5 Latest Technologies to Learn for 2020

. 5 min read

Emerging tech has changed the world in unprecedented ways in the last decade, making the world almost movie-like in its advancements. Space travel has come to the private sector, and someone somewhere conceived of driverless cars - possibly as a reaction to seeing several vehicular accidents. Who knows.

We’re just past the midpoint of 2019, and it is a good time to take stock of the trends that we can expect to transform our future in 2020. And perhaps gain an edge by upskilling early in those areas? So without further ado, here are the latest technologies to learn.

Augmented analytics

The next level up from natural language processing (NLP) and machine learning is the combination of the two: augmented analytics. It was coined by Gartner as the “future of data analytics”. Let’s take a closer look at what exactly this new term actually means.

As with everything else in data science, one starts with data. It is gathered from a multitude of sources, both public and private, then automatically processed to yield insights that are useful to business operations and readable by human beings.

The key difference between regular analytics and augmented analytics is that a data scientist isn’t required to interpret patterns in the data. It is understandable by the non-tech members of the team.

For instance, there is a 12% drop in sales in a certain region. There are factors that are obviously causing this downward trend, so the sales manager responsible wants to know what they are so as to combat them. To that end, the sales manager can then engage with the augmented analytics system directly, asking questions to elicit insights. The intervention of a technical professional to be the go-between is thus eliminated entirely.

This isn’t to say that organizations will be able to do away with data scientists altogether; far from it. Augmented analytics will help with the drudgery associated with analysis, like collection and cleaning of data, thus assisting data scientists in other, more intelligent aspects of their work.

Quantum computing

The next technology on our list strays into the domain of physics, at least to grasp the underlying concept: superposition

In classical computers - yes, in this context, normal computers are considered ‘classical’ - the fundamental base unit of all operations is a bit. A bit can take one of two states: 1 or 0. Never both at the same time, and thus it is considered to be binary in nature. Quantum computing uses something called a ‘qubit’ which, contrary to an ordinary bit, can hold both 1 and 0 at the same time. This phenomenon is known as superposition.

Superposition isn’t a magical state, but more the ability to predict the outcome of a state before it actually occurs. If this is done for lots of qubits, their proposed outcomes can be mathematically related to each other, another concept known as entanglement. Thus the outcomes are calculated before they ever occur.

As you can imagine, quantum computing would be invaluable in the financial sector, allowing banks to calculate risks rapidly. Same goes for the field of genetics; the human genome could be mapped much faster with this tech. As of now, molecular modelling is only possible with the simplest of molecules; quantum computing could change that.

The reason that quantum computing is so next-gen is that it is superfast. However, there are still kinks in the system, leading to unreliable outcomes. So their widespread use is still debatable as this stage.

Deep reasoning

Deep learning excels at relating inputs to outputs, when both are clearly defined. It relies on existing patterns derived from processing large datasets to make decisions. However, it doesn't have inherent reasoning, like a human being does. And that's where deep reasoning comes into the picture.

The premise of deep reasoning lies in being able to teach a system how to recognize the implicit relationships between different things. To put this into context, deep learning can learn to detect a disease rapidly based on the analysis of symptoms, checked against a library of saved symptom patterns, using decision trees to arrive at that conclusion. With deep reasoning, the system can understand what that disease is.

Deep reasoning aims to mimic human intelligence, by using common sense.

Big data

Replacing oil, data has today become the most important commodity on the global stage. There is so much data being churned out every second that traditional software techniques and normal databases cannot make sense of it any longer. Thus, Big data was born.

We all know that Big Data refers to large volumes of data that are analyzed to extract patterns through computations, which then in turn yield actionable business insights. The quantity of data makes it impossible for an individual or even a team of individuals to parse manually, and that’s where small data picks up the slack.

Big data has gained great momentum globally, as more and more companies start to use data to change their products and relationships with their customers.

But the interesting concept emerging now is Big data's little brother: Small data. The concept of Small Data isn’t a new one: it has been around for a long time, but it is experiencing a resurgence in popular consciousness.

So what is is exactly? A definition by Allen Bonde, research director at Forrester:

“Small data connects people with timely, meaningful insights (derived from big data and/or “local” sources), organized and packaged – often visually – to be accessible, understandable, and actionable for everyday tasks.”

In short, small data can be used to draw intuitive conclusions from data that can be actioned faster.

By no means is small data meant to replace big data; it is more to provide actionable insights faster, while big data is being processed.

Computer vision

Unsurprisingly, as the name suggests, computer vision is about teaching computers how to see. Simple enough, conceptually, it is incredibly hard to bring into being.

There is often confusion between digital image processing and computer vision. However, the former is quite simply a computer being able to identify components of images, whereas the latter is a computer being able to identify objects in images, understand what those objects are, and most importantly what to do about that information.

What's next?

Interested in these latest technologies to learn? GreyAtom has the program for you: Deep Learning with Computer Vision. Read more about it and how it can help boost your Data Science career further.

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