This week, what attracts you to a career in Data Science?
Welcome to The Digital Eye, your weekly roundup of the latest technology news.
Our team of experts has scoured the internet for the most exciting and informative articles so that you can stay up-to-date on Digital, Data, Blockchain, AI & Analytics, and Digital Transformation.
Fintech investment slows around the world
Once a key driver of global venture activity, investments slow.
Worth around a fifth of all venture dollars invested last year, fintech startups raised nearly unfathomable sums of capital but with good reason. While companies around the world turned to software during the pandemic to ensure that they could keep operating, accelerating the digital transformation, there has been analogous work going on in the consumer world.
In simple terms, financial technology has been busy digitizing consumers’ lives in recent years, just as enterprise software helped corporations ditch pencils, paper and generic spreadsheets. So it is not a huge surprise that fintech had a big part to play in the venture boom that is now behind us. Nor that as the boom faded, fintech did as well.
Why Insurance businesses cannot lag on digital
Jen Frost, of Insurance Business, sat down with Maha Santaram, Aspire Systems insurance practice leader, to discuss technology trends in the insurance market.
Carriers that do not invest in migrating from legacy platforms will either be “left over” or bought up, according to Aspire Systems insurance practice leader Maha Santaram.
The technology expert recommended that insurance businesses still running on legacy get on board and start to take an “incremental approach”, else get left behind their peers
Nor can agents rest on their laurels – insurance brokers should be looking to banking, where customers are often able to fill out details on a tablet or phone in-branch to speed up onboarding.
Quantum-Aided Machine Learning Shows Its Value
A machine-learning algorithm that includes a quantum circuit generates realistic handwritten digits and performs better than its classical counterpart.
Machine learning allows computers to recognize complex patterns such as faces and also to create new and realistic-looking examples of such patterns. Working toward improving these techniques, researchers have now given the first clear demonstration of a quantum algorithm performing well when generating these realistic examples, in this case, creating authentic-looking handwritten digits . The researchers see the result as an important step toward building quantum devices able to go beyond the capabilities of classical machine learning.
The most common use of neural networks is classification—recognizing handwritten letters, for example. But researchers increasingly aim to use algorithms on more creative tasks such as generating new and realistic artworks, pieces of music, or human faces. These so-called generative neural networks can also be used in automated editing of photos—to remove unwanted details, such as rain.
A metaverse fintech ecosystem: What the future of finance looks like
The Metaverse concept is predicted to grow as the digital universe for personal and business interactions, including financial transactions.
Metaverse unfolds a new dimension, a new universe – a space where our real world, augmented reality, and virtual reality intersect, seeding an immersive and collaborative shared virtual 3D environment. In the metaverse-sphere, cryptocurrency and digital art, namely non-fungible tokens, are commonplace. While technocrats, gaming platforms and social media fans are busy talking about the extraordinary experience one will witness in the metaverse, people are looking at alternative digital possibilities and eager to find new pathways to seamless financial transactions.
Sighting the near future, Metaverse has not only become the new favourite of large technology companies, but also the new favourite of the investment industry.
Upgrading AI proving tougher for insurers
Insurance has a heightened challenge of being able to get organizational life and culture driving towards the adoption and embracing of AI.
Businesses and corporations are increasingly interested in applying artificial intelligence (AI) to their operations, but see themselves falling behind in implementing AI, according to a research survey report from Talkdesk with relevance for insurtech.
Insurance has a “heightened challenge of being able to get organizational life and culture driving towards the adoption and embracing of AI,” said Antonio Gonzalez, senior manager, industries and AI research at Talkdesk.
Talkdesk surveyed 500 customer experience professionals from the U.S., Canada, France, Germany and the U.K., with about 7% of respondents from the financial services sector including insurance. 85% of these professionals said it’s important to leverage AI and automation, while 35% of these professionals said their organizations are advanced in their application of AI, down from 41% in last year’s edition of the survey.
What Attracts You To A Career In Data Science?
Ten years ago, the authors posited that being a data scientist was the “sexiest job of the 21st century.” A decade later, does the claim stand up? The job has grown in popularity and is generally well-paid, and the field is projected to experience more growth than almost any other by 2029. But the job has changed, in both large and small ways.
Ten years ago we published the article “Data Scientist: Sexiest Job of the 21st Century.”
Most casual readers probably remember only the “sexiest” modifier — a comment on their demand in the marketplace. The role was relatively new at the time, but as more companies attempted to make sense of big data, they realized they needed people who could combine programming, analytics, and experimentation skills. At the time, that demand was largely restricted to the San Francisco Bay Area and a few other coastal cities. Startups and tech firms in those areas seemed to want all the data scientists they could hire. We felt that the need would expand as mainstream companies embraced both business analytics and new forms and volumes of data.
At the time, we defined the data scientist as “a high-ranking professional with the training and curiosity to make discoveries in the world of big data.” Companies were beginning to analyze voluminous and less-structured data like online clickstreams, social media, and images and speech. Because there wasn’t yet a well-defined career path for people who could program with and analyze such data, data scientists had diverse educational backgrounds.
The most common qualification in our informal survey of 35 data scientists at the time was a PhD in experimental physics, but we also found astronomers, psychologists, and meteorologists. Most had PhDs in some scientific field, were exceptional at math, and knew how to code. Given the absence of tools and processes at the time to perform their roles, they were also good at experimentation and invention. It’s not that a science PhD was really required to do the work, but rather that these individuals had the rare ability to unlock the potential of data, wading through complex, messy data sets and building recommendation algorithms.