"Today's expert systems deal with domains of narrow specialization. For expert systems to perform competently over a broad range of tasks, they will have to be given very much more knowledge. ... The next generation of expert systems ... will require large knowledge bases. How will we get them?"
– Edward Feigenbaum, Pamela McCorduck, H. Penny Nii, from The Rise of the Expert Company. New York: Times Books, 1988.
THE HAGUE, NETHERLANDS – The global chemical weapons watchdog will in February begin to assign blame for attacks with banned munitions in Syria's war, using new powers approved by member states but opposed by Damascus and its key allies Russia and Iran. The agency was handed the new task in response to an upsurge in the use of chemical weapons in recent years, notably in the Syrian conflict, where scores of attacks with sarin and chlorine have been carried out by Syrian forces and rebel groups, according to a joint United Nations-OPCW investigation. A core team of 10 experts charged with apportioning blame for poison gas attacks in Syria will be hired soon, Fernando Arias, the new head of the Organisation for the Prohibition of Chemical Weapons (OPCW), told the Foreign Press Association of the Netherlands on Tuesday. The Syria team will be able to look into all attacks previously investigated by the OPCW, dating back to 2014. The OPCW was granted additional powers to identify individuals and institutions responsible for attacks by its 193 member states at a special session in June.
What's more, even AIs based on mechanisms inspired by human biology, such as neural networks, have only a distant relationship with biological neurons in the brain. NN are examples more of the importance of reinforcement and self-organisation of controller networks than any similarity with biology. The first, naive, approach to AI is to think that it is necessary to create a synthetic human, or a synthetic brain to produce cognition: in fact, cognition does not need to be anthropomorphic at all. Second attempt at a definition: "The ability of a machine to achieve performance equal to or better than certain human cognitive processes." This definition is based on the final outcome, without presupposing imitation of biological mechanisms.
Five new clinics will open in the UK next year that will use artificial intelligence to help speed up disease diagnosis. The medical technology centres in Leeds, Oxford, Coventry, Glasgow and London will be funded by the Government as it looks to increase its investment in AI and improve patient treatment. The centres will use AI software to digitalise scans and biopsies, and develop products to detect diseases early. The large investment, costing £50million, will ensure people get personalised treatment sooner, as well as freeing up doctors time. Business, Energy and Industrial Strategy Secretary Greg Clark said: 'AI has the potential to revolutionise healthcare and improve lives for the better.' 'The innovation at these new centres will help diagnose disease earlier to give people more options when it comes to their treatment, and make reporting more efficient, freeing up time for our much-admired NHS staff to spend on direct patient care.'
There's no foolproof way to know if someone's verbally telling lies, but scientists have developed a tool that seems remarkably accurate at judging written falsehoods. Using machine learning and text analysis, they've been able to identify false robbery reports with such accuracy that the tool is now being rolled out to police stations across Spain. Computer scientists from Cardiff University and Charles III University of Madrid developed the tool, called VeriPol, specifically to focus on robbery reports. In their paper, published in the journal Knowledge-Based Systems earlier this year, they describe how they trained a machine-learning model on more than 1000 police robbery reports from Spanish National Police, including those that were known to be false. A pilot study in Murcia and Malaga in June 2017 found that, once VeriPol identified a report as having a high probability of being false, 83% of these cases were closed after the claimants faced further questioning.
This installment of Research for Practice features a curated selection from Alex Ratner and Chris Ré, who provide an overview of recent developments in Knowledge Base Construction (KBC). While knowledge bases have a long history dating to the expert systems of the 1970s, recent advances in machine learning have led to a knowledge base renaissance, with knowledge bases now powering major product functionality including Google Assistant, Amazon Alexa, Apple Siri, and Wolfram Alpha. Ratner and Re's selections highlight key considerations in the modern KBC process, from interfaces that extract knowledge from domain experts to algorithms and representations that transfer knowledge across tasks.
Technology is changing rapidly, information is coming at us so fast that traditional methods of learning the information you need for the job - and improving your performance - are no longer effective. The challenge is giving your employees the tools they need to be able to access the requisite knowledge, and the ability to share it, is key to business success. SaaS had a major impact on the way companies consume cloud services. This ebook looks at how the as a service trend is spreading and transforming IT jobs. San Francisco, CA-based social workplace intelligence platform Bradio has launched a new platform that aims to address these issues.
In this case, the benchmarks are for running the GoogLetNet V1 convolutional neural network framework, with a batch size of 1. (Meaning that items to be identified are sent through in serial fashion rather than batched up to be chewed on all at once.) This framework came close to beating humans at image recognition, but it took Microsoft's ResNet in 2015 to accomplish this feat, with a 3.57 percent failure rate compared to humans at 5.1 percent. The baseline for performance that Xilinx chose was the smallest F1 FPGA-accelerated instance on the EC2 compute cloud at Amazon Web Services. This instance has a single Virtex UltraScale VU9P FPGA on it, which has 1.182 million LUTs, which is attached to a server slice that has eight vCPUs (Based on the "Broadwell" Xeon E5-2696 v4 processor and 122 GB of main memory.
The computer can't tell you the emotional story. It can give you the exact mathematical design, but what's missing is the eyebrows. Analytics has never been sexier in the world of business. Big data, artificial intelligence (AI) and machine learning are all terms that fill executives with excitement at their potential, or with dread at falling behind. Yet as recently as three years ago, an online job search would have returned very few AI-titled jobs.
Wherever artificial intelligence is deployed, you will find it has failed in some amusing way. Take the strange errors made by translation algorithms that confuse having someone for dinner with, well, having someone for dinner. But as AI is used in ever more critical situations, such as driving autonomous cars, making medical diagnoses, or drawing life-or-death conclusions from intelligence information, these failures will no longer be a laughing matter. That's why DARPA, the research arm of the US military, is addressing AI's most basic flaw: it has zero common sense. "Common sense is the dark matter of artificial intelligence," says Oren Etzioni, CEO of the Allen Institute for AI, a research nonprofit based in Seattle that is exploring the limits of the technology.
A new software system developed by a European Union-funded research project can determine if industrial machinery requires maintenance based on the sounds it makes. A European Union-funded research project has developed software based on the human auditory system that can analyze sound to determine if industrial machinery requires maintenance. The Horizon2020 neuronSW team integrated advanced algorithms, machine learning, and big data analysis to mimic the human auditory cortex and enable early detection and prediction of mechanical breakdowns. Said SME NeuronSW Ltd.'s Jiri Cermak, "The technology leverages machine learning, the cloud, and the Internet of Things to deliver a detection service which emulates human intuition about sound." The neuronSW solution lets manufacturers perform intelligent audio diagnostics and monitor key pieces of machinery by the sounds they generate.