Shamayita Acharya

Ever since Fritz Lang explored artificial intelligence (AI) in Metropolis (1927), filmmakers have used the idea to create tales of enslaved humans (Terminator, 1984) or explore our relationship with technology (Her, 2013). But AI in cinema is more than just a common theme to explore future inner and outer worlds; it’s increasingly an integral part of the filmmaking process itself. Like the introduction of sound in 1929, AI exhibits a similar potential to revolutionize the cinematic experience, both for filmmakers and audiences. Here are a few of the inroads AI is making on the silver screen.  Upscaling to Higher Resolutions A recent example of artificial intelligence and its merger with cinema is the recent documentary Apollo’s New Moon, made by MagellanTV. In an interview with the director of the documentary, David Sky Brody, he explained how AI technology was used to create the film. Brody and his team enhanced original NASA film footage from Apollo mission moon landings to 4K re

Tech giant Google is known to constantly release new projects in the market; some of them are huge hits and then there are some that just go unnoticed by a considerable margin. Speaking of huge hits, one of them is Flutter, Google's very own cross-platform app development framework that has been stealing the spotlight for the past few months. Well, to be exact, the whole hybrid mobile app development started back in 2011 when Xamarin decided to release its own solution i.e., Xamarin SDK (Software Development Kit) with C# for building hybrid mobile applications. Soon after, other companies decided to follow suit and that’s when Google launched the flutter framework to give some tough competition to the already existing market. Now the question that comes into the minds of app developers is ‘why does flutter use dart?’ This question is already haunting a majority of mobile app developers who are skilled in programming languages other than Google’s Dart but wish to make use of the

We know that artificial intelligence and machine learning seem to be all the rage right now, but there are also other equally powerful technologies. They stand to deliver as many benefits to pretty much every industry they touch. While that is an admittedly ambiguous statement, we’ll be more specific — augmented reality is a potent tool with the scope for application in virtually every single industry. Sure, some may tell you that augmented reality is too new a technology to have any real potential, but that couldn’t be any further away from the truth. From education to manufacturing and IT, you will be challenged to come up with one industry where augmented reality doesn’t offer a potential application. However, there’s one particularly exciting industry that offers unprecedented scope for both integrations of augmented reality as well as benefiting immensely from it. Say hello to the events industry. According to an international study, 87 percent of event planners have already org

Needless to say, there are many different factors that go into running a successful business or organization. Depending on what type it is, that business or organization will focus on different factors of success. However, the one thing that they all can agree on is the importance of data. No matter what type of data it is, data is what allows for growth and sustainability within a market. Specifically, it can be used for several reasons such as measuring performance, spotting weak points, and also transferring other data. That being said, perhaps even more important than analyzing data is the speed in which it can be done. In most cases, faster data analysis means more time to work on other areas. In the grand span of things, faster data analysis means greater success. With this in mind, let's go over six tips for speeding up data analysis. #1: Have Regular Data Cleaning Sweeps One of the first steps in having a faster data analysis process is cleaning it. To be more specifi

When organisations adopt new technologies, the context of that technology plays an important role. How people deal with the material properties of new technology is informed by their previous experience of using or not using similar technologies in the past. Since blockchain is still a new technology, how organisations adopt this technology also depends on how existing and related challenges are resolved. These affect how organisations apply blockchain and smart contracts, and whether the design and decision-making capabilities within will or can change at short notice. Currently, many enterprises are experimenting with blockchain, but few have implemented a decentralised solution. According to a 2019 Deloitte report on enterprise blockchain adoption, 53% of the 1386 interviewed executives stated that blockchain had become a critical priority. However, only 23 per cent have actually initiated a blockchain deployment. While blockchain is gaining traction and acceptance in more industr

Big data has dramatically changed the way most businesses operate. While the power of big data has been around for a while, it is only in recent years that companies across various industries have started leveraging it to its full potential. In its simplest form, big data is a collection of information from many sources. This data is usually collected in one of three ways: Asking users directly for permission to collect their information Tracking software  Cookies that collect data on users as they browse the web  It shouldn’t come as a surprise that data on most of our actions are tracked, documented, and then used in a variety of ways. With this in mind, the potential for the exploitation of big data is a major concern, but the malicious usage of this information isn’t the norm. Rather, this information is used to better our lives.  Businesses stand to greatly benefit from big data. Today, business leaders and project managers know how to use big data in order to get the

What are Data Scientists? How do you distinguish a genuine data scientist from a dressed-up business analyst, BI, or other related roles?  Truth be told, the industry does not have a standard definition of a data scientist. You've probably heard jokes like “a data scientist is a data analyst living in Silicon Valley”. Just for fun, take a look at the cartoon below Finding an “effective” data scientist is difficult. Finding people in the role of a data scientist can be equally difficult. Note the use of “effective” here. I use this word to highlight the fact that there could be people who might possess some of these data science skills yet may not be the best fit in a data science role. The irony is that even the people looking to hire data scientists might not fully understand data science. There are still some job advertisements in the market that describe a traditional data analyst and business analyst roles while labeling it a “Data Scientist” position. Instead of givi

By Lance Eliot, the AI Trends Insider [Ed. Note: For reader’s interested in Dr. Eliot’s ongoing business analyses about the advent of self-driving cars, see his online Forbes column: https://forbes.com/sites/lanceeliot/ ] Let’s examine the topic of things that work only intermittently, which as you’ll soon see is a crucial topic for intelligently designing and building AI systems, especially for self-driving autonomous cars. First, a story to illuminate the matter. My flashlight was only working intermittently, so I shook it to get the bulb to shine, hoping to cast some steady light. One moment the flashlight had a nice strong beam and the next moment it was faded and not of much use. At times, the light emanating from the flashlight would go on-and-off or it would dip so close to being off that I would shake it vigorously and generally the light would momentarily revive. We were hiking in the mountains as part of our Boy Scout troop’s wilderness-survival preparations and I was

By Deborah Borfitz Giving Medicare beneficiaries control of their healthcare data and otherwise accelerating the adoption of value-based care are among the priorities of the Centers for Medicare & Medicaid Services (CMS), according to two CMS representatives speaking from the stage at the 2019 AI World Government conference in June. The agency believes patient data belongs to the patient and needs to be available at the point-of-care as individuals shop around for high-value providers—meaning it needs to be free flowing across the entire healthcare system. Private-sector interest in the CMS Artificial Intelligence Health Outcomes Challenge, seeking ways to use AI to predict unplanned hospital and skilled nursing facility admissions and adverse events, has been particularly high with more than 300 qualified entries, reports Lisa Bari, who until recently was senior technical advisor for the U.S. Department of Health and Human Services’ Value-Based Transformation Initiative. The 2

By AI Trends Staff AI algorithms typically required thousands or millions of data points to work effectively, increasing the value of data. But thanks to GDPR and similar efforts, the days of grabbing data from individuals without explicitly getting permission is numbered. The pendulum seems to be swinging the other way, toward avenues for granting clear permission. One model for this is being offered by Oasis Labs, founded by Dawn Song, a professor at the University of California-Berkeley, to create a secure way for patients to share their data with researchers. Participating patients get paid when their data is used; participating researchers never see the data, even when it is used to train AI, according to an account in Wired . Government is getting in the mix too. US Senator Mark Warner (D-Virginia) has introduced a bill that would require firms to put a value on the personal data of each user. The idea is that companies should pay to use personal data. Health research is a g

Hackers get ready! Next month the AI World Conference & Expo will host its first hackathon on October 23-25 in Boston as part of AI World . The AI Data Science Hackathon will bring together innovative data scientists and developers from across the ecosystem to solve real-world data challenges in applying artificial intelligence and machine learning. Working in teams, participants will build and improve on pipelines, datasets, tools, and other projects from fintech, insurance, healthcare, pharma, and more. Participation is free, but space is limited, and interested teams and individuals are invited to apply as soon as possible. Application is simple. Interested individuals or groups can briefly outline their project or area of interest via the online form and explain why it’s important to the community. Projects can be data sets, pipelines, applications, or algorithms which our diverse teams will work on throughout the hackathon with the goal of addressing real world data chal

As artificial intelligence (AI) systems play bigger parts in our lives, people are asking whether they can be trusted. According to Mark Stefik, this concern stems from the fact that most AI systems are not designed to explain themselves. “The basic problem is that AIs learn on their own and cannot explain what they do. When bad things happen and people ask about an AI’s decisions, they are not comfortable with this lack of transparency,” says Stefik, Lead of Explainable AI, at PARC, a Xerox company. “We don’t know when we can trust them.” “Existing theories of explanation are quite rudimentary,” Stefik explains. “In DARPA’s eXplainable AI (XAI) program and other places, researchers are studying AIs and explanation. This research is changing how we think about machine learning and how AIs can work in society. We want AIs that can build common ground with people and communicate about what they are doing and what they have learned.” On behalf of AI Trends , Kaitlyn Barago spoke wit

I’ve already covered building a static dashboard with Cube.js and Chart.js in this tutorial . Now, I’m going to show you how to dynamically change the underlying chart’s data based on the user’s input. We’ll let the user pick a date range and based on that, reload the chart. When a user picks a new set of dates, a new request will be sent to the Cube.js server. The Cube.js server will generate new SQL code, execute it against the database, and send the result back to the client. And finally, the client re-renders a chart with the new data. Here is a Codesandbox demo of what we are going to build. You can click on the link below to check the source code and the demo. Chart.js Dynamic Data Example - CodeSandbox Setting up a Backend We are going to use our sample e-commerce Postgres dataset. Use the following commands to download it and import it into the ecom database. $ curl http://cube.dev/downloads/ecom-dump.sql > ecom-dump.sql $ createdb ecom $ psql --dbname ecom -f ecom-dum

Machine learning is an application of artificial intelligence. And it is no secret that this technology is revolutionizing the marketing niche. It enhances a system in a way it that it learns from current and past experiences. It involves the invention and development of computer programs that can read data and use it to influence performance. This, of course, happens without human intervention as the whole process is automated. Exploring technology for gains in business is a practice that has taken ground. More and more companies are employing artificial intelligence in their daily operations, in a bid to perfect customer experience and boost returns. Thanks to the rapid growth in technology, marketing has seen different improvements. So, how can machine learning improve your business and affect the flow of clients? This is an argument that has elicited mixed reactions from pundits in the industry. Some argue that this is the way to go for the future, while others are a bit skeptica

Predictive analytics is a process that involves passing historical data through statistical algorithms to identify the likelihood of future events. The technology has been around for decades now, with various statistical models in use as early as the 80s to predict the stock market. Only recently, however, have the powers that be come together and made an undeniably strong case for wider adoption of the once unstable technology. What is Driving Predictive Analytics in Sales? Growing volumes of data, in part due to more ways to collect them, faster computers for a fraction of the price and cloud computing have all come together to create the perfect environment for predictive analytics. Even as new technology such as machine learning, artificial intelligence and in-memory computing continue to take root, the impact on the internet economy is expected to be massive. In particular, the latter of these (in-memory computing) has spearheaded the availability of real-time data, greatl

App development is an inherently complex endeavour from the get-go, necessitating the close and thoughtful analysis of myriad factors and demanding many, many choices before and during the project. One such question any company or team planning to develop a cross-platform app is the library they will use. Now, this is a crucial question because it plays a critical role in deciding just how well the app’s UX turns out. Also, as any developer will tell you, the UX is the deciding factor when it comes to the app’s potential to succeed and make an impact. Much like anything else, there are plenty of options in the market in this regard as well, but we’ll focus our attention on just one leading name: JavaFX. The list of reasons why this name, in particular, stands out from the crowd is long! For example, JavaFX makes use of a high-quality graphics pipeline to deliver intricate UI rendering that functions exceptionally well. It is especially important when one is looking to put together a

By Lance Eliot, the AI Trends Insider [Ed. Note: For reader’s interested in Dr. Eliot’s ongoing business analyses about the advent of self-driving cars, see his online Forbes column: https://forbes.com/sites/lanceeliot/ ] One of the worst nightmares for any driver is the chance of backing up a car and running over someone. When you are backing up, it can be very difficult to know what’s behind the vehicle. There’s a famous video of a crawling baby in Brazil that unbeknownst to the parents crawled behind the family car as it was being backed out of the garage. The car got about halfway over the top of the baby when a person walking nearby pointed out there was a baby underneath. Getting out of the car, now stopped over the baby, the family members were fortunately able to pull out the baby and did so without much harm having come to the child. They were lucky. Statistics were against them in the sense that by-and-large once a backout is underway, whomever is getting hit is like