"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.
Raymond Bird, the electronics engineer who was tasked with developing the HEC, described the demonstration of the noughts and crosses game as a great success in showing the potential power of computers. It led to the development of expert systems – machines that could become domain experts in areas such as medical diagnosis, says Herbert. With ubiquitous internet access, much more data became available, which led to what is now called machine learning. A big driver was search engine development by the likes of Bing, Google and AltaVista and, later, the recommendation engines – all of which are based on pattern recognition technology.
People with the skills required to design, install, operate, and maintain robotic production systems are becoming more widely available, too. Advances in computing power, software-development techniques, and networking technologies have made assembling, installing, and maintaining robots faster and less costly than before. It will also make them viable for companies working with small batch sizes and significant product variety. In automotive production, expert systems can automatically make tiny adjustments in line speed to improve the overall balance of individual lines and maximize the effectiveness of the whole manufacturing system.
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Yago was one of the first knowledge bases, developed by scientists at the Max Planck Institute for Informatics in Saarbrücken and the Télécom ParisTech in Paris. "If you, for example, do a internet search for the German term'Allianz', this is merely a collection of letters for the search engine," explains Professor Gerhard Weikum, Scientific Director at the Max Planck Institute for Computer Science in Saarbrücken. Today, Yago is a collaboration of the Max Planck Institute, the Télécom ParisTech University, where Suchanek now holds a professorship, and the Max Planck spin-off, Ambiverse. Last week, the researchers behind Yago were awarded the Prominent Paper Award which recognizes outstanding papers published in the Artificial Intelligence Journal (AIJ) that are exceptional in their significance and impact over the past 5 years.
Forty years later, there are more architects, and more work for us, than ever--yet the existential angst remains: If recessions, construction managers, and liability insurance underwriters didn't manage to dismantle the profession, now what? The Susskinds argue that it will not be a loss of faith in architects, lawyers, and accountants, but rather the broad democratization of expertise through big data and data sharing, expert systems, and automation that will "transform the work of human experts." As knowledge work begins the same transfiguration in the world of computation that manufacturing experienced with machine automation, the bespoke relationships curated by architects with clients will be circumvented by widely accessible knowledge systems, architects will no longer be the anointed "gatekeepers" of professional knowledge or judgment, and the increasing complexity of building problems will face economic pressures demanding that architects provide even more service for less money. Large swaths of professional services will be routinized by computers, further decomposing those services into discrete automated tasks.
To tackle my aunt's puzzle, the expert systems approach would need a human to squint at the first three rows and spot the following pattern: The human could then instruct the computer to follow the pattern x * (y 1) z. Even when machines teach themselves, the preferred patterns are chosen by humans: Should facial recognition software infer explicit if/then rules, or should it treat each feature as an incremental piece of evidence for/against each possible person? And so they designed deep neural networks, a machine learning technique most notable for its ability to infer higher-level features from more basic information. These questions have constrained efforts to apply neural networks to new problems; a network that's great at facial recognition is totally inept at automatic translation.
Inference Engine It accepts and promotes human interpretation by making fuzzy inference according to inputs and IF-THEN rules. A number of other concepts are associated with fuzzy logic such as fuzzy set theory, fuzzy modelling, the fuzzy control system that have been developed for further enhancement. In control systems theory, if the fuzzy interpretation of the problem is appropriate and if the fuzzy theory is developed precise and correct, then fuzzy controllers can be accordingly designed and they work quite well to their advantages. Most of the fuzzy logic control systems are knowledge-based systems which mean either their fuzzy models or their fuzzy logic controllers are described by fuzzy logic IF-THEN rules.
If, like me, you're one of those people who worries that you haven't accomplished much in your life, you probably shouldn't read this profile of Kavya Kopparapu, a teenager who has probably done more in her time at high school than I've done since I graduated. Most recently, she created a cheap, portable diagnosis system for a common eye affliction her grandfather suffers from, but which often goes undetected and leads to blindness. A 3D-printed mount and lens lets retinal scans be taken with an iPhone, and a machine learning system using readily available services and trained on thousands of such images does the diagnosis. She presented her work last month at O'Reilly's AI conference.
A British computer expert who helped shut down the WannaCry cyber attack that crippled the NHS has been arrested in the US for his alleged role in an unrelated malware attack. Mr Hutchins is accused of creating, selling, and maintaining the malware, in collaboration with an unnamed codefendant, between July 2014 and July 2015. I'll be crowdfunding legal fees soon Andrew Mabbitt, a cyber security company founder who travelled to the conference with Mr Hutchins, says he does not believe the charges against him. The indictment alleges Mr Hutchins created the malware and attempted to sell it for $3,000.