Machine learning has evolved from an out-of-favor subdiscipline of computer science and artificial intelligence (AI) to a leading-edge frontier of research in both AI and computer systems architecture. Over the past decade investments in both hardware and software for machine learning have risen at an exponential rate matched only by similar investments in blockchain technology. This column is a technology check for professionals in a Q&A format on how this field has evolved and what big questions it faces. Q: The modern surge in AI is powered by neural networks. When did the neural network field start?

For more content like this, follow Insight and Emmanuel on Twitter. When trying to develop a scientific understanding of the world, most fields start with broad strokes before exploring important details. In Physics for example, we start with simple models (Newtonian physics) and progressively dive into more complex ones (Relativity) as we learn which of our initial assumptions were wrong. This allows us to solve problems efficiently, by reasoning at the simplest useful level. The exact same approach of starting with a very simple model can be applied to machine learning engineering, and it usually proves very valuable.

Deep learning, despite its remarkable successes, is a young field. While models called artificial neural networks have been studied for decades, much of that work seems only tenuously connected to modern results. It's often the case that young fields start in a very ad-hoc manner. Later, the mature field is understood very differently than it was understood by its early practitioners. For example, in taxonomy, people have grouped plants and animals for thousands of years, but the way we understood what we were doing changed a lot in light of evolution and molecular biology. In chemistry, we have explored chemical reactions for a long time, but what we understood ourselves to do changed a lot with the discovery of irreducible elements, and again later with models of the atom.

Whenever a hot new field starts to take off, you'll inevitably hear sighs of regret by the many who wish they'd gotten into it when they had the chance. The billion-dollar question is, why didn't they? The answer is, they chose not to. That's what separates successful people from the pack: the choices they make. Web service leaders Amazon, Google and Microsoft are scooping up talent and buying startups left and right in a race for facial and speech recognition technology used in cloud-based searches and other red-hot machine learning applications.

Join Entrepreneur's The Goal Standard Challenge and make 2017 yours. Whenever a hot new field starts to take off, you'll inevitably hear sighs of regret by the many who wish they'd gotten into it when they had the chance. The billion-dollar question is, why didn't they? The answer is, they chose not to. That's what separates successful people from the pack: the choices they make.

A grid maze is a binary matrix where fields containing a 0 are accessible while fields containing a 1 are blocked. A movement sequence consists of relative movements up, down, left, right – moving to a blocked field results in non-movement. The simultaneous maze solving problem asks for the shortest movement sequence starting in the upper left corner and visiting the lower right corner for all mazes of size n × m (for which a path from the upper left to the lower right corner exists at all). We present a theoretical problem analysis, including hardness results and a cubic upper bound on the sequence length. In addition, we describe several approaches to practically compute solving sequences and lower bounds despite the high combinatorial complexity of the problem.

The UK is at the forefront of the machine learning revolution, and the Early Stage investment team here at Amadeus is an active investor in the field. Start-ups applying artificial intelligence ('AI') and machine learning to a vast range of products come onto our radar almost daily, but investing in these complex technologies is not for the faint-hearted. So, I thought I would share some insights on start-ups we have backed and'where next?' for investors in a sector offering apparently endless possibilities. First, a quick analogy to explain what the difference between rule-based and machine learning-based systems is: remember when you were taught it was safe to cross a road when the light was green but not when it was red? That's just like old-fashioned computer programming: a set of conditions is described with a rule, or an'IF this, THEN that' statement, and some action or operation is chosen.