Is “Judgment Day” here? Hong Kong scientists bridged the gap between sci-fi and real life after creating a robot that can transform from solid to liquid like a real-life T-1000 robot from “Terminator 2.” A studying the cybernetic shapeshifter was published last month in the journal Matter.
Summary: Researchers are turning to artificial intelligence to find novel drugs that can block kappa opioid receptors with the hope to alleviate opioid addiction.
Source: Biophysical Society.
Approximately three million Americans suffer from opioid use disorder, and every year more than 80,000 Americans die from overdoses.
Generative AI is a catch-all term describing programs that use artificial intelligence to create new material from complex queries, such as “write a poem about monkeys in the style of Robert Frost” or “make an image of pandas draped over living room furniture.”
While AI more generally refers to software programs that can make themselves better by “learning” from new data, and which have been used behind the scenes in all kinds of software for years, generative AI is a fresh consumer-facing spin on the concept.
About 1,000 people from all over the world, including AI researchers and content marketers, attended Tuesday’s Gen AI Conference, which was organized by startup Jasper. It was a lavish affair, held at Pier 27 on the Embarcadero, overlooking San Francisco Bay.
Human beings are capable of processing several sound sources at once, both in terms of musical composition or synthesis and analysis, i.e., source separation. In other words, human brains can separate individual sound sources from a mixture and vice versa, i.e., synthesize several sound sources to form a coherent combination. When it comes to mathematically expressing this knowledge, researchers use the joint probability density of sources. For instance, musical mixtures have a context such that the joint probability density of sources does not factorize into the product of individual sources.
A deep learning model that can synthesize many sources into a coherent mixture and separate the individual sources from a mixture does not exist currently. When it comes to musical composition or generation tasks, models directly learn the distribution over the mixtures, offering accurate modeling of the mixture but losing all knowledge of the individual sources. Models for source separation, in contrast, learn a single model for each source distribution and condition on the mixture at inference time. Thus, all the crucial details regarding the interdependence of the sources are lost. It is difficult to generate mixtures in either scenario.
Taking a step towards building a deep learning model that is capable of performing both source separation and music generation, researchers from the GLADIA Research Lab, University of Rome, have developed Multi-Source Diffusion Model (MSDM). The model is trained using the joint probability density of sources sharing a context, referred to as the prior distribution. The generation task is carried out by sampling using the prior, whereas the separation task is carried out by conditioning the prior distribution on the mixture and then sampling from the resulting posterior distribution. This approach is a significant first step towards universal audio models because it is a first-of-its-kind model that is capable of performing both generation and separation tasks.
Did you know Google’s artificial intelligence company DeepMind has been working to solve one of the biggest problems in nuclear fusion?
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Link to Presentation Slides: https://www.dropbox.com/s/nz4hm3bnel7wqxq/Ep2.Artificial.Gen…e.pdf?dl=0
Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems.
Artificial General Intelligence is creating a computer that can understand or learn any intellectual task that a human being can and furthermore surpass brain power equivalent to that of all human brains combined.
Recently we have seen the power of Open AI’s Dall-E 2 and GPT3 take the internet by a storm with people thinking that the rate of technological change will soon take us to AGI.
But we believe there are some big challenges and barriers that need to be overcome.
CONTENTS OF THIS VIDEO
As mentioned, ChatGPT is available in free and paid-for tiers. You might have to sit in a queue for the free version for a while, but anyone can play around with its capabilities.
Google Bard is currently only available to limited beta testers and is not available to the wider public.
ChatGPT and Google Bard are very similar natural language AI chatbots, but they have some differences, and are designed to be used in slightly different ways — at least for now. ChatGPT has been used for answering direct questions with direct answers, mostly correctly, but it’s caused a lot of consternation among white collar workers, like writers, SEO advisors, and copy editors, since it has also demonstrated an impressive ability to write creatively — even if it has faced a few problems with accuracy and plagiarism.
Check out all the on-demand sessions from the Intelligent Security Summit here.
Artificial intelligence (AI) has already made its way into our personal and professional lives. Although the term is frequently used to describe a wide range of advanced computer processes, AI is best understood as a computer system or technological process that is capable of simulating human intelligence or learning to perform tasks and calculations and engage in decision-making.
Until recently, the traditional understanding of AI described machine learning (ML) technologies that recognized patterns and/or predicted behavior or preferences (also known as analytical AI).
Michal Kosinski, computational psychologist at Stanford University, has been testing several iterations of the ChatGPT AI chatbot developed by Open AI on its ability to pass the famous Theory of Mind Test. In his paper posted on the arXiv preprint server, Kosinski reports that testing the latest version of ChatGPT found that it passed at the level of the average 9-year-old child.
ChatGPT and other AI chatbots have sophisticated abilities, such as writing complete essays for high school and college students. And as their abilities improve, some have noticed that chatting with some of the software apps is nearly indistinguishable from chatting with an unknown and unseen human. Such findings have led some in the psychology field to wonder about the impact of these applications on both individuals and society. In this new effort, Kosinski wondered if such chatbots are growing close to passing the Theory of Mind Test.
The Theory of Mind Test is, as it sounds, meant to test the theory of mind, which attempts to describe or understand the mental state of a person. Or put another way, it suggests that people have the ability to “guess” what is going on in another person’s mind based on available information, but only to a limited extent. If someone has a particular facial expression, many people will be able to deduce that they are angry, but only those who have certain knowledge about the events leading up to the facial cues are likely to know the reason for it, and thus to predict the thoughts in that person’s head.
