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Feb 18, 2024

Google’s new version of Gemini can handle far bigger amounts of data

Posted by in category: robotics/AI

The updated AI model can now do some seriously impressive things with long videos or text.

Google DeepMind today launched the next generation of its powerful artificial-intelligence model Gemini, which has an enhanced ability to work with large amounts of video, text, and images.

Feb 18, 2024

OpenAI’s ‘Sora’ Has Rivals In The Works—Including From Google And Meta

Posted by in categories: economics, robotics/AI

ChatGPT maker OpenAI stepped up the race in generative artificial intelligence Thursday when it unveiled its text-to-video generation tool, Sora, viewed as an impressive but potentially dangerous step in the booming AI economy amid concerns about disinformation spread.


“Game on,” said the CEO and cofounder of rival video generator Runway after OpenAI teased content from its latest AI tool.

Feb 18, 2024

Reanimating Programmer Ada Lovelace With Magic Of AI

Posted by in category: robotics/AI

Marco tempest is a creative technologist at the NASA jet propulsion laboratory, a director’s fellow alumni at the MIT medialab, and the founder and director of the magiclab in new york city.


Marco Tempest uses AI to bring the world’s first programmer to life.

Feb 18, 2024

Host Cell Protein and Impurity Risk Mitigation in Monoclonal Antibody Therapeutic Manufacturing

Posted by in category: biotech/medical

Biopharmaceutical impurities such as host cell proteins can delay biologics development and production. These elements can have immunogenic effects in patients, forcing scientists to restart process development. Thus, detecting and removing biopharmaceutical impurities is necessary for maintaining drug efficacy and ensuring patient safety.

In this webinar brought to you by Cytiva, Andrew Hamilton and Joe Hirano will discuss how to identify, detect, and measure impurities in biologics manufacturing.

Feb 18, 2024

Startup Clones Three Piglets Gene-Hacked to Have Organs Transplanted Into Humans

Posted by in categories: biotech/medical, genetics

Japanese startup PorMedTec says it’s have cloned three piglets with the express purpose of having their organs be viable for transplantation to humans, without being rejected by the immune system.

The company imported gene-edited cells from a US biotech startup called eGenesis and used them to create genetically modified embryos, the Japan Times reports, which were then implanted into the uterus of a pig.

“The realization of xenotransplantation has been long awaited in Japan for several years, but it remained in the basic research stage because pigs that could withstand clinical application were still under development,” the company said in a statement.

Feb 17, 2024

Beer Goggles Don’t Exist, Scientists Find

Posted by in category: futurism

A study says that alcohol gives us extra confidence to approach people we already find attractive and does not give us beer goggles.

Feb 17, 2024

Mushroom sprouting from frog’s leg leaves scientists concerned

Posted by in category: biotech/medical

Sounds like the video game/movie “The Last of Us” though there was a somewhat similar X Files episode as well before that. Though I doubt it’ll be a zombie plague, it could be like another pandemic someday or an issue such as a deadly fungal outbreak they had in Portland, Oregon before if I recall.


Scientists have been left concerned after making the surprise discovery of a frog with a small mushroom sprouting from its leg.

The amphibian was discovered in the foothills of India’s Western Ghats and researchers stated that it’s the first time a mushroom has been found growing on live animal tissue.

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Feb 17, 2024

Donor/recipient enhancement of memory in rat hippocampus

Posted by in categories: mathematics, neuroscience

The critical role of the mammalian hippocampus in the formation, translation and retrieval of memory has been documented over many decades. There are many theories of how the hippocampus operates to encode events and a precise mechanism was recently identified in rats performing a short-term memory task which demonstrated that successful information encoding was promoted via specific patterns of activity generated within ensembles of hippocampal neurons. In the study presented here, these “representations” were extracted via a customized non-linear multi-input multi-output (MIMO) mathematical model which allowed prediction of successful performance on specific trials within the testing session. A unique feature of this characterization was demonstrated when successful information encoding patterns were derived online from well-trained “donor” animals during difficult long-delay trials and delivered via online electrical stimulation to synchronously tested naïve “recipient” animals never before exposed to the delay feature of the task. By transferring such model-derived trained (donor) animal hippocampal firing patterns via stimulation to coupled naïve recipient animals, their task performance was facilitated in a direct “donor–recipient” manner. This provides the basis for utilizing extracted appropriate neural information from one brain to induce, recover, or enhance memory related processing in the brain of another subject.

To understand the neural basis of memory, several features of the context in which the memories occur and are utilized, and the functional aspects of the brain areas involved, need to be identified and controlled (Hampson et al., 2008; Eichenbaum and Fortin, 2009). In prior studies we achieved both of these important contingencies as well as overcoming possible alternative interpretations of the relationship between recorded hippocampal ensemble activity and the behavioral task in which short-term memory formation is necessary (Deadwyler and Hampson, 2006; Deadwyler et al., 2007), and developing an effective mathematical/operational model for online prediction of CA1 hippocampal cell activity from simultaneously recorded input firing patterns from synaptically connected CA3 neurons (Song et al., 2009; Berger et al., 2011; Hampson et al., 2011).

Feb 17, 2024

Brain-inspired Cognition and Understanding for Next-generation AI: Computational Models, Architectures and Learning Algorithms Volume II

Posted by in categories: information science, robotics/AI

The human brain is probably the most complex thing in the universe. Apart from the human brain, no other system can automatically acquire new information and learn new skills, perform multimodal collaborative perception and information memory processing, make effective decisions in complex environments, and work stably with low power consumption. In this way, brain-inspired research can greatly advance the development of a new generation of artificial intelligence (AI) technologies.

Powered by new machine learning algorithms, effective large-scale labeled datasets, and superior computing power, AI programs have surpassed humans in speed and accuracy on certain tasks. However, most of the existing AI systems solve practical tasks from a computational perspective, eschewing most neuroscientific details, and tending to brute force optimization and large amounts of input data, making the implemented intelligent systems only suitable for solving specific types of problems. The long-term goal of brain-inspired intelligence research is to realize a general intelligent system. The main task is to integrate the understanding of multi-scale structure of the human brain and its information processing mechanisms, and build a cognitive brain computing model that simulates the cognitive function of the brain.

Feb 17, 2024

Geoffrey Hinton | Will digital intelligence replace biological intelligence?

Posted by in categories: biological, education, information science, life extension, robotics/AI

The Schwartz Reisman Institute for Technology and Society and the Department of Computer Science at the University of Toronto, in collaboration with the Vector Institute for Artificial Intelligence and the Cosmic Future Initiative at the Faculty of Arts & Science, present Geoffrey Hinton on October 27, 2023, at the University of Toronto.

0:00:00 — 0:07:20 Opening remarks and introduction.
0:07:21 — 0:08:43 Overview.
0:08:44 — 0:20:08 Two different ways to do computation.
0:20:09 — 0:30:11 Do large language models really understand what they are saying?
0:30:12 — 0:49:50 The first neural net language model and how it works.
0:49:51 — 0:57:24 Will we be able to control super-intelligence once it surpasses our intelligence?
0:57:25 — 1:03:18 Does digital intelligence have subjective experience?
1:03:19 — 1:55:36 Q&A
1:55:37 — 1:58:37 Closing remarks.

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