Collaborating Authors

An Introduction to Federated Learning


We all have experienced the benefits of crowdsourcing. In mobile apps such as Waze, clients using the platform can report all sorts of driving conditions such as car crashes, traffic jams, police speed traps, cars parked on the side of the road, etc. In turn, other users of the platform can take advantage of such collaboration to make better driving decisions. As a simple example, if there is an intense traffic jam on a given road, Waze might choose a different route to reach my destination. Similarly, when looking for a hotel to stay the next family holiday, we usually read multiple reviews from previous customers.

Automation Anywhere wants to bring software robots into the call center - SiliconANGLE


Robotic process automation firm Automation Anywhere Inc. is bringing its smarts into contact centers, helping call center agents become more efficient by quickly surfacing the information they need to handle customer calls. Automation Anywhere is widely considered one of the leaders in the fast-growing RPA market, valued at north of $6.8 billion. The company's Automation 360 platform enables companies to create artificial intelligence-based robots that can assume numerous repetitive manual tasks in a business that are normally performed by human workers. The bots work by observing how humans carry out these tasks, which might including copying records to and from different business applications, for example. They teach themselves how to replicate these workflows so they can be automated in future.

How to Use AI to Fight Financial Crime


Artificial intelligence (A.I.) is heavily used in Big Data and when it comes to the analysis of customers' behaviour. There's also anti-money laundering (AML) AI; it's used to fight financial crime and guard the reputation of app providers. FinTech is about trust, after all. How exactly is it done and how can you benefit? Because cybercrime is serious, there is a special word for it in the financial world.

Discovering exoplanets using artificial intelligence


By implementing artificial intelligence techniques similar to those used in autonomous cars, a team from the UNIGE and the UniBE, in partnership with the company Disaitek, has discovered a new method for detecting exoplanets. The majority of exoplanets discovered to date have been discovered using the transit method. This technique is based on a mini eclipse caused when a planet passes in front of its star. The decrease in luminosity observed makes it possible to deduce the existence of a planet and to estimate its diameter, after the observations have been periodically confirmed. However, theory predicts that in many planetary systems, interactions between planets alter this periodicity and make their detection impossible.

The Four Pillars of Trusted AI


I recently started an AI-focused educational newsletter, that already has over 100,000 subscribers. TheSequence is a no-BS (meaning no hype, no news etc) ML-oriented newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Quantifying trust and fairness is one of the most important challenges to ensure the mainstream adoption of deep learning systems. But what does trust truly means in the context of deep learning systems?

What is Machine Learning?


Machine learning is a branch of artificial intelligence (AI) and computer science that focuses on using data and algorithms to simulate the way humans learn, gradually increasing its accuracy. IBM has a rich history of machine learning. One of them, Arthur Samuel, is famous for coining the term "machine learning" in his research on the game of checkers. Robert Neely, a self-proclaimed checkers master, played the game on an IBM 7094 computer in 1962 and lost to the computer. This feat appears almost trivial in comparison to what can be done today, but it is regarded as a significant milestone in the field of artificial intelligence. Over the next two decades, data storage and processing technology will create some of the innovative products we know and love today, like the Netflix recommendation engine or self-driving cars.

First Steps Towards an Ethics of Robots and Artificial Intelligence


This article offers an overview of the main first-order ethical questions raised by robots and Artificial Intelligence (RAIs) under five broad rubrics: functionality, inherent significance, rights and responsibilities, side-effects, and threats. The

Cloud, microservices, and data mess? Graph, ontology, and application fabric to the rescue.


How do you solve the age-old data integration issue? We addressed this in one of the first articles we wrote for this column back in 2016. It was a time when key terms and trends that dominate today's landscape, such as knowledge graphs and data fabric, were under the radar at best. Data integration may not sound as deliciously intriguing as AI or machine learning tidbits sprinkled on vanilla apps. Still, it is the bread and butter of many, the enabler of all cool things using data, and a premium use case for concepts underpinning AI, we argued back then.

Robot taxi boats take to the water in Amsterdam


An autonomous boat taxi, years in the making, has now made it to the waterways of Amsterdam. The self-driving boat, dubbed Roboat III, is the creation of MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), the Senseable City Laboratory, and the Amsterdam Institute for Advanced Metropolitan Solutions. First developed and tested in pool environments back in 2015, Roboat has undergone extensive redesigns to become a full-scale, autonomous'taxi' service for city residents. Last year's version, Roboat II, was a blocky, eyesore-yellow model, two meters in length, that was able to carry two passengers. Despite its appearance, the technology behind the boat -- including sensors and mapping technologies -- meant that the boat was safely able to navigate itself for three hours with an error margin of 0.17m.

MIT will deploy robotic boats in Amsterdam that can carry five passengers


MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) is ready to deploy the autonomous passenger boat it's been developing over the past six years. The vehicle, called the Roboat, has been through multiple iterations -- just last year, the lab tested a version that can carry two passengers. This year, Roboat's creators are launching its full-scale version, which can carry up to five passengers, collect waste and deliver goods, in Amsterdam. The current Roboat has futuristic looks with its black and grey design and two seats facing each other. It's fully electric with 10 hours of battery life on a single charge and has wireless charging capabilities. MIT CSAIL Director Daniela Rus says it's more precise and has more robust perception, navigation and control systems that its predecessors.