Goto

Collaborating Authors

Results


The future of computing, a perspective

#artificialintelligence

Could it be possible to offer Einstein's intelligence, the context of Gandhi and the memory of all humanity in a consolidated computer platform? Will we be able, in the near future, to improve man - machine collaboration and connect both? So we are better able that to make more intelligent decisions? All of us have been generating enormous mountains of data for some years, thanks to 6 billion smartphones and around 30 billion connected sensors. To give you an idea; together we are producing 44 Zettabytes of data this year. One Zettabyte is comparable to 700,000 times the largest library in the world times 44.


GPT-3 Creative Fiction

#artificialintelligence

What if I told a story here, how would that story start?" Thus, the summarization prompt: "My second grader asked me what this passage means: …" When a given prompt isn't working and GPT-3 keeps pivoting into other modes of completion, that may mean that one hasn't constrained it enough by imitating a correct output, and one needs to go further; writing the first few words or sentence of the target output may be necessary.


QC Ware Touts Breakthrough in Quantum Machine Learning Algorithms

#artificialintelligence

PALO ALTO, Calif., July 22, 2020 – QC Ware, provider of enterprise software and services for quantum computing, announced a significant breakthrough in quantum machine learning (QML) that increases QML accuracy and speeds up the industry timeline for practical QML applications on near-term quantum computers. QC Ware's algorithms researchers have discovered how classical data can be loaded onto quantum hardware efficiently and how distance estimations can be performed quantumly. These new capabilities enabled by Data Loaders are now available in the latest release of QC Ware's Forgecloud services platform, an integrated environment to build, edit, and implement quantum algorithms on quantum hardware and simulators. "QC Ware estimates that with Forge Data Loaders, the industry's 10-to-15-year timeline for practical applications of QML will be reduced significantly," said Yianni Gamvros, Head of Product and Business Development at QC Ware. "What our algorithms team has achieved for the quantum computing industry is equivalent to a quantum hardware manufacturer introducing a chip that is 10 to 100 times faster than their previous offering. This exciting development will require business analysts to update their quad charts and innovation scouts to adjust their technology timelines."


Neuromorphic Chips Take Shape

Communications of the ACM

Chips designed specifically to model the neurons and synapses in the human brain are poised to change computing in profound ways.


The Race for Quantum Supremacy and the Quantum Artificial Intelligence of Things

#artificialintelligence

Both races are setting the stage for the next dominant world power. While research into AI and quantum technologies is being developed on a worldwide scale, with advances coming from different countries, China and the United States (US) are at the forefront of both races, with these technologies forming important stepping stones for geopolitical power accumulation. Indeed, China is currently playing the game for supremacy on both quantum technologies and AI, trying to surpass the US and become the leading world power (Smith-Goodson, 2019). If China wins the race for quantum supremacy then it will be in a leading geostrategic position, since it will become the major dominant power in the next technological infrastructure, if, along with quantum supremacy, China achieves AI supremacy (both classical and quantum), then it may topple the US, Russia, Europe and Asian geopolitical competition vectors. On the other hand, this race is not restricted to countries, it is a global geostrategic and geoeconomic race that includes cooperative networks involving the academia and the private sectors as well, indeed, the US geostrategic position depends strongly upon the private sector's US-based large technology companies' investment in quantum technologies. Regarding the issue of quantum supremacy, it is relevant to consider Kirkland (2020)'s reflection, quoting: "(…) One thing remains unchanged (…) and that is the glaring reality that those who manage to successfully harness the power of quantum mechanics will have supremacy over the rest of the world. How do you think they will use it?"


QC Ware Races Ahead With Breakthrough in Quantum Machine Learning Algorithms

#artificialintelligence

QC Ware, the leader in enterprise software and services for quantum computing, today announced a significant breakthrough in quantum machine learning (QML) that increases QML accuracy and speeds up the industry timeline for practical QML applications on near-term quantum computers. QC Ware's algorithms researchers have discovered how classical data can be loaded onto quantum hardware efficiently and how distance estimations can be performed quantumly. These new capabilities enabled by Data Loaders are now available in the latest release of QC Ware's Forge cloud services platform, an integrated environment to build, edit, and implement quantum algorithms on quantum hardware and simulators. "QC Ware estimates that with Forge Data Loaders, the industry's 10-to-15-year timeline for practical applications of QML will be reduced significantly," said Yianni Gamvros, Head of Product and Business Development at QC Ware. "What our algorithms team has achieved for the quantum computing industry is equivalent to a quantum hardware manufacturer introducing a chip that is 10 to 100 times faster than their previous offering. This exciting development will require business analysts to update their quad charts and innovation scouts to adjust their technology timelines."


3D hand-sensing wristband uses a Raspberry Pi for machine learning

#artificialintelligence

Researchers from Cornell and the University of Wisconsin, Madison, have designed a wrist-mounted device that tracks the entire human hand in 3D. The device (pictured) uses the contours from the wearer's wrist to create an abstraction of 20 finger joint positions. The FingerTrak bracelet uses low-resolution thermal cameras that read the wrist contours and a tethered Raspberry Pi 4 and machine learning to teach itself what the hand is doing based on these readings. Cheng Zhang, assistant professor of information science and director of Cornell's new SciFi Lab, where FingerTrak was developed said: "The most novel technical finding in this work is discovering that the contours of the wrist are enough to accurately predict the entire hand posture," Zhang said. "This finding allows the reposition of the sensing system to the wrist, which is more practical for usability."


QC Ware Races Ahead With Breakthrough in Quantum Machine Learning Algorithms

#artificialintelligence

"QC Ware estimates that with Forge Data Loaders, the industry's 10-to-15-year timeline for practical applications of QML will be reduced significantly," said Yianni Gamvros, Head of Product and Business Development at QC Ware. "What our algorithms team has achieved for the quantum computing industry is equivalent to a quantum hardware manufacturer introducing a chip that is 10 to 100 times faster than their previous offering. This exciting development will require business analysts to update their quad charts and innovation scouts to adjust their technology timelines." Apart from the Forge Data Loaders, the latest release of Forge includes tools for GPU acceleration, which allows algorithms testing to be completed in seconds versus hours, and turnkey algorithms implementations on a choice of simulators and quantum hardware. Quantum hardware integrations include D-Wave Systems, and IonQ and Rigetti architectures through Amazon Braket.


Getting Started with the NVIDIA Jetson Nano Developer Kit

#artificialintelligence

Over the last year or two there has been a flood of custom silicon intended to speed up machine learning on the edge. First to arrive was Intel with their Moividius-based hardware, and more recently we've seen the appearance of Google's Edge TPU-based hardware. However traditionally NVIDIA's offering in this space -- built as it was around their GPU-based hardware -- has been higher powered, and comparatively expensive. However, with everyone moving towards the edge it is perhaps unsurprising to see them introduce something a bit more affordable. So last month we saw the arrival of the Jetson Nano Module and Developer Kit. Still based around their existing GPU technology, the new Jetson Nano is therefore "upwards compatible" with the much more expensive Jetson TX and AGV Xavier boards. The Jetson Nano Developer Kit arrives in yet another unassuming box. Inside the box is the carrier board itself with the Jetson Nano module and a heatsink already fitted.


Zapata CEO Christopher Savoie: The QC and ML business use case is 'a when, not an if'

#artificialintelligence

The story of quantum computing hardware companies is well known. But as tech giants Amazon and Microsoft push the quantum computing conversation to the cloud, we're also seeing quantum computing software companies emerge. One such company, Zapata, is building an enterprise software platform for quantum computing. Businesses with deep pockets are increasingly exploring quantum computing, which depends on qubits to perform computations that would be much more difficult, or simply not feasible, on classical computers. Quantum advantage, the inflection point when quantum computers begin to solve useful problems, is a long way off (if it can even be achieved) but its potential is massive.