A new report from CB Insights has revealed that global funding in AI startups has seen a surge of 8% to $17.9 billion during Q3 2021.
Elon Musk’s revolutionary company Neuralink plans to insert Computer Chips into peoples brains but what if there’s a safer and even more performant way of merging humans and machines in the future?
Enter DARPAs plan to help the emergence of non-invasive brain computer interfaces which led to the organization Battelle to create a kind of Neural Dust to interface with our brains that might be the first step to having Nanobots inside of the human body in the future.
How will Neuralink deal with that potential rival with this cutting edge technology? Its possibilities in Fulldive Virtual Reality Games, Medical Applications, merging humans with artificial intelligence and its potential to scale all around the world are enormous.
Imagine a world in which smart packaging for supermarket-ready meals updates you in real-time to tell you about carbon footprints, gives live warnings on product recalls and instant safety alerts because allergens were detected unexpectedly in the factory.
But how much extra energy would be used powering such a system? And what if an accidental alert meant you were told to throw away your food for no reason?
These are some of the questions asked by team of researchers, including a Lancaster University Lecturer in Design Policy and Futures Thinking, who—by creating objects from a “smart” imaginary new world—are looking at the ethical implications of using artificial intelligence in the food sector.
Microsoft and Nvidia have been working hard to finally create an Artificial Intelligence Model which surpasses and beats OpenAI’s GPT3 with more than double the parameter count and almost reaching the amazing and intelligent amount of 1 Trillion Parameter models. Unless OpenAI comes out with GPT4, it seems like the Megatron-Turing NLP AI Model is to be the best and smartest Artificial Intelligence of 2021 which the most abilities of any Natural Language Processing AI ever.
It’s also much easier to train than GPT3. It requires much less hardware and maybe with the upcoming Nvidia Lovelace GPU’s, it’ll be even easier to run for regular consumers.
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TIMESTAMPS:
00:00 GPT-3 has been beaten.
02:09 How Transformers work.
03:53 What’s new about this AI Model?
05:45 The Future of Artificial Intelligence.
09:32 Last Words.
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#ai #agi #microsoft
Engineering students have designed an autonomous robot that can find and open doors in 3D digital simulations. Now they’re building the hardware for an autonomous robot that not only can open its own doors but also can find the nearest electric wall outlet to recharge without human help.
One flaw in the notion that robots will take over the world is that the world is full of doors.
The newest member of the Boston Dynamics family has a vacuum-powered grabber and a plethora of sensors.
Uncovering the mechanisms of learning via synaptic plasticity is a critical step towards understanding how our brains function and building truly intelligent, adaptive machines. Researchers from the University of Bern propose a new approach in which algorithms mimic biological evolution and learn efficiently through creative evolution.
Our brains are incredibly adaptive. Every day, we form new memories, acquire new knowledge, or refine existing skills. This stands in marked contrast to our current computers, which typically only perform pre-programmed actions. At the core of our adaptability lies synaptic plasticity. Synapses are the connection points between neurons, which can change in different ways depending on how they are used. This synaptic plasticity is an important research topic in neuroscience, as it is central to learning processes and memory. To better understand these brain processes and build adaptive machines, researchers in the fields of neuroscience and artificial intelligence (AI) are creating models for the mechanisms underlying these processes. Such models for learning and plasticity help to understand biological information processing and should also enable machines to learn faster.
Nvidia’s Omniverse, billed as a “metaverse for engineers,” has grown to more than 700 companies and 70,000 individual creators that are working on projects to simulate digital twins that replicate real-world environments in a virtual space.
The Omniverse is Nvidia’s simulation and collaboration platform delivering the foundation of the metaverse, the universe of virtual worlds that are all interconnected, like in novels such as Snow Crash and Ready Player One. Omniverse is now moving from beta to general availability, and it has been extended to software ecosystems that put it within reach of 40 million 3D designers.
And today during Nvidia CEO Jensen Huang’s keynote at the Nvidia GTC online conference, Nvidia said it has added features such as Omniverse Replicator, which makes it easier to train AI deep learning neural networks, and Omniverse avatar, which makes it simple to create virtual characters that can be used in the Omniverse or other worlds.
High level partnerships and a focus on using artificial intelligence and big data to find solutions to complex problems are backing VXV’s quiet climb to new all-time highs.
Although these social robots are two-dimensional computer animations, they helped MIT researchers decode the basics of human interactions.
