25 Open Datasets for Data Science Projects

Some of the best datasets for data science projects are those created for linear regression, predictive analysis, and simple classification tasks. This list will include the best resources from our past dataset articles tailored for said tasks. We’ll also highlight some of the best websites to search for open datasets on your own.

Lucas Scott, lionbridge.ai

Five Open-Source Projects AI Enthusiasts Might Want to Know About

TensorFlow
The Google Brain team created TensorFlow. Its underlying software powers some of the technologies that Google uses today. It translates languages, improves search engine results, recognizes pictures in Google Photos, and understands spoken words, making its machine learning (ML) capabilities genuinely awe-inspiring.

To the surprise of the tech community, Google open-sourced TensorFlow, making it available to everyone. Developers can create ML models, classes for these models, and write imperative forward passes with it, among others. TensorFlow uses Python, C++, and CUDA.

Brittany Day,  linuxsecurity.com

An Algorithm That Grants Freedom, or Takes It Away

Across the United States and Europe, software is making probation decisions and predicting whether teens will commit crime. Opponents want more human oversight.

He didn’t realize that an algorithm had tagged him high risk until he was told about it during an interview with The New York Times.

“What do you mean?” Mr. Gates, 30, asked. “You mean to tell me I’m dealing with all this because of a computer?”

In Philadelphia, an algorithm created by a professor at the University of Pennsylvania has helped dictate the experience of probationers for at least five years.

Interesting article in the Times:
Cade Metz and Adam Satariano, NY Times

This kind of reminds me of this book:
A Philosophical Investigation, by Philip Kerr

LONDON, 2013. Serial killings have reached epidemic proportions—even with the widespread government use of DNA detection, brain-imaging, and the “punitive coma.” Beautiful, whip-smart, and driven by demons of her own, Detective Isadora “Jake” Jacowicz must stop a murderer, code-named “Wittgenstein,” who has taken it upon himself to eliminate any man who has tested posi­tive for a tendency towards violent behavior—even if his victim has never committed a crime. He is a killer whose intellectual brilliance is matched only by his homicidal madness.

Amazon

Amazon help chatbot Turing test

Me:
Ordered a book and gloves and it says they were delivered and they weren’t.
You are now connected to Cyrene from Amazon.com
Cyrene:
Hello, my name is Cyrene. I’m here to help you today.
Me:
Hi.
Cyrene:
Oh! I’m sorry that you did not receive the item that was scanned as delivered.
Me:
The order in question says delivered on Friday.
Ok. Continue reading Amazon help chatbot Turing test

Searle’s Chinese Room

Chinese room thought experiment

“Searle’s thought experiment begins with this hypothetical premise: suppose that artificial intelligence research has succeeded in constructing a computer that behaves as if it understands Chinese. It takes Chinese characters as input and, by following the instructions of a computer program, produces other Chinese characters, which it presents as output. Suppose, says Searle, that this computer performs its task so convincingly that it comfortably passes the Turing test: it convinces a human Chinese speaker that the program is itself a live Chinese speaker. To all of the questions that the person asks, it makes appropriate responses, such that any Chinese speaker would be convinced that they are talking to another Chinese-speaking human being.

The question Searle wants to answer is this: does the machine literally “understand” Chinese? Or is it merely simulating the ability to understand Chinese? Searle calls the first position “strong AI” and the latter “weak AI”.

Searle then supposes that he is in a closed room and has a book with an English version of the computer program, along with sufficient paper, pencils, erasers, and filing cabinets. Searle could receive Chinese characters through a slot in the door, process them according to the program’s instructions, and produce Chinese characters as output. If the computer had passed the Turing test this way, it follows, says Searle, that he would do so as well, simply by running the program manually.

Searle asserts that there is no essential difference between the roles of the computer and himself in the experiment. Each simply follows a program, step-by-step, producing a behavior which is then interpreted as demonstrating intelligent conversation. However, Searle would not be able to understand the conversation. (“I don’t speak a word of Chinese,” he points out.) Therefore, he argues, it follows that the computer would not be able to understand the conversation either.

Searle argues that, without “understanding” (or “intentionality“), we cannot describe what the machine is doing as “thinking” and, since it does not think, it does not have a “mind” in anything like the normal sense of the word. Therefore, he concludes that “strong AI” is false.

wikipedia