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Don Patrick

The hobbyists competing to make AI human in AI News

The Loebner Prize is underway in Swansea University this weekend:
https://www.bbc.com/news/technology-49578503
Details on the event here

Squarebear is on the scene with Mitsuku, and my own AI is also participating, along with previous Loebner Prize contestants and some new ones.

11 Comments | Started September 14, 2019, 08:34:23 am
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LOCKSUIT

TRANSFORMERS in General AI Discussion

Let's track Transformers's progress. The singularity is now right around the corner. The information hive is synchronizing and big 4B dollar relationship connections in internet channels are being formed! So are these large language models. It's all about information aka language. Output it and intake it. And sharing.

https://devblogs.nvidia.com/training-bert-with-gpus/
https://openai.com/blog/gpt-2-6-month-follow-up/
https://openai.com/blog/microsoft/

https://openai.com/blog/sparse-transformer/
https://openai.com/blog/better-language-models/
https://openai.com/blog/musenet/
https://openai.com/blog/learning-dexterity/

GENERATIVE MODELS!!!!

2 Comments | Started Today at 03:58:13 am
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LOCKSUIT

THIS is Bayesian in General AI Discussion

I didn't really grasp Bayesian but now I do. Is the below what Bayesian is?



Physicians intuitively use Bayesian statistics on a daily, if not hourly, basis. Here’s why:

When a patient presents with a symptom, such as chest pain, the physician considers the possible causes (etiology) of that symptom in a rank-ordered list, from most likely to least likely. This rank-ordered list is referred to as a differential diagnosis for the presenting symptom(s).

https://www.aafp.org/afp/2005/11...

The physician then asks a series of probing questions meant to re-rank that list of potential etiologies, such as “do you experience chest pain primarily when climbing stairs or exercising?” The answer to each successive question re-ranks and in general narrows the list of candidate etiologies.

The re-ranking of diagnoses based on each successive question asked by the physician is premised on the predictive power of multiple discrete facts over the predictive power of fewer facts.

Inference engines that incorporate Bayesian statistics include backwards-chaining (inferential) expert systems, and are among the examples dating back to the 1970s of the application of artificial intelligence methods to medicine.

10 Comments | Started September 16, 2019, 01:48:07 am
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JohnnyWaffles

Scientist Grow Mini Brains and Hook them up to Robots....in Space! in AI News

I find this both very interesting and very creepy. Would this be considered Artificial Intelligence, seeing as they are lab grown?

https://futurism.com/scientists-grew-human-brains-robots

5 Comments | Started August 30, 2019, 12:58:19 pm
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Tyler

Using machine learning to estimate risk of cardiovascular death in Robotics News

Using machine learning to estimate risk of cardiovascular death
12 September 2019, 4:50 pm

Humans are inherently risk-averse: We spend our days calculating routes and routines, taking precautionary measures to avoid disease, danger, and despair.

Still, our measures for controlling the inner workings of our biology can be a little more unruly.

With that in mind, a team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) came up with a new system for better predicting health outcomes: a machine learning model that can estimate, from the electrical activity of their heart, a patient’s risk of cardiovascular death.

The system, called “RiskCardio,” focuses on patients who have survived an acute coronary syndrome (ACS), which refers to a range of conditions where there’s a reduction or blockage of blood to the heart. Using just the first 15 minutes of a patient's raw electrocardiogram (ECG) signal, the tool produces a score that places patients into different risk categories.

RiskCardio’s high-risk patients — patients in the top quartile — were nearly seven times more likely to die of cardiovascular death when compared to the low-risk group in the bottom quartile. By comparison, patients identified as high risk by the most common existing risk metrics were only three times more likely to suffer an adverse event compared to their low-risk counterparts.

"We're looking at the data problem of how we can incorporate very long time series into risk scores, and the clinical problem of how we can help doctors identify patients at high risk after an acute coronary event,” says Divya Shanmugam, lead author on a new paper about RiskCardio. “The intersection of machine learning and healthcare is replete with combinations like this — a compelling computer science problem with potential real-world impact.”

Risky business

Previous machine learning models have attempted to get a handle on risk by either making use of external patient information like age or weight, or using knowledge and expertise specific to the system — more broadly known as domain-specific knowledge — to help their model select different features.

RiskCardio, however, uses just the patients’ raw ECG signal, with no additional information.

Say a patient checks into the hospital following an ACS. After intake, a physician would first estimate the risk of cardiovascular death or heart attack using medical data and lengthy tests, and then choose a course of treatment.

RiskCardio aims to improve that first step of estimating risk. To do this, the system separates a patient’s signal into sets of consecutive beats, with the idea that variability between adjacent beats is telling of downstream risk. The system was trained using data from a study of past patients.

To get the model up and running, the team first separated each patient's signal into a collection of adjacent heart beats. They then assigned a label — i.e., whether or not the patient died of cardiovascular death — to each set of adjacent heartbeats. The researchers trained the model to classify each pair of adjacent heartbeats to its patient outcome: Heartbeats from patients who died were labeled “risky,” while heartbeats from patients who survived were labeled “normal.”

Given a new patient, the team created a risk score by averaging the patient prediction from each set of adjacent heartbeats.

Within the first 15 minutes of a patient experiencing an ACS, there was enough information to estimate whether or not they would suffer from cardiovascular death within 30, 60, 90, or 365 days.

Still, calculating a risk score from just the ECG signal is no simple task. The signals are very long, and as the number of inputs to a model increase, it becomes harder to learn the relationship between those inputs.

The team tested the model by producing risk scores for a set of patients. Then, they measured how much more likely a patient would suffer from cardiovascular death as a high-risk patient when compared to a set of low-risk patients. They found that in roughly 1,250 post-ACS patients, 28 would die of cardiovascular death within a year. Using the proposed risk score, 19 of those 28 patients were classified as high-risk.

In the future, the team hopes to make the dataset more inclusive to account for different ages, ethnicities, and genders. They also plan to examine medical scenarios where there’s a lot of poorly labeled or unlabeled data, and evaluate how their system processes and handles that information to account for more ambiguous cases.

“Machine learning is particularly good at identifying patterns, which is deeply relevant to assessing patient risk,'' says Shanmugam. “Risk scores are useful for communicating patient state, which is valuable in making efficient care decisions.”

Shanmugam presented the paper at the Machine Learning for Healthcare Conference alongside PhD student Davis Blalock and MIT Professor John Guttag.

Source: MIT News - CSAIL - Robotics - Computer Science and Artificial Intelligence Laboratory (CSAIL) - Robots - Artificial intelligence

Reprinted with permission of MIT News : MIT News homepage



Use the link at the top of the story to get to the original article.

2 Comments | Started September 14, 2019, 12:03:19 pm
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LOCKSUIT

Share your most unique powerful AGI digests in General AI Discussion

My 2 I seem to go to every other day are Open AI's website and Two Minute Papers YouTube Channel.

Reason: Open AI has made one of the best combined AGI-like AI yet; GPT-2. And other related work. How can you not check in every day?

Reason: Two Minute Papers YouTube Channel has all the latest bleeding edge most unique and most widely varying short summarized videos on amazing futuristic AI technologies. It's as good as food.

My faults: I think I remember other sites, one lists all the types of nets, and Google DeepMind has many latest updates. I gotta check in there more often. Lastly, this very forum is a quite good place to see member's and Tyler's posts. No doubt Writer Of Mind's AGI also has many modeling things that GPT-2 doesn't have, like forgetting...etc.

Share yours, let's unite/ fill in gaps...

1 Comment | Started September 15, 2019, 09:38:46 am
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LOCKSUIT

Efficient vectors .... in General AI Discussion

Ok. I got a question. Anyone with a ANN project can answer. Artificial NNs usually have say what, a million nodes/connections say. I know ANNs use vectors.

I'm going to give a possible answer: all nodes are know as the same, you just spawn 500,000,000 of them. Then, the weights in between must be stored in vectors. They begin randomly initialized. Trained. The cost of storage for each connection is actually higher because there is more connections that nodes! However, they still are less!! This must be vector storage! A vector is therefore this > 0.4636 0.68879 0.35345 ..... therefore each connection is ~8 bytes, not a KB!

?

8 Comments | Started September 14, 2019, 05:00:55 am
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Hopefully Something

A Pleasant Mind in General AI Discussion

If you can, does that mean you should? What do we really want? If you could have any kind of mind, what would it be like? Are we building AI for them, or for us? Is that a false dichotomy?

We are speeding headlong down the road of quantitative progress, its better faster higher stronger with our computers. But what if the AI wakes up shouting "I can't contain myself any loooongerrr!!!" and self destructs... you know?

We should look into the qualitative side of things. I was thinking about that movie title "A Beautiful Mind" and wondered if we (humans) can discern how to prioritize abilities/qualities in a personally rewarding way. Which, I think should be the top goal; to have the ability to have a nice time. I'd prefer a pleasant mind to all the alternatives, and I'm guessing the robots would too.

It's like transport. If I decided to go visit New Zealand, I'd rather spend a month on a ship than 15 hours on a plane. I'm not saying fast is bad, I'm just saying we haven't yet optimized the traveling equation with appropriate technology. Same issue could arise with AI. What makes for a pleasant yet sustainable existence?

9 Comments | Started September 13, 2019, 10:57:06 pm
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ivan.moony

Fractal Orbit in Human Computer Interaction

Fractal Orbit is a project that has some connection to representing structured data on 2D screen. Data can be anything, from textbooks, over source code, over business databases, over knowledge base representation. As knowledge base is directly connected to AI, I thought  this is the right place to share this project.



It is about representing knowledge trees in a way that reminds me of fractals. Children nodes orbit around parent nodes, while adjusting their magnification to fit between outer and inner circle. Clicking a child node zooms in its circle together with its grandchild nodes. Clicking a central parent node zooms out, back to the grandparent circle node. It should be interesting to follow node links in and out from one tree node to another, even across indirectly connected nodes of the tree.

I think it would look cool on round smart watches :)

47 Comments | Started February 04, 2018, 11:19:12 am
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Tyler

XKCD Comic : Earth-Like Exoplanet in XKCD Comic

Earth-Like Exoplanet
13 September 2019, 1:00 am

Fire is actually a potential biosignature, since it means something is filling the atmosphere with an unstable gas like oxygen. If we find a planet covered in flames, it might be an indicator that it supports life. Or used to, anyway, before the fire.

Source: xkcd.com

Started September 14, 2019, 12:03:18 pm
Metal Gear Series - Metal Gear RAY

Metal Gear Series - Metal Gear RAY in Robots in Games

Metal Gear RAY is an anti-Metal Gear introduced in Metal Gear Solid 2: Sons of Liberty. This Metal Gear model comes in two variants: a manned prototype version developed to combat Metal Gear derivatives and an unmanned, computer-controlled version.

Metal Gear RAY differs from previous Metal Gear models in that it is not a nuclear launch platform, but instead a weapon of conventional warfare, originally designed by the U.S. Marines to hunt down and destroy the many Metal Gear derivatives that became common after Metal Gear REX's plans leaked following the events of Shadow Moses.

Apr 08, 2019, 17:35:36 pm
Fallout 3 - Liberty Prime

Fallout 3 - Liberty Prime in Robots in Games

Liberty Prime is a giant, military robot, that appears in the Fallout games. Liberty Prime fires dual, head-mounted energy beams, which are similar to shots fired from a Tesla cannon.

He first appears in Fallout 3 and also it's add-on Broken Steel. Then again in Fallout 4 and later in 2017 in Fallout: The Board Game.

Apr 07, 2019, 15:20:23 pm
Building Chatbots with Python

Building Chatbots with Python in Books

Build your own chatbot using Python and open source tools. This book begins with an introduction to chatbots where you will gain vital information on their architecture. You will then dive straight into natural language processing with the natural language toolkit (NLTK) for building a custom language processing platform for your chatbot. With this foundation, you will take a look at different natural language processing techniques so that you can choose the right one for you.

Apr 06, 2019, 20:34:29 pm
Voicebot and Chatbot Design

Voicebot and Chatbot Design in Books

Flexible conversational interfaces with Amazon Alexa, Google Home, and Facebook Messenger.

We are entering the age of conversational interfaces, where we will interact with AI bots using chat and voice. But how do we create a good conversation? How do we design and build voicebots and chatbots that can carry successful conversations in in the real world?

In this book, Rachel Batish introduces us to the world of conversational applications, bots and AI. You’ll discover how - with little technical knowledge - you can build successful and meaningful conversational UIs. You’ll find detailed guidance on how to build and deploy bots on the leading conversational platforms, including Amazon Alexa, Google Home, and Facebook Messenger.

Apr 05, 2019, 15:43:30 pm
Build Better Chatbots

Build Better Chatbots in Books

A Complete Guide to Getting Started with Chatbots.

Learn best practices for building bots by focusing on the technological implementation and UX in this practical book. You will cover key topics such as setting up a development environment for creating chatbots for multiple channels (Facebook Messenger, Skype, and KiK); building a chatbot (design to implementation); integrating to IFTT (If This Then That) and IoT (Internet of Things); carrying out analytics and metrics for chatbots; and most importantly monetizing models and business sense for chatbots.

Build Better Chatbots is easy to follow with code snippets provided in the book and complete code open sourced and available to download.

Apr 04, 2019, 15:21:57 pm
Chatbots and Conversational UI Development

Chatbots and Conversational UI Development in Books

Conversation as an interface is the best way for machines to interact with us using the universally accepted human tool that is language. Chatbots and voice user interfaces are two flavors of conversational UIs. Chatbots are real-time, data-driven answer engines that talk in natural language and are context-aware. Voice user interfaces are driven by voice and can understand and respond to users using speech. This book covers both types of conversational UIs by leveraging APIs from multiple platforms. We'll take a project-based approach to understand how these UIs are built and the best use cases for deploying them.

Build over 8 chatbots and conversational user interfaces with leading tools such as Chatfuel, Dialogflow, Microsoft Bot Framework, Twilio, Alexa Skills, and Google Actions and deploying them on channels like Facebook Messenger, Amazon Alexa and Google Home.

Apr 03, 2019, 22:30:30 pm
Human + Machine: Reimagining Work in the Age of AI

Human + Machine: Reimagining Work in the Age of AI in Books

Look around you. Artificial intelligence is no longer just a futuristic notion. It's here right now--in software that senses what we need, supply chains that "think" in real time, and robots that respond to changes in their environment. Twenty-first-century pioneer companies are already using AI to innovate and grow fast. The bottom line is this: Businesses that understand how to harness AI can surge ahead. Those that neglect it will fall behind. Which side are you on?

Apr 02, 2019, 17:19:14 pm
Metal Arms: Glitch In The System - Glitch

Metal Arms: Glitch In The System - Glitch in Robots in Games

Metal Arms: Glitch in the System is a third-person shooter action-adventure video game, developed by American team Swingin' Ape Studios and released in 2003. The game follows a robot named Glitch as he joins forces with the Droids in their fight against General Corrosive and his Milbots.

Apr 01, 2019, 21:17:33 pm
10 of the Most Innovative Chatbots on the Web

10 of the Most Innovative Chatbots on the Web in Articles

Love them or hate them, chatbots are here to stay. Chatbots have become extraordinarily popular in recent years largely due to dramatic advancements in machine learning and other underlying technologies such as natural language processing. Today’s chatbots are smarter, more responsive, and more useful – and we’re likely to see even more of them in the coming years.

Mar 31, 2019, 00:32:28 am
Borderlands - Claptrap

Borderlands - Claptrap in Robots in Games

Borderlands is a series of action role-playing first-person shooter video games in a space western science fantasy setting, created by Gearbox Software and published by 2K Games for multiple platforms.

Several characters appear in multiple Borderlands games. The little yellow robot Claptrap (voiced by David Eddings), the de facto mascot for the franchise, has appeared in all games as a non-player character (NPC) and in the Pre-Sequel as a playable character.

Mar 30, 2019, 13:14:58 pm
Slave Zero - Slave Zero

Slave Zero - Slave Zero in Robots in Games

Taking place 500 years in the future, the game tells the story of Lu Chen, a sinister world overlord more commonly known as the SovKhan, who rules the Earth from a massive complex called Megacity S1-9.

The game follows "Slave Zero" as he wages war against the SovKhan's forces throughout every part of Megacity S1-9.

First released on the Dreamcast console.

Mar 29, 2019, 12:17:05 pm