Freddie McMahon

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AI 169 Insects are being converted into cyborgs – the scientific challenge is handling the power for the cybernetics; ethical issues could be huge

"Cyborg insects" (using remote-controlled or chip-based neural stimulators to control movements) could be powered by piezoelectric strips attached to their backs (generating 10 millivolts per fiber in in a proof-of-concept experiment), Tokyo University of Agriculture and Technology researchers have found.

Powering these "stimulator chips" is a big limitation. "Wires from an external power source restrict their motion, and most battery cells are too heavy and wouldn't fit on the insect," says Keisuke Morishima from the Tokyo University of Agriculture and Technology in Japan. Smaller batteries have been used, but run down in as little as a few minutes.

Instead, Morishima suggests that the insects themselves could power the slave-driving chips. As a proof of concept, he glued a piezoelectric fibre - 4 centimetres in length but just 200 micrometres across - to the back of a Madagascar hissing cockroach. As the cockroach walked, each step stretched and squeezed the piezoelectric fibre, generating electricity via mechanical stress.

His experiments show that the cockroach's movement can generate more than 10 millivolts in a single fibre. About 100 of these fibres would be enough to power the stimulators, he says (Comparative Biochemistry and Physiology Part B: Biochemistry and Molecular Biology, DOI: 10.1016/j.cbpb.2008.09.055).

Kevin Warwick, a cybernetics expert at the University of Reading in the UK, thinks it may be difficult to store the generated energy in order to provide a steady supply throughout a cyborg insect's mission. He also thinks this number of fibres may be too heavy for the insect to carry. "I'm quite sceptical of the leap to using this as a power supply," he says.


However, Warwick says the method may be more easily applied to larger animals like rats, which could generate greater power when controlled using a similar system.

AI 168 EXPERT SYSTEMS versus DECISIONALITY DECISION-TREES

EXPERT SYSTEMS

Primary Argument

The average life expectancy of an Expert System was 18 months as the inference engine weakness was that it lacked ‘human’ judgment to ‘know’ when to stop learning. As a consequence, it over learnt leading to decisioning distortions that are typically not obvious at first but then rapidly deteriorate. The problem was further exasperated as the Expert System is ‘black-box’ AI, which means there was no transparency of how decisions were made. The legacy of this issue has become more topical recently in the wider area of AI related to trust and ethical risks.

Secondary Argument

Knowledge acquisition required the coming together of two specialists: subject matter experts and knowledge engineers to develop the data and inference procedures. This created interpretation issues, plus high costs and elapsed time for knowledge acquisition.

Other Arguments

Wikipedia http://en.wikipedia.org/wiki/Expert_system/ summaries Expert Systems strengths and weaknesses quite well as:

Strengths:

• Provides consistent answers for repetitive decisions, processes and tasks
• Holds and maintains significant levels of information
• Encourages organizations to clarify the logic of their decision-making
• Never "forgets" to ask a question, as a human might

Weaknesses:

• Lacks common sense needed in some decision making
• Cannot make creative responses as human expert would in unusual circumstances
• Domain experts not always able to explain their logic and reasoning
• Errors may occur in the knowledge base, and lead to wrong decisions
• Cannot adapt to changing environments, unless knowledge base is changed


EXPERT SYSTEMS DECISION-TREES

Expert Systems use data driven Decision-Trees for forward and backward chaining. However, it is the use of data driven Decision-Trees combined with inference-based learning that led to the weakness of Expert Systems.

The use of Decision-Trees is fundamental to IF-THEN-ELSE so an alternative approach was needed that bypassed the weaknesses of Expert Systems.

DECISIONALITY DECISION-TREES

Many concept and technology challenges had to be overcome to provide an alternative set of technologies to Expert Systems.

One over riding challenge was that the tools had to be simple and natural to use. Though this has been achieved it does create the perception of familiarity from the past and being so simple how can it be any good.

This is our oxymoron as the technology is very simple to use but at the same time it can cope with the velocity, volatility and complexity of procedural knowledge.
Procedural knowledge is about IF-THEN-ELSE logic. Decision-Trees are a natural visualization for this type of logic. Of course, knowledge cannot be contained in one Decision-Tree. So by treating knowledge as a collection of Decision-Tree objects which can be linked enables an ecosystem to be created that can cover any breadth and depth. Indeed, this ecosystem of knowledge objects can grow and evolve without constraint.

This is quite a challenge as software convention is that the bigger a system becomes the more complex it is too build and there is a tipping point where it becomes untenable. Yet the ambition to cover all procedural knowledge that can continually evolve demanded a software solution that would have to be different. We solved this by:

• Automatically generating a stateless web-service from the Decision-Tree script – this means no database is used which is the opposite of an Expert System.

• Enable the Decision-Tree script to dynamically link with a web-service that required a handover to a different knowledge set.


This combination enables creation and change to happen at the ‘speed of articulation’ thus supporting the development of an ecosystem that can change and grow as fast as is needed.

At the same time this approach solved another problem and that is the usage of the knowledge. People only want to navigate down the pathways of their choice without having to be exposed to the landscape of the knowledge. We have found that knowledge interaction can go deep very quickly at the rate of say a step every 5 to 8 seconds.
This approach means the only limit to the number of pathways and outcomes that can be interacted with is a constraint of the Decision-Tree scripting and not a constraint of the technology.

Because we can automatically measure the interaction in terms of steps, flows, patterns and metrics the learning loops can be very fast but they are human controlled. This whole approach is white-box.

The combination of capabilities provides a sound foundation for the acquisition, application and usage of procedural knowledge that can be accessed by any connected device or application.

The DecisionFlows scripting tool embraces visualization, narration and simulation. It is designed to improve logical thought and the acquisition of knowledge.

The Decisionality Decision-Trees use forward and backward chaining techniques as do Expert Systems. The difference is Expert Systems are data driven whereas the Decisionality is driven by human interaction.



Here are some other points for consideration of advantages between Decisionality Decision-Trees over Expert Systems:

1. Decision-Trees are simple to script whereas an Expert System needs a knowledge engineer thus creating a skills shortage plus overheads for knowledge capture from subject matter experts.

2. Decision-Trees are an open system as it is easy to link the end of a pathway within a Decision-Tree to the start of another pathway in another Decision-Tree. An Expert System is a closed system as it is constrained by the design of its inference engine and application.

3. Decision-Trees are simple ‘natural’ programs that can adapt to complexity and chaotic conditions. Expert Systems are designed to cope with a complicated system, but have difficulty coping with the dynamics of complexity and chaos. (20 years validation Stephen Wolfram New Kind of Science).

4. Decision-Trees are stateless whereas Expert Systems are data driven, which has an embedded complexity cost as more data items are developed and used.

5. Decision-Trees are a white-box meaning it is transparent, simple to understand and interpret. People are able to understand Decision-Tree models and therefore are designed for organizational retention of knowledge. Expert Systems are a black-box that is complex to understand and has greater reliance upon tacit knowledge of a specialist that dilutes overtime.

6. Decision-Trees can have value very quickly even with a small number of nodes. Important insights can be gained from usage that often stimulates ideas for knowledge evolution that were not obvious at first. Expert Systems in comparison evolve slowly and to the point of decision contamination.

7. Decision-Trees can be created by subject matter experts without the need for software specialists as with Expert Systems.

8. Decision-Tree dynamic linking generates an exponential growth of pathways and outcomes compared to a far slow rate from Expert Systems that will then reach a plateau without further advancement being made.

9. Decision-Trees development enriches inductive and deductive reasoning as it focuses upon pathways and outcomes. This value is largely diluted with Expert Systems as it is in the hands of the knowledge engineer and not the subject matter experts.

10. The interactive use of Decision-Trees, via web-services, and the automated analyses of interactions provide short learning loops enabling Decision-Trees to be evolved rapidly based on behavioral insights. Expert Systems capabilities weaken overtime as mentioned.

11. The reuse of Decision-Trees and dynamic linking of Decision-Trees (via web-services) increases value overtime enabling exponential growth of pathways and outcomes in very short time frames.

12. Decision-Trees with stateless web-services do not create legacy systems unlike Expert Systems.

13. Expert Systems decreases in value overtime whenever there are high rates of complexity, velocity and volatility. The opposite is true for a Decision-Tree ecosystem.

14. Decision-Tree scripts have been extended to influence behaviour of usage so that other mediums like pictures can be used to influence decisioning. This is accomplished because of event-tags being available for each scripted step. This level of granularity is not available from Expert Systems.

15. As the Decision-Tree generates a web-service this can be accessed across all digital touchpoints. Expert Systems do not typically have this level of interoperability.

16. The construction of the Decision-Tree is simply not just focused on business logic but on good dialogue and choices to influence behaviour and decisioning. The automated analyses of behaviour enable the Decision-Tree to adapt to behavioral dynamics. Additionally, dialogue narrative can change according to pathways chosen. Expert Systems have limited capabilities for the nuances of different behaviors.

17. Decision-Tree source and its web-service representation are measurable as a knowledge asset. This is not the case with Expert Systems.

18. Decision-Trees can be developed in parallel with each one often being built in a matter of hours. Therefore the speed of building an ecosystem from existing procedural knowledge is very fast.

AI 167 MS&V is regarded as high growth sector within the current economic climate

The relatively new industry of modeling, simulation and visualization (MS&V) comprises numerous planning, analysis and training tools made possible by sophisticated computing. These tools can suggest and test concepts, minimizing reliance upon trial and error, and they can present information in ways that enhance comprehension.

For example, the tools might teach a medical student how to perform a surgical procedure without putting an actual patient in harm's way. Other applications are seen in simulations to test aircraft designs, in vehicular traffic models to simulate — and improve — flow along highways, in video games to teach algebra, and in models to predict the performance of a soldier or an athlete. Artificial intelligence, robotics and virtual environments also are part of MS&V.

AI 166 Big Stage launches a “Portable You” avatar for integration into 3rd party solutions

Big Stage Entertainment (for previous references go to AI 129 and AI 56) announced today the launch of its "Portable You" program, which opens up its 3D facial modeling system to third parties for integration in games, virtual worlds, websites, mobile apps, and more.

The first announced partner is Icarus Studios, which will integrate the system with its virtual worlds’ platform.

Virtual event solutions provider, The Venue Network, already plans on adopting the face creation tools for its customers, allowing them to network with their own animated, lip-synched faces.

“Given the complexity of human faces, developing high-fidelity, realistic facial construction systems for avatars is incredibly costly and requires highly specialized skill sets,” said James Hettinger CEO, Icarus Studios. “PortableYou” offers an easy and cost-effective way for us to integrate sophisticated cloning capabilities into the virtual worlds we create. Our clients are very excited that their users can now create accurate virtual representations of themselves for use in their worlds, at development costs that generate significant ROI for incorporating the innovation in user experience that the PortableYou system make possible.”
Portable You includes APIs, code samples, methods and reference libraries for customization.

“By offering a powerful, unified system for the integration of a realistically animated 3-D version of yourself into your digital life, PortableYou has the potential to revolutionize how we both entertain ourselves and communicate through digital media,” said Phil Ressler, Big Stage Entertainment CEO. “With the high level of personalization that PortableYou makes possible, digital media can evolve to truly reflect who we are in the real world, whether through life-like extension or alter ego fantasy. Faces are fundamental to human communication and adding recognizable, animated avatars to an application changes it fundamentally."

AI 165 AI Inventor creates his own robotic wife and he is pleased because she has 13,000 dialogue sentences and can feel pain!

Le Trung , an inventor in Canada has created his very own fembot called Aiko.
The living doll's skin has been created out of silicone and, according to its creator, "is the first android to mimic pain, and reacts to it."
Trung has created the 'pain' technology to aid others, explaining: "this technology can be beneficial for people born with or who have undergone amputations. This is the first step toward a life-like mechanical limb that has the ability to feel physical sensation."

To make the robot more lifelike, Trung has created something called Biometric Robot Artificial Intelligence Neural System (aka BRAINS) software, which means Aiko has the ability to talk and interact with humans, and houses a database of over 13,000 sentences.

The robot is so advanced that it can analyse the weather and if you are about to go outside, Aiko will tell you to bring an umbrella if it is going to rain or wear warmer clothes if it is windy.

AI 164 Study states that by 2020 social robots will be common place in our society with personalized interactions with humans

Spanish researchers have carried out a study looking into the potential future impact of robots on society.

Their conclusions show that the enormous automation capacity of robots and their ability to interact with humans will cause a technological imbalance over the next 12 years between those who have them and those who do not.


“Just as we depend upon mobile phones and cars in our daily lives today, the next 15 years will see mass hybridisation between humans and robots,” predicts Antonio López Peláez, a professor of sociology at Spain’s National Distance Learning University, UNED, and co-author of the study on the future social impact of robots, jointly carried out with the Institute for Prospective Technological Studies.

International experts working on inventing and adapting cutting edge robots for practical use were interviewed during the study, in order to find out by when we will be regularly using the models they are currently designing.

All agreed on 2020 as a technological inflection point, because by then robots “will be able to see, act, speak, manage natural language and have intelligence, and our relationship with them will have become more constant and commonplace”, said López Peláez.

This will follow a revolution in robotics after which they will no longer be sophisticated machines, but tools to be used on a daily basis, helping us with a large number of work and social activities.


The most striking feature of this technological revolution are social robots, machines with artificial intelligence, and with which we will have emotional and personalised interactions.

“A robot might be a more effective partner and a better person than the humans we actually have in our immediate lives: just as you can see dog owners talking to their pets today, soon we will be talking to robots,” says López Peláez

AI 163 HiPiHi continues to grown the Chinese virtual world market with 75,000 registered users

Chinese virtual world HiPiHi now claims 75,000 registered users on mainland China, said President Xu Hui in 21st Century Business Herald. While the total number of registrations is likely even higher, as HiPiHi has pursued an international strategy, though not particularly aggressively, its Mainland base contribute about 3,000 to 4,000 active users.

According to the report, HiPiHi is also working with the city government to build versions of historic Wuhan in the Hubei province.

AI 162 EKI One ai middleware now launched for avatar behaviour for authentic character simulation

Germany-based middleware developer Artificial Technology has released the first version of its AI and emotional intelligence middleware solution - EKI One - across Europe, with free trials being made available. We covered this as emergent technology before – please refer to AI 51.

EKI One 1.0 is designed to bolt onto existing game engines and aims to add depth to titles by giving developers an effective, affordable solution to AI and emotion.
"With EKI One 1.0, we proudly present a software solution that allows game and level designers, script designers and programmers to define character behaviour efficiently and with ease from cognition and movement characteristics to intelligent decision-making," said Serein Pfeiffer, technical director and co-founder of Artificial Technology GmbH.

"The specifications and requests we received from our developer partners have had a direct bearing on the development of EKI One 1.0. We have combined ease of use with the technological depth required for authentic character simulation in a single, well-rounded package."

This is commercially ahead of the work being done by The University of the Balearic Islands (refer to AI 161).

AI 161 Smart responsive facial expressions for avatars in 3d web and virtual worlds has been developed

The University of the Balearic Islands (UIB) has developed a computer application that enables the generation of faces that includes emotions and moods.

The study has been published in the latest edition of the magazine Computer Animation and Virtual Worlds.

“The aim of this work has been to design a model that reveals a person's moods and displays them on a virtual face”, SINC was informed by one of the authors of the study, Diana Arellano, from the UIB’s Computer and Artificial Intelligence Graphics and Vision Unit. “In the same 3-D space we have integrated personality, emotions and moods, which had previously been dealt with separately”, Arellano explained to SINC.

The designers have followed the theories of Albert Mehrabian to draw up the model, based on the five personality traits established by this American psychologist:

• Extraversion

• Neuroticism

• Openness

• Conscientiousness

• Agreeableness


“Every personality can be considered an emotional state by default”, indicated Arellano.

An introverted and neurotic personality is therefore related to an anxious emotional state.

The points of the face that define these emotions can be determined mathematically, and the algorithms developed by computer experts can be used to obtain different facial expressions “quickly and easily”.

The system, which uses the MPEG-4 video coding standard for creating images, makes it possible to display basic emotions (anger, disgust, fear, joy, sadness, surprise) and intermediate situations.

“Our next step is to leave the MPEG-4 standard aside and concentrate on a high-quality generic network which will enable the inclusion of both wrinkles and eye, eyelid and head movements, as well as synthesize the voice”, the researcher concluded.