LafargeHolcim will implement automation and robotics, artificial intelligence, predictive maintenance and digital twin technologies for its production process. The company is upgrading its production fleet for the future through its'Plants of Tomorrow" program. The program will be rolled out over four years as LafargeHolcim upgrades its technologies in the building materials industry. The company predicts a "Plants of Tomorrow" certified operation will show 15 to 20 percent of operational efficiency gains compared to a conventional cement plant. Among the technologies implemented are predictive operations that can detect abnormal conditions and process anomalies in real-time. This aims to reduce maintenance costs by more than 10 percent and significantly lower energy costs. Digital twins of plants will also be created to optimise training opportunities. Automation and robotics is another important element of the strategy. Unmanned surveillance is being performed for high exposure jobs in the entire plant. Partnering with Swiss start-up Flyability, the company is using drones that allow the frequency of inspections to increase while simultaneously reducing cost and increasing safety for employees by inspecting confined spaces. In addition, the new PACT (Performance and Collaboration) digital tool allows operational decision making from experience-based to data-centric, by combining data from various sources and enabling machine learning applications. LafargeHolcim is currently working on more than 30 pilot projects covering all regions where the company is active. The first integrated cement plant will be at LafargeHolcim's premises in Siggenthal, Switzerland, this plant will test all modules of the'Plants of Tomorrow' program. LafargeHolcim Global Head Cement Manufacturing, Solomon Baumgartner Aviles, said transforming the way we produce cement is one of the focus areas of our digitalisation strategy and the'Plants of Tomorrow' initiative will turn Industry 4.0 into reality at our plants. "These innovative solutions make cement production safer, more efficient and environmentally fit.
Artificial intelligence (AI) machine learning can have a considerable carbon footprint. Deep learning is inherently costly, as it requires massive computational and energy resources. Now researchers in the U.K. have discovered how to create an energy-efficient artificial neural network without sacrificing accuracy and published the findings in Nature Communications on August 26, 2020. The biological brain is the inspiration for neuromorphic computing--an interdisciplinary approach that draws upon neuroscience, physics, artificial intelligence, computer science, and electrical engineering to create artificial neural systems that mimic biological functions and systems. The human brain is a complex system of roughly 86 billion neurons, 200 billion neurons, and hundreds of trillions of synapses.
The Vatican's Pontifical Academy for Life, which began the year by urging the ethical development and application of artificial intelligence (AI), has announced an effort to use technology to fight world hunger, which has worsened during the pandemic. The Vatican institution, in collaboration with IBM, Microsoft and the UN Food and Agriculture Organization, or FAO, is encouraging governments, nonprofits and corporations to assure that technology is used to feed everyone, and to make farmers' lives more efficient and productive. In its quest to assure the transparent, responsible and inclusive use of AI, the Vatican and FAO are pushing for solutions in agriculture that will benefit not just the well off, but also the poor. "We need to face the biggest challenges on the planet," said John E. Kelly III, executive vice president of IBM. Kelly, who participated in the FAO and Pontifical Academy's Sept. 24 virtual conference announcing the effort against hunger, was one of the signers of the Vatican's call for AI ethics in February. The Vatican's effort to promote ethical AI for social good includes a new program to use digital technology to ensure a more sustainable and efficient global food supply.
Artificial Intelligence (AI) is the study of "intelligent agents" which can be define as any device that perceives its environment and takes appropriate action that makes the highest probability of achieving its goals. Additionally, it can also be define as a system's ability to interpret external data, learn from gathered data and use those learnings to realize specific goals through adaptation. It is also called as machine intelligence and attributed to the nature of intelligence demonstrated by machines. Some of the features of artificial intelligence are; successfully understanding human language, contending at the highest level in strategic games systems such as chess and go, autonomously operating cars, intelligent routing in content delivery networks and military simulations and others. To solve the problem of learning and perceiving the immediate environment, many approaches have been taken such as statistical methods, computational intelligence, versions of search and mathematical optimization, artificial neural networks, and methods based on statistic, probability and economics.
While GPT-3 has been bragging about achieving state-of-the-art performance on Complex NLP tasks with hundred billion parameters, researchers from the LMU Munich, Germany have proposed a language model who can show similar achievements with way fewer parameters. GPT-3 has been trained on 175 billion parameters and thus showed remarkable few-shot abilities, and by reformulating a few tasks and prompting inputs, it also showed immense capabilities on SuperGLUE benchmark. However it comes with two most significant drawbacks -- large models aren't always feasible for real-world scenarios, and with the context window of these monstrous models is limited to a few hundred tokens, it doesn't scale more than a few examples. And thus, the researchers proposed an alternative to priming, i.e. PET required unlabelled data, which is easier to gather than labelled data, thus making it usable for real-world applications.
Currently, the diagnosis of sleep disorders relies on polysomnographic recordings with a time-consuming manual analysis with low reliability between different manual scorers. Throughout the night, sleep stages are identified manually in non-overlapping 30-second epochs starting from the onset of the recording based on electroencephalography (EEG), electro-oculography (EOG), and chin electromyography (EMG) signals which require meticulous placement of electrodes. Moreover, the diagnosis of many sleep disorders relies on outdated guidelines. When assessing the severity of obstructive sleep apnea (OSA), the patients are classified based on thresholds of the apnea-hypopnea index (AHI), i.e. the number of respiratory disruptions during sleep. These thresholds are not fully based on solid scientific evidence and remain the same across different measurement techniques.
At an online event today, Daniel Ek, the founder of Spotify, said he would invest 1 billion euros ($1.2 billion) of his personal fortune in deeptech "moonshot projects", spread across the next 10 years. Ek indicated that he was referring to machine learning, biotechnology, materials sciences and energy as the sectors he'd like to invest in. "I want to do my part; we all know that one of the greatest challenges is access to capital," Ek said, adding he wanted to achieve a "new European dream". "I get really frustrated when I see European entrepreneurs giving up on their amazing visions selling early on to non-European companies, or when some of the most promising tech talent in Europe leaves because they don't feel valued here," Ek said. "We need more super companies that raise the bar and can act as an inspiration."
A scientist from Russia has developed a new neural network architecture and tested its learning ability on the recognition of handwritten digits. The intelligence of the network was amplified by chaos, and the classification accuracy reached 96.3%. The network can be used in microcontrollers with a small amount of RAM and embedded in such household items as shoes or refrigerators, making them'smart.' The study was published in Electronics. Today, the search for new neural networks that can operate on microcontrollers with a small amount of random access memory (RAM) is of particular importance.
You were pretty stressed yesterday. Are you feeling better today?" What might sounds like a concerned message from friends or parents is actually a query from Replika, a chatbot. "If you're feeling down, or anxious, or you just need someone to talk to, your Replika is here for you 24/7," the company behind the chatbot writes on its website. Chatbots – a combination of "chat" and robot" – are programs that simulate a conversation, usually by text message.
France's COVID-19 resurgence is palpable in the buzzing biology lab of this public hospital in the Paris suburb of Argenteuil. Tube after tube arrive with new nasal swabs, now about 240 per day. And the lab director struggles to obtain enough reagents to keep up with escalating demand. More than 1 million of France's 67 million people took a virus test over the past week, putting labs like this under growing strain. Getting a virus test in Paris this month has involved long waits, both to be tested and to receive the result, complicating authorities' efforts to trace the epidemic in real time. "Since Aug. 15, we're seeing a constant increase in the number of positive patients," Laurence Courdavault, head of the Argenteuil hospital's medical biology department, said Friday.