AI Weak and Strong

Capabilities currently classified as weak AI include successfully understanding human speech, competing at a high level in strategic game systems (such as chess and Go), self-driving cars, intelligent routing in content delivery networks, and interpreting complex data. Done through mainly calculus involving prediction problems, extrapolation based on feeding quality data.

The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects.

General intelligence is among the field’s long-term goals. Approaches include statistical methods, computational intelligence, and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics. The AI field draws upon computer science, mathematics, psychology, linguistics, philosophy, neuroscience and artificial psychology.

From a deep learning perspective, even neuroevolution approaches has its limits for a strong AI, no matter how much the population of networks grow to attain the objective the acknowledgment of the solution has to have an external “decidant.”

“That is, evolution in this case is not just deciding the architecture and weights, but also the rules that guide how and when particular weights change.” from Neuroevolution: A different kind of deep learning by O.Stanley

In the context of strong AI, I really think that we need a kind of “attractors” framework as a “loosely” decidant role (aka. “conscience”).

“Strong AI is related to the field of consciousness, sentience, mind. We are on the way to general intelligence with every AI field evolving (from natural language processing to cognitive computing, social intelligence, planning, learning, perception etc.) The rules that govern the universe are already there, our mind works under the patterns within the universes pattern. To realize the strong AI we need to hook the machine into this flow of our as-such universe of small-world networks*. Consciousness is not programmable, it is a result of the sensory, when hooked to the flow, it can be seen as a result of the “strange attractors” created into the flow. A strong AI will exist when all the sensory inputs will have a proper cognitive processing, conscience will arise de facto from the space where the inputs exists but under a proactive threshold. The triggering occurs under strange attractors influenza, thus the conscience manifests itself.” (Definition of Strong AI by Essential-Works, C.Stefan 04.2017).

* See: Watts and Strogatz model & Barabási–Albert model.
* Restricted Boltzmann Machine and Self-Organization systems#.
* Active inference#.

Personal Assistants

Siri voice assistant is available in portable electronic devices like the iPhone and the iPad.
Amazon which has developed the intelligent personal assistant Alexa baked into devices like the Echo smart speaker.
Alphabet subsidiary Google and its Google Now assistant available through Android and other Google software products.
Microsoft whose Cortana intelligent assistant is found in Microsoft products like the Xbox and its Windows operating systems.
IBMs Watson answering system. The intelligent personal assistants use natural language processing (NLP).

Digital Twin

The Digital Twin concept in Industry 4.0 come into practice when we addressed the question like: “Do we need to adjust the product?” and then we think “Let’s look at the original specs, at the drawings, at snapshots, at the BOM, at simulation results, at VR iterations, at the real performance parameters” – We simply say then: “Let’s look at the digital twin!”
Digital Twin is a mirroring (or twinning) of what exists in the real world and what exists in the virtual world. It contains all the informational sets of the physical ‘thing’ meaning its cross-discipline – not just a mechanical / geometric representation, but also including the electronics, wiring, software, firmware, etc. not just the CAD model.
Digital Twins are currently used in the context of monitoring, simulation and predictive maintenance which are all incredibly valuable and potentially transformative in their own right, however, there would seem to be much more to it. As products of all types move to include connectivity, sensors and intelligence we can’t just think about the data streaming back from the field.
Without accurate “Context” – Digital Twin – time series data generated during production and ongoing operation is difficult or even impossible to understand and analyze. In addition, the ability to interpret and act upon these data often require traceability to prior information from related revisions – Digital Thread. To complicate matters further as artificial intelligence / cognitive computing is introduced the necessity for the Digital Twin becomes even greater.