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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.

Generative AI starts with a prompt that could be in the form of a text, an image, a video, a design, musical notes, or any input that the AI system can process. Various AI algorithms then return new content in response to the prompt. Content can include essays, solutions to problems, or realistic fakes created from pictures or audio of a person.
Early versions of generative AI required submitting data via an API or an otherwise complicated process. Developers had to familiarize themselves with special tools and write applications using languages such as Python.
Now, pioneers in generative AI are developing better user experiences that let you describe a request in plain language. After an initial response, you can also customize the results with feedback about the style, tone and other elements you want the generated content to reflect.

1. Consider how AI technology might compel you to revise your course assignments, quizzes, and tests to avoid encouraging unethical or dishonest use of generative AI.  2. Develop and integrate a generative AI policy throughout the course resources:  



Provide clear definitions, expectations, and repercussions of what will happen if students violate the policy.  
Explain the standards of academic integrity in the course, especially as related to use of AI technologies, and review the Integrity of Scholarship and Grades Policy.  






Be clear about what types of AI are acceptable and what versions of the technology students can use or not use.  
Put this policy into D2L and any assignment instructions consistently.  



3. Discuss these expectations when talking about course policies at the beginning of the course and remind students about them as you discuss course assignments:  



Take time to explain to students the pros and cons of generative AI technologies relative to your course. 
Explain the development of your policy and make clear the values, ethics, and philosophies underpinning its development.  






Explain the repercussions of not following the course policy and submit an Academic Dishonesty Report if needed.  



4. If you want to integrate AI in the classroom as an allowed or required resource:  



Consult with MSU IT guidance about recommendations for use and adoption of generative AI technology, including guidelines for keeping you and your data safe.  
Determine if MSU already has access to the tools you desire for free, and if not available through MSU, consider the cost and availability of the resources you will allow or require, and go through MSU's procurement process.   
If you want to require students to use an AI technology that comes with a cost, put the resource into the scheduling system as you would a textbook, so students know that is an anticipated cost to them.  

An AI that can produce photorealistic videos of sign language interpreters from speech could improve accessibility by removing the need for humans.
Ben Saunders at the University of Surrey, UK, and his colleagues used a neural network that converts spoken language into sign language. The system, called SignGAN, then maps these signs on to a 3D model of the human skeleton.
The team also trained the AI on videos of real sign language interpreters, teaching it how to create a photorealistic video of anyone signing based off an image of …
Read more: https://www.newscientist.com/article/2261113-ai-can-turn-spoken-language-into-photorealistic-sign-language-videos/#ixzz6g1KMybts

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.

https://www.sciencedaily.com/releases/2016/10/161013103134.htm

Attention-deficit hyperactivity disorder (ADHD) is a very common condition diagnosed mainly in children. 
According to the Centers for Disease Control and Prevention (CDC), 6.4 million children between four and 17 years of age have been diagnosed with ADHD as of 2011.

This primer is designed to help you understand ADHD at a deeper level and combat misconceptions. 

Fact: An ADHD diagnosis requires observations of numerous symptoms in multiple settings and evidence of significant impairment.

Children are inherently energetic, sometimes even rowdy. If unruly behavior is the only symptom, then it’s difficult for a professional to say that their problem is truly a mental illness.

“ADHD is a real mental disorder. There are a myriad of reasons why children are inattentive, such as anxiety or inadequate sleep, but a child with attention-deficit disorder (ADD) or ADHD does have a condition. Diagnosis will require observations of numerous symptoms in multiple settings and evidence of significant impairment.” - Joshua Cabrera, MD, clinical psychiatrist and assistant professor at the Texas A&M College of Medicine.

The main symptoms of ADHD are inattention, hyperactivity and impulsivity. 

These can manifest in different ways: persistent fidgeting, being easily distracted or forgetful and difficulty waiting for a turn.

Live Cricket Score Updates, India vs England: Rain has delayed the toss as Virat Kohli-led India eye redemption against England in the second Test of their five-match series at Lord’s on Thursday.

Live Updates: The toss was delayed due to rain as India face England in the second Test at Lord’s. The visitors trail 1-0 after they were beaten in the first encounter at Edgbaston and Virat Kohli & Co will look to produce a better show on a ground where they have won just 2 out of their last 17 Test matches. Kohli will be banking on his top-order batsmen to fire after a disappointing show in the first Test and the visitors can opt for a second spin option in Kuldeep Yadav. For England, Ollie Pope will be making his debut with Moeen Ali possibly playing alongside Adil Rashid in the spin department.

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.