Publication: Forbes story title: Human 2.0: is coming faster than you think deck: Will you evolve with the times? section: Innovation topic: artificial intelligence + big data special label: contributor group | Cognitive World author: by Neil Sahota date: October 1, 2018.
IonQ was founded on a gamble that ‘trapped ion quantum’ computing could outperform the silicon-based quantum computers that Google and others are building. As of right now, it does. IonQ has constructed a quantum computer that can perform calculations on a 79-qubit array, beating the previous king Google’s efforts by 7 qubits.
Their error rates are also the best in the business, with their single-qubit error rate at 99.97% while the nearest competitors are around the 99.5 mark, and a two-qubit error rate of 99.3% when most competitors are beneath 95%. But how does it compare to regular computers?
According to IonQ, in the kinds of workloads that quantum computers are being built for, it’s already overtaking them. The Bernstein-Vazirani Algorithm, a benchmark IonQ is hoping will take off, tests a computer’s ability to determine a single encoded number (called an oracle) when the computer can only ask a single yes/no question.
Continue reading “A new type of quantum computer has smashed every record” »
This is happening in the city of Tianjin, about an hour’s drive south of Beijing, within a gleaming office building that belongs to iFlytek, one of China’s rapidly rising artificial-intelligence companies. Beyond guarded gates, inside a glitzy showroom, the US president is on a large TV screen heaping praise on the Chinese company. It’s Trump’s voice and face, but the recording is, of course, fake—a cheeky demonstration of the cutting-edge AI technology iFlytek is developing.
Jiang Tao chuckles and leads the way to some other examples of iFlytek’s technology. Throughout the tour, Tao, one of the company’s cofounders, uses another remarkable innovation: a hand-held device that converts his words from Mandarin into English almost instantly. At one point he speaks into the machine, and then grins as it translates: “I find that my device solves the communication problem.”
IFlytek’s translator shows off AI capabilities that rival those found anywhere in the world. But it also highlights a big hole in China’s plan, unveiled in 2017, to be the world leader in AI by 2030. The algorithms inside were developed by iFlytek, but the hardware—the microchips that bring those algorithms to life—was designed and made elsewhere. While China manufactures most of the world’s electronic gadgets, it has failed, time and again, to master the production of these tiny, impossibly intricate silicon structures. Its dependence on foreign integrated circuits could potentially cripple its AI ambitions.
Continue reading “China has never had a real chip industry. Making AI chips could change that” »
Investigators from the Neurodegenerative Diseases Research Group at the University of Extremadura are studying signaling mediated by a pathway known as planar cell polarity (PCP), which regulates the coordinated orientation of cells during organogenesis, the process of organ formation in living organisms. This pathway has been highly conserved on the evolutionary scale, and one of its key functions in vertebrates is the regulation of the coordinated positioning of centrioles/ciliary basal cells inside cells.
This signaling pathway was discovered initially in the fruit fly genus Drosophila, although the majority of the pathway components have been retained in humans. It has likewise been observed that certain pathologies such as hydrocephaly, infertility and some kinds of cancers are associated with defective functioning of this signaling.
Under the auspices of the project EPICENTR within the Spanish national research plan, whose objective is to study the planar polarisation of centrioles in epithelial cells, the UEx researchers have now published the first results of their investigation in the journal Development. These results are related to the polarised positioning mechanism of centrioles in Drosophila and its correlation with actin.
Facial recognition technology is being tested by businesses and governments for everything from policing to employee timesheets. Even more granular results are on their way, promise the companies behind the technology: Automatic emotion recognition could soon help robots understand humans better, or detect road rage in car drivers.
But experts are warning that the facial-recognition algorithms that attempt to interpret facial expressions could be based on uncertain science. The claims are a part of AI Now Institute’s annual report, a nonprofit that studies the impact of AI on society. The report also includes recommendations for the regulation of AI and greater transparency in the industry.
“The problem is now AI is being applied in a lot of social contexts. Anthropology, psychology, and philosophy are all incredibly relevant, but this is not the training of people who come from a technical [computer science] background.” says Kate Crawford, co-founder of AI Now, distinguished research professor at NYU and principal researcher at Microsoft Research. “Essentially the narrowing of AI has produced a kind of guileless acceptance of particular strands of psychological literature that have been shown to be suspect.”
The promise of quantum computing brings with it some mind-blowing potential, but it also carries a new set of risks, scientists are warning.
Specifically, the enormous power of the tech could be used to crack the best cyber security we currently have in place.
A new report on the “progress and prospects” of quantum computing put together by the National Academies of Sciences, Engineering, and Medicine (NASEM) in the US says that work should start now on putting together algorithms to beat the bad guys.
Continue reading “We Need to Prepare For Quantum Attacks Now, Top US Scientists Warn” »
Over the past few years, classical convolutional neural networks (cCNNs) have led to remarkable advances in computer vision. Many of these algorithms can now categorize objects in good quality images with high accuracy.
However, in real-world applications, such as autonomous driving or robotics, imaging data rarely includes pictures taken under ideal lighting conditions. Often, the images that CNNs would need to process feature occluded objects, motion distortion, or low signal to noise ratios (SNRs), either as a result of poor image quality or low light levels.
Although cCNNs have also been successfully used to de-noise images and enhance their quality, these networks cannot combine information from multiple frames or video sequences and are hence easily outperformed by humans on low quality images. Till S. Hartmann, a neuroscience researcher at Harvard Medical School, has recently carried out a study that addresses these limitations, introducing a new CNN approach for analyzing noisy images.
