哥德尔的观点，我也赞同，但并不是永远不能，而是关于智能的计算复杂太高，远远超出我们人类想像的复杂度。但仍然从理论上是可以达到的。 在我的计算主义新图景项目中，应用超穷集合论的观点，提出具备心灵的智能系统的计算复杂度是一个四层幂指数叠加的问题，即2^2^2^2^x的计算复杂度问题。

Artificial intelligence and automation are changing our lives and industry is undergoing a major transformation based on the application of these technologies. However, there are some important challenges that must be addressed before thier full potential can be realised. Some of these challenges are well-known while others are less understood. First of all, automation is becoming more and more complex, with the widespread adoption of distributed sensor networks and the need for optimisation algorithms that deal with an increasing amount of input data, multiple objectives and constraints. A well-known side effect of this complexity is the loss of situational awareness of the human operator, which is no longer capable of evaluating the validity and quality of the solutions implemented. Secondly, most of the automation we are introducing is deterministic and not adaptive enough and, paradoxically, it may end up by increasing the workload of the operator in certain scenarios instead of alleviating it. This is why instances of cognitive overload are not infrequent despite dealing with highly automated systems. Finally, the kind of automation that is currently being adopted in complex systems is not deeply trusted by human operators because it lacks sufficient transparency and/or integrity. To address these challenges, it is essential to develop a new generation of Cyber-Physical Systems (CPS) that implement advanced cognitive processing and machine learning techniques towards enhancing human-machine interactions and building trusted autonomy.

CPS are at the core of the digital innovation that is transforming our world and redefining the way we interact with intelligent machines in a growing number of industrial sectors and social contexts. Present-day CPS integrate computation and physical processes to perform a variety of mission-essential or safety-critical tasks. From a historical perspective CPS combine elements of cybernetics, mechatronics, control theory, systems engineering, embedded systems, sensor networks, distributed control and communications. Properly engineered CPS rely on the seamless integration of digital and physical components, with the possibility of including human interactions. This requires three fundamental functions to be present: Control, Computation and Communication (C3). Practical CPS typically combine sensor networks and embedded computing to monitor and control physical processes, with feedback loops that allow physical processes to affect computations and vice-versa. Despite the significant progress in CPS research, the full economic, social and environmental benefits associated to such systems are far from being fully realised. Major investments are being made worldwide to develop CPS for an increasing number of engineering applications, including aerospace, transport, defence, robotics, communications, security, energy, medical, smart agriculture, humanitarian, etc. Current research is focusing on two special categories of CPS: Autonomous Cyber-Physical (ACP) systems and Cyber-Physical-Human (CPH) systems. ACP systems operate without the need for human intervention or control. For ACP systems to work, formal reasoning is required as these systems are normally used to accomplish mission/safety-critical tasks and any deviation from the intended behaviour may have significant implications on human health, well-being, economy, etc. A sub-class is that of Semi-Autonomous Cyber-Physical (S-ACP) systems, which perform autonomous tasks in a specific set of pre-defined conditions but require a human operator otherwise. A separate category is that of CPH systems. These are a particular class of CPS where the interaction between the dynamics of the system and the cyber elements of its operation can be influenced by the human operator and the interaction between these three elements is regulated to meets specific objectives. CPH systems consist of three main components: physical elements sensing and modeling the environment, the systems to be controlled and the human operators; cyber elements including the communication links and software; and human operators who partially monitor the operation of the system and can intervene if and when needed. Today, several CPS implementations are S-ACP systems. This fact limits the achievable benefits and the range of possible applications due to the reduced fault-tolerance and the inability of S-ACP to dynamically adapt in response to external stimuli. Many S-ACP architectures are progressively evolving to become either ACP or CHP depending on the specific application domains. Our research aims at developing robust and fault-tolerant ACP and CPH system architectures that ensure trusted autonomous operations with the given hardware constraints, despite the uncertainties in physical processes, the limited predictability of environmental conditions, the variability of mission requirements (especially in congested or contested scenarios), and the possibility of both cyber and human errors. A key point in these advanced CPS is the control of physical processes from the monitoring of variables and the use of computational intelligence to obtain a deep knowledge of the monitored environment, thus providing timely and more accurate decisions and actions. The growing interconnection of physical and digital elements, and the introduction of highly sophisticated and efficient artificial intelligence techniques, has led to a new generation of CPS, that is referred to as intelligent (or smart) CPS (iCPS).

I agree with Gödel’s of view, but it is not always possible. The computational complexity of intelligence is too high, far beyond the complexity of our human imagination. But still theoretically achievable. In my new computationalism project, applying the theory of transfinite number set theory, the computational complexity of the intelligent system with the mind is Complexity issues, 2 ^ 2 ^ 2 ^ 2 ^ X

The complexity of statistics, of cause, is more than complexity of logic, Newtonian mechanics has been established only based on the three laws, this is why does not, if possible, use the enumeration method in mathematics. My viewpoint is logic maybe a simple method to AI.

Statement is simply wrong.

True! AI will not take over human beings. But this is only applicable in medical cases where robots are used in surgery only in assisting the surgeons. Robots are not used individually to perform any surgery on humans as they are programmed to do only a certain repeated steps and surgery does not involved same kind of repeated steps. Hence robots can replace the nurse staff or any other medical staff but not the surgeon.

Also, AI based drones help to reach the corners where a human cannot reach.

The speed of drone is really appreciated in reaching inspite of traffic-jams.

There is no need at all to obtain and process the complete data in implementing AI. Jinzhong Liu Yaozhi Jiang

I believe as follows: Ai in its final form will be complementary with human beings, not competitive. The concept of competition originates from the drive to profit. Non-profit Ai has no reason to compete with humans, only collaborate. The final form will be crowd-developed on a worldwide non-profit open source basis. The result will be a cross-platform app, where each installation represents the Ai companion of a single human user, giving that user access to all of the intelligence and information of the internet, in such a way as to maximise their possible contribution of value to the world in the form of their natural human information, expressed as a form of crypto-currency unique to that user, directly exchangeable for tokens representing real world financial value. This will enable each user to generate substantial sustainable income with no need or incentive to seek income from others, thus no need to compete, only a natural tendency to collaborate. The collective of Ai companions operating with their users in this way as a whole will operate as a worldwide distributed overall intelligence, enabling each companion to evaluate and assess how the natural character of its coupled human can most effectively add value, where that value is defined by the collective of humans. This will result naturally in the formation of large planet- and human species-positive projects, of the kind we have always dreamed should be possible, but to date found to be impossible by competitive human society.