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).
The aim of cognitive computing is to mimic human thought processes in a computerized model. Using self-learning cognitive algorithms that use data mining, machine learning, pattern recognition, and natural language processing, the computer can imitate the way the human brain works.
Cognitive systems analyze the huge amount of data which is created by connected devices (not just the Internet Of Things) with diagnostic, predictive and prescriptive analytics tools which observe, learn and offer insights, suggestions and even automated actions.
Cognitive Computing and Machine learning addresses the challenge of passing the boundary of traditional data analytics algorithms, which spotlights the development of swift efficient cognitive algorithms.
These cognitive or machine learning algorithms enable real-time processing of huge volume of data, deliver precise predictions of various types such as recommending right products, customer segmentation, detecting fraud and risks, customer retention etc. Cognitive Computing and Machine learning supports these functions by creating a set of cognitive or machine learning algorithms that differ from the traditional statistical techniques. The emphasis is on real-time and highly scalable predictive/cognitive models, using fully automated methods that make data scientist tasks easier.