Scientists are rethinking how to implement automation for biologists to reduce costs, simplify adoption, and increase reproducibility.
Category: robotics/AI – Page 270
Analog in-memory computing recent hardware implementations focused mainly on accelerating inference deployment. In this work, to improve the training process, the authors propose algorithms for supervised training of deep neural networks on analog in-memory AI accelerator hardware.
The brain-computer interface offers real-time feedback to boost rehab adherence.
Rehabilitation robots could help patients in the future by reading their neural activity via a headset.
Many fundamental processes of life, and their synthetic counterparts in nanotechnology, are based on the autonomous assembly of individual particles into complex patterns. LMU physicist Professor Erwin Frey, Chair of Statistical and Biological Physics at LMU Munich and member of the ORIGINS Excellence Cluster, investigates the fundamental principles of this self-organization.
The rapid development of technologies such as the internet, mobile communications, and artificial intelligence has dramatically increased the demand for high-capacity communication systems. Among various solutions, mode-division multiplexing (MDM) has emerged as a crucial technique, utilizing spatial modes like orbital angular momentum (OAM) to enhance communication capacity.
Science fiction is riddled with artificial intelligence going rogue and turning on their human creators. HAL-9000. The Matrix. Skynet. GLaDOS. Cylons. Humanity, it seems, has a deep fear of the rebellion of the machine.
With the rise of ever more sophisticated large language models (LLMs), such as Chat GPT, the question of what dangers AI may pose has become even more pertinent.
And now, we have some good news. According to a new study led by computer scientists Iryna Gurevych of the Technical University of Darmstadt in Germany and Harish Tayyar Madabushi of the University of Bath in the UK, these models are not capable of going rogue.
This just in!
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While large language models (LLMs) have demonstrated remarkable capabilities in extracting data and generating connected responses, there are real questions about how these artificial intelligence (AI) models reach their answers. At stake are the potential for unwanted bias or the generation of nonsensical or inaccurate “hallucinations,” both of which can lead to false data.
That’s why SMU researchers Corey Clark and Steph Buongiorno are presenting a paper at the upcoming IEEE Conference on Games, scheduled for August 5–8 in Milan, Italy. They will share their creation of a GAME-KG framework, which stands for “Gaming for Augmenting Metadata and Enhancing Knowledge Graphs.”
The research is published on the arXiv preprint server.
The singularity is already here.
Since that pioneering work first appeared, AI has become a household word, most dramatically since OpenAI’s iterations of ChatGPT began rolling out starting on November 30, 2022. Now, from smoke-analyzin g AI aiding firefighters in California, to instant AI translation of most languages, to almost daily AI innovations in health care, this technology is already central to our lives. Last year, private investment in AI was more than $25 billion, according to the Li’s Center at Stanford, an estimate I believe on the conservative side. By next year, annual AI investment will reach some $200 billion, according to Goldman Sachs.
At my company, data.world, we’ve been building the foundation of our platform for AI since our founding in 2016. We knew back then that data would be the essential feedstock of AI, the oxygen of its metabolism. And in a world where data grows exponentially, data silos, data errors, missing context, and sheer data deluge are the bane of many companies and institutions. Our mission is to transform data into tools of institutional cognition, the most recent advance of which is our AI Context Engine™. The most important product we’ve ever launched, this tool makes corporate data now inaccessible to AI an essential part of companies’ strategic toolkit. The chat-with-your-data future has never been closer than it is right now, and our AI Context Engine is our fastest new product takeoff in our company’s history.
So back to the journey that we are all on. Let’s explore the essentials of Nearer together in summary.
Despite these challenges, the potential rewards of edge AI are driving innovation in model optimization, device management and security solutions. As these advancements continue, the barriers to edge AI deployment are gradually being lowered, paving the way for its widespread adoption across industries.
Ultimately, edge computing democratizes AI by removing it from complex, costly cloud execution and moving it to the local, accessible devices companies already own and use. This means that small and medium-sized businesses can gain access to tools previously reserved for much larger companies.
As we move forward, AI in business and edge computing are intertwined. The ebb and flow of progress is already noticeable in edge computing applications, and AI will continue this trajectory. As edge devices become more powerful, the proliferation of intelligent applications that operate seamlessly at the edge will transform industries.