Lifeboat Foundation

Will we ever decipher the language of molecular biology? Here, I argue that we are just a few years away from having accurate in silico models of the primary biomolecular information highway — from DNA to gene expression to proteins — that rival experimental accuracy and can be used in medicine and pharmaceutical discovery.

Since I started my PhD in 1996, the computational biology community had embraced the mantra, “biology is becoming a computational science.” Our ultimate ambition has been to predict the activity of biomolecules within cells, and cells within our bodies, with precision and reproducibility akin to engineering disciplines. We have aimed to create computational models of biological systems, enabling accurate biomolecular experimentation in silico. The recent strides made in deep learning and particularly large language models (LLMs), in conjunction with affordable and large-scale data generation, are propelling this aspiration closer to reality.

LLMs, already proven masters at modeling human language, have demonstrated extraordinary feats like passing the bar exam, writing code, crafting poetry in diverse styles, and arguably rendering the Turing test obsolete. However, their potential for modeling biomolecular systems may even surpass their proficiency in modeling human language. Human language mirrors human thought providing us with an inherent advantage, while molecular biology is intricate, messy, and counterintuitive. Biomolecular systems, despite their messy constitution, are robust and reproducible, comprising millions of components interacting in ways that have evolved over billions of years. The resulting systems are marvelously complex, beyond human comprehension. Biologists often resort to simplistic rules that work only 60% or 80% of the time, resulting in digestible but incomplete narratives. Our capacity to generate colossal biomolecular data currently outstrips our ability to understand the underlying systems.

ChatGPT is not officially available in China.

China can’t buy US chips required for advanced artificial intelligence models, but that’s not stopping the country from churning out AI models. After receiving regulatory approval from the country’s internet watchdog, several Chinese tech companies launched their respective AI chatbots last week. This comes in response to OpenAI’s ChatGPT, which, since its launch, has prompted rival tech companies around the globe to launch their own chatbots.

According to a Reuters report, Baidu CEO Robin Li has claimed that over 70 large language models (LLMs) with over 1 billion parameters have been released in China.

Continue reading “China approves chatbots for public use in tech war with US” »

Can we learn robot manipulation for everyday tasks, only by watching videos of humans doing arbitrary tasks in different unstructured settings? Unlike widely adopted strategies of learning task-specific behaviors or direct imitation of a human video, we develop a a framework for extracting agent-agnostic action representations from human videos, and then map it to the agent’s embodiment during deployment. Our framework is based on predicting plausible human hand trajectories given an initial image of a scene. After training this prediction model on a diverse set of human videos from the internet, we deploy the trained model zero-shot for physical robot manipulation tasks, after appropriate transformations to the robot’s embodiment. This simple strategy lets us solve coarse manipulation tasks like opening and closing drawers, pushing, and tool use, without access to any in-domain robot manipulation trajectories. Our real-world deployment results establish a strong baseline for action prediction information that can be acquired from diverse arbitrary videos of human activities, and be useful for zero-shot robotic manipulation in unseen scenes.

These all-purpose, desktop machines can reproduce a seemingly infinite variety of items. In fact, they are like miniature factories. In appearance, they resemble a combined washing machine/microwave oven. Raw materials are purchased separately and can be loaded in solid, liquid or powder form. An interior compartment is accessed via a small hatch, where objects are constructed atom-by-atom. The process takes a matter of minutes and the assembled items can be used immediately. New schematics can be accessed from the web and programmed into the machine.

I did not create this animation video and i do not gain any profit from it. This is for educational purposes only.

I HOPE YOU ENJOY.

“When checking the system this morning, I noticed your account logged in for over 20 hours,” begins a December 1998 email from the president of my dial-up Internet service provider (ISP) at the time. “Our service is unlimited, but we ask that you actually be using the connection while logged in.”

But I’m getting ahead of myself—why do I have an email from 1998?

The Internet just changed forever, but most people living in the United States don’t even realize what just happened. A draconian new law known as the “Digital Services Act” went into effect in the European Union on Friday, and it establishes an extremely strict regime of Internet censorship that is far more authoritarian than anything we have ever seen before.

From this point forward, hordes of European bureaucrats will be the arbiters of what is acceptable to say on the Internet. If they discover something that you have said on a large online platform that they do not like, they can force that platform to take it down, because someone in Europe might see it. So even though this is a European law, the truth is that it is going to have a tremendous impact on all of us.

NASA Commercial Space Capabilities office and SpaceX have a 24 page agreed plan to develop the SpaceX starship into a low earth orbit space station design by 2028. This is unfunded and aspirational. The parties will cooperate to try and make it happen. SpaceX with successful Starlink commercial services will have the funds to make this happen.

There are many other milestones that NASA and SpaceX are trying to achieve with Starship.

It will take another 2–4 years to actually launch the Starship space station.

Unmanned aerial vehicles (UAVs), commonly known as drones, are already used in countless settings to tackle real-world problems. These flying robotic systems can, among other things, help to monitor natural environments, detect fires or other environmental hazards, monitor cities and find survivors of natural disasters.

To tackle all of these missions effectively, UAVs should be able to reliably detect targets and objects of interest in their surroundings. Computer scientists have thus been trying to devise new computational techniques that could enable these capabilities, using deep learning or other approaches.

Researchers at Yunnan University and the Chinese Academy of Sciences recently introduced a new object-detection system based on edge computing. Their proposed system, introduced in the IEEE Internet of Things Journal, could provide UAVs with the ability to spot relevant objects and targets in their surroundings without significantly increasing their power-consumption.

Elon Musk’s rocket manufacturing company SpaceX has completed 60 launches this year, much to the CEO’s excitement.

What Happened: SpaceX’s Falcon 9 rocket launched 22 Starlink satellites to low-Earth orbit from Florida on Thursday at about 10:21 p.m. ET, marking the company’s 60th launch this year.

“Congrats to the SpaceX team on launch 60 of 2023!” Musk wrote.