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Artificial intelligence for anti-aging research

artificial intelligence aging data base

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#1 Engadin

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Posted 04 March 2019 - 03:32 PM


Artificial intelligence (IA): a fascinating (re)discovery

The principle of what we call AI is not actually new. It was Alan Turing, who in 1936, stated that everything that is calculable is calculable by a logical machine[2]. This is the common foundation of all current informatics.
Later, in 1943, McCulloch and Pitts described the working of neurons via electrical circuits. This is where the idea of “neural networks” takes shape. The perceptron was born in 1957 and is the first system that could be described as artificial intelligence, it could learn from experience. This computational pathway has been neglected for a long time due to technical limitations and skepticism.
Today, we use AI in almost all areas that require a statistical approach. Big Data, i. e. large amounts of data, constitutes the pantry of AIs.

 

In short, an AI is a statistical analysis tool that works via neural networks among other techniques. Neural networks gather data from a variety of sources. They apply modifications to them, before outputting a result or transferring it to the next layer of neurons when it is a multi-layer network.
Talking about AI “in general” doesn’t necessarily make sense. An AI is in fact an algorithm, and its capabilities will depend entirely on the programming of the algorithm. There are many methods depending on what you are trying to achieve[3]. First, “deep learning” is part of automatic learning that implies large data quantities and treatment capabilities. Then, once the information has been assimilated, we can influence the type of neural networks programmed at will and thus change the processing of this information.

There are supervised learning neural networks, and unsupervised learning networks.
The big difference is that the unsupervised network (reinforcement learning) does not need any indication from the programmer. The AI will try to extract a “meaning” from the data (which may have none).

 

 

A huge database would train artificial intelligence to fight aging

We know that the amount of data generated by the Internet is huge. The Google search engine processes more than 40,000 searches per second, every minute about 4,150,000 videos are viewed on YouTube, Instagram users post 46,740 photos per minute: all this to tell you that we have no lack of data. They must also have an interest.
These examples underline a crucial truth: more and more people are using IT tools. As a result, more and more data is being generated and stored.
This also applies to hospitals and health centres, which are finally becoming more computerized.

Approaches such as microfluidics in biology can also be very effective in producing biological data. This technology allows the study of cell response on a microscopic scale on tiny replicas of our tissues, biotechnology devices called organs-on-chip. Organs-on-chip are a good way to reduce the range of possibilities offered by artificial intelligence in order to take over after data analysis. They make it possible to test a proposed molecule in vitro quickly and efficiently, without the need for animal testing. The results of the data analyses are thus tested on the organs-on-chip, and at the end of this step, only a quantity of information remains that the human brain is now able to process, with the certainty that they are relevant, because how could we understand the volumes of data on which we feed AIs?

These masses of data make no sense as they are, we are not able to comprehend such large amounts. Like the list of biological interactions and mechanisms that make our body work: we cannot hope to predict anything by looking at this gigantic amount of information and interactions. The extreme complexity of the workings of living organisms requires a very large amount of data generated during analyses. Not to mention the data we don’t even have….
Managing and organizing data for future use is a major challenge for Big Data and its integration into AI analysis[4]. Could artificial intelligence help us to solve these challenges?

 

 

The need for artificial intelligence to decode aging

In the field of aging and medicine, the potential of artificial intelligence is very interesting. It allows us to make connections that we would not otherwise have seen, to identify invisible trends, to propose models, generalizations, determine causalities… And all this can be extremely beneficial for preventive and regenerative medicines[1, 6].

As we have mentioned, the data we produce can no longer be processed by our primate brains. Our brains are obviously conditioned by our evolutionary history and are not capable of everything. To analyze a phenomenon as complex and dependent on so many factors as aging, we need help to process the resulting amounts of data.
We have the statistics to help us extract meaning from a lot of information. Neural Networks, originating from Artificial Intelligence research are one of these statistical tools, and it is the most efficient to date: we need the computational and synthesis capabilities of these algorithms. In addition, they sometimes reveal human cognitive biases.

This is the case, for example, of Word2Vec: an AI from Google whose objective is to recognize and determine the meaning of words, semantics. For W2Vec, “Doctor” + “Woman” = “Nurse”.  This result may seem unfair, even sexist, but artificial intelligence does not make morals, it makes statistics. What the algorithm reveals is not its bias, but our own bias that is reflected in the statistical sample.

AIs may therefore represent our best chance at this time to make sense of the data collected. This is the challenge undertaken by BioViva, a company founded by Elizabeth Parrish, which specializes in the analysis of medical data and the development of custom treatments. Since 2015, data management has evolved enormously and bioinformatics tools allow BioViva and its doctors to offer treatments adapted to each patient, with personalized medicine as an ultimate goal. The aim is to provide fast access to effective treatments, especially in the field of gene therapy against aging.

 

Continue here: http://www.longlongl...aging-research/

 

 


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#2 Dream Big

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Posted 22 March 2019 - 09:24 PM

I agree. This is what Longecity needs to focus on 80x more. Give it a huge box of data and out come desired output. It will have updating goals. AGI will duplicate everywhere and so much thinking/testing (R&D) will get done so fast using ALL data possible. Things in digital tech grow fast, and this one is so the most. Tons of sensors/types, fast motors, body types, wireless cooperation, big memory, simulations, list goes on for human-level digital-AI. This is the fastest way, it's a trillion AGI-times more effective! You see? You just need persistence. AGI is just around the corner in the Machine Learning field - AGI is an efficient simple algorithm in the end -- it clusters (minimizes energy) relations in big data and knows what's what. I'm researching/working on true AGI.

 

I agree here, you read my mind recently!:

For W2Vec, “Doctor” + “Woman” = “Nurse”.  This result may seem unfair, even sexist, but artificial intelligence does not make morals, it makes statistics. What the algorithm reveals is not its bias, but our own bias that is reflected in the statistical sample.

Yes, it tells the truth, it is our bias, and therefore its too - it believes it. These biases are real - woman are nurses. I thought so too. I also know things can change, anything is possible in the universe/VR. Turtles can be talking doctors.


Edited by Dream Big, 22 March 2019 - 09:36 PM.


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#3 Mind

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Posted 23 March 2019 - 10:53 AM

AI should be a tremendous tool going forward, however, much caution must be exercised in the beginning because most medical research is false or can't be reproduced. If AI is going to reference medical literature, that data needs to be heavily curated.


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#4 Zed

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Posted 24 March 2019 - 10:28 AM

I know Kaggle has had numerous ML comps around using ML for medical research ( like  X-ray classification of tumors or vision learning on cells etc) .

Does anyone know of other references of Big Data/ AI used in specific diseases?  Even just in downloading pubmed articles and text analysis / ML through them for patterns etc ?



#5 Dream Big

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Posted 24 March 2019 - 10:33 AM

Well researched Mind.

 

What you put in is what you get out, in terms of data and goals. The Filter IS the Goals: When a child is Taught/Told [enough] [true] facts, it can Search the web/brain for what is Desired and Verify it seems correct when given a fact that may actually be trash, or is said to be good but is really is trash. While half the data is really far off when recorded, you can still find related data if they both have enough similar contexts.


Edited by Dream Big, 24 March 2019 - 11:16 AM.


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#6 Dream Big

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Posted 24 March 2019 - 11:19 AM

Zed,

 

This doesn't just work for diseases, but everything....fixing cars, improving hard drives, fashion design.....all you need is a solution discovery engine based on text. A text AGI. I'm researching/starting implementation on this already.


Edited by Dream Big, 24 March 2019 - 11:21 AM.






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