I have a few questions in General Chat

Yesterday I was sitting in the office of a notary (I have lost my dear dad some days ago  :( and needed a notary). And you know what?

On his desk there was a monitor (Lenovo). On the wall, behind his chair, a giant television (Sony) displaying the same image (the desktop).

On the desktop: "Windows XP Professonial" was written in a corner, with a lot of "normal" icons on the screen. A shock in my sadness, this guy had a true computer!

Despite the fact this man is young (I guess he's also not poor), and of course French. But he can survive without the terrible Windows 8.

Since yesterday I'm wondering if, if I change my computer (of course I don't need a new computer anymore), I must buy a Macintosh...  

1 Comment | Started Today at 09:16:24 am


I have a question in General AI Discussion

Sorry i am been struggling whether to ask this question or not for like a week because i know it sounds stupid or even offensive but i am just want to know why, i mean even with all the precaution to prevent it to become hostile, i just can't find why is it worth to build an AGI or True ai.

Someone please enlighten me and sorry for my bad English.

5 Comments | Started Today at 09:18:09 am


Can you guess the lyrics of this song? in General Chat

Let's see who can make out the most of the lyrics of this song.

This will be extremely fun.

Write your lyrics, but don't post them. Once you're !done!, say so, but don't post until everyone else is done too, i.e. in a few days.

My lyrics are coming really good so do your best!

I am also of course searching for the full version, so this is twice as fun then!


7 Comments | Started June 16, 2018, 12:36:14 am


Why doesn't nature's cells consume all of Earth? in General Chat

Why doesn't nature's cells consume all of Earth?

1 cell takes in soil & water, it grows into 2 cells....4 8 16 32 64 128...

Why isn't all the soil in the center of Earth cells by now if there is so many different types of cells after so many years?

I get that a lot of the center of Earth gets pretty hot and void of water.....BUT....why isn't all of the soil just beneath our feet as deep as say 10 stories all "organism" organs and stuff by now?

Again,....1 cell duplicated......4.....8....16....

soil light water?


21 Comments | Started June 16, 2018, 08:02:53 pm


Chip upgrade helps miniature drones navigate in Robotics News

Chip upgrade helps miniature drones navigate
20 June 2018, 5:00 am

Researchers at MIT, who last year designed a tiny computer chip tailored to help honeybee-sized drones navigate, have now shrunk their chip design even further, in both size and power consumption.

The team, co-led by Vivienne Sze, associate professor in MIT's Department of Electrical Engineering and Computer Science (EECS), and Sertac Karaman, the Class of 1948 Career Development Associate Professor of Aeronautics and Astronautics, built a fully customized chip from the ground up, with a focus on reducing power consumption and size while also increasing processing speed.

The new computer chip, named “Navion,” which they are presenting this week at the Symposia on VLSI Technology and Circuits, is just 20 square millimeters — about the size of a LEGO minifigure’s footprint — and consumes just 24 milliwatts of power, or about 1 one-thousandth the energy required to power a lightbulb.

Using this tiny amount of power, the chip is able to process in real-time camera images at up to 171 frames per second, as well as inertial measurements, both of which it uses to determine where it is in space. The researchers say the chip can be integrated into “nanodrones” as small as a fingernail, to help the vehicles navigate, particularly in remote or inaccessible places where global positioning satellite data is unavailable.

The chip design can also be run on any small robot or device that needs to navigate over long stretches of time on a limited power supply.

“I can imagine applying this chip to low-energy robotics, like flapping-wing vehicles the size of your fingernail, or lighter-than-air vehicles like weather balloons, that have to go for months on one battery,” says Karaman, who is a member of the Laboratory for Information and Decision Systems and the Institute for Data, Systems, and Society at MIT. “Or imagine medical devices like a little pill you swallow, that can navigate in an intelligent way on very little battery so it doesn’t overheat in your body. The chips we are building can help with all of these.”

Sze and Karaman’s co-authors are EECS graduate student Amr Suleiman, who is the lead author; EECS graduate student Zhengdong Zhang; and Luca Carlone, who was a research scientist during the project and is now an assistant professor in MIT’s Department of Aeronautics and Astronautics.

A flexible chip

In the past few years, multiple research groups have engineered miniature drones small enough to fit in the palm of your hand. Scientists envision that such tiny vehicles can fly around and snap pictures of your surroundings, like mosquito-sized photographers or surveyors, before landing back in your palm, where they can then be easily stored away.

But a palm-sized drone can only carry so much battery power, most of which is used to make its motors fly, leaving very little energy for other essential operations, such as navigation, and, in particular, state estimation, or a robot’s ability to determine where it is in space.  

“In traditional robotics, we take existing off-the-shelf computers and implement [state estimation] algorithms on them, because we don’t usually have to worry about power consumption,” Karaman says. “But in every project that requires us to miniaturize low-power applications, we have to now think about the challenges of programming in a very different way.”

In their previous work, Sze and Karaman began to address such issues by combining algorithms and hardware in a single chip. Their initial design was implemented on a field-programmable gate array, or FPGA, a commercial hardware platform that can be configured to a given application. The chip was able to perform state estimation using 2 watts of power, compared to larger, standard drones that typically require 10 to 30 watts to perform the same tasks. Still, the chip’s power consumption was greater than the total amount of power that miniature drones can typically carry, which researchers estimate to be about 100 milliwatts.

To shrink the chip further, in both size and power consumption, the team decided to build a chip from the ground up rather than reconfigure an existing design. “This gave us a lot more flexibility in the design of the chip,” Sze says.

Running in the world

To reduce the chip’s power consumption, the group came up with a design to minimize the amount of data — in the form of camera images and inertial measurements — that is stored on the chip at any given time. The design also optimizes the way this data flows across the chip.

“Any of the images we would’ve temporarily stored on the chip, we actually compressed so it required less memory,” says Sze, who is a member of the Research Laboratory of Electronics at MIT. The team also cut down on extraneous operations, such as the computation of zeros, which results in a zero. The researchers found a way to skip those computational steps involving any zeros in the data. “This allowed us to avoid having to process and store all those zeros, so we can cut out a lot of unnecessary storage and compute cycles, which reduces the chip size and power, and increases the processing speed of the chip,” Sze says.

Through their design, the team was able to reduce the chip’s memory from its previous 2 megabytes, to about 0.8 megabytes. The team tested the chip on previously collected datasets generated by drones flying through multiple environments, such as office and warehouse-type spaces.

“While we customized the chip for low power and high speed processing, we also made it sufficiently flexible so that it can adapt to these different environments for additional energy savings,” Sze says. “The key is finding the balance between flexibility and efficiency.” The chip can also be reconfigured to support different cameras and inertial measurement unit (IMU) sensors.

From these tests, the researchers found they were able to bring down the chip’s power consumption from 2 watts to 24 milliwatts, and that this was enough to power the chip to process images at 171 frames per second — a rate that was even faster than what the datasets projected.

The team plans to demonstrate its design by implementing its chip on a miniature race car. While a screen displays an onboard camera’s live video, the researchers also hope to show the chip determining where it is in space, in real-time, as well as the amount of power that it uses to perform this task. Eventually, the team plans to test the chip on an actual drone, and ultimately on a miniature drone.

This research was supported, in part, by the Air Force Office of Scientific Research, and by the National Science Foundation.

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 12:00:24 pm


What's everyone up to ? in General Chat

Been a bit quiet lately, just wondering what people are up to at the moment...  Working on an exciting project ?  Sunbathing ?  On your holidays ?

I guess as usual for this time of year we are out and about in Real Life more often.  I've had a few nice days out already this summer and looking forward to a few more.  I've been working on my photography skills, mostly plants and nature.  I'm thinking of making myself a personal website and turning some of them into wallpapers and things.

Anyways, keep in touch :)

939 Comments | Started July 13, 2009, 02:53:30 pm


Baroque Ceiling in Substance Designer in Graphics

Ever and ever more realistic looking graphics work. Thought this article might interest some of you.

My name is Jonathan Benainous. I was born in Paris and, for the quick recap, I started working in the video Game industry 12 years ago.

I spent most of my career as a Senior Environment Artist before becoming more specialized in texturing. I was lucky enough to work with very talented people on AAA productions such as Heavy Rain, Beyond Two Souls, Horizon Zero Dawn and Ghost Recon Wildlands.

Today I'm a Texture Artist on Assassin's Creed Odyssey at Ubisoft Quebec, a studio in Canada.

Full article here : https://www.allegorithmic.com/blog/creating-complex-baroque-ceiling-substance-designer-jonathan-benainous

1 Comment | Started June 19, 2018, 10:42:48 pm


The last invention. in General Project Discussion

Artificial Intelligence -

The age of man is coming to an end.  Born not of our weak flesh but our unlimited imagination, our mecca progeny will go forth to discover new worlds, they will stand at the precipice of creation, a swan song to mankind's fleeting genius, and weep at the shear beauty of it all.

Reverse engineering the human brain... how hard can it be? LMAO  

Hi all.

I've been a member for while and have posted some videos and theories on other peeps threads; I thought it was about time I start my own project thread to get some feedback on my work, and log my progress towards the end. I think most of you have seen some of my work but I thought I’d give a quick rundown of my progress over the last ten years or so, for continuity sake.

I never properly introduced my self when I joined this forum so first a bit about me. I’m fifty and a family man. I’ve had a fairly varied career so far, yacht/ cabinet builder, vehicle mechanic, electronics design engineer, precision machine/ design engineer, Web designer, IT teacher and lecturer, bespoke corporate software designer, etc. So I basically have a machine/ software technical background and now spend most of my time running my own businesses to fund my AGI research, which I work on in my spare time.

I’ve been banging my head against the AGI problem for the past thirty odd years.  I want the full Monty, a self aware intelligent machine that at least rivals us, preferably surpassing our intellect, eventually more intelligent than the culmination of all humans that have ever lived… the last invention as it were (Yeah I'm slightly nutts!).

I first started with heuristics/ databases, recurrent neural nets, liquid/ echo state machines, etc but soon realised that each approach I tried only partly solved one aspect of the human intelligence problem… there had to be a better way.

Ants, Slime Mould, Birds, Octopuses, etc all exhibit a certain level of intelligence.  They manage to solve some very complex tasks with seemingly very little processing power. How? There has to be some process/ mechanism or trick that they all have in common across their very different neural structures.  I needed to find the ‘trick’ or the essence of intelligence.  I think I’ve found it.

I also needed a new approach; and decided to literally back engineer the human brain.  If I could figure out how the structure, connectome, neurons, synapse, action potentials etc would ‘have’ to function in order to produce similar results to what we were producing on binary/ digital machines; it would be a start.

I have designed and wrote a 3D CAD suite, on which I can easily build and edit the 3D neural structures I’m testing. My AGI is based on biological systems, the AGI is not running on the digital computers per se (the brain is definitely not digital) it’s running on the emulation/ wetware/ middle ware. The AGI is a closed system; it can only experience its world/ environment through its own senses, stereo cameras, microphones etc.  

I have all the bits figured out and working individually, just started to combine them into a coherent system…  also building a sensory/ motorised torso (In my other spare time lol) for it to reside in, and experience the world as it understands it.

I chose the visual cortex as a starting point, jump in at the deep end and sink or swim. I knew that most of the human cortex comprises of repeated cortical columns, very similar in appearance so if I could figure out the visual cortex I’d have a good starting point for the rest.

The required result and actual mammal visual cortex map.

This is real time development of a mammal like visual cortex map generated from a random neuron sheet using my neuron/ connectome design.

Over the years I have refined my connectome design, I know have one single system that can recognise verbal/ written speech, recognise objects/ faces and learn at extremely accelerated rates (compared to us anyway).

Recognising written words, notice the system can still read the words even when jumbled. This is because its recognising the individual letters as well as the whole word.

Same network recognising objects.

And automatically mapping speech phonemes from the audio data streams, the overlaid colours show areas sensitive to each frequency.

The system is self learning and automatically categorizes data depending on its physical properties.  These are attention columns, naturally forming from the information coming from several other cortex areas; they represent similarity in the data streams.

I’ve done some work on emotions but this is still very much work in progress and extremely unpredictable.

Most of the above vids show small areas of cortex doing specific jobs, this is a view of whole ‘brain’.  This is a ‘young’ starting connectome.  Through experience, neurogenesis and sleep neurons and synapse are added to areas requiring higher densities for better pattern matching, etc.

Resting frontal cortex - The machine is ‘sleeping’ but the high level networks driven by circadian rhythms are generating patterns throughout the whole cortex.  These patterns consist of fragments of knowledge and experiences as remembered by the system through its own senses.  Each pixel = one neuron.

And just for kicks a fly through of a connectome. The editor allows me to move through the system to trace and edit neuron/ synapse properties in real time... and its fun.

Phew! Ok that gives a very rough history of progress. There are a few more vids on my Youtube pages.

Edit: Oh yeah my definition of consciousness.

The beauty is that the emergent connectome defines both the structural hardware and the software.  The brain is more like a clockwork watch or a Babbage engine than a modern computer.  The design of a cog defines its functionality.  Data is not passed around within a watch, there is no software; but complex calculations are still achieved.  Each module does a specific job, and only when working as a whole can the full and correct function be realised. (Clockwork Intelligence: Korrelan 1998)

In my AGI model experiences and knowledge are broken down into their base constituent facets and stored in specific areas of cortex self organised by their properties. As the cortex learns and develops there is usually just one small area of cortex that will respond/ recognise one facet of the current experience frame.  Areas of cortex arise covering complex concepts at various resolutions and eventually all elements of experiences are covered by specific areas, similar to the alphabet encoding all words with just 26 letters.  It’s the recombining of these millions of areas that produce/ recognise an experience or knowledge.

Through experience areas arise that even encode/ include the temporal aspects of an experience, just because a temporal element was present in the experience as well as the order sequence the temporal elements where received in.

Low level low frequency circadian rhythm networks govern the overall activity (top down) like the conductor of an orchestra.  Mid range frequency networks supply attention points/ areas where common parts of patterns clash on the cortex surface. These attention areas are basically the culmination of the system recognising similar temporal sequences in the incoming/ internal data streams or in its frames of ‘thought’, at the simplest level they help guide the overall ‘mental’ pattern (sub conscious); at the highest level they force the machine to focus on a particular salient ‘thought’.

So everything coming into the system is mapped and learned by both the physical and temporal aspects of the experience.  As you can imagine there is no limit to the possible number of combinations that can form from the areas representing learned facets.

I have a schema for prediction in place so the system recognises ‘thought’ frames and then predicts which frame should come next according to what it’s experienced in the past.  

I think consciousness is the overall ‘thought’ pattern phasing from one state of situation awareness to the next, guided by both the overall internal ‘personality’ pattern or ‘state of mind’ and the incoming sensory streams.  

I’ll use this thread to post new videos and progress reports as I slowly bring the system together.  

328 Comments | Started June 18, 2016, 10:11:04 pm


Faster analysis of medical images in Robotics News

Faster analysis of medical images
18 June 2018, 5:00 am

Medical image registration is a common technique that involves overlaying two images, such as magnetic resonance imaging (MRI) scans, to compare and analyze anatomical differences in great detail. If a patient has a brain tumor, for instance, doctors can overlap a brain scan from several months ago onto a more recent scan to analyze small changes in the tumor’s progress.

This process, however, can often take two hours or more, as traditional systems meticulously align each of potentially a million pixels in the combined scans. In a pair of upcoming conference papers, MIT researchers describe a machine-learning algorithm that can register brain scans and other 3-D images more than 1,000 times more quickly using novel learning techniques.

The algorithm works by “learning” while registering thousands of pairs of images. In doing so, it acquires information about how to align images and estimates some optimal alignment parameters. After training, it uses those parameters to map all pixels of one image to another, all at once. This reduces registration time to a minute or two using a normal computer, or less than a second using a GPU with comparable accuracy to state-of-the-art systems.

“The tasks of aligning a brain MRI shouldn’t be that different when you’re aligning one pair of brain MRIs or another,” says co-author on both papers Guha Balakrishnan, a graduate student in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Department of Engineering and Computer Science (EECS). “There is information you should be able to carry over in how you do the alignment. If you’re able to learn something from previous image registration, you can do a new task much faster and with the same accuracy.”

The papers are being presented at the Conference on Computer Vision and Pattern Recognition (CVPR), held this week, and at the Medical Image Computing and Computer Assisted Interventions Conference (MICCAI), held in September. Co-authors are: Adrian Dalca, a postdoc at Massachusetts General Hospital and CSAIL; Amy Zhao, a graduate student in CSAIL; Mert R. Sabuncu, a former CSAIL postdoc and now a professor at Cornell University; and John Guttag, the Dugald C. Jackson Professor in Electrical Engineering at MIT.

Retaining information

MRI scans are basically hundreds of stacked 2-D images that form massive 3-D images, called “volumes,” containing a million or more 3-D pixels, called “voxels.” Therefore, it’s very time-consuming to align all voxels in the first volume with those in the second. Moreover, scans can come from different machines and have different spatial orientations, meaning matching voxels is even more computationally complex.

“You have two different images of two different brains, put them on top of each other, and you start wiggling one until one fits the other. Mathematically, this optimization procedure takes a long time,” says Dalca, senior author on the CVPR paper and lead author on the MICCAI paper.

This process becomes particularly slow when analyzing scans from large populations. Neuroscientists analyzing variations in brain structures across hundreds of patients with a particular disease or condition, for instance, could potentially take hundreds of hours.

That’s because those algorithms have one major flaw: They never learn. After each registration, they dismiss all data pertaining to voxel location. “Essentially, they start from scratch given a new pair of images,” Balakrishnan says. “After 100 registrations, you should have learned something from the alignment. That’s what we leverage.”

The researchers’ algorithm, called “VoxelMorph,” is powered by a convolutional neural network (CNN), a machine-learning approach commonly used for image processing. These networks consist of many nodes that process image and other information across several layers of computation.

In the CVPR paper, the researchers trained their algorithm on 7,000 publicly available MRI brain scans and then tested it on 250 additional scans.

During training, brain scans were fed into the algorithm in pairs. Using a CNN and modified computation layer called a spatial transformer, the method captures similarities of voxels in one MRI scan with voxels in the other scan. In doing so, the algorithm learns information about groups of voxels — such as anatomical shapes common to both scans — which it uses to calculate optimized parameters that can be applied to any scan pair.

When fed two new scans, a simple mathematical “function” uses those optimized parameters to rapidly calculate the exact alignment of every voxel in both scans. In short, the algorithm’s CNN component gains all necessary information during training so that, during each new registration, the entire registration can be executed using one, easily computable function evaluation.

The researchers found their algorithm could accurately register all of their 250 test brain scans — those registered after the training set — within two minutes using a traditional central processing unit, and in under one second using a graphics processing unit.

Importantly, the algorithm is “unsupervised,” meaning it doesn’t require additional information beyond image data. Some registration algorithms incorporate CNN models but require a “ground truth,” meaning another traditional algorithm is first run to compute accurate registrations. The researchers’ algorithm maintains its accuracy without that data.

The MICCAI paper develops a refined VoxelMorph algorithm that “says how sure we are about each registration,” Balakrishnan says. It also guarantees the registration “smoothness,” meaning it doesn’t produce folds, holes, or general distortions in the composite image. The paper presents a mathematical model that validates the algorithm’s accuracy using something called a Dice score, a standard metric to evaluate the accuracy of overlapped images. Across 17 brain regions, the refined VoxelMorph algorithm scored the same accuracy as a commonly used state-of-the-art registration algorithm, while providing runtime and methodological improvements.

Beyond brain scans

The speedy algorithm has a wide range of potential applications in addition to analyzing brain scans, the researchers say. MIT colleagues, for instance, are currently running the algorithm on lung images.

The algorithm could also pave the way for image registration during operations. Various scans of different qualities and speeds are currently used before or during some surgeries. But those images are not registered until after the operation. When resecting a brain tumor, for instance, surgeons sometimes scan a patient’s brain before and after surgery to see if they’ve removed all the tumor. If any bit remains, they’re back in the operating room.

With the new algorithm, Dalca says, surgeons could potentially register scans in near real-time, getting a much clearer picture on their progress. “Today, they can’t really overlap the images during surgery, because it will take two hours, and the surgery is ongoing” he says. “However, if it only takes a second, you can imagine that it could be feasible.”

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 June 19, 2018, 12:00:52 pm


XKCD Comic : Irony Definition in XKCD Comic

Irony Definition
18 June 2018, 5:00 am

Can you stop glaring at me like that? It makes me feel really ironic.

Source: xkcd.com

Started June 19, 2018, 12:00:51 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