The advanced global tech market is set to experience the effect of quantum computing on artificial intelligence in 2022 and beyond. It is essential to integrate quantum computing into artificial intelligence models to boost decision-making abilities more efficiently.
Selmer Bringsjord, and his colleagues have proposed the Lovelace test as a substitute for the flawed Turing test. The test is named after Ada Lovelace.
Bringsjord defined software creativity as passing the Lovelace test if the program does something that cannot be explained by the programmer or an expert in computer code.2 Computer programs can generate unexpected and surprising results.3 Results from computer programs are often unanticipated. But the question is, does the computer create a result that the programmer, looking back, cannot explain?
When it comes to assessing creativity (and therefore consciousness and humanness), the Lovelace test is a much better test than the Turing test. If AI truly produces something surprising which cannot be explained by the programmers, then the Lovelace test will have been passed and we might in fact be looking at creativity. So far, however, no AI has passed the Lovelace test.4 There have been many cases where a machine looked as if it were creative, but on closer inspection, the appearance of creative content fades.
China is emerging as a pioneer in artificial intelligence as it makes strides in filing AI patents and experimenting with the latest AI technology to power industrial applications, industry experts said.
Their comments came after a Stanford University report that shows China filed more than half of all the world’s AI patent applications last year and Chinese researchers produced about one-third of AI journal papers and AI citations in 2021.
Wu Hequan, an academician at the Chinese Academy of Engineering, said China has been working to build a solid foundation to support its AI economy and is making significant contributions to AI globally.
A cutting-edge AI development that could boost smartphone battery life by 30 percent and shave countless kilowatts from energy bills will be unveiled to technology giants. The ground-breaking University of Essex-developed work has been rolled into an app called EOptomizer—which will be demonstrated to expert researchers and designers as well as major manufacturing companies like Nokia and Huawei.
It is hoped the EOptomizer app will be adapted across the industry and help drive down carbon emissions, by making consumers’ goods last longer.
It will do this by using software to dramatically increasing efficiency and reliability in phones, tablets, cars, smart fridges and computers’ batteries—delaying when consumers need to buy carbon-footprint-producing replacements. The event—which takes place in Robinson College, in Cambridge, on 11July—will showcase the impact EOptomizer could have across the globe.
“China claims to have developed an AI that can read the minds of Communist Party members to determine how receptive they are to ‘thought education’ in since-deleted article”.
AI developed by experts at Hefei Comprehensive National Science Centre is reportedly intended to check loyalty of Communist Party members.
On-chip training of machine learning algorithms is challenging for photonic devices. Here, the authors construct nonlinear mapping functions in silicon photonic circuits, and experimentally demonstrate on-chip bacterial foraging training for projection-based classification.
Neuromorphic photonics/electronics is the future of ultralow energy intelligent computing and artificial intelligence (AI). In recent years, inspired by the human brain, artificial neuromorphic devices have attracted extensive attention, especially in simulating visual perception and memory storage. Because of its advantages of high bandwidth, high interference immunity, ultrafast signal transmission and lower energy consumption, neuromorphic photonic devices are expected to realize real-time response to input data. In addition, photonic synapses can realize non-contact writing strategy, which contributes to the development of wireless communication.
The use of low-dimensional materials provides an opportunity to develop complex brain-like systems and low-power memory logic computers. For example, large-scale, uniform and reproducible transition metal dichalcogenides (TMDs) show great potential for miniaturization and low-power biomimetic device applications due to their excellent charge-trapping properties and compatibility with traditional CMOS processes. The von Neumann architecture with discrete memory and processor leads to high power consumption and low efficiency of traditional computing. Therefore, the sensor-memory fusion or sensor-memory-processor integration neuromorphic architecture system can meet the increasingly developing demands of big data and AI for low power consumption and high performance devices. Artificial synaptic devices are the most important components of neuromorphic systems. The performance evaluation of synaptic devices will help to further apply them to more complex artificial neural networks (ANN).
Chemical vapor deposition (CVD)-grown TMDs inevitably introduce defects or impurities, showed a persistent photoconductivity (PPC) effect. TMDs photonic synapses integrating synaptic properties and optical detection capabilities show great advantages in neuromorphic systems for low-power visual information perception and processing as well as brain memory.
Making pizza is not rocket science, but for this actual rocket scientist it is now. Benson Tsai is a former SpaceX employee who is now using his skills to launch a new venture: Stellar Pizza, a fully automated, mobile pizza delivery service. When a customer places an order on an app, an algorithm decides when to start making the pizza based on how long it will take to get to the delivery address. Inside Edition Digital’s Mara Montalbano has more.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
How groups of humans working together collaboratively should redistribute the wealth they create is a problem that has plagued philosophers, economists, and political scientists for years. A new study from DeepMind suggests AI may be able to make better decisions than humans.
AI is proving increasingly adept at solving complex challenges in everything from business to biomedicine, so the idea of using it to help design solutions to social problems is an attractive one. But doing so is tricky, because answering these kinds of questions requires relying on highly subjective ideas like fairness, justice, and responsibility.
For an AI solution to work it needs to align with the values of the society it is dealing with, but the diversity of political ideologies that exists today suggests that these are far from uniform. That makes it hard to work out what should be optimized for and introduces the danger of the developers’ values biasing the outcome of the process.
