Artificial Intelligence (AI) Trends to Watch in 2022

Mesh of Artificial Intelligence

Mesh of Artificial Intelligence

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Mesh of Artificial Intelligence AI basic ||  Job Prospective ||your work ||Research area || Threws role

In Mesh of  Artificial Intelligence  I tried to answer several Unanswerd questions in One blog which is very useful for  all of us to know . hope  you like this

What is Artificial Intelligence?

What do you have to do?

What are the research areas?

Our role in  Your success?

What are the job prospects and Market in AI?

 

What is Artificial Intelligence?

Mesh of Artificial Intelligence

The term artificial intelligence was coined in 1956, but AI has become more popular today thanks to increased data volumes, advanced algorithms, and improvements in computing power and storage.

Early AI research in the 1950s explored topics like pro

blem-solving and symbolic methods. In the 1960s, the US Department of Defense took interest in this type of work and began training computers to mimic basic human reasoning. For example, the Defense Advanced Research Projects Agency (DARPA) completed street mapping projects in the 1970s. And DARPA produced intelligent personal assistants in 2003, long before Siri,

Alexa or Cortana were household names.

This early work paved the way for the automation and formal reasoning that we see in computers today, including decision support systems and smart search systems that can be designed to complement and augment human abilities.

While Hollywood movies and science fiction novels depict AI as human-like robots that take over the world, the current evolution of AI technologies isn’t that scary – or quite that smart. Instead, AI has evolved to provide many specific benefits in every industry. Keep reading for modern examples of artificial intelligence in health care, retail, and more.

Artificial intelligence is a constellation of many different technologies working together to enable machines to sense, comprehend, act, and learn with human-like levels of intelligence. Maybe that’s why it seems as though everyone’s definition of artificial intelligence is different: AI isn’t just one thing.

Technologies like machine learning and natural language processing are all part of the AI landscape. Each one is evolving along its own path and, when applied in combination with data, analytics and automation, can help businesses achieve their goals, be it improving customer service or optimizing the supply chain.

Narrow (or “weak”) AI
Some go even further to define artificial intelligence as “narrow” and “general” AI. Most of what we experience in our day-to-day lives is narrow AI, which performs a single task or a set of closely related tasks. Examples include:

  • Weather apps
  • Digital assistants
  • Software that analyzes data to optimize a given business function

These systems are powerful, but the playing field is narrow: They tend to be focused on driving efficiencies. But, with the right application, narrow AI has immense transformational power—and it continues to influence how we work and live on a global scale.

General (or “strong”) AI
General AI is more like what you see in sci-fi films, where sentient machines emulate human intelligence, thinking strategically, abstractly and creatively, with the ability to handle a range of complex tasks. While machines can perform some tasks better than humans (e.g. data processing), this fully realized vision of general AI does not yet exist outside the silver screen. That’s why human-machine collaboration is crucial—in today’s world, artificial intelligence remains an extension of human capabilities, not a replacement.

What are the job prospects and Market in AI?

Mesh of Artificial Intelligence

COVID-19 has radically changed our lives. Its impacts can be felt across workplaces, particularly where it has forced industries to reduce their activities including leisure, restaurants, oil and gas, and airlines.

Yet throughout COVID-19, the technology industry remains strong. The pandemic has spurred technological innovation. Technology enables work to continue despite lockdowns and other pandemic mitigation measures. Analysts project the tech industry will be worth $5 trillion in 2021, with a growth rate comparable to what it was prior to the pandemic (4 to 5%).

Artificial intelligence has a strong foothold in the tech industry and has gained influence during COVID-19. Artificial intelligence and its jobs are future-proof — unlikely to become obsolete.

Artificial Intelligence and Future Job Prospects

Mesh of Artificial Intelligence

Artificial intelligence describes computing to mimic human behaviors and complete human tasks. AI technologies are progressive; they aim to improve people’s lives. Examples include surgical robots, wildfire-monitoring drones, and naturally speaking artificial personal assistants.

Experts predict the global AI software market will be worth $126 billion by 2025. A 2019 Gartner survey indicates 37% of business enterprises already implement AI in their workplaces.

Industries that benefit from AI now and will benefit from AI in the future include:

  • Cybersecurity— AI technologies can improve threat detections, mitigate and prevent cybercrimes.
  • Healthcare— Healthcare practitioners use AI to detect and diagnose diseases (e.g. cancers, respiratory diseases) and perform surgeries (with robotic arms).
  • Business Intelligence— Businesses employ AI and big data to extract meaningful insights about their performance metrics.
  • Information Technology (IT) and Cloud Computing— Cloud computing enables specialists like data scientists and AI-product developers to run demanding AI learning models (e.g. deep neural networks for humanized robots).
  • Transportation— Self-driving cars use AI technologies like computer vision to “see” and safely navigate their surroundings.
  • Manufacturing— Businesses use AI to assemble factory equipment.

Jobs in AI have great salary potential. People working in areas relevant to AI — like software development and data science — can expect to see increases in the jobs available during this decade.

The World Economic Forum estimates that 97 million new jobs may arise by 2025. Analysts forecast these jobs will be better adapted to a future with different divisions of labor between humans and machines than you are used to now — a future more mechanized and automated with AI technologies.

Which Skills Do You Use in AI Jobs?

Mesh of Artificial Intelligence

The skills you need to work in AI are diverse and interesting. They include:

  • Algorithms and Data Structures. Artificial intelligence specialists create and use algorithms and data structures to enable AI technologies to function.
  • Computers. A solid understanding of computers enables you to design artificially intelligent driving drones or autonomous vehicles. AI-relevant computer skills include software development, computer programming, computer design, and information processing.Computer programming languages include:
  • Machine Learning: Machine learningis a focal area of AI in which computer algorithms improve on their own through experience. Self-driving cars, surgical robots, and game-playing computers use machine learning to develop their skills.Machine learning frameworks include:
    • TensorFlow
    • PyTorch
    • Caffe
    • Keras
  • Robotics. Roboticistsdesign machines like planetary rovers that explore Mars or Jupiter, flying drones that survey landscapes, or humanoid robots who socialize and help with everyday tasks.
  • Data Science. Data scientists analyze and prepare massive amounts of data for processing by AI technologies and enable AI to function.Data science engines and frameworks include:
    • Apache Spark
    • Apache Kafka
    • Hive
    • Apache Hadoop

Learn more about industry-relevant AI job skills.

Employers foresee job skills like critical thinking, active learning, problem solving, and analysis rising in importance as we head toward 2025. You can keep your skill set relevant to AI and future-proof jobs through reskilling (acquiring new skills) and upskilling (improving existing skills).

What Types of Jobs Can You Get in AI?

Mesh of Artificial Intelligence

In-demand occupations connected to artificial intelligence include:

  • Data scientist
  • Machine learning engineer
  • Software engineer
  • Robotics engineer
  • Self-driving car engineer

But the list doesn’t stop here. AI influences employees across industries like biologists, professional gamers, content developers, designers, astronomers, physicists, and business analysts.

Top companies with AI jobs include Amazon, Alphabet, Microsoft, Netflix, and Apple.

 

What do you have to do?

Mesh of Artificial Intelligence

Artificial Intelligence Courses and Curriculum

As the table below demonstrates, AI consists of several overlapping disciplines. Understanding statistical methods, for example, is just as important as a background in computer science. In addition to the subjects listed here, it can be helpful to take interdisciplinary courses in areas like cognitive science to provide a conceptual framework for AI applications.

Sample Core Subjects in an AI Curriculum

MATH AND STATISTICS COMPUTER SCIENCE AI CORE SUBJECTS
  • Linear Algebra
  • Differential and Integral Calculus
  • Matrices and Linear Transformations
  • Integration and Approximation
  • Modern Regression
  • Probability Theory
  • Bayesian Networking
  • Probabilistic Graphical Models
  • Computer Systems and Programming
  • Principles of Imperative Computation
  • Principles of Functional Programming
  • Data Science Essentials
  • Parallel and Sequential Data Structures and Algorithms
  • Logic Programming and Computational Logic
  • Agile Software Development
  • Machine Learning, Deep Learning, and Reinforcement Learning
  • Information Theory, Inference, and Learning Algorithms
  • Neural Networks for Machine Learning
  • AI Representation and Problem-Solving
  • Natural Language Processing
  • Computer Vision and Image Analysis
Sources: Carnegie Mellon University; Stanford University; Northwestern University; Learning Artificial Intelligence; Microsoft Professional Program

Once you master some of the fundamentals, find the AI subfields that most interest you and shape your coursework accordingly. The next table shows more specialized subjects you might take as electives while earning a degree; these topics are also worth exploring at any stage of your career. Additional classes may be available that teach students specific AI applications in fields like biology, healthcare, and neuroscience.

Sample Artificial Intelligence Clusters and Subjects

MACHINE LEARNING DECISION-MAKING AND ROBOTICS PERCEPTION AND LANGUAGE HUMAN-AI INTERACTION
  • Deep Reinforcement Learning and Control
  • Applied Machine Learning
  • Machine Learning for Text Mining
  • Advanced Data Analysis
  • Neural Computation
  • Autonomous Agents
  • Cognitive Robotics
  • Strategic Reasoning for AI
  • Robot Kinematics and Dynamics
  • Information Retrieval and Search Engines
  • Speech Processing
  • Computational Perception
  • Computational Photography
  • Vision Sensors
  • Designing Human -Centered Systems
  • Human-Robot Interaction
  • Robotic Manipulation
  • Safe and Interactive Robots
Sources: Carnegie Mellon University; Stanford University

 

What are the Research areas in AI?

Mesh of Artificial Intelligence

Imagine a future in which intelligence is not restricted to humans!!! A future where machines can think as well as humans and work with them to create an even more exciting universe. While this future is still far away, Artificial Intelligence has still made a lot of advancements in these times. There is a lot of research being conducted in almost all fields of AI like Quantum Computing, Healthcare, Autonomous Vehicles, the Internet of Things, Robotics, etc. So much so that there is an increase of 90% in the number of annually published research papers on Artificial Intelligence since 1996.
Keeping this in mind, if you want to research and write a thesis based on Artificial Intelligence, there are many sub-topics that you can focus on. Some of these topics along with a brief introduction are provided in this article. We have also mentioned some published research papers related to each of these topics so that you can better understand the research process.

1. Machine Learning

Machine Learning involves the use of Artificial Intelligence to enable machines to learn a task from experience without programming them specifically about that task.  This process starts with feeding them good quality data and then training the machines by building various machine learning models using the data and different algorithms. The choice of algorithms depends on what type of data do we have and what kind of task we are trying to automate.
However, generally speaking, Machine Learning Algorithms are divided into 3 types i.e. Supervised Machine Learning Algorithms, Unsupervised Machine Learning Algorithms, and Reinforcement Machine Learning Algorithms.

2. Deep Learning

Deep Learning is a subset of Machine Learning that learns by imitating the inner working of the human brain in order to process data and implement decisions based on that data. Basically, Deep Learning uses artificial neural networks to implement machine learning. These neural networks are connected in a web-like structure like the networks in the human brain (Basically a simplified version of our brain!).
This web-like structure of artificial neural networks means that they are able to process data in a nonlinear approach which is a significant advantage over traditional algorithms that can only process data in a linear approach. An example of a deep neural network is RankBrain which is one of the factors in the Google Search algorithm.

3. Reinforcement Learning

Reinforcement Learning is a part of Artificial Intelligence in which the machine learns something in a way that is similar to how humans learn. As an example, assume that the machine is a student. Here the hypothetical student learns from their own mistakes over time (like we had to!!). So the Reinforcement Machine Learning Algorithms learn optimal actions through trial and error.
This means that the algorithm decides the next action by learning behaviors that are based on its current state and that will maximize the reward in the future. And like humans, this works for machines as well! For example, Google’s AlphaGo computer program was able to beat the world champion in the game of Go (that’s a human!) in 2017 using Reinforcement Learning.

4. Robotics

Robotics is a field that deals with creating humanoid machines that can behave like humans and perform some actions like human beings. Now, robots can act like humans in certain situations but can they think like humans as well? This is where artificial intelligence comes in! AI allows robots to act intelligently in certain situations. These robots may be able to solve problems in a limited sphere or even learn in controlled environments.
An example of this is Kismet, which is a social interaction robot developed at M.I.T’s Artificial Intelligence Lab. It recognizes the human body language and also our voice and interacts with humans accordingly. Another example is Robonaut, which was developed by NASA to work alongside the astronauts in space.

5. Natural Language Processing

It’s obvious that humans can converse with each other using speech but now machines can too! This is known as Natural Language Processing where machines analyze and understand language and speech as it is spoken (Now if you talk to a machine it may just talk back!). There are many subparts of NLP that deal with language such as speech recognition, natural language generation, natural language translation, etc.
NLP is currently extremely popular for customer support applications, particularly the chatbot. These chatbots use ML and NLP to interact with the users in textual form and solve their queries. So you get the human touch in your customer support interactions without ever directly interacting with a human.

Some Research Papers published in the field of Natural Language Processing are provided here. You can study them to get more ideas about research and thesis on this topic.

6. Computer Vision

The internet is full of images! This is the selfie age, were taking an image and sharing it has never been easier. In fact, millions of images are uploaded and viewed every day on the internet. To make the most use of this huge amount of images online, it’s important that computers can see and understand images. And while humans can do this easily without a thought, it’s not so easy for computers! This is where Computer Vision comes in.
Computer Vision uses Artificial Intelligence to extract information from images. This information can be object detection in the image, identification of image content to group various images together, etc. An application of computer vision is navigation for autonomous vehicles by analyzing images of surroundings such as AutoNav used in the Spirit and Opportunity rovers which landed on Mars.

7. Recommender Systems

When you are using Netflix, do you get a recommendation of movies and series based on your past choices or genres you like? This is done by Recommender Systems that provide you some guidance on what to choose next among the vast choices available online. A Recommender System can be based on Content-based Recommendation or even Collaborative Filtering.
Content-Based Recommendation is done by analyzing the content of all the items. For example, you can be recommended books you might like based on Natural Language Processing done on the books. On the other hand, Collaborative Filtering is done by analyzing your past reading behavior and then recommending books based on that.

8. Internet of Things

Artificial Intelligence deals with the creation of systems that can learn to emulate human tasks using their prior experience and without any manual intervention. Internet of Things, on the other hand, is a network of various devices that are connected over the internet and they can collect and exchange data with each other.
Now, all these IoT devices generate a lot of data that needs to be collected and mined for actionable results. This is where Artificial Intelligence comes into the picture. Internet of Things is used to collect and handle the huge amount of data that is required by Artificial Intelligence algorithms. In turn, these algorithms convert the data into useful actionable results that can be implemented by IoT devices.

 

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