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Anticipation and Anticipatory Systems: Humans Meet Artificial Intelligence

Senior Professor and responsible of the Experimental Phenomenology Laboratory at the University of Trento, (Italy).Projected title of her talk: “Anticipatory Structures in Vision: perception from inside out” Canada Research Chair in Race, Inequalities and Global Change, University of British Columbia (Canada).

Projected title of her talk: “Stories as Anticipatory Models” Project Lead of the AI Narratives project, Leverhulme Centre for the Future of Intelligence, investigating the representation of AI in fictional, cinematic and other cultural narratives and the effects of these on the development, reception and regulation of artificial intelligence.

Projected title of his talk: “Revisiting Hippocrates’ On ancient medicine to inform natural and artificial anticipation” Philosopher and sociologist, Unesco Chair in Anticipatory Systems, Principal Investigator of the International Anticipation Project, University of Trento (Italy).

Projected title of her talk: “Anticipatory science fiction to foster ethical debates on AI and robotics” Computer scientists and roboticist, Author of Artificial Cognitive Systems (MIT Press), Professor at Carnegie Mellon University Africa (Rwanda).

Artificial intelligence in the control and operation of construction plant - the autonomous robot excavator.

The particular application concerns the problem of autonomous excavation, and the subtlety of the approach lies in the fact that the machine can cope with highly variable ground conditions and even underground obstructions without human interference.

The hardware systems are briefly described, but the main emphasis of the paper concerns the method by which the AI technique of a rule-based “production system” is used to control the tip of the excavator bucket.

The particular application concerns the problem of autonomous excavation, and the subtlety of the approach lies in the fact that the machine can cope with highly variable ground conditions and even underground obstructions without human interference.

The hardware systems are briefly described, but the main emphasis of the paper concerns the method by which the AI technique of a rule-based “production system” is used to control the tip of the excavator bucket.

Meet Hemingway: The Artificial Intelligence Robot That Can Copy Your Handwriting

The everyday tasks that humans often take for granted, such as walking and handwriting, have, until recently, been challenging to program a robot to do.

The Handwriting Company now has a robot that can create beautifully handwritten communication that mimics the style of an individual's handwriting while a robot from Brown University can replicate handwriting from a variety of languages even though it was just trained on Japanese characters.

Not only can Hemingway write faster than a human, in one example completing the sample text provided in 2 minutes that took the human who provided it 15, it’s also surprisingly accurate.

While script from the same language shares specific characteristics no matter who is doing the writing, every individual has nuances in their handwriting that makes it unique to them including the pressure applied by the writing utensil, flourishes to various characters, inconsistencies and the like.

While the robotic handwriting doesn’t yet fool a graphologist, who was able to tell automated handwriting from a human handwriting sample only because the robotic sample lacked a bit of fluidity, it’s good enough to fool others.

The roboticists at Brown University split their training system into two models—a local model that is concerned with the stroke of the pen and a global model that moves the writing utensil to the next stroke of the character.

The future of artificial intelligence: stories

the humanoid robot who uses AI and facial recognition to imitate human gestures and facial expressions.

Information Sciences is focusing on developing technologies that will allow AI to understand narratives so they can become contextually aware and reason intelligently about the world.

Computers in the near future will be able to achieve tasks such as educating doctors and assessing the artistic quality of a novel.

For the technology to get better, machines will have to become more flexible in understanding context and when they are in a novel situation they are not designed for.

Currently, AI systems can understand and respond to simple language: AI can grammatically analyze sentences, identifying nouns and verbs, and figure who or what is the subject or object of the sentence.

Motifs are small bundles of important cultural information that are often identified by key phrases: for example, the idea of a troll under a bridge or a golden fleece. After teaching AI to identify these motifs, he plans to track them as people employ them (such as in social media) and model how they are used in context so as to allow machines to better mimic human cognition.

Finlayson’s research has various real-world applications, for example, in the domains of education that incorporate story-driven learning, such as law, business and medicine.

However, as weapons, such as drones, become more autonomous and independent, there is a desperate need for them to reason ethically and understand when they need to ask a human for help.

These devices may have situational awareness and the ability to assist decision makers to make the best possible choice given the context.

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