The same dog with another collar?
finances
internet
Advertisement
press
TV
entertainment
Web
Web 2.0
Web3
Maybe.
But the dog this time
will be Synthetic.
202X
2023
AI abyssus
Hic svn Drac0nes
A brief introduction to:
Synthetic Media.
Synthetic media is an umbrella term that covers data that is artificially generated or altered by AI. Synthetic data is
information generated artificially and that may be used instead of actual historical data in training AI models, where the
actual dataset is lacking in quality, quantity, or diversity.
Synthetic data can also be a critical tool in an enterprises AI efforts where the data that is available does not fit the
needs of the enterprise, or may pose a privacy concern when used for training machine learning models, testing software, or
similar tasks. Synthetic data, or data created artificially instead of captured from the real world, is becoming increasingly
used in data science as demand for AI systems grows. Synthetic data, or data generated by computers to act as an alternative
to data in the real world, has the potential to transform the current paradigm of AI development and break traditional data-
to-insight pipelines.
The means for synthesizing data could use deep learning models, machine learning, data science techniques, or any of the
commercial tools for synthesizing data that are out there. Using synthetic data methods, companies can obtain trained data
much faster and cheaper than the alternative collecting this data in a painstaking manner from the real world. This allows
systems to be built and tested virtually, and allows AI developers to iterate orders of magnitude faster, because the
training data can be created on-demand.
Rather than using just real data or just synthetic data, researchers can achieve dramatic improvements in performance by
combining both real data and synthetic data into a training dataset, which allows AIs to learn from both, while increasing
the overall training corpus size. With synthetic data, practitioners feed information into their AI models and gain
artificially generated data, which is much more valuable than straight observations. Generating synthetic datasets that are
statistically significant and that capture actual data in ways relevant to the use cases can be challenging. A STAT report
found that IBMs troubled Watson Health division, which deals with the medical treatment of cancer, frequently provided bad,
unsafe advice about treatment, because the platforms models were trained using flawed, synthetic patient records instead of
real-world data.
According to a widely referenced study from Gartner, by 2024, 60% of all data used to develop AI will be synthetic instead of
real. Many people working in technology have probably encountered synthetic scanned documents that were used to train models
for text analysis and data mining. Many people perceive synthetic media to be deep false-image technologies, but synthetic
content is far more expansive, spanning text, images, videos, voices, and data, with wide applications.
By merging technologies from the visual effects industry and neural networks for generative computation, Synthetic data
delivers accurately labelled, real-world data sets, and emulated environments on a large scale -- meaning data scientists can
leverage it to break through massive barriers to entry. Synthesis AIs cloud-based platform allows companies to create
synthetic, labeled imagery data using a combination of artificial intelligence, procedural generation, and rendering
technology from visual effects. Amazon uses synthetic data for training Alexa, Facebook acquired synthetic data creator
AI.Reverie, and Nvidia has realized the NVIDIA Omniverse Replicator, a powerful synthetic-data-generation engine that
produces synthetic, physically-simulated data to train deep neural networks.
Tom F. fitzgerald
for Futurespoilers, 2022
"The American sci-fi TV show Westworld presents a free open world in which each “receptionist” is an independent and intelligent individual that the player can interact with to create their own unique experience.
People have tried to do the same in games, but everything to date is scripted, from encounters that seem random to NPC dialogues. Traditional production processes are limited; “GTA 5” and “Red Dead Redemption 2” employed thousands of designers and writers to create “personalized” storylines and “realistic” NPC characters.
rct’s “Chaos Box” algorithm uses Deep Reinforcement Learning to intelligently generate almost infinite storylines without requiring an extensive number of designers and scriptwriters, allowing players to freely interact with NPCs.
To showcase this gameplay’s potential, rct is launching “The Extractors”, a game experience consisting entirely of AI-generated NPCs. “The Extractors” ushers in the next generation of gaming experiences—not only a long-awaited revolution in the game industry, but also an important milestone in the path of mankind's discovery of ourselves."
Intelligent generation of storyline and personalized dynamic narrative based on Deep Reinforcement Learning.
Intro text of The Extractors
Please visit rtc.ai to discover more about it's awesom works
What if a wetware that can learn to play pong, maybe will be an autonomous NPC?
Abstract
Integrating neurons into digital systems to leverage their innate intelligence may enable performance infeasible with silicon alone, along with providing insight into the cellular origin of intelligence. We developed DishBrain, a system which exhibits natural intelligence by harnessing the inherent adaptive computation of neurons in a structured environment. In vitro neural networks from human or rodent origins, are integrated with in silico computing via high-density multielectrode array. Through electrophysiological stimulation and recording, cultures were embedded in a simulated game-world, mimicking the arcade game ‘Pong’. Applying a previously untestable theory of active inference via the Free Energy Principle, we found that learning was apparent within five minutes of real-time gameplay, not observed in control conditions. Further experiments demonstrate the importance of closed-loop structured feedback in eliciting learning over time. Cultures display the ability to self-organise in a goal-directed manner in response to sparse sensory information about the consequences of their actions.
In vitro neurons learn and exhibit sentience when embodied in a simulated game-world.
In vitro neurons learn and exhibit sentience when embodied in a simulated game-world
Brett J. Kagan, Andy C. Kitchen, Nhi T. Tran, Forough Habibollahi, Moein Khajehnejad, Bradyn J. Parker, Anjali Bhat ,Ben Rollo, Adeel Razi, Karl J. Friston
Open AccessPublished:October 12, 2022DOI:https://doi.org/10.1016/j.neuron.2022.09.001
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