To understand “What is Artificial Intelligence (AI)?” we must go through the article below which cites enough examples.
It is safe to say that you are considering Bishop, Terminator, and Robby? Mindful robots are nearer to turning into a reality than you might suspect. Machines that can challenge or surpass human insight is the purpose of artificial Intelligence. Artificial Intelligence can be described as a simulation of human intelligence by machines. A straightforward enough definition, isn’t that so?
Clearly, there is significantly more to it. Artificial intelligence is an expansive point going from easy to self-directing innovation that may totally change what’s to come.
ARTIFICIAL INTELLIGENCE vs. MACHINE LEARNING vs. DEEP LEARNING
AI: The capability of a machine to imitate intelligent human behavior.
ML: Programs that alter themselves.
DL: More accuracy, more math, more compute.
Deep learning is a subset of machine learning, and machine learning is a subset of AI.
Artificial intelligence can be loosely interpreted to mean incorporating human intelligence into machines. Such machines can move and manipulate objects, recognize whether someone has raised the hands, or solve other problems.
Application Example: Amazon Echo
Machine Learning can be loosely interpreted to mean empowering computer systems with the ability to “learn”. The intention of ML is to enable machines to learn by themselves using the provided data and make accurate predictions. It is a method of training algorithms such that they can learn how to make decisions. Training in machine learning entails giving a lot of data to the algorithm and allowing it to learn more about the processed information.
Application Example: GOOGLE
Deep Learning is a technique for realizing machine learning. In other words, Deep Learning is the next evolution of machine learning. DL algorithms are roughly inspired by the information processing patterns found in the human brain.
Application Example: Natural Language Processing
Figure 1: AI, ML, DL hierarchy.
AI IN THE FUTURE BUSINESS
· Machine Learning (ML), globally recognized as a key driver of digital transformation, will be responsible for cumulative investments of $58 billion by the end of 2021.
· The global ML industry, growing at a CAGR (Compound Annual Growth rate) of 42 percent, will be worth almost $9 billion in the latter part of 2022.
· The neural networks market will be worth over $23 billion in 2024. The Deep Learning (DL) applications market in the US alone has been predicted to shoot from $100 million to $935 million in 2025.
Figure 2: AI in the future business.
How Artificial Intelligence is rapidly changing everything around you!
We live in an era which is interesting than never before. It is surprising to know that Apollo 11, the computer that put Man on the Moon in 1969, whose assembly language code operated on 64 KB memory, whereas today’s kids have 64 GB iPhones to click selfies to upload on Instagram and play viral games that marginally break all-time daily active users record in no time. Technically, 1 million times more memory and 100 million times more computational power at your disposal.
Not a long ago, we were struggling to artificially replicate the brain, read intelligence, of an insect and just so overwhelmingly, we are not too far from artificially achieving the intelligence of the most superior species on the planet earth.
The journey so far hasn’t been easy. Starting from the Big bang, to the birth of life on the earth, to the development of human civilizations, to the million science experiments, everything has contributed in making today’s Machine learning with humongous data to take intelligent decisions of its own, to probably build their own society tomorrow.
Figure 3: Growth of AI.
How AI is Everywhere Facilitated by Machine Learning
Right from a cell phone App that recommends you the close by inexpensive shopping outlet you may be keen on, to Facebook’s photograph labeling Algorithm that distinguishes your face with others, to Google’s self-driving vehicle, AI is all over the place and is profoundly installed in our lives without us understanding it.
It is a beautiful fact that the roots of Artificial intelligence of a machine taking decisions live on complicated Machine Learning algorithms that actually start with a few lines of code on your computer. Anyone who has no background of coding or whatsoever, getting in touch with programming and basics of Image processing can land up writing a simple algorithm for predictions. Soon with his/her growing interest, can build deep learning algorithms and could further bring a positive change in the field of Artificial Intelligence.
Figure 4: AI modeling is all about coding.
Machine Learning and the power of Datasets
With huge computational power, machines can process and be trained to make decisions utilizing huge information. Imagine an individual having superpowers who can foresee the eventual fate of everything, the world would fall at his/her feet. In a progressively legitimate sense, if a machine can process the humongous amount of verifiable and continuous information with predictive models and learns after some time to show signs of improvement and gets better and better, imagine the intensity of huge information is nothing less than magical.
A few ventures vastly use Machine Learning to take information-driven choices and make life smarter with the intensity of information aggregated throughout the years. Human services have just been emphatically affected by the gigantic measure of work in this field that has contributed to sparing a huge number of lives. From Governance to Economy to Healthcare, name a field and you have smart models upheld by huge information, performing predictive examination. Super intelligent machines can now decide your fate to survive in a Multi-billion-dollar stock market.
But one thing is for sure, whatever happens, at the heart of it lie several complicated Algorithms that actually started with a few lines of code.
Figure 5: Power of huge datasets in ML.
Each one of us talks about how good is AI, performing frequent, high-volume, computerized tasks reliably and without fatigue. However human tendency is to not focus on the back end process involved in this type of automation. The core process involved in AI-driven systems is the training of the system. The training of the system comes with a requirement of huge datasets, labeling, and manpower. Labeling typically takes a set of unlabeled data and augments each piece of that unlabeled data with meaningful tags that are informative. Performance of an AI product totally depends upon the training which in turn depends upon labeling.
We need to capture datasets as quickly and inexpensively as possible. There are quite a few steps in capturing data, they are:
· Source an existing dataset: Find an existing dataset (through partnering etc) which could be manipulated for your application. In most cases, however, the data simply isn’t available.
· Generate your own dataset: This approach makes the most sense.
This involves the following steps,
1. Acquiring the initial data:
Tons of image and video data from various sources like social media, healthcare, space, agriculture, etc., need to be collected which explicitly requires reasonable manpower. Once you have found a valuable proprietary dataset that you want to capture, we need to go with data analyzing.
2. Data Analysis:
Generally, labels can be obtained by asking humans to make judgments about a given piece of unlabeled data (e.g., “Does this photo contain a horse or a cow?”). It can also be analyzed through existing labeled data sets. Further according to the requirement or a specific application, labels need to be sorted and finalized with its features.
3. Data Labeling:
After the raw data has been captured, it needs to be annotated before it is useful to train a machine learning algorithm.
Labeling is generally accomplished through a few manual, semi-automatic or automatic tools. Labeling of data types such as image, text, video audio datatypes can be accomplished. Developing a data annotation process through the life cycle of a machine learning product vary from collecting initial data, setting up the annotation infrastructure, optimizing performance to running the process in production.
Machine learning models are used to make practical business decisions. The cost of errors can be huge but optimizing model accuracy mitigates that cost. This optimization is possible through precise labeling and this precision is guaranteed through different annotation
shapes. Annotation can be carried out by certain shapes like box, polygon, polyline, points, etc.
Figure 6: Importance of Data Labeling.
Figure 7: An image labeled with box as an annotation shape.
Goals and Applications of AI
· Powering Infrastructure, Solutions and Services
Leveraging AI/ML in security, services, and network infrastructure. An AI platform to build conversational interfaces to power the next generation of chat and voice assistants.
· Cybersecurity Defense
In addition to traditional security measures, AI can be adopted to assist with cybersecurity defense. The AI system constantly analyses the network packets and maps out what is normal traffic. The AI wins over traditional firewall rules, it works automatically without prior signature knowledge to find anomalies.
· Health Care Benefits
It can help doctors with diagnoses and tell when patients are deteriorating so medical intervention can occur sooner before the patient needs hospitalization. It’s a win-win for the healthcare industry, saving costs for both the hospitals and patients. The precision of machine learning can also detect diseases such as cancer sooner, thus saving lives.
· Recruiting Automation
With unemployment at historic lows, the recruitment of qualified workers remains one of the most difficult challenges. By harnessing the power of recruiting automation, savvy employers are using AI-powered sourcing tools to find candidates who may not have been considered for roles in the past, not because they weren’t qualified, but because they weren’t surfaced in the first place.
· Intelligent Conversational Interfaces
Using machine learning and AI to build intelligent conversational chatbots and voice skills. These AI-driven conversational interfaces answers questions from frequently asked questions and answers, helping users with concierge services in hotels, and to provide information about products for shopping.
· Reduced Energy Use And Costs
Use of AI to cut energy use, reduce energy costs for drilling, crude and natural gas transportation, storage, and petroleum refining operations. AI-based applications can learn and predict future energy load at levels as granular as a single blending activity. This opens up an entire range of opportunities to reduce waste, reduce peak demand and cut costs.
· Predicting Vulnerability Exploitation
Using machine learning to predict if a vulnerability in a piece of software will end up being used by attackers. This allows us to stay days or weeks ahead of new attacks. It’s a large scope problem, but by focusing on the simple classification of “will be attacked” or “won’t be attacked,” we’re able to train precise models with high recall.
· Becoming More Customer-Centric
Using AI to better analyze customer responses to surveys and activities over time. This enables us to understand not only the feedback they provide but whether or not
there are specific qualities and attributes that correlate to their response rate and likelihood to engage. This information will allow customers to alter their own client survey strategies.
· Market Prediction
Using AI in a number of traditional places like personalization, intuitive workflows, enhanced searching and product recommendations. Baking AI into the go-to-market operations to be first to the market by predicting the future.
· Accelerated Reading
AI is accelerating the understanding of the written text. Simply put, humans cannot read fast enough, and cannot mentally mine and structure the vast quantity of data that is available. Advance AI reads and understands life science articles, helping researchers to accelerate the discovery of cures for diseases and the development of new treatments and medications.
When we think about agriculture, we tend to think about old-school farming. But although many of us might think that the agricultural community is behind the curve when it comes to implementing new technologies, there is lots of evidence that farmers are actually moving quite quickly to modernize almost everything about the farming process, they’re using artificial intelligence in new and amazing ways to bring the process of food cultivation into the future. All sorts of artificial intelligence work are being done behind the scenes on predictions like where a seed will grow best, what soil conditions are likely to be, etc. The power of artificial intelligence is being applied to agricultural big data in order to make farming much more efficient and that’s only the beginning.