Today, Artificial intelligence (AI) plays an instrumental role in strengthening and transforming the business world. From deep learning and predictive analytics to image recognition and chatbots, AI, alongside machine learning (ML), is revolutionizing how organizations engage with their customers and deliver more in less time.
We are already witnessing a few material benefits for the first movers, making them disruptors in their fields – driving productivity and innovation, at the same time cutting costs and boosting profits. While tech companies, including Google, IBM, and Salesforce, have scaled up their AI adoption, more and more non-tech firms are jumping onto the bandwagon.
Furthermore, AI will significantly impact national and global economies. The cutting-edge technology, for instance, will auger well for GDP, mainly through enabling companies to achieve greater workforce efficiency by automating recurring tasks, expanding employee skill-sets, and making their jobs more meaningful.
What is Artificial Intelligence?
AI is the science and engineering of leveraging intelligent machines, especially computer systems, to mimic a human’s problem-solving and decision-making abilities. It is a stack of sophisticated algorithms and mathematical functions that perform tasks intelligently without the need for explicit instructions, think and act rationally, and deliver results accordingly.
AI resorts to various technologies to empower machines to plan, behave, understand, learn, and sense with human-like intelligence. Additionally, it might recognize objects, observe surroundings, make decisions, fix issues, and learn from the experiences.
AI systems combine these abilities to carry out actions that would otherwise require humans to do, such as tailoring products or services on demand or displaying advertisements based on users' search or purchasing history.
How does Artificial Intelligence Work?
AI is built on two pillars – engineering and cognitive science. It utilizes large data sets (past and present) and algorithms alongside constraints and other elements to create a propensity model.
With the help of ML, deep learning, and natural language processing (NLP), AI systems parse through heaps of data, interpreting both images and text to uncover patterns. They then learn from the patterns and experiences to execute cognitive tasks that ordinarily come within the human brain's radar. All these processes occur at an unimaginable rate and scale.
Types of Artificial Intelligence
Based on functionality, AI includes:
Reactive machines are the primary form of AI that does not store memories or utilize past experiences to figure out future actions. Instead, they only focus on the existing scenarios and react to them based on the best possible action. Reactive machines are assigned particular tasks and are not capable enough to go beyond those tasks.
Google's AlphaGo and IBM’s Deep Blue are common examples of reactive machines.
Limited memory machines learn from past data or experiences to make decisions for a limited duration. The memory of such AI systems is short-lived. Also, these machines cannot add the data to a library of their experiences.
Limited memory AI is commonplace in self-driving vehicles. Mitsubishi Electric, for instance, has been striving to capitalize on limited memory AI for autonomous driving applications.
Theory Of Mind
Theory of mind AI is an advanced tech grade that exists only as a concept. Such machines need to comprehend people, emotions, beliefs, and emulate humans when it comes to social interaction. These AI machines are still under development stage, and researchers are putting in immense efforts to bring them to the surface.
Real-world examples of theory of mind AI include Sophia from Hanson Robotics and Kismet, developed by the Massachusetts Institute of Technology (MIT).
Self-awareness AI system is only a speculative possibility. Regarded as the future of AI, these machines will perceive the emotions of those they interact with as well as have their own sentiments, consciousness, and self-awareness.
Based on capability, AI includes:
Narrow AI focuses on one specific task and cannot function beyond its radar. Hence, it is also called weak AI. Narrow AI targets a single subgroup of cognitive capabilities and progresses within that scope.
IBM Watson supercomputer and Apple Siri are prominent narrow artificial intelligence examples. Other common examples include speech recognition, spam filtering, and page-ranking algorithms.
General AI, also called strong AI, can understand and perform any intellectual activity with efficiency nearly akin to humans. It empowers machines to employ skills and expertise in various contexts.
Artificial General Intelligence (AGI) is still a theoretical concept. Think tanks worldwide are going to great lengths on developing machines with general AI, for instance, Fujitsu's K computer and Tianhe-2 by the National University of Defense Technology (China).
Super AI outsmarts human intelligence and can perform any activity better versus humans with cognitive properties. It is a by-product of general AI. They will be the most powerful in this AI category, with capabilities in research, emotions, and even disaster response.
Super AI is still a hypothetical concept. Some of its key characteristics include solving puzzles, making judgments, reason, and communicating on its own.
A Brief History of AI
The history of AI goes as far back as ancient Egypt and Greece. However, the emergence of electronic computing made AI a real possibility. So let us walk through the timeline of AI.
1943: Walter Pits and Warren McCulloch presented a model of artificial neurons.
1949: Donald Hebb introduced the Hebbian Theory or Cell Assembly Theory to modify the connection strength between neurons.
1950: Alan Turing outlines the Turing Test in his publication titled “Computing Machinery and Intelligence" that ascertains whether computers are as intelligent as human beings.
1955: Herbert A. Simon and Allen Newell organized the "first AI program" named "Logic Theorist.” This program proved 38 of 52 mathematics theorems and discovered new proofs for some hypotheses.
1956: John McCarty coined the term "Artificial Intelligence" at the Dartmouth conference.
1966: Joseph Weizenbaum developed the first chatbot - ELIZA.
1972: WABOT-1, the first intelligent humanoid robot, was developed in Japan.
1980: Expert systems came into existence that mimics a human’s decision-making capability.
1997: IBM’s Deep Blue first computer to beat a world chess champion, Gary Kasparov.
2002: iRobot Corporation created Roomba, the world’s first robotic vacuum cleaner.
2006: Incumbents, such as Twitter, Facebook, and Netflix, began leveraging AI’s potential.
2011: IBM's Watson proved that it could understand natural language and solve tricky questions significantly fast.
2012: Google launched "Google Now," an Android app feature that proactively offers personalized insights to users in the form of information cards.
2014: Chatbot "Eugene Goostman" won a Turing test competition held in the UK.
2018: IBM’s "Project Debater" debated with two master debaters on complex topics.
6 Key Components of AI
1. Deep Learning
Deep learning involves learning by processing and examining the input data by different techniques until the computer system uncovers the single desirable output. It performs this by implementing various algorithms, such as neuroevolution and gradient descend on a neural network.
Voice recognition, image identification, and fraud detection are some direct applications of deep learning.
2. Machine Learning
ML equips computers to automatically assess the problems and develop a solution by using already fed data and facts or past self-learning without the explicit programming – the training phase. It finds immense use in healthcare institutions for disease diagnostics, drug development, and medical image interpretation.
3. Computer Vision
Computer vision banks on pattern recognition and deep learning to analyze digital pictures or videos. Critical use cases of this subset include automatic inspection in manufacturing, species identification, and visual surveillance.
4. Neural Network
Neural networks are the replica of the neural connections in the human brain. They gather knowledge by processing multiple training examples before producing a desirable outcome. Additionally, this method will provide a solution for several related unanswered queries by using various learning models.
Alongside deep learning, neural networks unfurl numerous layers of hidden data, including the output layer of complicated problems. As a result, they are instrumental in medicine, character recognition, and detecting tumors.
5. Cognitive Intelligence
Cognitive intelligence improves human-machine collaboration for faster problem-solving and completion of complex tasks. It learns and understands human behavior in several distinct situations and rebuilds the human thought process in a computer model.
In tandem with AI, cognitive intelligence can develop a machine stacked with human-comparable actions and data processing capabilities. As this AI component takes accurate decisions during complex problems, companies implement it in areas where cost-effective development of solutions is overdue.
Google Assistant is a prevalent use case of cognitive intelligence.
6. Natural Language Processing
Natural language processing (NLP) enables computer systems to interpret, recognize, locate, and process human speech and language, which includes both active and passive modes of utilizing algorithms. This AI component smoothens the human-machine collaboration in order to provide logical responses to human queries.
Real-world applications of NLP include Skype Translator and Google voice search engine.
Artificial Intelligence Market Trends 2022
The global AI market registered revenues around $36 Bn in 2019, and the figure will surpass $112 Bn in 2022, at a 45.7% CAGR, as per the recent release of Beroe’s category intelligence on artificial intelligence worldwide.
The market is receiving major impetus from soaring data generation and rigorous investments in the R&D of innovative technologies. Moreover, thanks to pandemic-induced rapid digitization, AI is witnessing a demand upsurge in healthcare, automotive, and retail. However, the lack of skilled professionals to properly deploy AI-powered solutions across businesses as well as the current under-skilled workforce continues to itch market players.
Looking ahead, North America will remain the ground zero of growth prospects for AI providers as the region is home to tech giants. In addition, the regional governments are heavily investing in cyber security solutions armed with AI and real-time analytics. The report reveals that the North American AI market revenue will multiply at around 41% CAGR during 2018-2023.
Beroe’s market intelligence on the global AI market provides timely and accurate market size (by value and volume) and vendor share across continents. With patented research tools and methods, Beroe delivers in-depth insights into every category and sub-category. Furthermore, the industry report covers key global market players, thus offering a well-documented competitive landscape across all the categories.
For more information, subscribe to Beroe’s market study.
Artificial Intelligence: Latest Updates
AI & ML, cloud computing, and 5G will dominate 2022, finds study
According to a new IEEE survey titled “The Impact of Technology in 2022 and Beyond,” artificial intelligence (AI) and machine learning (ML), cloud computing, and 5G will be the technologies attracting the most attention from businesses globally in 2022.
The study included 350 chief technology officers (CTO), chief information officers (CIO), and IT directors from China, the US, the UK, India, and Brazil. It revealed that over a fifth of the overall polled cohort believe AI and ML to be the most important technologies next year, followed by cloud computing (20%) and 5G (17%).
The COVID-19 pandemic compelled about 6 out of 10 tech leaders to expedite their cloud computing adoption, with respondents within the third quintile rapidly shifting toward AI and ML and 5G. Moreover, almost every tech incumbent agreed, including 66% who strongly agreed, that AI would lead the innovation wave across nearly all industries over the coming five years.
The survey emphasized that 5G will remain a game-changer in telemedicine, remote learning and education, and personal and professional day-to-day communications. Additionally, it opined that manufacturing, financial services, and healthcare would be the major technology end-users.
Regarding tech-related hurdles, 8 out of 10 respondents viewed ensuring robust cybersecurity for their hybrid employees as a significant obstacle. At the same time, about three-quarters found it tough to manage health and safety protocols, return-to-office, and digital assets.
The study highlights that nearly 80% of the respondents credit robotics to enhance their business operations over the next five years, from HR and sales to IT and marketing.
Furthermore, given the global health crisis and the subsequent shift to hybrid work, over half of the tech giants believe the number of web-browsing devices required to monitor and manage their businesses surged by 1.5X.
Clarion call for governments to join hands with the AI community to support climate resilience
Recently, Climate Change AI and Centre for AI & Climate presented a study titled “Climate Change and AI: Recommendations for Government” for the Global Partnership on AI (GPAI) at COP26 in Glasgow, the 2021 United Nations Climate Change Conference, during the second week of November 2021.
Co-authored by 15 prominent AI think-tanks worldwide, the report motivates policymakers to realize the potential of artificial intelligence (AI) to expedite the shift to net zero and encourage AI-for-climate technologies.
"There are many ways that AI can be a powerful tool in enabling and accelerating climate action, from monitoring carbon stock using satellite imagery, to optimizing heating and cooling in buildings, forecasting crop yield, and helping design next-generation batteries," said David Rolnick, Co-founder and Chair, Climate Change AI; Canada CIFAR AI Chair and Assistant Professor, McGill University.
The report highlights 48 specific recommendations for how governments can support AI usage in fighting climate challenges and tackle the climate-associated threats that the technology poses. To address these, the report influences policymakers to:
- Keep climate transition at the center while creating AI strategies to develop AI-powered solutions meaningfully.
- Promote at-scale capacity building and foreign synergy to fuel the development and supervision of AI-for-climate technologies.
- Step up the support for R&D and deployment through infrastructure, focused investments, and better market layouts.
- Strengthen data ecosystems, especially in industries critical to climate change, for instance, the energy industry.
5 top AI summits in 2022 technology buffs must look forward to
2022 promises several large-scale AI and ML events and conferences for technology enthusiasts. Read on to know about some of them.
- International Conference on Artificial Intelligence and Applications (ICAIA): Organized by the International Research Conference (IRC), ICAIA brings together eminent academic researchers and scholars to comprehensively exchange their thoughts and research findings on AI and its applications. The conference will take place during 7-8 January 2022 in Singapore.
- International Conference on Artificial Intelligence in Education (ICAIE): The ICAIE will occur in January 2022 in Chengdu, China, marking its second year wherein the industrial sector sees AI’s growth in education. Think tanks from industries, academia, and government agencies will be sharing the recent findings and innovations of AI in education at the summit.
- SXSW Conference: The SXSW Conference provides a platform for digital creatives worldwide to uncover new interests, create innovative ideas, and interact with like-minded professionals in film, technology (AI and ML), and music. Organized in March 2022 in Austin, Texas (The US), the iconic event highlights that “discussion between people from diverse backgrounds on diverse topics lead to unexpected discoveries.”
- Big Data and Analytics Summit: Centered on corporate leaders, the Big Data and Analytics Summit features data experts from top brands, including Uber, sharing strategic insights and best practices around AI, ML, data mining, and big data. The event will enable participants to understand the thought process of leaders and pioneers behind the world’s most reputed firms and utilize their analytics to extract useful information. It will be held by Q3 2022 in Montreal, Canada.
- Deep Learning World: Featuring industry deep learning specialists, Deep Learning World covers practical deep learning applications. The event aims to drive innovations in the value-based operationalization of existing deep learning techniques. As part of the ML week, the event will occur in June 2022 at Caesars Palace, Las Vegas (the US), alongside the five key Predictive Analytics World summits.
Frequently Asked Questions
What does artificial intelligence mean?
Artificial intelligence is the technology that empowers computer systems to mimic a human’s ability to function and react. Specific AI applications include natural language processing (NLP), expert systems, and speech recognition.
How does artificial intelligence work?
In general, AI-driven machines work by parsing through large volumes of labeled training data, assessing the data for patterns and correlations, and leveraging these patterns to predict future possibilities.
Are artificial intelligence and machine learning the same?
Machine learning is a subdivision of artificial intelligence. While ML contains algorithms whose performance enhances as machines ingest more data with time, AI is a program that reasons, senses, adapts, and acts nearly akin to humans.
When did artificial intelligence start?
While the beginnings of artificial intelligence date back to ancient prehistoric Greece and Egypt, the term was officially founded as an academic field in 1956.
How will artificial intelligence change the future?
AI is not only super-powering the workforce but also offers an opportunity for organizations to reimagine themselves. As specific tasks shift from humans to AI-based AI, collaborating with AI assistance will only unfold new avenues for multiple kinds of work.
Moving ahead, businesses and researchers globally will harness the untapped potential of AI and strive to develop self-learning robots. They will explore reinforcement training algorithms derived from generative adversarial networks (GAN).
Furthermore, AI will remain a frontrunner for sustainable solutions and contribute substantially to climate resilience by minimizing pollution levels and promoting eco-friendly AI R&D.
What can artificial intelligence do for businesses?
AI helps businesses in various processes, including data analysis and customer service, and enables them to make faster, laser-focused, and more consistent decisions by using datasets. Consequently, the employees will have more time to focus on other core activities.
Further, AI-driven systems are increasingly crucial in a post-pandemic economy, where scalable AI solutions can help businesses stay prepared even during unprecedented circumstances.
Leverage AI to accelerate your business growth
Before the advent of AI and ML, organizations took critical decisions based on weeks or even months of researching and data accumulation. However, rapid and successive advancements in AI-enabled analytics have significantly made it easier to make those decisions.
Today, including AI in workflows will be more crucial for businesses than minting more money. The ever-evolving technology helps decrease human workload, particularly in big data analytics. Additionally, for companies making important decisions after processing large data chunks, AI-enabled solutions offer clarity. They drive businesses to convert all that data into tangible outcomes, from marketing and sales through supply chains and demand planning.
Get Beroe’s market intelligence report on Artificial Intelligence from there.