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Latest AI Opinions


10 data storytelling mistakes to avoid

We live in a data-saturated world. But data only matters if you can turn information into action. To get from one to the other, you need to tell a story about your data that any person at your company can understand. 

Why? Because numbers and charts don’t motivate people to seek better outcomes. Stories do.  

So how do you tell these stories? 

Here are ten mistakes to avoid in order to tell a good story (about your data) that will drive strategic business.


Automation – the bedrock of creating value

According to the 2019 Gartner fourth annual Chief Data Officer (CDO) Survey, the implementation of a data and analytics strategy was ranked the third most-critical success factor when it comes to a CDO’s organisation.

When it comes to data, we’re all aware of the four ‘Vs’ – variety, velocity, veracity and volume – yet for many organisations, their data warehousing infrastructure is no longer equipped to handle them. Additionally, value, the fifth ‘V’, is even more elusive. So, taking into account the scale of data that many modern companies have means that meeting these challenges requires a new approach – with automation being the foundation.


How Verizon deploys machine learning to improve energy efficiency

Over the past few decades, technological advances have enabled telecom service providers to consolidate their network infrastructure. The reduction in equipment size, coupled with an improved ability to bridge larger distances, has also allowed service providers to reduce their data centre footprint.

But with the reduction in data centre sites, the modern facility has also become comparatively supersized and energy hungry, mainly due to the never-ending increase of network traffic. To mitigate this challenge, companies such as Verizon are turning to machine learning and data analytics to improve the energy efficiency of facilities.


Cyber-physical systems explained

Cyber-Physical Systems (CPS) are a mix of computation, networking and physical processes, in which the embedded computational algorithms and networks have the power to monitor and control the physical components.

By using a combination of machines, sensory devices, embedded computational intelligence and various communication mechanisms, CPS monitor physical elements with computer-based algorithms tied to the internet. This means they are capable of autonomously functioning based on their physical surroundings.

In light of advancements in analytics, artificial intelligence (AI) and communications, there is increased demand for intelligent machines that can interact with the environment around them, such as driverless cars which monitor and communicate with their surroundings, and smart appliances that optimise energy consumption. CPS are stimulating significant changes in quality of life and forming the basis of smart infrastructure, products, and services.

As this kind of technology continues to become more integrated into our everyday lives, here are four areas of CPS we can expect to come to the fore.


Identity and AI: The recipe for a strong fraud defence

The market has long demanded quicker and more convenient payment methods, and the industry is now answering with ubiquitous payments. Yet at what cost? PSD2 and open banking are now established in the legislative framework, and many third-party providers are springing up with new offerings that democratise access to payments and offer complementary value-added services.

Banks and other financial organisations are already seeing a surge in the volume and value of electronic transactions through digitalisation. And new channels, like PSD2, are set to exacerbate the pressure on existing fraud defences. Faster payments, through SWIFT gpi and other means, virtually eliminate the window of investigation. And, therefore, necessitate automated real-time detection.


Big Data World Frankfurt 2019: The real risk that society will reject AI
The career of Big Data World Frankfurt speaker Bruno Kramm has spanned gaming, music and politics. Ahead of his appearance at Messe Frankfurt on November 14, Techerati spoke to Bruno about his new organisation Digitalgrid, which focuses on the cultural and social effects of machine learning, artificial intelligence, and the comprehensive automation and digitisation of... Read More

Big Data World Asia: Machine learning in insurance, with Munich Re’s regional head of data analytics

While machine learning has transformed many industries over the past decade, one area that is still playing catch-up is insurance. It’s a sector used to finding itself trailing behind other industries’ tech adoption, where high running costs of legacy systems squeeze budgets to such an extent that it’s hard for firms to stump up the cash necessary for driving innovation. While online comparison services have proliferated in recent years, signing up to and managing the policy invariably involves the pens, paper, and printers that other digitally-transformed industries have long since left behind. 


How open source is accelerating innovation in AI

No other technology has captured the world’s imagination quite like AI, and there is perhaps no other that has been as disruptive. AI has already transformed the lives of people and businesses and will continue to do so in endless ways as more startups uncover its potential. According to a recent study, venture capital funding for AI startups in the UK increased by more than 200 percent last year, while a Stanford University study observed a 14-times increase in the number of AI startups worldwide in the last two years.


Will AI ever be emotionally intelligent?

Being able to understand another’s emotions is undeniably a vital and defining characteristic for anyone looking to be successful in a customer-facing role.

But as we enter a new digital era, artificial intelligence (AI)-supported technology like chatbots are increasingly automating more of the customer experience. And more is on its way, from AI that can decipher good from bad calls, to machine learning that can translate phone conversations in real time.


ad:tech 2019: How technology is enhancing one-to-one cultural experiences

Ahead of Eleni Sarla’s presentation at ad:tech London in September, Techerati sat down with the self-described culture vulture to discover how she is finding her new leadership role, why she decided to move from a media agency to an arts and entertainment specialist, and how technology is being used to heighten one-to-one cultural experiences.


AI at the edge – challenges and opportunities

The convergence of AI and edge computing is still in its infancy, but together they hold the potential to revolutionise the lives of consumers and businesses alike. But it is not a marriage without its challenges.


eCommerce Expo 2019: How Simon Calvert helped Bonmarché balance machine learning with human intelligence

As of this summer, the fashion retailer is using machine learning to ensure it has the right stock, in the right place, at the right time. Techerati talked to eCommerce Expo speaker Simon Calvert about his role in helping the retailer intelligently pre-empt markdown.



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