Getting Started with the future of everything! — A Deep dive on ‘AI’

What is AI?

AI (Artificial intelligence) is an area of computer science that targets to create smart, automatic and intelligent machines. A more nuanced definition is that AI is an interdisciplinary concept that studies the possibility of creating machines capable of interacting with their environment and acting upon the received data in a manner considered as intelligent devices. There are certain things a machine/computer program must be capable of to be considered as AI. First, it should be able to mimic the human thought process and behavior. Second, it should act in a human-like way — intelligent, rational, social, economic and ethical. AI-based devices and approaches are specifically designed to solve the highly sophisticated computational, statistical, economic, social, mathematical, engineering, psychological, linguistics, neuroscience, ethical, critical, functional, mechanical, programmatical, logical and philosophical problems which include:

  • Speech recognition
  • Learning
  • Planning
  • Problem-solving
  • Knowledge
  • Reasoning
  • Perception
  • Analysis
  • Thinking
  • Feeling

AI often revolves around the use of algorithms. An algorithm is a set of unambiguous instructions that a computer or any other smart device can execute. A complex algorithm is often built on top of other, simpler, algorithms to solve super-sophisticated problems. These are math formulas and/or programming commands that inform a computer on how to solve problems with artificial intelligence. Algorithms are rules or set of instructions that teach computers how to figure things out on their own.

Many AI algorithms are capable of learning from data and analysis, they can also enhance and enrich themselves by understanding the heuristics and writing the algorithms. If AI was given with infinite data, time, and memory to learn any function, mathematical calculations and other processes, then it can outreach for newer possibilities. So, the AI derive all possible knowledge, by considering hypothesis, strategies, principles and matching it against the data. In practice, it is almost never possible to consider every possibility, because of the phenomenon of “combinatorial explosion”, where the amount of time needed to solve a problem grows exponentially. Oodles of the AI research involves in determining how to discover and avoid eclectic paths of probabilities that are unlikely to be worthwhile.

Computer-enhanced artificial intelligence (AI) has been around since the 1950s, but recent hardware innovations have reinvigorated the field. New sensors help machines have more accurate sight, hear sounds, and understand the location. Powerful processors can help computers make complex decisions, sort through possibilities, plan outcomes, and learn from mistakes. The possibilities are thrilling; the implications are vast.

AI is progressing rapidly. AI research also overlaps with tasks such as robotics, control systems, sensing, scheduling, processing, fingerprint, scanning, analysis, data mining, logistics, speech recognition, facial recognition, voice recognition, iris recognition, and many others. Computational intelligence involves iterative development or learning based on experiential data and is associated with non-symbolic AI, scruffy AI, and soft computing.

Basic Terms in AI:

  • Analytical AI: It deals with cognitive intelligence generating a cognitive representation of the world and using learning-based strategy on past experiences to predict future decisions
  • Human-inspired AI: It mainly focuses on cognitive, emotional intelligence, understanding, and considering them in the right decision making
  • Humanized AI: It accords to all types of core abilities of AI and it is aware of everything when interacting with others
  • Weak AI or Narrow AI: It is mainly focused on one narrow task, the phenomenon that machines which are not too intelligent to do their own work can be built in such a way that they seem smart. Here each and every possible scenario need to be entered beforehand manually. Each and every weak AI will contribute to the building of strong AI
  • Strong AI: The machines that can actually think and perform tasks on its own just like a human being. There are no proper existing examples for this but the development of it is getting close to developing one in a rapid way
  • Autonomous: This area of AI has gathered a lot of attention. The list of vehicles includes cars, buses, trucks, trains, ships, submarines, and autopilot flying drones etc. Autonomous means that an AI doesn’t require help from humans. Driverless or Self-driving cars are the perfect illustrations for the term ‘Autonomous’. It doesn’t need a human inside to operate. Anything beyond that would be called sentient
  • ML (Machine Learning): The term ML is typically acceptable to substitute the terms artificial intelligence for one another. They aren’t quite the same, however, but connected and related in various means. It’s the process by which an AI uses algorithms to perform functions. It’s the result of applying rules to create outcomes through an AI
  • Black Box: An AI does a lot of complex math when rules are applied to it. This math, often, can’t even be understood by humans, yet the system provides useful information. When this happens, it’s called black box learning
  • Neural Network: We create a neural network or probably ANN (Artificial Neural Network) when we want an AI to get better at something. These networks are designed to be very similar to the human nervous system and brain. It trains and learns in a step-wise approach to give AI the ability to solve complex problems by breaking them down into levels of data. The first level of the network check for similarities in other files. Once the initial stage is done, the neural network will pass its findings to the next level which will try to understand a few more files, and perhaps some metadata. This process continues at every level of a neural network
  • Reactive Machines: This is one of the basic forms of AI. It doesn’t have past memory and cannot use past information for future actions
  • Limited Memory: AI systems can use past experiences to inform future decisions. Some of the decision-making functions in self-driving cars have been designed this way
  • Theory of Mind: This type of AI should be able to understand people’s emotion, belief, thoughts, expectations, trust, hope, faith, love, hatred, peace and be able to interact socially. Even though a lot of improvements are there in this field, this kind of AI is not complete yet
  • Self-awareness: An AI that has its own conscious, super intelligent, self-awareness and sentient. In simple words a complete human being. Of course, this kind of bot also doesn’t exist and if achieved it will be one of the milestones in the field of AI
  • DL (Deep Learning): It happens when a neural network gets to work. As the layers process data, the AI gains a basic understanding. You might be teaching your AI to understand maths, but once it learns what the math is, then AI can apply that knowledge to a different task to perform. It means that instead of understanding what something is, the AI begins to learn ‘why’
  • NLP (Natural Language Processing): It takes an advanced neural network to parse human language. When an AI is trained to interpret human communication is called NLP. This is useful for chatbots and translation services, but it’s also represented at the cutting-edge technological AI assistants like Google Assistant, Bixby, Cortana, Alexa and Siri
  • RL (Reinforcement Learning): One method of teaching a machine, just like a person, is to use reinforcement learning. This involves giving the AI a goal that isn’t defined with a specific metric, such as telling it to “improve efficiency” or “find solutions.” Instead of finding one specific answer the AI will run scenarios and report results, which are then evaluated by humans and determined. The AI takes the feedback and adjusts the next scenario to achieve better results in an iterative way
  • SL (Supervised Learning): This is a very complex way of proving things. When you train an AI model using an SL method you provide the machine with the correct answer ahead of time. Basically, the AI knows the answer and it knows the question. This is the most common method of training because it yields the most data. With this, it defines and draws the patterns between a question and an answer. We can get to know why something happens, or how something happens, an AI can look at the data and determine connections using this method
  • USL (Unsupervised learning): In many ways, the uncanny and spookiest part of AI research is realizing that the machines are really capable of learning, and they’re using layers upon layers of data and processing capability to do so. With USL we don’t give the AI an answer. Rather than finding patterns that are predefined, we simply feed a machine or smart device a bunch of data so that it can find whatever patterns it is able to
  • TL (Transfer learning): Another weirdest way machines can learn is through TL. Once an AI has successfully learned something, like how to determine if an image is a human or not, it can continue to build on its knowledge even if you aren’t asking it to learn anything about humans. You could take an AI that can determine if an image is a human with 90-percent accuracy, hypothetically, and after it spent a week training on identifying patterns it could then return to its work on humans with a noticeable improvement in accuracy
  • Vision: It can be said as a field which enables the machines to see. Machine vision captures and analyses visual information using a camera, analog-to-digital conversion, and DSP (Digital Signal Processing). It can be compared to human eyesight but it is not bound by the human limitation which can enable it to see through walls. It is usually achieved through ML to get the best possible results so we could say that these two fields are interlinked
  • Robotics: It is a field of engineering focused on the design and manufacturing of robots. Robots are often used to perform tasks that are difficult for humans to perform consistently. Examples include car assembly lines, in hospitals, office cleaner, serving foods, and preparing foods in hotels, patrolling farm areas and even as police officers. Recently ML has been used to achieve certain good results in building robots that interact socially, for example, Sophia
  • CNN (Convolutional Neural Networks): CNN’s, like neural networks, are made up of neurons with learnable weights and biases. Each neuron receives several inputs, takes a weighted sum over them, pass it through an activation function and responds with an output. The whole network has a loss function. CNN’s use a variation of multilayer perceptron’s designed to require minimal pre-processing. They are also known as shift invariant or space invariant artificial neural networks (SIANN), based on their shared-weights architecture and translation invariance characteristics
  • Capsule Networks: It would outpace CNN’s on multiple fronts. CNN’s have certain limitations that lead to a lack of performance or gaps in security. Capsule Networks would allow AI’s to identify general patterns with fewer data and be less susceptible to false results. Capsule Networks would take relative positions and orientation of an object into consideration without needing to be trained exhaustively on variations
  • AIOps: It refers to multi-layered technology platforms that automate and enhance IT operations by using analytics and machine learning to analyze huge data collected from various IT operations tools and devices, in order to automatically spot and react to issues in real time

Future of AI:

By 2040 or 2050, AI will likely be everywhere and embedded in everything. AI unlocks better possibilities and practices to cope up with humans and solve the problems with ease and efficiency. The big question is how humans manage, control and maximize their power. The decisions people make today will have a profound impact on the way our companies, governments, societies, organizations, and institutions operate in the future. The future may be uncertain and cannot be inevitable. But the approaches what we make, implement and practice today should be for a better, positive and effective way to help for the future needs but not at the cost of human lives.

Applications of AI:

  • Drones & Self-driving Cars
  • Medical diagnosis
  • Search engines (such as Google search) & Online assistants (such as Siri, Alexa, Google Assistant)
  • Targeting online advertisements
  • Finance and Insurance
  • Gaming, Military & Auditing
  • Computer Vision & Cybersecurity
  • Emotion Detection, Predictions, and Patterns in various fields

Developer Zone: Take-A-Ways! — Best Sources & Software’s to look at!

Here’s the list:

Conclusion:

Creating AI is one of the most remarkable events for humankind. After all, AI is considered a major component of the 4th Industrial Revolution, and its potential on socio-economic impact is believed to be huge. The AI industry is on the verge of tech-driven shift and stands out as a transformational technology of our digital age — and its pragmatical application throughout the economy is growing expeditiously.

Let’s view AI as an ecosystem that unlocks new values by enhancing, accelerating, and automating decisions that drive growth and profitability.

“AI shouldn’t stop at reaching the human level it must go beyond the capabilities of humans for it to reach its peak potential” — Unknown