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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.

Big data:Big data refers to the large, diverse sets of information that grow at ever-increasing rates. It encompasses the volume of information, the velocity or speed at which it is created and collected, and the variety or scope of the data points being covered (known as the "three v's" of big data).

Big data is a great quantity of diverse information that arrives in increasing volumes and with ever-higher velocity.
Big data can be structured (often numeric, easily formatted and stored) or unstructured (more free-form, less quantifiable).
Nearly every department in a company can utilize findings from big data analysis, but handling its clutter and noise can pose problems.
Big data can be collected from publicly shared comments on social networks and websites, voluntarily gathered from personal electronics and apps, through questionnaires, product purchases, and electronic check-ins.
Big data is most often stored in computer databases and is analyzed using software specifically designed to handle large, complex data sets.
How Big Data Works
Big data can be categorized as unstructured or structured. Structured data consists of information already managed by the organization in databases and spreadsheets; it is frequently numeric in nature. Unstructured data is information that is unorganized and does not fall into a predetermined model or format. It includes data gathered from social media sources, which help institutions gather information on customer needs.






 






Big data can be collected from publicly shared comments on social networks and websites, voluntarily gathered from personal electronics and apps, through questionnaires, product purchases, and electronic check-ins. The presence of sensors and other inputs in smart devices allows for data to be gathered across a broad spectrum of situations and circumstances.

Augmented reality (AR) is an interactive experience of a real-world environment where the objects that reside in the real world are enhanced by computer-generated perceptual information, sometimes across multiple sensory modalities, including visual, auditory, haptic, somatosensory and olfactory.

Speech perception is the process by which the sounds of language are heard, interpreted, and understood. The study of speech perception is closely linked to the fields of phonology and phonetics in linguistics and cognitive psychology and perception in psychology.  In the speech, perception seeks to understand how human listeners recognize speech sounds and use this information to understand spoken language. Speech perception research has applications in building computer systems that can recognize speech, in improving speech recognition for hearing- and language-impaired listeners, and in foreign-language teaching.
The process of perceiving speech begins at the level of the sound signal and the process of audition. (For a complete description of the process of the audition, After processing the initial auditory signal, speech sounds are further processed to extract acoustic cues and phonetic information. This speech information can then be used for higher-level language processes, such as word recognition.
 
Link URL : https://iteach-testing.venturit.org/browse/articles

Culture of Collaboration
Number of Directions: Give a minimal number of directions or steps at a time.If necessary, have students repeat the directions to the teacher or a peer partner.
Form of Directions: Provide written directions or steps, or a visual model of acompleted project. Teach students how to refer to these items as reminders ofprocess steps to complete tasks. This strategy is particularly helpful for long-termprojects.
Written AssignmentsMany students with ADHD have particular challenges with written work due to finemotorskills difficulties, motor planning issues, and difficulty alternating their attentionfrom a book to their written responses. 
Students with ADHD may also need assistance breaking a larger task or project into smaller, more workable units.
The following strategies can be used to address these needs.
Deconstructing Tasks: Break tasks into smaller units.o Limit amount of work per page.o Cover up part of the work on a page.o Allow extra time for completing tasks.o Provide work breaks.o Allow student to use a computer to type or to use speech-to-text software.o Reduce the length of written assignments.
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Limit amount of work per page.
Cover up part of the work on a page.
Allow extra time for completing tasks.

NAIC System.
Changes in industry demand and technological innovations are important factors affecting future occupational employment, as we saw in the previous section. Furthermore, the projected employment published by the BLS is given for detailed industries and occupations. Thus, I describe the industry classification systems used by the BLS and other federal agencies. These systems provide a framework for assigning codes to establishments, allowing for consistent data collection and analyses of economic statistics in industries over time.
Federal statistical agencies used the Standard Industrial Classification (SIC) system in 1939 when it was first published by the former Bureau of the Budget, which is now the Office of Management and Budget (OMB). Like all classification systems, it was updated periodically. However, economic changes, such as the emerging services-oriented economy, increased use of computers, rapidly evolving technology, and globalization, motivated the need to change the industry classification system.

In 1957, people started figuring out new ways to build computer programs. They wanted to make the process better over time, so they came up with iterative and incremental methods.
In the 1970s, people started using adaptive software development and evolutionary project management. This means they were adjusting and evolving how they built software.
In 1990s, there was a big change. Some people didn't like the strict and super-planned ways of doing things in software development. They called these old ways "waterfall." So, in response, lighter and more flexible methods showed up.

Edited

Christopher Savoie, founder and chief executive of a start-up called Zapata, offered jobs this year to three scientists who specialize in an increasingly important technology called quantum computing. They accepted.

Several months later, the Cambridge, Mass., company was still waiting for the State Department to approve visas for the specialists. All three are foreigners, born in Europe and Asia.

Christopher Savoie, founder and chief executive of a start-up called Zapata, offered jobs this year to three scientists who specialize in an increasingly important technology called quantum computing. They accepted.

Several months later, the Cambridge, Mass., company was still waiting for the State Department to approve visas for the specialists. All three are foreigners, born in Europe and Asia.