervised Learning.
Can we get rid of AI bias? Bias is one of the many imperfections of humanity that causes us to make mistakes and holds us back from growing and innovating. However, bias is not only a human reality, but is also a reality for artificial intelligence as well. AI bias is a well-documented phenomenon that is widespread among machine learning tools from a variety of sectors, and it is notoriously difficult to get rid of.
How do we apply ethics to artificial intelligence? Why do we now need AI ethics? What is AI ethics? The ever-expanding availability of big data and cloud computing, improved computing power, and recent developments in deep learning algorithms have paved the way for machine learning algorithms to transform nearly every industry.
Explainable AI, or XAI, refers to a collection of ways we can analyse machine learning models. It is the opposite of the so-called ‘black box’, a machine learning model whose decisions can’t be understood or explained. Here’s a short video we have made about explainable AI.
Technical Due Diligence on companies with AI products and technologies Are you thinking about making an investment in a startup that allegedly uses AI or machine learning and would like a completely impartial assessment of their actual AI technology or products?
Key data science concepts from A to Z I’ve put together a short selection of some intermediate-level data science concepts which give you a good grounding in the field. A lot of these are based on a series of articles which I wrote for the excellent data science resource deepai.org. I’ve biased the list of data science concepts a bit towards natural language processing, because that’s the area I mainly work in.
What is explainable AI? Explainable AI, or XAI, is a set of methods and techniques that allow us to understand how a machine learning model works and why it makes the decisions it does. Without XAI, a machine learning model might be a “black box”, where even the developers cannot understand it they arrived at a certain decision.
How does Deep Learning work and are we really still in control? Deep Learning models are evolving, as are we Your mobile phone receives an alert. You take it from your pocket and look at the screen. Face recognition unlocks the phone, and you read a message your sister just sent. You laugh and then share it by selecting a group of friends who will also find it amusing. You add an instant voice message to the share and tell the phone to send it.
What we can do for you
