Artificial intelligence

As AI capabilities have made their way into mainstream enterprise operations, a new term is evolving: adaptive intelligence. Adaptive intelligence applications help enterprises make better business decisions by combining the power of real-time internal and external data with decision science and highly scalable computing infrastructure.

artificial intelligence examples

Artificial intelligence terms

AI has become a catchall term for applications that perform complex tasks that once required human input such as communicating with customers online or playing chess. The term is often used interchangeably with its subfields, which include machine learning and deep learning. There are differences, however. For example, machine learning is focused on building systems that learn or improve their performance based on the data they consume. It’s important to note that although all machine learning is AI, not all AI is machine learning.

To get the full value from AI, many companies are making significant investments in data science teams. Data science, an interdisciplinary field that uses scientific and other methods to extract value from data, combines skills from fields such as statistics and computer science with business knowledge to analyze data collected from multiple sources.

How AI Technology can help organizations

The central tenet of AI is to replicate—and then exceed—the way humans perceive and react to the world. It’s fast becoming the cornerstone of innovation. Powered by various forms of machine learning that recognize patterns in data to enable predictions, AI can add value to your business by:

AI technology is improving enterprise performance and productivity by automating processes or tasks that once required human power. AI can also make sense of data on a scale that no human ever could. That capability can return substantial business benefits. For example, Netflix uses machine learning to provide a level of personalization that helped the company grow its customer base by more than 25 percent in 2017.

Most companies have made data science a priority and are investing in it heavily. In Gartner’s recent survey of more than 3,000 CIOs, respondents ranked analytics and business intelligence as the top differentiating technology for their organizations. The CIOs surveyed see these technologies as the most strategic for their companies; therefore, they are attracting the most new investment.

Types of artificial intelligence—weak AI vs. strong AI

Weak AI—also called Narrow AI or Artificial Narrow Intelligence (ANI)—is AI trained and focused to perform specific tasks. Weak AI drives most of the AI that surrounds us today. ‘Narrow’ might be a more accurate descriptor for this type of AI as it is anything but weak; it enables some very robust applications, such as Apple’s Siri, Amazon’s Alexa, IBM Watson, and autonomous vehicles.

Strong AI is made up of Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI). Artificial general intelligence (AGI), or general AI, is a theoretical form of AI where a machine would have an intelligence equaled to humans; it would have a self-aware consciousness that has the ability to solve problems, learn, and plan for the future. Artificial Super Intelligence (ASI)—also known as superintelligence—would surpass the intelligence and ability of the human brain. While strong AI is still entirely theoretical with no practical examples in use today, that doesn’t mean AI researchers aren’t also exploring its development. In the meantime, the best examples of ASI might be from science fiction, such as HAL, the superhuman, rogue computer assistant in 2001: A Space Odyssey.

Deep learning vs. machine learning

Since deep learning and machine learning tend to be used interchangeably, it’s worth noting the nuances between the two. As mentioned above, both deep learning and machine learning are sub-fields of artificial intelligence, and deep learning is actually a sub-field of machine learning.

Visual Representation of how AI, ML and DL relate to one another

Deep learning is actually comprised of neural networks. “Deep” in deep learning refers to a neural network comprised of more than three layers—which would be inclusive of the inputs and the output—can be considered a deep learning algorithm. This is generally represented using the following diagram:

Diagram of Deep Neural Network

The way in which deep learning and machine learning differ is in how each algorithm learns. Deep learning automates much of the feature extraction piece of the process, eliminating some of the manual human intervention required and enabling the use of larger data sets. You can think of deep learning as "scalable machine learning" as Lex Fridman noted in same MIT lecture from above. Classical, or "non-deep", machine learning is more dependent on human intervention to learn. Human experts determine the hierarchy of features to understand the differences between data inputs, usually requiring more structured data to learn.

"Deep" machine learning can leverage labeled datasets, also known as supervised learning, to inform its algorithm, but it doesn’t necessarily require a labeled dataset. It can ingest unstructured data in its raw form (e.g. text, images), and it can automatically determine the hierarchy of features which distinguish different categories of data from one another. Unlike machine learning, it doesn’t require human intervention to process data, allowing us to scale machine learning in more interesting ways.

A Brief History of Artificial Intelligence

Intelligent robots and artificial beings first appeared in the ancient Greek myths of Antiquity. Aristotle’s development of syllogism and its use of deductive reasoning was a key moment in mankind’s quest to understand its own intelligence. While the roots are long and deep, the history of artificial intelligence as we think of it today spans less than a century. The following is a quick look at some of the most important events in AI.

1940s

1950s

  • (1950) Alan Turing publishes "Computing Machinery and Intelligence, proposing what is now known as the Turing Test, a method for determining if a machine is intelligent.
  • (1950) Harvard undergraduates Marvin Minsky and Dean Edmonds build SNARC, the first neural network computer.
  • (1950) Claude Shannon publishes the paper "Programming a Computer for Playing Chess."
  • (1950) Isaac Asimov publishes the "Three Laws of Robotics."
  • (1952) Arthur Samuel develops a self-learning program to play checkers.
  • (1954) The Georgetown-IBM machine translation experiment automatically translates 60 carefully selected Russian sentences into English.
  • (1956) The phrase artificial intelligence is coined at the "Dartmouth Summer Research Project on Artificial Intelligence." Led by John McCarthy, the conference, which defined the scope and goals of AI, is widely considered to be the birth of artificial intelligence as we know it today.
  • (1956) Allen Newell and Herbert Simon demonstrate Logic Theorist (LT), the first reasoning program.
  • (1958) John McCarthy develops the AI programming language Lisp and publishes the paper "Programs with Common Sense." The paper proposed the hypothetical Advice Taker, a complete AI system with the ability to learn from experience as effectively as humans do.
  • (1959) Allen Newell, Herbert Simon and J.C. Shaw develop the General Problem Solver (GPS), a program designed to imitate human problem-solving.
  • (1959) Herbert Gelernter develops the Geometry Theorem Prover program.
  • (1959) Arthur Samuel coins the term machine learning while at IBM.
  • (1959) John McCarthy and Marvin Minsky founded the MIT Artificial Intelligence Project.

1960s

1970s

1980s

  • (1980) Digital Equipment Corporations develops R1 (also known as XCON), the first successful commercial expert system. Designed to configure orders for new computer systems, R1 kicks off an investment boom in expert systems that will last for much of the decade, effectively ending the first "AI Winter."
  • (1982) Japan’s Ministry of International Trade and Industry launches the ambitious Fifth Generation Computer Systems project. The goal of FGCS is to develop supercomputer-like performance and a platform for AI development.
  • (1983) In response to Japan’s FGCS, the U.S. government launches the Strategic Computing Initiative to provide DARPA funded research in advanced computing and artificial intelligence.
  • (1985) Companies are spending more than a billion dollars a year on expert systems and an entire industry known as the Lisp machine market springs up to support them. Companies like Symbolics and Lisp Machines Inc. build specialized computers to run on the AI programming language Lisp.
  • (1987-1993) As computing technology improved, cheaper alternatives emerged and the Lisp machine market collapsed in 1987, ushering in the "Second AI Winter." During this period, expert systems proved too expensive to maintain and update, eventually falling out of favor.

1990s

2000s

2010-2014

2015-2021

  • (2016) Google DeepMind’s AlphaGo defeats world champion Go player Lee Sedol. The complexity of the ancient Chinese game was seen as a major hurdle to clear in AI.
  • (2016) The first "robot citizen", a humanoid robot named Sophia, is created by Hanson Robotics and is capable of facial recognition, verbal communication and facial expression.
  • (2018) Google releases natural language processing engine BERT, reducing barriers in translation and understanding by machine learning applications.
  • (2018) Waymo launches its Waymo One service, allowing users throughout the Phoenix metropolitan area to request a pick-up from one of the company’s self-driving vehicles.
  • (2020) Baidu releases its LinearFold AI algorithm to scientific and medical teams working to develop a vaccine during the early stages of the SARS-CoV-2 pandemic. The algorithm is able to predict the RNA sequence of the virus in just 27 seconds, 120 times faster than other methods.

Resources:

https://www.oracle.com/artificial-intelligence/what-is-ai/
https://www.ibm.com/cloud/learn/what-is-artificial-intelligence
https://builtin.com/artificial-intelligence
Artificial intelligence

There’s also a related myth that people who worry about AI think it’s only a few years away. In fact, most people on record worrying about superhuman AI guess it’s still at least decades away. But they argue that as long as we’re not 100% sure that it won’t happen this century, it’s smart to start safety research now to prepare for the eventuality. Many of the safety problems associated with human-level AI are so hard that they may take decades to solve. So it’s prudent to start researching them now rather than the night before some programmers drinking Red Bull decide to switch one on.

Artificial intelligence

Benefits & Risks of Artificial Intelligence

Everything we love about civilization is a product of intelligence, so amplifying our human intelligence with artificial intelligence has the potential of helping civilization flourish like never before – as long as we manage to keep the technology beneficial.

From SIRI to self-driving cars, artificial intelligence (AI) is progressing rapidly. While science fiction often portrays AI as robots with human-like characteristics, AI can encompass anything from Google’s search algorithms to IBM’s Watson to autonomous weapons.

Artificial intelligence today is properly known as narrow AI (or weak AI), in that it is designed to perform a narrow task (e.g. only facial recognition or only internet searches or only driving a car). However, the long-term goal of many researchers is to create general AI (AGI or strong AI). While narrow AI may outperform humans at whatever its specific task is, like playing chess or solving equations, AGI would outperform humans at nearly every cognitive task.

Future of Artificial Intelligence

When you look around you, you will notice that Artificial Intelligence has impacted almost every industry and it will continue to do so in the future. It has emerged as one of the most exciting and advanced technologies of our time. Robotics, Big Data, IoT, etc. are all fueled by AI. There are companies around the world conducting extensive research on Machine Learning and AI. At the current growth rate, it is going to be a driving force for a very long time in the future as well.

AI helps computers generate huge amounts of data and use it to make decisions and discoveries in the fraction of a time that it would have taken a human to. It has already had a lot of impact on our world. If used responsibly, It can end up massively benefiting human society in the future.

Artificial Intelligence

The journal of Artificial Intelligence (AIJ) welcomes papers on broad aspects of AI that constitute advances in the overall field including, but not limited to, cognition and AI, automated reasoning and inference, case-based reasoning, commonsense reasoning, computer vision, constraint processing, ethical AI, heuristic search, human interfaces, intelligent robotics, knowledge representation, machine learning, multi-agent systems, natural language processing, planning and action, and reasoning under uncertainty. The journal reports results achieved in addition to proposals for new ways of looking at AI problems, both of which must include demonstrations of value and effectiveness.

Papers describing applications of AI are also welcome, but the focus should be on how new and novel AI methods advance performance in application areas, rather than a presentation of yet another application of conventional AI methods. Papers on applications should describe a principled solution, emphasize its novelty, and present an indepth evaluation of the AI techniques being exploited.

Apart from regular papers, the journal also accepts Research Notes, Research Field Reviews, Position Papers, and Book Reviews (see details below). The journal will also consider summary papers that describe challenges and competitions from various areas of AI. Such papers should motivate and describe the competition design as well as report and interpret competition results, with an emphasis on insights that are of value beyond the competition (series) itself.

From time to time, there are special issues devoted to a particular topic. Such special issues must always have open calls-for-papers. Guidance on the submission of proposals for special issues, as well as other material for authors and reviewers can be found at http://aij.ijcai.org/special-issues .

AIJ caters to a broad readership. Papers that are heavily mathematical in content are welcome but should include a less technical high-level motivation and introduction that is accessible to a wide audience and explanatory commentary throughout the paper. Papers that are only purely mathematical in nature, without demonstrated applicability to artificial intelligence problems may be returned. A discussion of the work’s implications on the production of artificial intelligent systems is normally expected.

There is no restriction on the length of submitted manuscripts. However, authors should note that publication of lengthy papers, typically greater than forty pages, is often significantly delayed, as the length of the paper acts as a disincentive to the reviewer to undertake the review process. Unedited theses are acceptable only in exceptional circumstances. Editing a thesis into a journal article is the author’s responsibility, not the reviewers’.

The Research Notes section of the Journal of Artificial Intelligence will provide a forum for short communications that cannot fit within the other paper categories. The maximum length should not exceed 4500 words (typically a paper with 5 to 14 pages). Some examples of suitable Research Notes include, but are not limited to the following: crisp and highly focused technical research aimed at other specialists; a detailed exposition of a relevant theorem or an experimental result; an erratum note that addresses and revises earlier results appearing in the journal; an extension or addendum to an earlier published paper that presents additional experimental or theoretical results.

The AIJ invests significant effort in assessing and publishing scholarly papers that provide broad and principled reviews of important existing and emerging research areas, reviews of topical and timely books related to AI, and substantial, but perhaps controversial position papers (so-called "Turing Tape" papers) that articulate scientific or social issues of interest in the AI research community.

Research Field Reviews: AIJ expects broad coverage of an established or emerging research area, and the articulation of a comprehensive framework that demonstrates the role of existing results, and synthesizes a position on the potential value and possible new research directions. A list of papers in an area, coupled with a summary of their contributions is not sufficient. Overall, a field review article must provide a scholarly overview that facilitates deeper understanding of a research area. The selection of work covered in a field article should be based on clearly stated, rational criteria that are acceptable to the respective research community within AI; it must be free from personal or idiosyncratic bias.

Research Field Reviews are by invitation only, where authors can then submit a 2-page proposal of a Research Field Review for confirmation by the special editors. The 2-page proposal should include a convincing motivational discussion, articulate the relevance of the research to artificial intelligence, clarify what is new and different from other surveys available in the literature, anticipate the scientific impact of the proposed work, and provide evidence that authors are authoritative researchers in the area of the proposed Research Field Review. Upon confirmation of the 2-page proposal, the full Invited Research Field Reviews can then be submitted and then undergoes the same review process as regular papers.

Resources:

https://futureoflife.org/background/benefits-risks-of-artificial-intelligence/
https://intellipaat.com/blog/what-is-artificial-intelligence/
https://journals.elsevier.com/artificial-intelligence
Artificial intelligence

With the help of radiological tools like MRI machines, X-rays, and CT scanners, AI can identify diseases such as tumors and ulcers in the early stages. For diseases like cancer, there is no solid treatment, but the risk of premature death can be greatly reduced if the tumor is detected in its early stage. Similarly, It can suggest medication and tests by analyzing their R-Health records.

Artificial intelligence

28 Examples of Artificial Intelligence Shaking Up Business as Usual

Examples of artificial intelligence (AI) in pop culture usually involve a pack of intelligent robots hell-bent on overthrowing the human race, or at least a fancy theme park. Sentient machines with general artificial intelligence don’t yet exist, and they likely won’t exist anytime soon, so we’re safe. for now.

That’s not to make light of AI’s potential impact on our future. In a recent survey, more than 72% of Americans expressed worry about a future in which machines perform many human jobs. Additionally, tech billionaire Elon Musk, long an advocate for the regulation of artificial intelligence, recently called AI more dangerous than nukes. Despite these legitimate concerns, we’re a long way from living in Westworld.

Future of Artificial Intelligence

When you look around you, you will notice that Artificial Intelligence has impacted almost every industry and it will continue to do so in the future. It has emerged as one of the most exciting and advanced technologies of our time. Robotics, Big Data, IoT, etc. are all fueled by AI. There are companies around the world conducting extensive research on Machine Learning and AI. At the current growth rate, it is going to be a driving force for a very long time in the future as well.

AI helps computers generate huge amounts of data and use it to make decisions and discoveries in the fraction of a time that it would have taken a human to. It has already had a lot of impact on our world. If used responsibly, It can end up massively benefiting human society in the future.

Examples of Artificial Intelligence

As humans, we have always been fascinated by technological changes and fiction, right now, we are living amidst the greatest advancements in our history. Artificial Intelligence has emerged to be the next big thing in the field of technology. Organizations across the world are coming up with breakthrough innovations in artificial intelligence and machine learning. Artificial intelligence is not only impacting the future of every industry and every human being but has also acted as the main driver of emerging technologies like big data, robotics and IoT. Considering its growth rate, it will continue to act as a technological innovator for the foreseeable future. Hence, there are immense opportunities for trained and certified professionals to enter a rewarding career. As these technologies continue to grow, they will have more and more impact on the social setting and quality of life.

Career Opportunities in AI

AI & ML Engineer/Developer is responsible for performing statistical analysis, running statistical tests, and implementing statistical designs. Furthermore, they develop deep learning systems, manage ML programs, implement ML algorithms, etc.

So, basically, they deploy AI & ML-based solutions for the company. For becoming n AI & ML developer, you will need good programming skills in Python, Scala, and Java. You get to work on frameworks like Azure ML Studio, Apache Hadoop, Amazon ML, etc. If you proceed on the set ai engineer learning path, success is all yours! The average salary of an AI engineer in India is found to be ranging from INR 4 Lakhs p.a. to INR 20 Lakhs p.a.

The role of an ai analyst or specialist is similar to that of an ai engineer. The key responsibility is to cater to AI-oriented solutions and schemes to enhance the services delivered by a certain industry using the data analyzing skills to study the trends and patterns of certain datasets. Whether you talk about the healthcare industry, finance industry, geology sector, cyber security, or any other sector, AI analysts or specialists are seen to have quite a good impact all over. An AI Analyst/Specialist must have a good programming, system analysis, and computational statistics background. A bachelor’s or equivalent degree can help you land an entry-level position, but a master’s or equivalent degree is a must for the core AI analyst positions. The average salary of an ai analyst can be anywhere between INR 3 Lakhs per year and 10 Lakhs per year, based on the years of experience and company you are working for.

Owing to the huge demand for data scientists, there are high chances that you are already familiar with the term. The role of a data scientist involves identifying valuable data streams and sources, working along with the data engineers for the automation of data collection processes, dealing with big data, analyzing massive amounts of data to learn the trends and patterns for developing predictive ML models. A data scientist is also responsible for coming up with solutions and strategies for the decision-makers with the help of intriguing visualization tools and techniques. SQL, Python, Scala, SAS, SSAS, and R are the most useful tools to a data scientist. They are required to work on frameworks such as Amazon ML, Azure ML Studio, Spark MLlib, and so on. The average salary of a data scientist in India is INR 5-22 Lakhs per year, depending on their experience and the company they are hired in.

Research Scientist is one of the other fascinating artificial intelligence jobs. This ai job position holds responsibilities related to researching the field of Artificial Intelligence and Machine Learning to innovate and discover AI-oriented solutions to real-world problems. As we know, research in whatever streams it may be demands core expertise. Likewise, the role of a research scientist calls for mastery in various AI disciplines such as Computational Statistics, Applied Mathematics, Deep Learning, Machine Learning, and Neural Networks. A research scientist is expected to have Python, Scala, SAS, SSAS, and R programming skills. Apache Hadoop, Apache Signa, Scikit learn, H20 are some common frameworks to work on as a research scientist. An advanced master’s or doctoral degree is a must for becoming an AI research scientist. As per the current studies, an AI research scientist earns a minimum of INR 35 Lakhs annually in India.

Following the lead of global automation trends and the emergence of robotics in the field of ai, we can tell it is definitely a sign of sprouting demand for robotics scientists. In this fast-paced world where technology is becoming the pioneer, robots are indeed stealing the job of people handling manual or repetitive & boring tasks. On the contrary, it is giving employment to professionals having expertise in the field of robotics. In order to build and manage these robotic systems, we need a robotics engineer. To pursue a career as a robotics engineer, you must have a master’s degree in robotics, Computer Science or Engineering. A robotics scientist is among one of the other interesting and high paying ai careers take upon. Since we are already aware of how complicated robots are, tackling them demands knowledge in different disciplines. If the field of robotics intrigues you and you are good at programming, mechanics, electronics, electrics, sensing, and psychology and cognition to some extent, you are good to go with this career option.

Important FAQs on Artificial Intelligence (AI)

Ans. Artificial Intelligence is used across industries globally. Some of the industries which have delved deep in the field of AI to find new applications are E-commerce, Retail, Security and Surveillance. Sports Analytics, Manufacturing and Production, Automotive among others.

Ans. The virtual digital assistants have changed the way w do our daily tasks. Alexa and Siri have become like real humans we interact with each day for our every small and big need. The natural language abilities and the ability to learn themselves without human interference are the reasons they are developing so fast and becoming just like humans in their interaction only more intelligent and faster.

Ans. AI makes every process better, faster, and more accurate. It has some very crucial applications too such as identifying and predicting fraudulent transactions, faster and accurate credit scoring, and automating manually intense data management practices. Artificial Intelligence improves the existing process across industries and applications and also helps in developing new solutions to problems that are overwhelming to deal with manually.

Ans. Artificial Intelligence is an intelligent entity that is created by humans. It is capable of performing tasks intelligently without being explicitly instructed to do so. We make use of AI in our daily lives without even realizing it. Spotify, Siri, Google Maps, YouTube, all of these applications make use of AI for their functioning.

Ans. We are currently living in the greatest advancements of Artificial Intelligence in history. It has emerged to be the next best thing in technology and has impacted the future of almost every industry. There is a greater need for professionals in the field of AI due to the increase in demand. According to WEF, 133 million new Artificial Intelligence jobs are said to be created by Artificial Intelligence by the year 2022. Yes, AI is the future.

Ans. AI has paved its way into various industries today. Be it gaming, or healthcare. AI is everywhere. Did you now that the facial recognition feature on our phones uses AI? Google Maps also makes use of AI in its application, and it is part of our daily life more than we know it. Spam filters on Emails, Voice-to-text features, Search recommendations, Fraud protection and prevention, Ride-sharing applications are some of the examples of AI and its application.

Resources:

https://builtin.com/artificial-intelligence/examples-ai-in-industry
https://intellipaat.com/blog/what-is-artificial-intelligence/
https://www.mygreatlearning.com/blog/what-is-artificial-intelligence/


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