Machines can’t read text or look at images like us: they need their inputs to be transformed into numbers in order to perform calculations. Machine learning algorithms operate on a numeric feature space, even if they involve text data.
But how do you turn text into numbers?
Vectors and matrices represent inputs like text as numbers, so that we can train and deploy our models.
When we use vectors as inputs, the main use is their ability to encode information in a format that our model can process, and then output something useful to our end goal. …
I’m a huge fan of Lex Fridman and the awesome content he produces to promote ideas and advances in different sciences. In this regard, I want to share some of the concepts that blew my mind when I first heard them in his Podcasts.
He is the President and Chief Scientific Officer of the Allen Institute for Brain Science in Seattle. From 1986 until 2013, he was a professor at CalTech. Cited more than 105,000 times. Author of several books including “Consciousness: Confessions of a Romantic Reductionist”.
I’m sure you’ve heard about Deep Learning, and the awesome accomplishments this discipline has reached in the past years. Whether is solving protein structures or beating the South Korean Go champion Lee Se-dol (causing him to retire), Deep Learning has been all over the news recently.
But what is Deep Learning exactly?
Deep Learning is a subset of Machine Learning where Artificial Neural Networks (ANNs), which are algorithms inspired by the human brain, learn from large amounts of data.
Deep Learning uses a multi-layered structure of ANNs, enabling models to disentangle the kinds of complex and hierarchical patterns found in…
Natural Language Processing (NLP) is probably the hottest topic in Artificial Intelligence (AI) right now. After the breakthrough of GPT-3 with its ability to write essays, code and also create images from text, Google announced its new trillion-parameter AI language model that’s almost 6 times bigger than GPT-3. These are massive advances in the discipline that keep pushing the boundaries to new limits.
How is this possible? How can machines interact with human language? There are dozens of subfields in NLP, but we must start with the basics. In another post I went through some tips on how to begin…
Natural Language Processing (NLP) is one of the most exciting fields in Artificial Intelligence. It allows machines to process and understand human language in a variety of ways, and it’s triggering a revolution in the way we interact with systems and technology.
In a previous post I talked about NLP, its real-world applications, and some of its core concepts. Now I want to show you that NLP is as real as it gets, and anyone can start learning it. How? Let’s start with a simple text, and perform some Exploratory Data Analysis (EDA) around it using some NLP techniques. …
I’m a huge fan of Lex Fridman and the great content he produces to promote ideas and advances in different sciences. In this regard, I selected some of the Artificial Intelligence (AI) concepts that blew my mind when I first heard them on the several interviews he shares through his Podcast.
He is a professor of Computer Science at UC Berkeley and a co-author of the book “Artificial Intelligence: A Modern Approach”.
Do you think your actions are the result of your own free choices? What if those actions are the inevitable and necessary consequence of antecedent states of affairs? What does this mean for your free will?
In a deterministic world where there’s an exclusive future for all our actions, digital users can become more predictable and monetizable than ever. In fact, by using creative designs and deceptive strategies, companies can create deterministic worlds and exploit the fact that human behaviour is hardwired to choose the path of least resistance.
You might be wondering if machines are a threat to the world we live in, or if they’re just another tool in our quest to improve ourselves. If you think that AI is just another tool, you might be surprised to hear that some of the biggest names in technology have a clear concern for it. As Mark Ralston wrote, “The great fear of machine intelligence is that it may take over our jobs, our economies, and our governments”.
If you disagree with this idea, that’s OK, because I didn’t write the previous paragraph. An Artificial Intelligence (AI) solution did…
Modern fake news has evolved into a complex organism, carefully designed to hide its deceptive mechanisms from any potential victim. But although the fake news field has grown into every possible digital channel, the truth is that at its core, in order to be effective, it still relies on exploiting our most basic human characteristics.
Almost 200 years ago, Arthur Schopenhauer examined different methods that work to produce a victory in an argument, regardless of where the truth lies. And the tricks he describes are meant to persuade not just the opponent but, more importantly, the audience of the dispute…
Interpretability is one of the biggest challenges in machine learning. A model has more interpretability than another one if its decisions are easier for a human to comprehend. Some models are so complex and are internally structured in such a way that it’s almost impossible to understand how they reached their final results. These black boxes seem to break the association between raw data and final output, since several processes happen in between.
But in the universe of machine learning algorithms, some models are more transparent than others. Decision Trees are definitely one of them, and Linear Regression models are…