AI News, BOOK REVIEW: Could Artificial Intelligence Become Conscious? 33 Researchers Contribute Their Opinion

Could Artificial Intelligence Become Conscious? 33 Researchers Contribute Their Opinion

Over the last five months, we’ve combined a mix of interviews and surveys with some of our most experienced artificial intelligence guests –

Clicking on an individual researcher will: (NOTE: If you’re interested in the full data set from our surveys, including 12 guest responses that didn’t make this graphic, and expert predictions on the biggest AI risks within the next 20 years, you can download the complete data set from this interview series here via Google Spreadsheets, simply fill out this form)

AI programs exhibit racial and gender biases, research reveals

An artificial intelligence tool that has revolutionised the ability of computers to interpret everyday language has been shown to exhibit striking gender and racial biases.

The findings raise the spectre of existing social inequalities and prejudices being reinforced in new and unpredictable ways as an increasing number of decisions affecting our everyday lives are ceded to automatons.

The approach, which is already used in web search and machine translation, works by building up a mathematical representation of language, in which the meaning of a word is distilled into a series of numbers (known as a word vector) based on which other words most frequently appear alongside it.

For instance, in the mathematical “language space”, words for flowers are clustered closer to words linked to pleasantness, while words for insects are closer to words linked to unpleasantness, reflecting common views on the relative merits of insects versus flowers.

The machine learning tool used in the study was trained on a dataset known as the “common crawl” corpus – a list of 840bn words that have been taken as they appear from material published online.

Sandra Wachter, a researcher in data ethics and algorithms at the University of Oxford, said: “The world is biased, the historical data is biased, hence it is not surprising that we receive biased results.” Rather than algorithms representing a threat, they could present an opportunity to address bias and counteract it where appropriate, she added.

In contrast, we do not expect algorithms to lie or deceive us.” However, Wachter said the question of how to eliminate inappropriate bias from algorithms designed to understand language, without stripping away their powers of interpretation, would be challenging.

Risks of AI – What Researchers Think is Worth Worrying About

and a flurry of attention around celebrity comments around AI dangers (including the now well-known statements of Bill Gates and Elon Musk), it’s safe to say that the risks of AI has embedded itself as a topic of pop-culture discourse — even if it’s not a very serious one amongst the populace at present.

Recently, we interviewed and reached out to a total of over 30 artificial intelligence researchers (all except one hold a PhD) and asked them about the risks of AI that they believe to be the most pressing in the next 20 years and the next 100 years.

(NOTE: If you’re interested in the full data set from our surveys, including 12 guest responses that didn’t make this graphic and expert predictions on the biggest AI risks within the next 100 [not just 20] years, you can download the complete data set from this interview series here via Google Spreadsheets;

risk list with 36 percent of responses, a positive correlation with the massive amount of media attention on autonomous vehicles and improved robotic manufacturing, among other industries.

In Dr. Stephen Thaler’s opinion, the greatest risk that human beings face is “the revelation that human minds may not be as wonderful as we all thought, leading to the inevitable humiliation and denial that accompanies significant technological breakthroughs,”

Will the onslaught of AI technologies inspire an overcoming of human disparities as societies come together to address underlying faults, or catalyze growing rifts that escape our eventual control?

was done after the survey (it could be argued that other categories could have been used to couch these responses), and that 33 researchers is by no means an extensive consensus, the resulting trends and thoughts of PhDs, most of whom have spent their careers in various segments of AI, are interesting and worth considering.

We conducted this survey to mainly spurn debate and consideration for the reasonable risks of AI. Interacting with and getting the thoughts of readers is always valuable, which why we’re asking you to make your own predictions and compare them to other TechEmergence readers’:

Will artificial intelligence becomeconscious?

Forget about today’s modest incremental advances in artificial intelligence, such as the increasing abilities of cars to drive themselves.

Waiting in the wings might be a groundbreaking development: a machine that is aware of itself and its surroundings, and that could take in and process massive amounts of data in real time.

In addition to driving people around, it might be able to cook, clean, do laundry – and even keep humans company when other people aren’t nearby.

As a professor of electrical engineering and computer science who works in machine learning and quantum theory, I can say that researchers are divided on whether these sorts of hyperaware machines will ever exist.

Some believe that consciousness involves accepting new information, storing and retrieving old information and cognitive processing of it all into perceptions and actions.

They’ll be able to gather more information than a human, store more than many libraries, access vast databases in milliseconds and compute all of it into decisions more complex, and yet more logical, than any person ever could.

Since it takes consciousness as a given and no attempt is made to derive it from physics, the Copenhagen Interpretation may be called the “big-C” view of consciousness, where it is a thing that exists by itself – although it requires brains to become real.

A well-known example of this is the paradox of Schrödinger’s cat, in which a cat is placed in a situation that results in it being equally likely to survive or die – and the act of observation itself is what makes the outcome certain.

For instance, dreams or visions are supposed to have inspired Elias Howe’s 1845 design of the modern sewing machine, and August Kekulé’s discovery of the structure of benzene in 1862.

His notebook, which was lost and forgotten for about 50 years and published only in 1988, contains several thousand formulas, without proof in different areas of mathematics, that were well ahead of their time.

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