Artificial Intelligence – Good or Evil?
There has been much attention in the press recently to the warnings of Mssrs. Musk, Gates and Hawking concerning the implications of strong AI without controls on usage and intent.  They appear to be worried that strong AI could lead to large scale social disruption, either via unemployment or exploitation by elites using strong AI as a tool.

The Epoch Times compiled a list of academics in the field of AI research who are offering their own opinions. From the article: “A 2014 survey conducted by Vincent Müller and Nick Bostrom of 170 of the leading experts in the field found that a full 18 percent believe that if a machine super-intelligence did emerge, it would unleash an ‘existential catastrophe’ on humanity. A further 13 percent said that advanced AI would be a net negative for humans, and only a slight majority said it would be a net positive.” That leaves 69% that were neutral or positive opinion so what’s the verdict? Good or evil? Here’s a few examples of the “good” benefit of AI and machine learning.

For those that are worried about AI and the “singularity”, this article is more representative of the state of the art:  A lot of work by human researchers to come up with an artificial simulacrum of a complex human intelligence activity, but for the sole purpose of extending human intelligence.

Emerging Technology From the arXiv, May 22, 2015 “Computational Aesthetics Algorithm Spots Beauty That Humans Overlook”

The photo-sharing site Flickr is home to about 200 million images, but most of them go largely unseen and unappreciated. A team of researchers from the University of Turin and Yahoo Labs in Barcelona, Spain, have developed a machine-vision algorithm that can identify and highlight beautiful images, enabling it to spot the hidden gems among Flickr’s millions of obscure images. The team began by crowd-sourcing human opinion on the aesthetic quality of 10,000 pictures taken from the Flickr database, a mix of popular and unpopular images in four categories: people, nature, animals, and urban subjects. Each image was rated by at least five humans according to five aesthetic categories. Their beauty ratings were used to train the team’s machine-vision system, CrowdBeauty, which used criteria such as contrast, brightness, color patterns, and composition to predict the beauty rating of any given image. CrowdBeauty was then turned loose on a database of 9 million images from Flickr that have fewer than five favorites. The team then crowd-sourced opinion on the images selected by the system and found they were rated almost as favorably as Flickr’s most popular images.

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Use of AI to emulate specific human intelligence capabilities, such as voice recognition, can bring great benefits.

Our work on the Sayin’ It app for Appropo Software, LLC is an excellent example.  In developing this app, Telegraph Hill brought together these essential components for a successful implementation:

  • creative domain experts (experience with autism),
  • artists (graphics, music, colors)
  • technologists with a wide variety of expertise in mobile iOS, graphics, UI and voice recognition technology
  • Objective C developers consisting of overseas talent, local interns, and experienced software engineers, each working on different parts of the app, some in solely advisory roles
  • experienced agile development managers
  • commercial voice recognition software

All these elements were brought together through THPI leadership to create a deceptively simple, though in reality complex, useful, mobile application benefiting children with autism.

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