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The basic idea of intelligence:An explosion is that once machines reach a certain level of intelligence, they’ll be able to work on AI just like we do and improve their own capabilities — redesign their own hardware and so on — and their intelligence will zoom off the charts. There’s an area emerging called “cyber-physical systems” about systems that couple computers to the real world. With a cyber-physical system, you’ve got a bunch of bits representing an air traffic control program, and then you’ve got some real airplanes, and what you care about is that no airplanes collide. You’re trying to prove a theorem about the combination of the bits and the physical world. What you would do is write a very conservative mathematical description of the physical world — airplanes can accelerate within such-and-such envelope — and your theorems would still be true in the real world as long as the real world is somewhere inside the envelope of behaviors.
Yet you’ve pointed out that it might not be mathematically possible to formally verify AI systems.
There’s a general problem of “undecidability” in a lot of questions you can ask about computer programs. Alan Turing showed that no computer program can decide whether any other possible program will eventually terminate and output an answer or get stuck in an infinite loop. So if you start out with one program, but it could rewrite itself to be any other program, then you have a problem, because you can’t prove that all possible other programs would satisfy some property.

Categories of AI
Artificial intelligence:
can be divided into two different categories: weak and strong. Weak artificial intelligence embodies a system designed to carry out one particular job. Weak AI systems include video games such as the chess example from above and personal assistants such as Amazon's Alexa and Apple's Siri. You ask the assistant a question, it answers it for you.
 
Strong artificial intelligence systems are systems that carry on the tasks considered to be human-like. These tend to be more complex and complicated systems. They are programmed to handle situations in which they may be required to problem solve without having a person intervene. These kinds of systems can be found in applications like self-driving cars or in hospital operating rooms.
 
 

common myths
for Advanced
AI:A captivating conversation is taking place about the future of artificial intelligence and what it will/should mean for humanity. There are fascinating controversies where the world’s leading experts disagree, such as AI’s future impact on the job market; if/when human-level AI will be developed; whether this will lead to an intelligence explosion; and whether this is something we should welcome or fear. But there are also many examples of boring pseudo-controversies caused by people misunderstanding and talking past each other. 

TIMELINE MYTHS



The first myth regards the timeline: how long will it take until machines greatly supersede human-level intelligence? A common misconception is that we know the answer with great certainty.
One popular myth is that we know we’ll get superhuman AI this century. In fact, history is full of technological over-hyping. Where are those fusion power plants and flying cars we were promised we’d have by now? AI has also been repeatedly over-hyped in the past, even by some of the founders of the field. For example, John McCarthy (who coined the term “artificial intelligence”), Marvin Minsky, Nathaniel Rochester, and Claude Shannon wrote this overly optimistic forecast about what could be accomplished during two months with stone-age computers: “We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College […] An attempt will be made to find how to make machines use language, form abstractions, and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer.”

CONTROVERSY MYTHS



Another common misconception is that the only people harboring concerns about AI and advocating AI safety research are Luddites who don’t know much about AI. When Stuart Russell, author of the standard AI textbook, mentioned this during his Puerto Rico talk, the audience laughed loudly. A related misconception is that supporting AI safety research is hugely controversial. In fact, to support a modest investment in AI safety research, people don’t need to be convinced that risks are high, merely non-negligible — just as a modest investment in home insurance is justified by a non-negligible probability of the home burning down.

Nature heals
Being in nature, or even viewing scenes of nature, reduces anger, fear, and stress and increases pleasant feelings. Exposure to nature not only makes you feel better emotionally, but it also contributes to your physical wellbeing, reducing blood pressure, heart rate, muscle tension, and the production of stress hormones.
In addition, nature helps us cope with pain. Because we are genetically programmed to find trees, plants, water, and other nature elements engrossing, we are absorbed by nature scenes and distracted from our pain and discomfort.

muscle tension
physical wellbeing
reducing blood pressure

Nature restores
One of the most intriguing areas of current research is the impact of nature on general wellbeing. In one study in Mind, 95% of those interviewed said their mood improved after spending time outside, changing from depressed, stressed, and anxious to more calm and balanced. Other studies by Ulrich, Kim, and Cervinka show that time in nature or scenes of nature are associated with a positive mood, and psychological wellbeing, meaningfulness, and vitality.

Nature
Flowers
Plants

Business Intelligence and Data Science are two of the most recurring terms in the digital era. While both of them involve the use of data, they are totally different from one another. Data Science is the bigger pool containing greater information, BI can be thought of as a part of the bigger picture. 
What is Business Intelligence?
Business Intelligence is a process of collecting, integrating, analyzing, and presenting the data. With Business Intelligence, executives and managers can have a better understanding of decision-making. This process is carried out through software services and tools.
Using Business Intelligence, organizations are able to several strategic and operational business decisions. Furthermore, BI tools are used for the analysis and creation of reports. They are also used for producing graphs, dashboards, summaries, and charts to help the business executives to make better decisions.

Effective Management Information System:
Essential characteristics of an effective management information system are 1. MIS is management-oriented 2. MIS is developed under the direction of management 3. MIS is an integrated system 4. common data flow 5. MIS is based upon the future needs of the business 6. MIS is composed of sub-systems 7. MIS requires flexibility 8. distributed data processing and 9. MIS is mostly computerized.
Management Information System is established in an organization to provide relevant information to the managers to operate effectively and efficiently.
1. MIS is management-oriented:
The design of MIS starts with an appraisal of the information needs of the management. The system is usually designed from top to bottom. However, this does not mean that MIS fulfills the information needs of top management only.
 
It only implies that the information needs of the top management will serve as a basis for the assessment of the information needs of lower-level managers. In every case, the system should be designed to cater to the information needs of all levels of management.
2. MIS is developed under the direction of management:
Because of the management orientation of MIS, it is imperative that the management of an organization actively directs the development and establishment of the MIS in an organization.
It is rare to find an MIS where the manager himself, or a high-level representative of his department, is not spending a good deal of time in the system design.
 

Key Method
Educator provides evidence of their understanding of communication and outlines and provides evidence of a lesson that uses technology to support students’ use of communication in learning.
Method Components
What are the 4Cs?
The 4Cs for 21st century learning are Creativity, Critical Thinking, Communication, and Collaboration. They are part of the framework for 21st Century Learning and are designed to support student learning in today’s world and are skills they can use in college and career.
What is communication (and what isn’t it)?
The P21 framework emphasizes effective use of oral, written, and nonverbal communication skills for multiple purposes (e.g., to inform, instruct, motivate, persuade, and share ideas). It also focuses on effective listening, using technology to communicate, and being able to evaluate the effectiveness of communication efforts—all within diverse contexts (adapted from P21). Note that working in partners is a great way to collaborate or build shared understanding but a critical part of communication is sharing with an authentic audience.

Through a case study on Mozambique's Gorongosa National Park, learners will explore how scientists study ecosystem
 
The idea that food webs and ecosystem functioning are intimately linked harkens back at least to the work of Forbes (1887). He pondered, in his lake as a microcosm paper, the complexity of lake ecosystems and how this complexity could be maintained given the complex network of trophic interactions. He also emphasized that spatial structure, both within and among lakes, could be important. Lindeman (1942) built on Forbes’s vision of a food web as a microcosm by linking a simplified view of food webs to ecosystem metabolism. Since then, much thinking has gone into understanding food webs and their links to ecosystem attributes (Odum 1957; Margalef 1963), but until recently the importance of space has not sufficiently been integrated into these thoughts. By contrast, the importance of space to populations and communities has been recognized for some time (Watt 1947; Skellam 1951; MacArthur & Wilson 1967), but the connection between this literature and food webs and ecosystems is only now being resolved (Loreau et al. 2003; Polis et al. 2004; Holt & Hoopes 2005; Pillai et al. 2009; Gravel et al. 2010a). Some progress has been made (e.g. Polis et al. 2004; Holyoak et al. 2005), but most of the work on the spatial food web and ecosystem properties has progressed along with two relatively independent traditions.
REF :links https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1461-0248.2011.01588.x
YouTube: https://youtu.be/C6YrPt1ygX8