This week, AI Literacy – 3 fabulous things you need to know to become an expert.
Welcome to The Digital Eye, your weekly roundup of the latest technology news.
Our team of experts have scoured the internet for the most exciting and informative articles so that you can stay up-to-date on all things digital, data, blockchain, AI & analytics.
- How AI and ML are transforming data quality management?
- Toward Optoelectronic Chips That Mimic the Human Brain
- Data driven insurance companies will be frontrunners of their industry
- AI Could Monitor Drivers More Closely for Danger
- Humans are incapable of effectively preventing identity fraud. Here’s how AI can help
We hope you find this information valuable and would appreciate your help in sharing it with others who may also be interested.
In recent years technology has become prominent, both at work and at home. Machine learning (ML) and Artificial Intelligence (AI) are evolving quickly today. Almost everyone will have some interaction with a form of AI daily.
Some common examples include Siri, Google Maps, Netflix, and Social media (Facebook/Snapchat).AI and ML have popularly used buzzwords right now, often used interchangeably. Most experimentation has been geared to finding specific solutions to specific problems. Artificial Intelligence (AI) is an application in which a machine can perform human-like tasks.
At the same time, Machine Learning (ML) is a system that can automatically learn and improve from experience without being directly programmed.
Article by @IEEESpectrum
Toward Optoelectronic Chips That Mimic the Human Brain
An interview with a NIST researcher keen to improve spiking neuromorphic networks
IEEE Spectrum recently spoke with Jeffrey Shainline, a physicist at the National Institute of Standards and Technology in Boulder, Colo., whose work may shine some light on this question. Shainline is pursuing an approach to computing that can power advanced forms of artificial intelligence—so-called spiking neural networks, which more closely mimic the way the brain works compared with the kind of artificial neural networks that are widely deployed now.
Today, the dominant paradigm uses software running on digital computers to create artificial neural networks that have multiple layers of neurons. These “deep” artificial neural networks have proved immensely successful, but they require enormous computing resources and energy to run. And those energy requirements are growing quickly: in particular, the calculations involved in training deep neural networks are becoming unsustainable.
Article by @ITI_Insurtech
Data driven insurance companies will be frontrunners of their industry
Extracting value from that data is easier said than done, with many data transformation projects across the world failing to reap their full benefits.
Insurance companies today (both property & casualty, health and life insurance) face a range of challenges due to several converging developments in society, the technology space, and the world of insurance.
These developments are forcing insurers to take significant steps in future-proofing their operating model, including taking steps to improve processes, automation of tasks, digitization, and how customer needs are satisfied.
Article by @lifewiretech
AI Could Monitor Drivers More Closely for Danger
Your robotic minder
Car systems that use increasingly sophisticated artificial intelligence (AI) could keep you safer by monitoring your driving, but some experts say AI isn’t ready to replace human drivers.
Toyota is developing a system called Guardian that uses a dashboard camera to check to see if a driver falls asleep. It’s part of a growing movement to increase automation in vehicles, but some experts say we’re a long way off from cars that are safe enough to fully drive themselves.
“I’ve been a bit of a skeptic of full automation in terms of the timelines,” MIT professor John Leonard, who is working on Guardian, said at a recent MIT Mobility Forum, according to the news release. “[It] is going to take a lot longer to have this sort of ubiquitous robo taxi fleet, whereby, you know, a teenager today would never need a driver’s license or never need to have a real human Uber driver because all cars would drive themselves autonomously.”
Article by @FastCompany
Humans are incapable of effectively preventing identity fraud. Here’s how AI can help
Fraudsters will always find an exploitable vulnerability in ID verification until predictability and human empathy are entirely disengaged from the process.
Do you think you could spot the differences between a fake ID and a person standing in front of you? Now add the pressures of a lineup of impatient people, a tight schedule, and a long working day; how confident would you be in your continuous accuracy? There’s no shame in admitting you’d make mistakes.
While it’s fair to consider the negative impacts of exhaustion, poor lighting, and time constraints, the heart of the problem is this: humans are not wired to identify fraud accurately.
Article by @Forbes
AI Literacy is fast becoming a requirement for professionals from all industries. I recently participated in an overview of AI for Finance Professionals, organized by SLASSCOM Sri Lanka for finance professionals in Asia. Here are the key items that I covered:
- AI can seem intimidating. It was only recently (and sometimes even now!) that many people believed that AI is only accessible to those with Ph.Ds and deep knowledge of math. This is not true however. If you want to create new types of AI, yes this level of knowledge is required. It is however not required if your goal is to use AI in your domain (where you have relevant expertise). In this case, it is only required that you understand enough about AI to know how to apply it effectively in your domain, understand what tools and services are available to you, and be aware of what AI regulations you will need to follow for your domain to use the AI safely and securely.
- The rest of this article answers these three questions for the finance industry in general.