<\/span><\/h2>\nWhile browsing the internet, we knowingly or unknowingly come across AI\/ML-based algorithmic bots that try to collect our data or manipulate our user-interface decisions as per the objectives defined for them. They frequently track our usage stats, browsing flow, & even local storage to comprehend our interests, our mood, preferences, needs, personal issues, or even paying potential.<\/p>\n
The close enough examples could be Google Ads, Facebook suggestions, LinkedIn Ad campaigns, YouTube video recommendations or even the Amazon\/ Flipkart product advertisements<\/strong> that we must have viewed on web-pages that are nowhere related to these websites.<\/p>\nFor example, while booking airline tickets, we must have seen \u2018dynamic price updations\u2019 that may offer different prices to different sets of users at the same moment.<\/p>\n
Apart from our usage statistics, all these scenarios depend upon the \u2018digital-profiling\u2019 done by these bots on us & how much \u2018exploitable-potential\u2019 a profile has.<\/p>\n
The stock market trading<\/strong> on International stock exchanges, big financial transactions within multinational banks or even intelligence sharing among governments, all of them require hefty investments in the futuristic technologies like AI & ML for filtering out \u2018fraudulent\u2019 & \u2018suspicious\u2019 transactions among a dynamic list of gazillion financial transactions that could update within a fraction of a second.<\/p>\n<\/span>Process of training intelligent machines (AI\/ML) bots<\/u><\/strong><\/span><\/h2>\nIn all the above examples, massive chunks of \u2018user data sets\u2019 & ‘financial data sets’ are exposed to these self-learning bots, which in turn are helping these bots to enhance their quality of future predictions based on the patterns in existing data.<\/p>\n
Lately, humans were creating abstract algorithms with limitations on what could be achieved.<\/p>\n
The initial bots were also trained using these abstract man-made instructions:<\/p>\n
E.g. \u201cIF<\/strong> <this> \u00e0 THEN <\/strong><that>\u201d<\/p>\nTo address the complexities that arose while solving large & complicated scenarios, this manual training process fell short in potential.<\/p>\n
But with the advent of AI & ML, the complete cyberspace has taken a giant leap forward and the process of training these Algorithmic bots has been automated.<\/p>\n
They don\u2019t rely on humans to supervise or train them. Once their artificial neural structure (ANN) achieves a certain level of maturity, they start learning on their own when exposed to different training sets\/ data sets. Meanwhile, the effort that goes into training a bot in its nascent stage can\u2019t just be denied.<\/p>\n
Unfortunately, these pieces of training are \u2018highly guarded trade secrets\u2019. But here we will try to make sense of these pieces of training, using a case study of our own.<\/p>\n
<\/span>Structure of Artificial Neural Network (ANN)<\/u><\/strong><\/span><\/h2>\nLet\u2019s first get a birds-eye view of the input, processing, & output layers within an Artificial Neural Network that could also be considered a prototype to the biological human brain.<\/p>\n
<\/p>\n
<\/p>\n
<\/p>\n
While academia is well aware of the working of an artificial\/ biological neuron and how a cluster of neurons communicate with each other is vaguely grasped, detailing out the working of \u2018complete package of neural wirings\u2019 & \u2018how a certain level of intelligence\u2019 is achieved is still beyond scientific comprehension.<\/p>\n
<\/span>\u201cImage-recognition\u201d- A case study in AI\/ML bots training<\/u><\/strong><\/span><\/h2>\nIt is easier for humans (even children) to differentiate b\/w two different objects by looking at their respective instances, e.g. say, the deformed images a dog & a bear.<\/p>\n
This happens because wirings in our brain help us \u2018relate\u2019 the current visuals with memories of similar visuals & do the \u2018memory-based pattern-recognition\u2019.<\/p>\n
We as humans have different shades of thought processes. Apart from memory-based learning we could relate, infer, assume, imagine, introspect, & innovate solutions that have never even existed before. Such cases are never perceived with machines\/bots.<\/p>\n
Their conditional programming makes them think linearly.<\/p>\n
Therefore, in this scenario, it\u2019s almost impossible for a bot to differentiate a dog & a bear by looking at their deformed images without incorporating AI\/ML features into that bot.<\/p>\n
This could only be achieved by incorporating ANN to bot\u2019s programmable sets, creating infinite copies of that bot, & grilling all those bots through vast, rigorous, diverse, & indefinite \u2018user-data sets\/ training-sets\u2019(e.g. Internet) that would allow them to make mistakes & re-wire their ANN.<\/p>\n
In nascent stages, the tweaks made in their ANN would be manual & hence, more or less random. One can\u2019t be sure if the individual modifications would result in an improved \u2018image recognition\u2019 skill or not.<\/p>\n
Hence, an iterative \u2018test-err-build-test\u2019 cycle would be repeated indefinitely till a desired level of performance is attained by any of the bots. Let\u2019s look into this training scenario.<\/p>\n
<\/span>\u201cTest-Err-Build-Test-Repeat\u201d: A intelligent machines training scenario for nascent bots<\/u><\/strong><\/span><\/h2>\nAs we know that humans are unable to train a complex scenario to these bots. We would create two bots (say, a teacher bot & a builder bot) that would train a bunch of other bots (say, student bots). The ANN of these bots would be simpler and could easily be programmed by a human programmer.<\/p>\n
At first, the wirings in student bot\u2019s brains would be done at random & they are exposed to \u2018training sets\u2019 by Teacher-bot. The \u2018training sets\/ tests\u2019 & \u2018answer keys\u2019 to each set is provided by the human overseer. The teacher bot \u2018evaluates\u2019 the tests under a controlled setup & maps the respective performances to each of the student bots.<\/p>\n
These ML bots are then transferred to Builder-bot that sort out the best performers, creates multiple copies of them & does some non-calculated tweaks in their neural wiring intending to improve performance.<\/p>\n
The bots are again transferred to Teacher-bot\u2019s end that tests them with even harder training-sets. This \u2018test-err-build-test\u2019<\/strong> cycle repeats indefinitely until a desired level of performance is achieved.<\/p>\nThe success of this process<\/u><\/strong> also banks-on \u2018discarding the under-performing bots\u2019 and \u2018re-wiring the top performers\u2019 in each subsequent cycle. Furthermore, the human overseer is expected to keep the \u2018bot\u2019s-count\u2019 & \u2018training-sets\/ user-data-sets\u2019 as magnanimous as possible.<\/p>\n The initial performers would be lucky but with passing iterations & gradually increasing the complexity of ANN-wirings, the bots would incorporate \u2018data-pattern-recognition\u2019, \u2018object-differentiation\u2019, \u2018logical reasoning\u2019 & \u2018calculated-decision-making\u2019 skills depending on the nature of training sets they are exposed to.<\/p>\n
E.g. In our case study, a bot is supposed to emerge who could successfully differentiate a dog & a bear even by looking at their deformed instances.<\/p>\n
A real-life example similar to our case study is Google\u2019s \u2018image search algorithm\u2019.<\/p>\n
<\/span>Intelligent Machines Training scenario for Mature enough ML bots<\/u><\/strong><\/span><\/h2>\nWhen these bots cross their nascent stage, they acquire an ability to learn on their own (similar to humans). Hence, from thereon, it becomes easier to deploy them over the internet where they would get continuous exposure to a large chunk of \u2018user-data\u2019\/ \u2018training sets\u2019 and without the help of a human overseer or evaluator, they would self-assess & improve.<\/p>\n
E.g. you must have come across \u2018image-based captchas<\/strong> asking to identify a particular object (say, statues) among a bunch of other images before allowing you to access specific web content.<\/p>\nThese captchas are covertly acting as training sets & helping these bots develop their object differentiation & image recognition skills.<\/p>\n
Other such examples include \u201cAre you human?<\/strong>\u201d or \u201cConfirm you are not a robot<\/strong>\u201d kinds of tests that are helping such bots learn how to read & count.<\/p>\n <\/p>\n
Tests on the social media sites like \u201cpick the photos<\/strong> of your friends that are most likable, or choose the articles that have been shared the most<\/strong>\u201d, all have this covert objective that goes behind the scenes.<\/p>\nThe more such user-data these bots would be exposed to, the better their humanistic-skills would shape up.<\/p>\n
This is the reason why big corporates are obsessed with collecting user-data these days.<\/p>\n
<\/span>The dark side of intelligent training machines: Data Privacy issue<\/u><\/strong><\/span><\/h2>\nThere is also a contrasting side of all we have gone through.<\/p>\n
Countries nowadays are becoming more & more overprotective about their citizens\u2019\/user\u2019s data privacy, the reason being the malpractices that go into training these bots to manipulate user\u2019s interaction on the internet in such a way that they can\u2019t be termed legitimate.<\/p>\n
For instance, Cambridge Analytica\u2019s case in which Facebook & Twitter were tracking their user\u2019s tweets, posts, & activities without even their consent, which was later found to be used in manipulating the overall mood of people before the USA\u2019s presidential elections of 2015.<\/p>\n
<\/span>Conclusion<\/u><\/strong><\/span><\/h2>\nAI\/ML is the most fascinating field for the entire academia. Not just because a slight glimpse of it is showcasing immense possibilities for the future, but also because it\u2019s a quest to create \u2018ourselves\u2019. To create our own-selves, it is pre-requisite to \u2018understand\u2019 our own-selves & this is where the trick lies.<\/p>\n
Concluding this blog with a simple question,<\/p>\n
\u201cIf AI is a quest to create consciousness, how come an unconscious being is going to create a conscious machine?\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"
We all must have heard about smart devices (e.g. smartphones) & intelligent machines or virtual assistants like Siri, Google Assistant, Cortana, Alexa, & AI-driven support bots on the internet. How come these simple abstract bunches of code are becoming so-called \u2018intelligent\u2019 or \u2018smart\u2019? The simplest of the answers could be\u2014 \u201cBy allowing them to make […]<\/p>\n","protected":false},"author":2,"featured_media":10119,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[18,487,16],"tags":[65,66,33,67],"acf":[],"yoast_head":"\n
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