Lifeboat Foundation

Recent advancements in the field of electronics have enabled the creation of smaller and increasingly sophisticated devices, including wearable technologies, biosensors, medical implants, and soft robots. Most of these technologies are based on stretchy materials with electronic properties.

While material scientists have already introduced a wide range of flexible materials that could be used to create electronics, many of these materials are fragile and can be easily damaged. As damage to materials can result in their failure, while also compromising the overall functioning of the system they are integrated in, several existing soft and conductive materials can end up being unreliable and unsuitable for large-scale implementations.

Researchers at Harbin University of Science and Technology in China recently developed a new conductive and self-healing hydrogel that could be used to create flexible sensors for wearables, robots or other devices. This material and its composition was outlined in the Journal of Science: Advanced Materials and Devices.

Generative artificial intelligence (AI) has put AI in the hands of people, and those who don’t use it could struggle to keep their jobs in future, Jaspreet Bindra, Founder and MD, Tech Whisperer Lt. UK, surmised at the Mint Digital Innovation Summit on June 9.

“We never think about electricity until it’s not there. That’s how AI used to be. It was always in the background and we never thought about it. With generative AI it has come into our hands, and 200–300 million of us are like, wow!” said Bindra.

He noted that while AI won’t replace humans at their jobs, someone using AI very well could. He urged working professionals to “recalibrate” and embrace generative AI as a “powerful tool” created by humans, instead of looking at it as a threat.

Summary: A novel study put the diagnostic prowess of generative AI, specifically the chatbot GPT-4, to the test, yielding promising results.

The study involved evaluating the AI’s diagnostic accuracy in handling complex medical cases, with GPT-4 correctly identifying the top diagnosis nearly 40% of the time and including the correct diagnosis in its list of potential diagnoses in 64% of challenging cases.

The success of AI in this study could provide new insights into its potential applications in clinical settings. However, more research is needed to address the benefits, optimal use, and limitations of such technology.

Clinical stage generative AI-driven drug discovery company Insilico Medicine has today published a paper on a new multimodal transformer-based aging clock; the new clock is capable of processing diverse data sets and providing insights into biomarkers for aging, mapping them to genes relevant to both aging and disease, and discovering new therapeutic targets to slow or reverse both aging and aging-related diseases.

Insilico calls the aging clock Precious1GPT, in a nod to the powerful “One Ring” in Tolkien’s Lord of the Rings; the findings have been published in the journal Aging.

Longevity. Technology: Insilico has been at the forefront of both generative AI and aging research, and has been publishing studies on biomarkers of aging using advanced bioinformatics since 2014. Later, the company trained deep neural networks (DNNs) on human “multi-omics” longitudinal data and retrained them on diseases to develop its end-to-end Pharma. AI platform for target discovery, drug design, and clinical trial prediction.

NASA is focusing increasingly on interplanetary missions to faraway places like Mars, and such highly ambitious voyages will require robotic equipment to assist astronauts with a range of tasks.

With that in mind, a team of researchers at the Ecole Polytechnique Fédérale de Lausanne (EPFL) in Switzerland is developing a remarkable robot called Mori3 that’s capable of changing its size, shape, and function, morphing from 2D triangles into almost any 3D object. You can see it in action in the video above.

In large organizations, complexity can lead to less efficient processes and projects that aren’t aligned with business strategy. A modern business management tool can help enterprise architects, leaders and relevant stakeholders gain control and ensure efforts are prioritized and anchored correctly. Cutting projects that aren’t business critical or growth enablers might be the right thing to do, even if the ideas they’re based upon are great. As a result, freeing up time and saving costs can empower the organization to onboard new projects faster and leverage AI and its possibilities before its competitors do.

How can technology leaders best leverage AI to benefit their companies and their customers? Here at Ardoq, we’ve focused on a few key areas that all technology leaders could benefit from.

1. Allow for and encourage continuous innovation. This includes always evaluating the type of technology your business is based on. If your organization is already based on modern technology and has a data-driven approach, you can be more agile when it comes to adopting and leveraging AI. AI will create disruptions as well as open up new opportunities. This is an opportunity for leaders to create momentum and embrace an iterative approach that will help their people feel that they’re staying ahead of the curve.

A great video on the history of electric cars. I love the AI voice. Also notice Tesla was incorporated in July 2003 by Martin Eberhard and Marc Tarpenning as Tesla Motors. The company’s name is a tribute to inventor and electrical engineer. Elon Musk was an investor.

While electric vehicles (EV) have only recently begun to challenge the internal combustion engine (ICE) for the future of our roads, EVs have been around for over a century. The long history of EVs has been one of many twists and turns.

Continue reading “The amazing long history (1826 — 2022) of ELECTRIC CARS!” »

In short, data-driven solutions themselves are only part of the overall approach. It is the effective integration of this fast-evolving technology into existing workflows and processes that leads to successful business outcomes.

The first step to integrating AI is identifying places and processes where it can help increase efficiency or accuracy. Businesses should step back and identify their pain points, creating a list of processes that are slow, tedious, cumbersome or suffering from a lack of staff. They should also analyze where additional data or information could help make better decisions.

In the pharma industry, data-driven AI solutions have been widely adopted in sales and marketing processes. For example, by analyzing patient and physician data, electronic medical records and demographic information, AI algorithms can identify trends, patterns and insights that help sales representatives tailor their messaging and presentations to specific HCPs.

NEW YORK, June 13 (Reuters) — Meta Platforms (META.O) said on Tuesday that it would provide researchers with access to components of a new “human-like” artificial intelligence model that it said can analyze and complete unfinished images more accurately than existing models.

The model, I-JEPA, uses background knowledge about the world to fill in missing pieces of images, rather than looking only at nearby pixels like other generative AI models, the company said.

That approach incorporates the kind of human-like reasoning advocated by Meta’s top AI scientist Yann LeCun and helps the technology to avoid errors that are common to AI-generated images, like hands with extra fingers, it said.