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Have You Watched Salt? This Sci-Fi Series Is Entirely Created By Dall.E-2!

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Have You Watched Salt? This Sci-Fi Series Is Entirely Created By Dall.E-2!

With facial recognition technology, AI algorithms can recognize the key characters and sort certain scenes for human editors.

The experimental project is called Salt (Opens in a new window), and it’s built entirely with AI-generated art. To create the visuals, Stelzer has been tapping publicly available programs such as Midjourney, Stable Diffusion, and DALL-E 2, which can essentially draw anything you want by relying on a mere text description from the user. On Twitter, Stelzer has been releasing Salt in short clips, called “story seeds.” As you can see, the film project is a bit like watching a Ken Burns’ documentary revolving around still images. That’s because AI-generated art can’t render moving pictures, at least not yet. Nevertheless, Stelzer can create the feeling of motion through video editing and even some deepfake programs, which can make the characters’ faces move. All the voices in the film—including the female ones—also come from Stelzer, who’s been using an AI voice generator called Murf (Opens in a new window) to create them.

“It’s all AI, except for one voice, which is… mine,” Stelzer told PCMag via Twitter.

 

Artificial intelligence in filmmaking

Artificial intelligence in filmmaking might sound futuristic, but we have reached this place. Technology is already making a significant impact on film production.

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Today, most of the outperforming movies that come under the visual effects category are using machine learning and AI for filmmaking. Significant pictures like ‘The Irishman’ and ‘Avengers: Endgame’ are no different.

It won’t be a wonder if the next movie you watch is written by AI, performed by robots, and animated and rendered by a deep learning algorithm.

But why do we need artificial intelligence in filmmaking? In the fast-moving world, everything has relied on technology. Integrating artificial intelligence and subsequent technologies in film production will help create movies faster and obtain more income. Besides, employing technology will also ease almost every task in the film industry.

 

Applications of AI in film production

Writing scripts

‘Artificial intelligence writes a story is what happens here. Humans can imagine and script amazing stories, but they can’t assure that they will perform well in theatres. Fortunately, AI can. Machine learning algorithms are fed with large amounts of movie data, which analyses them and comes up with unique scripts that the audience love.

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Simplifying pre-production

Pre-production is an important but stressful task. However, AI can help streamline the process involved in pre-production. AI can plan schedules according to actors and others’ timing, and find apt locations that will go well with the storyline.

 

Character making

Graphics and visual effects never fail to steal people’s hearts. Digital domain applied machine learning technologies are used to design amazing fictional characters like Thanos of Avengers: Infinity War.

 

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Subtitle creation

Global media publishing companies have to make their content suitable for viewers from different regions to consume. To deliver video content with multiple language subtitles, production houses can use AI-based technologies like Natural language generation and natural language processing.

 

Movie Promotion

To confirm that the movie is a box-office success, AI can be leveraged in the promotion process. AI algorithm can be used to evaluate the viewer base, the excitement surrounding the movie, and the popularity of the actors around the world.

 

Movie editing

In editing feature-length movies, AI supports film editors. With facial recognition technology, AI algorithms can recognize the key characters and sort certain scenes for human editors. By getting the first draft done quickly, editors can focus on scenes featuring the main plot of the script.

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The post Have You Watched Salt? This Sci-Fi Series is Entirely Created by Dall.E-2! appeared first on Analytics Insight.

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Top 10 AI Universities Leading In Research And Innovation In 2023

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Top 10 AI Universities Leading In Research And Innovation In 2023

Here are the top 10 AI universities engaged in research and innovation in 2023

Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. AI research is the field of study of intelligent agents, which refers to any system that perceives its environment and takes actions that maximize its chance of achieving its goals. A professional degree in AI from a reputed university will help you get started in this AI industry. It has signified a great number of developing technologies including robotics, IoT, and big data. Here are the top 10 AI universities engaged in research and innovation in 2023.

 

Harvard University:

Harvard University opened the Lawrence Scientific School as its first formal effort to provide higher-level education in engineering and the sciences. Under the category of Computer Science, Artificial Intelligence and the subsets of Computational Linguistics, Machine Learning, Multi-agent Systems, and Robotics, are researched in detail by various faculty members.

 

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Stanford University:

Standford University ranks #2 on Nature’s Index for top academic institutions in AI. The Stanford Artificial Intelligence Laboratory is set up to advance knowledge and foster learning. The lab team is always working on new AI research projects like the MetaMorph model and task-specific fine-tuning for robotics.

 

Carnegie Mellon University:

Carnegie Mellon University is frequently heard of in the AI research community. CMU’s research leads in upcoming AI concepts of self-driving cars, speech recognition, advanced sensor design, facial recognition software, and more. CMU has worked on several projects, including five interactive maps, etc.

 

MIT:

The Massachusetts Institute of Technology is known for its MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), the lab has housed some of the top researchers and won several awards, and mentored leaders that have gone ahead to create notable innovations.

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University of California Berkeley:

UC Berkeley is consistently ranked among the five top research universities globally with an AI research unit. The Berkeley Artificial Intelligence Centre lab includes ML, NLP, planning, control, and robotics.

 

Cornell University:

Cornell University is ranked fourth in the world for research in engineering. Cornell’s research is conducted in several areas like robotics, NLP, vision, ML, and interdisciplinary topics related to AI.

 

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Vellore Insititute of Technology:

The school of computer science at Vellore Insititute of Technology has more than 1500 publications. It aims to further apply research in computer science and offers Ph.D. courses. The team of VIT students and alumni worked on the prevalent XOR problem and found a solution.

 

Indian Institute of Technology, Bombay:

The IIT Bombay research lab, Center for Indian Language Technology, the department offers Ph.D. programs in computer science and is one of the most well-known for its AI research. The latest research like Breaking the trial-and-error loop in Material Design through Machine Learning and ‘Computational techniques to measure small amounts of soot.

 

Indian Institute of Technology, Delhi:

IIT-D is among the top five IITs and encourages AI research in India. The university opened the School of Artificial Intelligence center for Ph.D. programs to multiply research productivity. IIT-D also houses a research group, Data Analytics & Intelligence Research, to build AI applications for India.

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Indian Institute of Technology, Madras:

IIT-M has developed Ai4Bharat, an NLP model to process eleven regional languages, and the Robert Bosch Centre for Data Science and Artificial Intelligence was introduced to further AI research. The research applications focus on addressing Indian issues and leveraging AI for local, vernacular, healthcare, and agricultural problems.

The post Top 10 AI Universities Leading in Research and Innovation in 2023 appeared first on Analytics Insight.

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DCNN’s Vision Incapability Could Put Real-World AI Apps In Danger

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DCNN’s Vision Incapability Could Put Real-World AI Apps In Danger

AI is currently facing several complex challenges that might jeopardize the existing models 

Artificial intelligence has become the largest innovation that the tech industry has ever witnessed. The innovation and research directed by AI are aiding global industries like healthcare, retail, and finance toward advancements. Even though evolving technology has become an important element in business development, AI is bringing new perspectives to the global entrepreneurial paradigm with its avant-garde approaches. AI applications and models are being deployed by some of the major tech companies in the world like Amazon, Apple, Facebook, and IBM. These companies constantly evolve their AI integration pipeline based on the evolving tech landscape. AI’s most important and gradually advancing branch is deep learning since the importance of convolutional neural networks has become quite evident to business leaders.

The growing demand for deep neural networks and deep convolutional neural networks has forced researchers and scientists to advance their prospects. But unfortunately, after several rapid advancements, convolutional deep neural networks or DCNNs still do not possess the same accuracy as humans do, and since AI is literally supposed to automate and perform tasks for humans, AI neural networks need accuracy and efficiency to imitate human intelligence. According to recent reports, DCNNs do not envision objects the way humans do and this could prove to be quite dangerous for the AI applications that are already being deployed in projects.

 

Deep Convolutional Neural Networks have Failed to Attain Sensitivity like Humans

In a collaborative study conducted by Elder, from York Research Chair in Human and Computer Vision and the Co-Director of York’s Centre for AI and Society, and Assistant Psychology professor Nicholas Baker from Loyola College in Chicago explores how human brains and DCNNs react to ‘Frankensteins’, a novel visual stimuli. Frankensteins are basically simple objects that have been taken apart and put back together, in the wrong way. Generally, when presented with a Frankenstein object, humans get confused, whereas, DCNNs do not reveal any insensitivity to these configural object properties.

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The researchers claimed that the streamlining of these AI models has failed under certain critical conditions and clearly demonstrate that AI development should focus more on tasks beyond object recognition. Apparently, these models use “shortcuts” while solving complex recognition tasks. Sometimes these shortcuts work, based on several cases, but unfortunately, these might become quite dangerous when it comes to real-world AI applications that the industry and government are currently using.

 

Bottom Line

Based on the predicaments released by researchers, modifications to the training and architecture of AI models can make these neural network projects act more as the human brain would usually do. They speculate that to accurately match human configural sensitivity, all networks should be trained to solve a broader range of cutting-edge tasks which goes beyond object recognition. In a nutshell, these are the basic shortcomings that AI researchers should work on overcoming. AI is not just a technology that can be used to automate complex human tasks, it was innovated for human intelligence, it might sound dangerous, but scientists should now focus on meeting complex challenges to successfully attain these goals.

The post DCNN’s Vision Incapability Could Put Real-World AI Apps in Danger appeared first on Analytics Insight.

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AI Is Not Just Your Best Friend But Is A Mirror To Your Soul

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AI Is Not Just Your Best Friend But Is A Mirror To Your Soul

Psychologists say that the growing involvement of AI and the adjoining digital technologies has stunted the mental growth of today’s youth. Young people, who were earlier connected with each other socially and physically are now only connected digitally, indicating high reports of loneliness. The depression rate is higher among teenagers and millennials who spend a massive amount of working towards building a career and only focus on digital interaction. But the actual debate is that since AI is the problem, AI is also the solution.

The emergence of AI friends or AI companions has truly changed the dynamics of friendship and social interaction. But these AI friend applications are not just your friends, but are a mirror to your soul. Users get permission to customize the AI avatar to whoever and however, they like. AI companions reassure users by asking questions about how they are as if they are really interested and always try to give an answer that fits into the context of the conversation.

We can take the Replica AI application, for example. It’s a personalized chatbot that learns to replicate the text patterns and personality of the user who created it, almost like a best friend, who is always on point. Replika provides investors with the ability to choose personality traits, and interests, and change the nature of the relationship like friend, girlfriend, mother, sister, and mentor. Users can also give a realistic-seeming voice call to their Replica buddy. Well, the whole concept of Replica truly reflects that AI friends are just proper mirrors to what the soul actually wants, with only minor room for error.

The post AI is Not Just Your Best Friend but is a Mirror to Your Soul appeared first on Analytics Insight.

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