Simple high level discussion on 4 key components AI, ML, ANN and DL. Sometimes its ok and good for everyone to un-develop something existing to uncover the hidden gems which are already there and are useful. May be its like Un-Develop to Innovate? Alan Turing published “Turing Test” that speculates the possibility of creating machines that think. In order to pass the test, a computer must be able to carry on a conversation that was indistinctive from a conversation with a human being. AI apart from its traditional definition also includes things like planning, understanding language, recognizing objects and sounds, learning, and problem solving.
Confusing jargons like AI, Machine Learning, Data Science, Deep Learning and Neural Networks with no scientific reasoning and definitions can create huge understanding issues and usability. “Artificial neural systems, or neural networks, are physical cellular systems which can acquire, store, and utilize experiential knowledge.” – Zurada (1992). Like this each on has their own meaning and use cases so be careful when to use what for what reasons.
We have excellent young professionals working in today’s time with dream of changing the world by brining the values to society, business and people. During my time (around 19 years back) we had no such option to learn or to choose popular professions. It was mainly R&D for anything unusual and use regression techniques if anyone wishing to be a data scientist or simply an engineer in computer software or hardwares. Blockchain is a new approach to manage/monitor financial and other transactions. Time, and the right learning algorithms made all the difference. In below examples let me succinctly demystify for understanding the basic concept of artificial intelligence better.
- A simple mathematical calculation or product of two given numbers; any computer can beat any human in terms of speed and accuracy.
- On the other hand to identify whether an image has a dog or a cat most of the humans (even a four year old kid) will easily outperform computers.
Scenarios like example 2 as above where artificial intelligence has to play critical role to show its power. To simulate the mapping of inputs to outputs as it happens in a human brain which makes very difficult tasks for computers like image recognition, sarcasm detection, voice recognition, etc.
Artificial Intelligence— Human intelligence exhibited by machines. AI is today’s increasingly used as a wide term to describe machines that can mimic human functions such as learning and problem solving, but was originally founded on the premise that human intelligence can be precisely described, and machines made to simulate it. Speech processing which is basically time series analysis using RNN/LSTM deep neural nets also has to be trained on known ‘good speech’. Technology and non technology companies are now investing and brining out the real and materialistic values of Artificial Intelligence to the real world.
Artificial intelligence is now considered as a new factor of production and has the conceivable potential to introduce new sources of growth, reinventing the business in currently in place, changing how work is done and reinforcing the role of people to drive growth in business. There is strong perception but sadly its wrong (in my personal opinion) that Deep Learning is unsupervised. This came out of earlier projects where image gets recognised as cat or not cat which required millions of images of cat and not cat but almost all of them were labeled.
AI for real life not just phd or scholar books material this was the first serious proposal in the philosophy of artificial intelligence, which can be explained as: a science developing technology to mimic humans to respond in a circumstance. In simple words AI involves machines that behave and think like humans i.e Algorithmic Thinking in general. Computers start simulating the brain’s sensation, action, interaction, perception and cognition abilities. Strategy for chatbots is simple but tricky by training RNN for contents with simple conversational english datasets of audio and video.
AI has started delivering values. Using the contemporary view of computing exemplified by recent models and results from non-uniform complexity theory has proven the fact. Are we ready to relinquish control to autonomous cars, software bots, and AI-based recommendation engines? Investment in artificial intelligence is growing fast. Tech giants like Google, Microsoft, Apple and Baidu known for their dominance in digital technologies globally are spending couple of tens of billions united state dollars on AI with 90 percent of this spent on R&D and deployment, and 10 percent on AI acquisitions.
Machine Intelligence or Machine Learning— An approach to achieve artificial intelligence. Machine Learning as a subset of AI was carrying neural networks as an important ingredient for some time, but only recently became the focus of AI and deep learning. What is all the fuss about machine learning any ways! Can machines be creative? Can machines empathise?
What machines can do and how creative they can be; I guess that we will see in another upcomig post “Machine Learning Evolution followed by Machine Learning Transformation”. Machine Learning is (mostly) a mathematics specific AI technique for classification, regression and clustering. Machine learning is responsible for assessing the impact of data. In machine learning algorithms are used for gaining knowledge from data sets. It completely focus on algorithms.
Machine learning is where the traditional Statistical modeling of data meets the algorithmic and computational field of data science. It focuses primarily on the development of several computer programs that can transform if and when exposed to newer sets of data. Machine learning and data mining follow the relatively same process. Algorithms are built through which input is received and after statistical analysis output value is predicted. There are three general classes of machine learning —
- Supervised machine learning
- Unsupervised machine learning
- Reinforcement learning
Artificial Neural Networks— Inspired from biological neuronal structure. The transmission of a signal from one neuron to another through synapses is a complex chemical process in which specific transmitter substances are released from the sending side of the junction. The effect is to raise or lower the electrical potential inside the body of the receiving cell. If this graded potential reaches a threshold, the neuron fires. It is this characteristic that the artificial neuron model attempt to reproduce.
The biological neuron model is widely used in artificial neural networks with some minor modifications on it. The artificial neural network we train for the prediction of image and stock data has an arbitrary number of hidden layers, and arbitrary number of hidden nodes in each layer, both of which the user decides during run-time. Neural networks find great application in data mining used in sectors. For example economics, forensics, etc and for pattern recognition.
A neural network may contain the following 3 layers:
- Input layer – The activity of the input units represents the raw information that can feed into the network.
- Hidden layer – To determine the activity of each hidden unit. The activities of the input units and the weights on the connections between the input and the hidden units. There can be several hidden layers.
- Output layer – The behavior of the output units depends on the activity of the hidden units and the weights between the hidden and output units.
Deep Learning— A technique for implementing machine learning. At the same time I also claim It is absolutely wrong to call Deep Learning as Machine Learning (in my personal opinion) as a techniques to achieve a goal not necessarily come out of same goal. Deep learning’s main driver are Artificial neural networks system or neural networks or neural nets; are the main vehicle for deep learning. Some specialized versions, such as convolutional neural networks and recurrent neural networks, which address special problem domains are also provide huge. Two of the best use cases for Deep Learning which are unique as well are image processing and text/speech processing that are based on methodologies like Deep Neural Nets.
In practice Deep Learning methods, specifically Recurrent Neural Networks (RNN) models are used for complex predictive analytics like share price forecasting and it consist of several stages. DL also includes decision tree learning, inductive logic programming, clustering, reinforcement learning, and Bayesian networks, among others.
Deep learning is the first class of algorithms that is scalable. Performance just keeps getting better as we feed the algorithms more data. Speach/Text and image processing can make perfect robot to start with and actions based on triggers makes it the best. It has to pass basic 4 tests. Turning test i.e needs to acquire college degree, needs to work as an employee for atleast 20 years and perform well to get promotions and attain ASI status.
Data Science— Data science succinctly communicate the value of analysis & benefits in real life to stakeholders. Data science is the smartest BI or most intelligent BI i.e IBI to do laborious task of ‘solving intelligence’ with a range of tools and specialty areas. Fundamentally solving intelligence means the artificial seeking and production of knowledge that answers questions whether they were asked or not, known or unknown. Successful data science requires more than just programming and coding. However, in data mining algorithms are only combined that too as the part of a process and entirely focuses on.
Mixing Up All— On the other hand i.e. thinking with machines or intelligence augmentation is serious evolutionary epistemology, and semiotic. And it has extreme potentials to take business intelligence to competitive intelligence that can infer competitive measures using augmented site-centric data. One of the best strength of DeepLearning does not require predefined features to find peculiar patterns that humans will always struggle or probably would never be able to define before hand. . Robots or AI enabled AI taking our job is still far not in next 50 years or so.
Conclusion – Artificial intelligence is a broad and active area of research, but it’s no longer the sole province of academics; increasingly, companies are incorporating AI into their products. For now AI is controlled by humans and I wish in long term it should remain the same i.e. should never starts or think to control us or should not turns out uncontrollable. Baidu’s speech-to-text services are outperforming humans in similar tasks. Amazon is also applying deep learning for best-in-class product recommendations. Robots can fail; as reason is very simple and known which is machine learning models are not sufficiently accurate or can’t be accurate without lots of data and lots of training. Artificial Intelligence with cloud computing add advancements using new use cases to improvise the systems developed so far.
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