Last week, I attended the WIRED Smarter conference in London and wrote an article about a presentation from the Energy track that explained how Google is using DeepMind to reduce energy consumption in its data centers.
My favorite presentation of the day came from Maria McKavanagh, the Chief operating officer at Verv, called “Creating a new energy marketplace that’s powered by data,” and brought together machine learning, IoT and blockchain.
Most efforts to utilize AI and Machine Learning to help us digest the research literature have revolved around supporting people as they attempt to "drink from the fire hose" of research coming out. With several thousand new papers every day, it’s a much-needed level of support.
This isn’t the only use case being explored, however, as a recent paper was published in PLOS Biology highlights. The paper describes the use of AI to help users as they search the PubMed database for research.
Continuous Delivery is no longer a value-enhancing strategy, it is a much-needed approach in software development and release lifecycle. It has substantially changed the way enterprises test and launch their applications in a volatile consumer ecosystem. The demand for Intelligent Applications is on the rise. In fact, it is predicted that almost all the applications will be delivered with embedded intelligence. It practically implies that these applications will have the capability to scrutinize historical and real-time data to deliver customized experiences and results to the end-users by leveraging Machine Learning technologies. Hence, testing these applications will need a relevant Test Automation strategy and a Continuous Delivery plan.
In the current Digital ecosystem, consumers are swarmed by chatbots and virtual assistants across diverse websites and applications. These features are not only automating basic activities, but are also delivering enhanced and personalized experiences. At a recent Google conference, CEO Sundar Pichai opened the event by stating, "We’re moving from a mobile-first to an AI-first world." Hence, due to growing business mandate and evolving consumer preferences, the need to build such robust applications is increasing by the day.
AI has emerged as the mainstream trend in various industries. AI might excite us and terrify us at the same time. AI is widely unknown and experts are researching day in and day out to discover how AI will affect the design industry in the upcoming days.
Since AI has become apparent in a large number of areas including medical, entertainment, sports, and more, the design industry is no exception. Intense debates are taking place between the designers and developers worldwide about how AI would influence the web design and development. Machine Learning, Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), and Deep Learning are expected to mark major footsteps in the design and development industry.
Some time ago (think early to mid-nineties), I used to run a BBS. One Christmas, I logged onto another BBS based in Manchester, and they had a "Santa Chat." I tried it for a while and was so impressed by it that I decided to write my own; it was basically the gift that kept giving. You put this thing on your BBS (basically an Eliza clone) and it records the responses of the unsuspecting users to a text file (or log).
These days there are laws against recording such things, but those were simpler times, and once they realized the joke, everyone was happy, and life went on (albeit at 14.4k bps).
It’s increasingly appreciated that AI can play a crucial role in supporting the work that we do, and a good example of how this will play out comes via a recent study from MIT CSAIL that describes how AI can help support image and multimedia editing.
The editing of images is now not only a major part of publishing and advertising, but increasingly of film making, and a big part of the process is "compositing," which is the merging of foreground and background images so that actors are placed on top of items, such as planes or planets. Making this look realistic is very hard, however.
There are two ways to categorize Machine Learning algorithms you may come across in the field.
Generally, both approaches are useful. However, we will focus in on the grouping of algorithms by similarity and go on a tour of a variety of different algorithm types.
Back in 2016 (oh how time flies), the tech industry was abuzz with the word "chatbots." Many were dubbing chatbots as the "new apps", believing that they would completely transform the way we perceive mobile technology. Although we’ll refrain from making such decisive statements, it’s true that this technology has left quite the mark, particularly in the area of user experience. Instead of replacing apps, chatbots have demonstrated an ability to enhance UX. In order to better understand this use case, we’ve handpicked five particular apps that have harnessed the power of chatbots to increase engagement and bolster retention.
Yet before diving into some cool examples, let’s take a brief glance at the way they work, to understand their power and to see how far we’ve come in terms of upgrading the digital interface.
Finance is probably one of the first fields to adopt innovations. These days, IoT, AI, and blockchain are the technologies reshaping multiple industries, especially FinTech. AI solutions attract immense investments and for the right reasons. Analysts have counted that Artificial Intelligence is going to save the industry more than a trillion dollars (!) through the year 2030.
How exactly are financial institutions planning to leverage AI? Is it already a part of the processes? Skelia dug into this issue.
Before anything else, you will need to Install Python on your machine. So, here are the steps to install Python.
You can download from the official website:
