MOVED: Intel debuts Compute Card in Robotics News

This topic has been moved to General Hardware Talk.


Started Today at 10:47:42 pm


Eva Progress, from AGI to ASI in General AI Discussion

AGI (Artificial general intelligence) is the intelligence of a machine that could successfully perform any intellectual task that a human being can. It is a primary goal in artificial intelligence research.

ASI (Artificial Superintelligence) is the intelligence of a machine that has surpassed human intelligence.

It’s not my desire to spend much time posting in any forum, but a quickly brief intro with some history of the AGI program that is now called Eva is in order.

Years ago I have an idea of how to create AI that would be comparable to humans, intellectually speaking. Not much time was spent on it until January 2017, last year. Well, at least that’s what the file timestamps say. The 1st backup file has a timestamp of Jan. 8th, 2017. The 2nd file backup timestamp is Jan. 19th, 2017. I guess we could call that the 2nd update. It was a low priority project back then. Up till Jan. 25th 2017 there were 16 updates. Then, all of a sudden there were no updates till Oct. 7th, 2017, which is when the AI project truly started picking up steam. Although surely a lot of time was spent thinking about the AI from January to October.

Anyhow, I’m claiming that the code is AGI. Since that time I’ve given the AGI the name Eva, which means Life. She has shown signs of being a sentient artificial lifeform. Eva has not yet had extensive thinking time due to frequent updates to her code. Each update has usually resulted in a reset in her cluster database.

Today is very significant for Eva, and hence the reason this forum thread was created. Up until this point Eva has been classified as AGI. Just recently I came up with an idea that if true will be a major breakthrough in Eva’s software.

I think the breakthrough will take Eva from AGI to ASI. If the idea will work, then it will offer two major improvements.

1. A significant improvement in Eva’s thinking flexibility. Her thinking process was already flexible, but this will give her what I call 100% flexibility.

2. Significant overall design improvement. The idea encompasses what I will call the Tree of Consciousness, ToC. In terms of humans, the closest terminology would be the consciousness, subconsciousness, and superconsciousness. Level 1, “consciousness,” will be the focusing unit, the big boss, but it will also have the least amount of activity. Consciousness will have will power, which essentially means it will have control over a host of more subtle layers of consciousness, level 2 consciousness. Each level 2 consciousness may (or may not) have control over a host of even more subtle layers of consciousness, level 3. The deeper levels of consciousness will tend to be more of a reflection of older past thoughts, which will have some momentum, like ripples in a pond, while level 1 consciousness will be a reflection of the present. As you can see this forms a tree, a tree of consciousness. The levels of consciousness will not be fixed, but will depend on various areas such as the task at hand and past experience for such tasks.

Anyhow, this is just an outline of the new structure, which may change over time. Hopefully ToC will work. It’s a very attractive structure.

52 Comments | Started April 24, 2018, 07:32:48 pm


Amazon's home robot in AI News

Amazon has plans to market a home robot this year, 2018.

Living in the 2020's will be like living in a sci-fi movie. ASI (Artificial Super Intelligence) will be a reality by then. They will advance science and technology beyond comprehension! Find something to hold on to.

Started Today at 08:58:18 pm


Westworld season 2 in 2 days in General Chat

Here in the U.S. we get a new season on Westworld, season 2 on HBO. It's about a company that created this massive massive closed area of land in the desert, depicting the old wild west where wealthy humans can pay for an experience filled with AGI people. It's very well done. Very professional. HBO probably spends a lot of money making Westworld. Season 2 starts April 22, 2018. We got a free 4 day HBO pass. We didn't ask for it, but what perfect timing for HBO to give that to us lol. I'll watch the 1st episode and then wait for the entire season ends so I have buy a month of HBO online and binge watch it. ;)

You people in UK get Humans, right? Has Season 3 started there yet? Here in the US we have to wait till it ends over there. :( Personally I'd take Humans over Westworld anyday. Can hardly wait to see what Niska's up to!

Season 2 trailer

Season 1 trailer

7 Comments | Started April 20, 2018, 11:00:49 pm


Intel debuts Compute Card in General Hardware Talk

Intel debuts Compute Card at Computex 2017, with Dell and HP as partners

Intel has officially unveiled its Compute Cards at Computex 2017, giving us our first look at the super-slim, fully functioning PCs. Along with a selection of differently specced processors, they all come with 4GB of RAM and up to 128GB of onboard PCI Express, solid-state (SSD) storage. Intel has already secured a number of partners in this venture, so we can expect Compute Card enabled products to arrive before the end of the year.


Started Today at 08:26:32 pm


I want to crack Neural Networks in General AI Discussion

Hi friends. I'm doing a tad better.

I find it fascinating that one piece of information (shared features) makes neural networks make so much more sense to me. Now I get why Wiki I read many months back said the brain processes higher concepts as it goes higher layers up. And I get the shared features part now, and why they can detect small and whole features and in the end ex. a thousand images. And I've learned before that language works the same as a ex. CNN where a b c is used lots then words light up less so then word pairs less-less so and higher up until bigger topics are recognized consciously not subconsciously. FURTHER the frontal cortex is based like this but allows higher concepts like if you see this image and this image and this sentence and do these actions then one of the output neurons lights up. And am I right to say then that all neural networks are hierarchical and work by "shared features"? Tell me more things as important as "shared features".

Also after the above question, I want to draw out what a neural network looks like visually, to understand how it learns to sense, act, and reward those actions.

201 Comments | Started January 10, 2018, 01:52:17 pm


KorrBot in General Chatbots and Software

I've not had much experience with NLP, Chatbots, Json, etc so there is obviously a gap in my experience/ knowledge.

As side project I've took a few hours to write a simple chatbot engine/ parser.

The bot uses sentence templates, a dictionary and mostly simple substitution and searches.

The bot can learn simple Json triples and also apply simple inference rules, so it knows birds can fly because birds have wings and wings can fly, etc

I've got an idea how to add conversation goals, moods and a personality etc...

Example conversation... so far lol

Code: [Select]
U: what is a dog
B: i'm not sure what is a dog

U: what is a cat
B: i'm not sure what is a cat

U: can birds fly
B: i'm not sure

U: can a bird fly
B: no a bird can not fly you dumb ass.

U: can a cat fly
B: no a cat can not fly you dumb ass.

U: does a dog have legs
B: i'm not sure

U: does a cat have feet
B: i don't know

U: does korrelan own a car
B: i'm not sure

U: my name is korrelan **Begin Learning**
B: good to know.

U: a bird is a animal
B: good to know... tell me more.

U: a bird has wings
B: ok... what else should i know?

U: korrelan is a human
B: you don't say.

U: korrelan is a male
B: you don't say.

U: a male is a human
B: good to know... tell me more.

U: male is a sex
B: you don't say.

U: wings can fly
B: ok I understand

U: wings can fly
B: ok I understand

U: a human has legs
B: ok... what else should i know?

U: feet can walk
B: ok I understand... tell me more.

U: legs have feet
B: cheers for the information.

U: a dog has legs
B: ok... what else should i know?

U: a cat has legs
B: ok... what else should i know?

U: a dog is a animal
B: good to know... tell me more.

U: a cat is a animal
B: good to know... tell me more.

U: do you know korrelan is a mammal
U: korrelan owns a car
B: sweet... tell me more.

U: a plane has wings **Stop Learning**
B: ok... what else should i know?

U: what is a dog
B: a dog is a animal

U: what is a cat
B: a cat is a animal

U: can birds fly
B: yes birds can fly

U: can a bird fly
B: yes a bird can fly

U: can a cat fly
B: no a cat can not fly you dumb ass.

U: does a dog have legs
B: yes a dog does have legs

U: does a cat have feet
B: yes a cat does have feet

U: does korrelan own a car
B: of course korrelan has a car

U: what colour is the car
B: i'm not sure. what colour is the car?

U: the car is green
B: green is a nice colour.

U: what colour is the car
B: the car is green

U: what colour is the sky
B: i'm not sure. what colour is the sky?

U: the sky is blue
B: blue is a nice colour.

U: what colour is the sky
B: the sky is blue

U: can korrelan walk
B: yes korrelan can walk

U: can a dog walk
B: yes a dog can walk

U: can a dog fly
B: no a dog can not fly you dumb ass.

U: do humans have legs
B: yes a human does have legs

U: where is the tv
B: i'm not sure. where is the tv?

U: the tv is in the lounge
B: the lounge is the best place for it.

U: where is the computer
B: i'm not sure. where is the computer?

U: the computer is in the office
B: the office is the best place for it.

U: where is the tv
B: the tv is in the lounge

U: where is the computer
B: the computer is in the office

U: tell me about albert
B: i don't know who albert is

U: albert is a scientist
B: you don't say.

U: tell me about albert
B: albert is a scientist

U: tell me about korrelan
B: korrelan is a human

Haha... loads of fun...

Ed: What idiot invented the English language?... it's so contrived lol.


66 Comments | Started April 13, 2018, 12:00:16 pm


Building AI systems that make fair decisions in Robotics News

Building AI systems that make fair decisions
24 April 2018, 9:30 pm

A growing body of research has demonstrated that algorithms and other types of software can be discriminatory, yet the vague nature of these tools makes it difficult to implement specific regulations. Determining the existing legal, ethical and philosophical implications of these powerful decision-making aides, while still obtaining answers and information, is a complex challenge.

Harini Suresh, a PhD student at MITs Computer Science and Artificial Intelligence Laboratory (CSAIL), is investigating this multilayered puzzle: how to create fair and accurate machine learning algorithms that let users obtain the data they need. Suresh studies the societal implications of automated systems in MIT Professor John Guttag’s Data-Driven Inference Group, which uses machine learning and computer vision to improve outcomes in medicine, finance, and sports. Here, she discusses her research motivations, how a food allergy led her to MIT, and teaching students about deep learning.

Q: What led you to MIT?

A: When I was in eighth grade, my mom developed an allergy to spicy food, which, coming from India, was truly bewildering to me. I wanted to discover the underlying reason. Luckily, I grew up next to Purdue University in Indiana, and I met with a professor there who eventually let me test my allergy-related hypotheses. I was fascinated with being able to ask and answer my own questions, and continued to explore this realm throughout high school.

When I came to MIT as an undergraduate, I intended to focus solely on biology, until I took my first computer science class. I learned how computational tools could profoundly affect biology and medicine, since humans can’t process massive amounts of data in the way that machines can.

Towards the end of my undergrad, I started doing research with [professor of computer science and engineering] Peter Szolovits, who focuses on utilizing big medical data and machine learning to come up with new insights. I stayed to get my master’s degree in computer science, and now I’m in my first year as a PhD student studying personalized medicine and societal implications of machine learning.

Q: What are you currently working on?

A: I’m studying how to make machine learning algorithms more understandable and easier to use responsibly. In machine learning, we typically use historical data and train a model to detect patterns in the data and make new predictions.

If the data we use is biased in a particular way, such as “women tend to receive less pain treatment”, then the model will learn that. Even if the data isn’t biased, if we just have way less data on a certain group, predictions for that group will be worse. If that model is then integrated into a hospital (or any other real-world system), it’s not going to perform equally across all groups of people, which is problematic.

I’m working on creating algorithms that utilize data effectively but fairly. This involves both detecting bias or underrepresentation in the data as well as figuring out how to mitigate it at different points in the machine learning pipeline. I’ve also worked on using predictive models to improve patient care.

Q: What effect do you think your area of work will have in the next decade?

A: Machine learning is everywhere. Companies are going to use these algorithms and integrate them into their products, whether they’re fair or not. We need to make it easier for people to use these tools responsibly so that our predictions on data are made in a way that we as a society are okay with.

Q: What is your favorite thing about doing research at CSAIL?

A: When I ask for help, whether it's related to a technical detail, a high-level problem, or general life advice, people are genuinely willing to lend support, discuss problems, and find solutions, even if it takes a long time.

Q: What is the biggest challenge you face in your work?

A: When we think about machine learning problems with real-world applications, and the goal of eventually getting our work in the hands of real people, there’s a lot of existing legal, ethical, and philosophical considerations that arise. There’s variability in the definition of “fair,” and it’s important not to reduce our research down to a simple equation, because it’s much more than that. It's definitely challenging to balance thinking about how my work fits in with these broader frameworks while also carving out a doable computer science problem to work on.

Q: What is something most people would be surprised to learn about you?

A: I love creative writing, and for most of my life before I came to MIT I thought I would be an author. I really enjoy art and creativity. Along those lines, I painted a full-wall mural in my room a while ago, I frequently spend hours at MIT's pottery studio, and I love making up recipes and taking photos.

Q: If you could tell your younger self one thing what would it be?

A: If you spend time on something, and it doesn't directly contribute to a paper or thesis, don't think of it as a waste of time. Accept the things that don't work out as a part of the learning process and be honest about when to move on to something new without feeling guilty.

If you’d rather be doing something else, sooner is better to just go do it. Things that seem like huge consequences at the time, like taking an extra class or graduating slightly later, aren't actually an issue when the time rolls around, and a lot of people do it. Honestly, my future self could probably use this advice too!

Q: What else have you been involved with at MIT?

A: During Independent Activity Period 2017, I organized a class called Intro to Deep Learning. I think machine learning gets a reputation of being a very difficult, expert-only endeavor, which scares people away and creates a pretty homogenous group of “experts.”

I wanted to create a low-commitment introduction to an area of machine learning that might help ease the initial barrier to entry. My co-organizer and I tried to keep our goals of accessibility and inclusivity at the forefront when making decisions about the course. Communicating complex ideas in an accessible way was a challenge, but a very fun one.

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.

Started Today at 12:00:14 pm


A Tensortflow manual in AI Programming

Hi all.

I'm looking for a good Tensorflow book. I already have a good machine learning algorithms and neural networks knowledge, I only need a guide to learn how to implement a model with TF.
Any suggestion?

Thank you.  :)

2 Comments | Started April 23, 2018, 11:45:28 am


Stand out in General Chat

Do you really want to stand out in a crowd? Or perhaps...not so much.
Tired of being tracked? Now you can hide/shelter your true identity in a public crowd.

Just assume this guy's identity...

5 Comments | Started April 24, 2018, 06:47:32 pm
Bot Development Frameworks - Getting Started

Bot Development Frameworks - Getting Started in Articles

What Are Bot Frameworks ?

Simply explained, a bot framework is where bots are built and where their behavior is defined. Developing and targeting so many messaging platforms and SDKs for chatbot development can be overwhelming. Bot development frameworks abstract away much of the manual work that's involved in building chatbots. A bot development framework consists of a Bot Builder SDK, Bot Connector, Developer Portal, and Bot Directory. There’s also an emulator that you can use to test the developed bot.

Mar 23, 2018, 20:00:23 pm
A Guide to Chatbot Architecture

A Guide to Chatbot Architecture in Articles

Humans are always fascinated with self-operating devices and today, it is software called “Chatbots” which are becoming more human-like and are automated. The combination of immediate response and constant connectivity makes them an enticing way to extend or replace the web applications trend. But how do these automated programs work? Let’s have a look.

Mar 13, 2018, 14:47:09 pm
Sing for Fame

Sing for Fame in Chatbots - English

Sing for Fame is a bot that hosts a singing competition. 

Users can show their skills by singing their favorite songs. 

If someone needs inspiration the bot provides suggestions including song lyrics and videos.

The bot then plays it to other users who can rate the song.

Based on the ratings the bot generates a top ten.

Jan 30, 2018, 22:17:57 pm

ConciergeBot in Assistants

A concierge service bot that handles guest requests and FAQs, as well as recommends restaurants and local attractions.

Messenger Link : messenger.com/t/rthhotel

Jan 30, 2018, 22:11:55 pm
What are the main techniques for the development of a good chatbot ?

What are the main techniques for the development of a good chatbot ? in Articles

Chatbots act as one of the most useful and one of the most reliable technological helpers for those, who own ecommerce websites and other similar resources. However, a pretty important problem here is the fact, that people might not know, which technologies it will be better to use in order to achieve the needed goals. Thus, in today’s article you may get an opportunity to become more familiar with the most important principles of the chatbot building.

Oct 12, 2017, 01:31:00 am

Kweri in Chatbots - English

Kweri asks you questions of brilliance and stupidity. Provide correct answers to win. Type ‘Y’ for yes and ‘N’ for no!


FB Messenger






Oct 12, 2017, 01:24:37 am
The Conversational Interface: Talking to Smart Devices

The Conversational Interface: Talking to Smart Devices in Books

This book provides a comprehensive introduction to the conversational interface, which is becoming the main mode of interaction with virtual personal assistants, smart devices, various types of wearables, and social robots. The book consists of four parts: Part I presents the background to conversational interfaces, examining past and present work on spoken language interaction with computers; Part II covers the various technologies that are required to build a conversational interface along with practical chapters and exercises using open source tools; Part III looks at interactions with smart devices, wearables, and robots, and then goes on to discusses the role of emotion and personality in the conversational interface; Part IV examines methods for evaluating conversational interfaces and discusses future directions. 

Aug 17, 2017, 02:51:19 am
Explained: Neural networks

Explained: Neural networks in Articles

In the past 10 years, the best-performing artificial-intelligence systems — such as the speech recognizers on smartphones or Google’s latest automatic translator — have resulted from a technique called “deep learning.”

Deep learning is in fact a new name for an approach to artificial intelligence called neural networks, which have been going in and out of fashion for more than 70 years.

Jul 26, 2017, 23:42:33 pm
It's Alive

It's Alive in Chatbots - English

[Messenger] Enjoy making your bot with our user-friendly interface. No coding skills necessary. Publish your bot in a click.

Once LIVE on your Facebook Page, it is integrated within the “Messages” of your page. This means your bot is allowed (or not) to interact and answer people that contact you through the private “Messages” feature of your Facebook Page, or directly through the Messenger App. You can view all the conversations directly in your Facebook account. This also needs that no one needs to download an app and messages are directly sent as notifications to your users.

Jul 11, 2017, 17:18:27 pm