BACKGROUND

These are exciting times for data science, artificial intelligence and everyone involved with data in general. Often perceived to be a few steps behind industries, governments are fast realising the importance of regulation, investment and innovation and they’re upping their game too. In the United Kingdom, data policy and governance were controlled by the Cabinet Office until February 2018, when the Department for Digital, Culture, Media and Sport (DCMS) was tasked with them. This move was controversial at first, but it necessitated the building of a young and dynamic team and the creation of a new approach. …


Case study — Lloyd’s of London

Data literacy is a relatively new concept. The expression showed up in the past decade and quickly became a buzzword around all industries. By now, companies are considering it one of their top priorities, and the importance of a “data driven culture”, “all around data literacy” and the role of the emerging star position, the Chief Data Officer are being increasingly emphasised at industry meetings, conferences and in countless articles.


A couple of weeks ago we introduced 10 terms everyone should be familiar with in the areas of data science and artificial intelligence. As a follow-up, we thought we should go a little deeper and examine a few more:

Data scientists and data engineers

These two professions (yet to be formally recognised or supported by a standards body) have emerged from the data revolution are arguably now among the most sought-after job roles. Companies are expected to need and hire more people in these categories, and the growth rate alone in the last five years has been 256%. Both data scientists and data engineers use data analytics and programming skills in their workloads. The difference lies in their focus. …


Lessons AI can learn from history’s tech advances — and must learn now!

Artificial intelligence is now the undisputed star sector of the tech industry. Billions of dollars, pounds, yuans and euros are being pumped into AI research, development and business and the projected contribution of the sector for 2030 (that’s just 11 years away) is 15 trillion dollars. A growth rate this large will inevitably brings pitfalls with it. Pitfalls, dead ends and less than ideal solutions can bring disappointment, bad publicity and without a doubt millions of working hours and investments lost.

What can the AI sector learn from other sectors? How can we work smarter to develop responsible machines, systems and algorithms? All of these things will increasingly govern our lives. …


Is your data big enough to be called “big data”? Are you confused about how artificial intelligence and machine learning relate to each other?

We’ve collected some definitions to help you navigate through the maze of today’s tech buzzwords.

Data

Data is at the very heart of the 4th industrial revolution happening today. Data is defined in the Cambridge Dictionary as “information, especially facts or numbers, collected to be examined and considered and used to help decision-making, or information in an electronic form that can be stored and used by a computer.” …


We are now in the midst of the 4th industrial revolution characterised by many as “’a fusion of technologies blurring the lines between the physical, digital, and biological spheres.

Data is right at the epicentre of this revolution: it continues to turn entire industries upside down (e.g.transport) and has changed leadership directives at many large companies — and the disciplines of public relations (PR) and communications as well as the organisations, business and brands professionals work for are not immune from this revolution — in fact they need to be at the forefront of it!


Why we must have diversity baked in!

I recently posted on LinkedIn a wonderful New York Times article about the pioneering women of computer programming and the reasons for their fall in numbers. From the dawn of computer science to today, a remarkable cultural transformation has taken place in the Western World that created today’s “white men” culture of programming, computer science and data science. The article is well worth reading (do use it for your free allowance if you are not a NYT subscriber), and I posted it with the following intro:

“We must achieve more diversity in technology, including women, minorities, people of all ages and abilities.”


Disputes over unfair scoring and judging in sports are as old as sports themselves are. In many sports like athletics, tennis and football, modern technologies, photography and film have been around for a while and have assisted line and finish line judges for decades. Goal-line technology, approved by FIFA in 2012, finally put an end to goal disputes at major football events where it’s been put to use. Unfortunately in some other sports, like figure skating and gymnastics, where skills are performed in split seconds and artistic presentation also affects the final score, the controversies are still ongoing and seem to plague major competitions, even high profile international ones like the Olympics. In some of these sports medals are lost and won over just a few degrees difference in the athlete’s rotation in jumps, bent elbows or knees, and wobbles or steps during landing. …



Some burning questions regarding data security, data breaches and the prosecution of hackers have been brought up in Hungary, fuelled on by an ongoing criminal trial. At the centre of the case is a young, unnamed IT student, now dubbed the “ethical hacker” who discovered a major security flaw in the customer database of Hungary’s leading telecommunication provider, the German-based Telekom. After he was able to get access to confidential data records, he duly disclosed his findings and alerted the company to its vulnerability. …

About

Bogi Szalacsi

Senior Associate, infoNation

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