Toggle light / dark theme

Research: AI tailors artificial DNA for future drug development

With the help of AI, researchers at Chalmers University of Technology, Sweden, have succeeded in designing synthetic DNA that controls the cells’ protein production. The technology can contribute to the development and production of vaccines, drugs for severe diseases, as well as alternative food proteins much faster and at significantly lower costs than today. How our genes are expressed is a process that is fundamental to the functionality of cells in all living organisms. Simply put, the genetic code in DNA is transcribed to the molecule messenger RNA (mRNA), which tells the cell’s factory which protein to produce and in which quantities.

Researchers have put a lot of effort into trying to control gene expression because it can, among other things, contribute to the development of protein-based drugs. A recent example is the mRNA vaccine against Covid-19, which instructed the body’s cells to produce the same protein found on the surface of the coronavirus. The body’s immune system could then learn to form antibodies against the virus. Likewise, it is possible to teach the body’s immune system to defeat cancer cells or other complex diseases if one understands the genetic code behind the production of specific proteins. Most of today’s new drugs are protein-based, but the techniques for producing them are both expensive and slow, because it is difficult to control how the DNA is expressed. Last year, a research group at Chalmers, led by Aleksej Zelezniak, Associate Professor of Systems Biology, took an important step in understanding and controlling how much of a protein is made from a certain DNA sequence.

“First it was about being able to fully ‘read’ the DNA molecule’s instructions. Now we have succeeded in designing our own DNA that contains the exact instructions to control the quantity of a specific protein,” says Aleksej Zelezniak about the research group’s latest important breakthrough. The principle behind the new method is similar to when an AI generates faces that look like real people. By learning what a large selection of faces looks like, the AI can then create completely new but natural-looking faces. It is then easy to modify a face by, for example, saying that it should look older, or have a different hairstyle. On the other hand, programming a believable face from scratch, without the use of AI, would have been much more difficult and time-consuming. Similarly, the researchers’ AI has been taught the structure and regulatory code of DNA. The AI then designs synthetic DNA, where it is easy to modify its regulatory information in the desired direction of gene expression.

Can an AI-powered insect trap solve a $220 billion pest problem?

Pests destroy up to 40% of the world’s crops each year, causing $220 billion in economic losses, according to the UN Food and Agriculture Organization (FAO). Trapview is harnessing the power of AI to help tackle the problem.

The Slovenian company has developed a device that traps and identifies pests, and acts as an advance warning system by predicting how they will spread.

“We’ve built the biggest database of pictures of insects in the world, which allows us to really use modern AI-based computing vision in the most optimal way,” says Matej Štefančič, CEO of Trapview and parent company EFOS.

Deepmind’s new video game AIs learn from humans

Deepmind introduces a new research framework for AI agents in simulated environments such as video games that can interact more flexibly and naturally with humans.

AI systems have achieved great success in video games such as Dota or Starcraft, defeating human professional players. This is made possible by precise reward functions that are tuned to optimize game outcomes: Agents were trained using unique wins and losses calculated by computer code. Where such reward functions are possible, AI agents can sometimes achieve superhuman performance.

But often – especially for everyday human behaviors with open-ended outcomes – there is no such precise reward function.

Ubisoft’s New AI: Breathing Life Into Games!

11/26/22


❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.com/papers.

📝 The paper “ZeroEGGS: Zero-shot Example-based Gesture Generation from Speech” is available here:
https://github.com/ubisoft/ubisoft-laforge-ZeroEGGS

🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:
Aleksandr Mashrabov, Alex Balfanz, Alex Haro, Andrew Melnychuk, Benji Rabhan, Bryan Learn, B Shang, Christian Ahlin, Eric Martel, Geronimo Moralez, Gordon Child, Jace O’Brien, Jack Lukic, John Le, Jonas, Jonathan, Kenneth Davis, Klaus Busse, Kyle Davis, Lorin Atzberger, Lukas Biewald, Luke Dominique Warner, Matthew Allen Fisher, Matthew Valle, Michael Albrecht, Michael Tedder, Nevin Spoljaric, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Rajarshi Nigam, Ramsey Elbasheer, Steef, Taras Bobrovytsky, Ted Johnson, Thomas Krcmar, Timothy Sum Hon Mun, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi.
If you wish to appear here or pick up other perks, click here: https://www.patreon.com/TwoMinutePapers.

Thumbnail background design: Felícia Zsolnai-Fehér — http://felicia.hu.

Microsoft Uses Transfer Learning to Train Autonomous Drones

Originally published on Towards AI the World’s Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses.

The model is able to transfer knowledge between a simulated environment and real-world settings.

Researchers are building robots that can build themselves

Researchers at MIT’s Center for Bits and Atoms are working on an ambitious project, designing robots that effectively self-assemble. The team admits that the goal of an autonomous self-building robot is still “years away,” but the work has thus far demonstrated positive results.

At the system’s center are voxels (a term borrowed from computer graphics), which carry power and data that can be shared between pieces. The pieces form the foundation of the robot, grabbing and attaching additional voxels before moving across the grid for further assembly.

The researchers note in an associated paper published in Nature, “Our approach challenges the convention that larger constructions need larger machines to build them, and could be applied in areas that today either require substantial capital investments for fixed infrastructure or are altogether unfeasible.”

Robotics Breakthrough Builds Anything

MIT researchers have devised an algorithm using voxels robotics devices to build anything from houses to planes to cars and even other robots by using a grid system that transfers knowledge to determine when to build what, and when to build other robot builders. New Google Deepmind video game artificial intelligence develops agents that can talk, listen, ask questions, navigate, search and retrieve information, control things, and do a range of other intelligent tasks in real-time. New Non-invasive brain computer interface device transmits information through optic nerve to compete with Neuralink BCI.

Tech News Timestamps:
0:00 Robotics Breakthrough Builds Anything — Even Robots.
2:44 New Google Deepmind Video Game AI
5:25 New Neuralink BCI Competitor.

#robot #ai #neuralink

How Will AI And 5G Power the Next Wave Of Innovation?

The combined force of these disruptive technologies (AI and 5G) enables fast, secure, and ubiquitous connectivity of cost-efficient smart networks and IoT (Internet-of-Things) devices. This convergence point is essential to concepts like intelligent wireless edge.

5G and AI, the connected digital edge

Artificial intelligence and 5G are the two most critical elements that would empower futuristic innovations. These cutting-edge technologies are inherently synergistic. The rapid advancements of AI significantly improve the entire 5G ecosystem, its performance, and efficiency. Besides, 5G-connected devices’ proliferation helps drive unparalleled intelligence and new improvements in AI-based learning and inference. Moreover, the transformation of the connected, intelligent edge has commenced as on-device intelligence has garnered phenomenal traction. This transformation is critical to leveraging the full potential of 5G’s future. With these prospects, these technologies hold enough potential to transform every industry. Here’s how the combination of AI and 5G has been reshaping industries.