Changing the way numbers are stored in computers could improve the accuracy of calculations without needing to increase energy consumption or computing power, which could prove useful for software that needs to quickly switch between very large and small numbers. Numbers can be surprisingly difficult for computers to work with. The simplest are integers – a whole number with no decimal point or fraction.

Medical records are a rich source of health data. When combined, the information they contain can help researchers better understand diseases and treat them more effectively. But to unlock this rich resource, researchers first need to read it. We may have moved on from the days of handwritten medical notes, but the information recorded in modern electronic health records can be just as hard to access and interpret. It's an old joke that doctors' handwriting is illegible, but it turns out their typing isn't much better.

These algorithms are extremely complex. They need to understand context, long strings of words and medical concepts, distinguish current events from historic ones, identify family relationships and more. We teach them to do this by feeding them existing written information so they can learn the structure and meaning of language – in this case, publicly available English text from the internet – and then use real medical records for further improvement and testing.

Amy Barrett: So Girl Decoded was published earlier this year by Penguin Business. Can you tell me, what is your book about? Rana el Kaliouby: So my book is a memoir. It's a juxtaposition of my personal journey intertwined with my journey building emotional intelligence into technology. AB: What made you actually want to start writing it? ReK: So the initial idea was to talk about emotion A.I. or artificial emotional intelligence and kind of tease apart the different applications of the technology and the ethical and moral implications of building technology like that. But very early on, I remember meeting with the publisher Penguin, Random House, and the editor there said, you know, your story is really fascinating. I grew up in the Middle East, found my way to the US by way of studying in the UK, actually. Ane he said, that's the story, you got to interweave your personal stories. So it ended up being this, again, kind of inter woven mix of my personal background and how I went from what I call "a nice Egyptian girl" to a CEO of a tech company. AB: And what some of the biggest challenges you say you faced to getting where you are today? ReK: I think the biggest kind of challenge is that I was always kind of doing some… I'm a misfit. Like, I grew up in the Middle East, but I really wanted to be a computer scientist. I left home to do my PhD, which was quite unusual at the time because my husband at the time had to stay back in Cairo for work.

As humans, we've gotten pretty good at shaping the world around us. We can choose the molecular design of our fruits and vegetables, travel faster and further and stave off life threatening diseases with personalized medical care. However, what continues to elude our molding grasp is the airy notion of "time" – how to see further than our present moment, and ultimately how to make the most of it. As it turns out, robots might be the ones who can answer this question. Computer scientists from the University of Bonn in Germany wrote this week that they were able to design a software that could predict a sequence of events up to five minutes in the future with accuracy between 15 and 40 percent.

Most IT leaders have heard of deep learning, but few really understand how this new technology works. Deep learning burst onto the public consciousness in 2016 when Google's AlphaGo software, which was based on deep learning, beat the human world champion at the board game Go. Since then, deep learning has begun appearing in news reports and product literature with more frequency, but few organizations are actually using it today. The 2018 O'Reilly survey report How Companies Are Putting AI to Work Through Deep Learning found that only 28% of the more than 3,300 respondents were currently using deep learning. However, 92% believed that deep learning would play a role in their future projects, with 54% saying it would play a large or essential role in those initiatives.

It could be argued there is only one proven Big Data application -- web search. Nothing so far has met the sheer size and complexity of indexing the web at the precision, recall, and freshness Google delivers. In its quest to structure the web well beyond text documents, around 2011 Google realized it had to fundamentally change the way it was indexing images. Google's DistBelief system -- the inception of the newly formed Google Brain team -- pushed the boundaries of how deep learning could be applied to massive problems by training on a highly distributed configuration of thousands of CPUs. The publication of this system marked a key milestone for Google and the tech industry at-large. By applying the deep learning techniques Geoff Hinton and Yann LeCun had been researching for over a decade, Google was finally able to create a production system that could scale to understand and structure information from images.

Last Friday, we mentioned how Google's artificial intelligence software DeepMind has the ability to teach itself many things. It can teach itself how to walk, jump and run. Or defeat the world's best player of the Chinese strategy game, Go. The science of teaching computers how to do things is called Deep Learning. Offered through Udacity, the course is taught by Vincent Vanhoucke, the technical lead in Google's Brain team.

Artificial Intelligence (AI) and Machine Learning (ML) are two very hot buzzwords right now, and often seem to be used interchangeably. They are not quite the same thing, but the perception that they are can sometimes lead to some confusion. So I thought it would be worth writing a piece to explain the difference. Both terms crop up very frequently when the topic is Big Data, analytics, and the broader waves of technological change which are sweeping through our world. Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider "smart".

Wondering whether you should invest in AI and Machine Learning? That's a question that the most innovative companies are considering. One good reason is because your competitors have already started. If that doesn't give you some reason to get motivated, I hope you get started before you are put out of business. To make sure that doesn't happen, there are a few things to consider to help you start to explore an investment in machine learning.