One such competitor, LaMDA, is the work of Google’s AI division. Built on Transformer – the company’s open-source neural network architecture – it can produce non-generic, open-ended dialogue after training on 1.56 trillion words of multi-content, public data and web text. By contrast, a typical chatbot is dependent on topic-specific datasets and has a limited conversation flow. LaMDA has 137 billion parameters, which can be thought of as the individual “synapses” combining to form the AI.
The sheer scale and complexity of models like LaMDA is leading some experts to ask profound questions about the nature of AI. In February, the Chief Scientist and Co-Founder of OpenAI, one of the leading research labs for artificial intelligence, claimed that the latest generation of neural networks are now large enough to be “slightly conscious”.
This month, another expert in machine learning has spoken out. Blake Lemoine, Senior Software Engineer at Google, believes that a form of self-awareness might be starting to emerge from the billions of connected parameters.
As artificial intelligence and deep learning techniques become increasingly advanced, engineers will need to create hardware that can run their computations both reliably and efficiently. Neuromorphic computing hardware, which is inspired by the structure and biology of the human brain, could be particularly promising for supporting the operation of sophisticated deep neural networks (DNNs).
Researchers at Graz University of Technology and Intel have recently demonstrated the huge potential of neuromorphic computing hardware for running DNNs in an experimental setting. Their paper, published in Nature Machine Intelligence and funded by the Human Brain Project (HBP), shows that neuromorphic computing hardware could run large DNNs 4 to 16 times more efficiently than conventional (i.e., non-brain inspired) computing hardware.
“We have shown that a large class of DNNs, those that process temporally extended inputs such as for example sentences, can be implemented substantially more energy-efficiently if one solves the same problems on neuromorphic hardware with brain-inspired neurons and neural network architectures,” Wolfgang Maass, one of the researchers who carried out the study, told TechXplore. “Furthermore, the DNNs that we considered are critical for higher level cognitive function, such as finding relations between sentences in a story and answering questions about its content.”
How the Matrix begins…
The technology I want to talk about today is something out of this world, but also a bit controversial There is a startup in Australia who are actually growing live human neurons and then integrating it into traditional computer chips… mind-blowing stuff!
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WATCH NEXT:
➞ This AI Technology is a Game Changer: https://youtu.be/i5Hsx_cQY4Q
➞ Million Quibit Silicon Quantum Computer from Intel: https://youtu.be/j9eYQ_ggqJk.
➞ New WoW Processor explained: https://youtu.be/-NeRIrRSFs4
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Lemoine, an engineer for Google’s responsible AI organization, described the system he has been working on since last fall as sentient, with a perception of, and ability to express thoughts and feelings that was equivalent to a human child.
“If I didn’t know exactly what it was, which is this computer program we built recently, I’d think it was a seven-year-old, eight-year-old kid that happens to know physics,” Lemoine, 41, told the Washington Post.
He said LaMDA engaged him in conversations about rights and personhood, and Lemoine shared his findings with company executives in April in a GoogleDoc entitled “Is LaMDA sentient?”
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Can AI enable us to live forever? In A.rtificial I.mmortality, filmmaker Ann Shin sets out on a journey, exploring the latest AI and biotech with scientists and visionaries who foresee a ‘post-biological’ world where humans and AI merge. Will AI be the best, or the last thing we ever do?
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Ken OtwellIt’s an awkward situation. Was the engineer continuing to do his job? Was his public claim about internal corporate technology interfering with his duties or causing harm to Google? Was it breaking a voluntary non-disclosure?
Kevin CuevasWith neurocyte based computing, it is a question worth exploring since we are already blurring that line anyway.
Neurons are amazing little microbes capable of learning and making decisions. Modern AI tries to take inspiration from living neurons, but why settle for the synthetic version? By growing human neurons directly connected to a computer it’s possible to make a living AI of sorts capable of even complex tasks like flying a plane in a simulation.
Today we explore our first attempt at doing exactly that. We cover building the first prototype multi electrode array, growing the neurons and attempting to take some readings from them. This is the first part of what will hopefully be a many part series, so stay tuned for updates!
More reading/sources:
In January, a law came into effect in South Korea called the Serious Disasters Punishment Act. The new regulation states that if workers die or sustain serious injuries during work, courts could fine the CEO or high-ranking managers of the firms or even send them to jail.
An increase in robot investments
This event has spurred an increase in investment in robots in the nation, according to a report by Rest of the World published on June 6.
Imagine if it were possible to produce infinite amounts of the world’s most valuable resource, cheaply and quickly. What dramatic economic transformations and opportunities would result?
This is a reality today. It is called synthetic data.
Synthetic data is not a new idea, but it is now approaching a critical inflection point in terms of real-world impact. It is poised to upend the entire value chain and technology stack for artificial intelligence, with immense economic implications.
For plant biologists, understanding how plants grow and interact with soil is vital for selecting resilient crops that can efficiently take up water and nutrients. But how do you monitor what is happening underground?
To address this challenge, a team from KAUST has developed a low-cost system for imaging plant growth dynamics, noninvasively and at high throughput.
Unlike other imaging tools, which are costly and stationary, the new system called MutipleXLab, is modular, mobile and, at a low cost, can continuously monitor thousands of seeds, from germination to root development.