What Is Artificial Intelligence (AI)?

Artificial intelligence (AI) is a broad field of computer science concerned with creating intelligent machines capable of doing activities that normally require human intelligence. While AI is an interdisciplinary discipline with many techniques, advances in machine learning and deep learning in particular are causing a paradigm shift in almost every sector of the IT industry.

Artificial intelligence enables machines to mimic, if not outperform, the capabilities of the human mind. From self-driving cars to the growth of smart assistants like Siri and Alexa, AI is fast becoming a part of everyday life — and an area in which corporations across all industries are investing.

Understanding Artificial intelligence (AI)

In general, artificially intelligent systems can execute tasks generally associated with human cognitive functions, such as voice interpretation, game play, and pattern recognition. They often learn how to do so by sifting through enormous volumes of data in search of patterns to model in their own decision-making. Humans will often supervise an AI’s learning process, praising positive decisions while discouraging negative ones. However, other AI systems are designed to learn without supervision, for as by repeatedly playing a video game until they figure out the rules and how to win.

History of Artificial intelligence (AI)

The first intelligent robots and artificial entities appeared in Greek mythology. And the discovery of syllogism and its application of deductive reasoning by Aristotle was a watershed moment in humanity’s search to comprehend its own intelligence. While the roots are extensive, the history of AI as we know it today is less than a century old. The following is a summary of some of the most significant AI events.

1940s

• • (1942) Isaac Asimov releases The Three Laws of Robotics, a popular theory in science fiction media about how artificial intelligence should not hurt people.
  • (1943) Warren McCullough and Walter Pitts write “A Logical Calculus of Ideas Immanent in Nervous Activity,” the first mathematical model for developing a neural network.
  • (1949) Donald Hebb claims in his book The Organization of Behavior: A Neuropsychological Theory that neural pathways are formed from experiences and that connections between neurons become stronger the more frequently they are used. Hebbian learning remains an essential model in AI.

1950s

• • (1950) Alan Turing publishes the paper “Computing Machinery and Intelligence,” in which he proposes what is now known as the Turing Test, a method for judging whether a machine is intelligent.
  • (1950)SNARC, the first neural network computer, is built by Harvard undergraduates Marvin Minsky and Dean Edmonds.
  • (1950)Claude Shannon publishes “Programming a Computer for Playing Chess.”
  • (1952) Arthur Samuel creates a self-learning checkers program.
  • (1954) The Georgetown-IBM machine translation experiment converts 60 carefully selected Russian words into English automatically.
  • (1956) At the Dartmouth Summer Research Project on Artificial Intelligence, the term “artificial intelligence” is coined. The symposium, led by John McCarthy, is widely regarded as the genesis of artificial intelligence.
  • (1956) Allen Newell and Herbert Simon exhibit the first reasoning program, Logic Theorist (LT).
  • (1958) John McCarthy creates the AI programming language Lisp and publishes “Programs with Common Sense,” a paper that proposes the hypothetical Advice Taker, a complete AI system that can learn from experience as well as humans.
  • (1959) Allen Newell, Herbert Simon, and J.C. Shaw create the General Problem Solver (GPS), a program that mimics human problem-solving.
  • (1959) Herbert Gelernter creates the Geometry Theorem Prover program.
  • (1959) While working at IBM, Arthur Samuel coined the term “machine learning”.
  • (1959) The MIT Artificial Intelligence Project was founded by John McCarthy and Marvin Minsky.

1960s

• • (1963) John McCarthy establishes the AI Lab at Stanford.
  • (1966) The United States government’s Automatic Language Processing Advisory Committee (ALPAC) study shows the lack of progress in machine translations research, a significant Cold War program with the promise of automatic and instantaneous Russian translation. All government-funded MT projects will be canceled as a result of the ALPAC findings.
  • (1969) DENDRAL and MYCIN, the first successful expert systems, are developed at Stanford.

1970s

• • (1972) PROLOG, a logic programming language, is invented.
  • (1973) The British government issues the Lighthill Report, which details the failures of AI research and results to major cuts in funding for AI initiatives.
  • (1974-1980) Frustration with the pace of AI development leads to significant DARPA cuts in university grants. When combined with the prior ALPAC report and the Lighthill Report from the previous year, AI funding and development dries up. This is referred to as the “First AI Winter.”

1980s

• • (1980) Digital Equipment Corporations creates R1 (also known as XCON), the first commercially effective expert system. R1, which is designed to configure orders for new computer systems, kicks off an investment boom in expert systems that will persist for the rest of the decade, thus putting an end to the first AI Winter.
  • (1982) The ambitious Fifth Generation Computer Systems project is launched by Japan’s Ministry of International Trade and Industry. FGCS’s goal is to create supercomputer-like performance as well as a platform for AI development.
  • (1983) In response to Japan’s FGCS, the United States creates the Strategic Computing Initiative, which provides DARPA-funded research in advanced computing and artificial intelligence.
  • (1987-1993) As computing technology advanced, more affordable competitors appeared, and the Lisp machine market crashed in 1987, ushering in the “Second AI Winter.” During this time, expert systems were too expensive to maintain and update, and they gradually fell out of favor.

1990s

• • (1991) During the Gulf War, US forces use DART, an automated logistics planning and scheduling technology.
  • (1992) Japan cancels the FGCS project in 1992, alleging failure to reach the grandiose goals laid out a decade earlier.
  • (1993) DARPA terminates the Strategic Computing Initiative in 1993 after over $1 billion was spent and expectations were significantly exceeded.
  • (1997) Deep Blue, an IBM computer, defeats world chess champion Gary Kasparov.

2000s

• • (2005) STANLEY, a self-driving automobile, wins the DARPA Grand Challenge.
  • (2005) The United States military begins to invest in self-driving robots such as Boston Dynamics’ “Big Dog” and iRobot’s “PackBot.”
  • (2008) Google makes advances in speech recognition and incorporates the technology into its iPhone app.

2010s

• • (2011) On Jeopardy!, IBM’s Watson easily defeats the competitors.
  • (2011) Through its iOS operating system, Apple introduces Siri, an AI-powered virtual assistant.
  • (2012) Andrew Ng, founder of the Google Brain Deep Learning project, inputs 10 million YouTube videos into a neural network using deep learning methods as a training set. The neural network learns to recognize a cat without being informed what a cat is, ushering in a new era of financing for neural networks and deep learning.
  • (2014) Google’s self-driving automobile passes a state driving test.
  • (2014) The virtual home smart gadget Alexa from Amazon is released.
  • (2016) AlphaGo, developed by Google DeepMind, defeats world champion Go player Lee Sedol. The complexity of the ancient Chinese game was regarded as a significant barrier to overcome in AI.
  • (2016) Hanson Robotics creates the first “robot citizen,” Sophia, a humanoid robot capable of facial recognition, verbal dialogue, and facial expression.
  • (2018) Google unveils BERT, a natural language processing engine that reduces barriers to translation and interpretation by machine learning applications.
  • (2018) Waymo introduces the Waymo One service, which allows consumers in the Phoenix metropolitan region to order a pick-up from one of the company’s self-driving vehicles.

2020s

• • (2020) Baidu makes available its LinearFold AI algorithm to scientific and medical teams working on vaccine development during the early stages of the SARS-CoV-2 epidemic. The program can anticipate the virus’s RNA sequence in just 27 seconds, which is 120 times faster than prior methods.
  • (2020) OpenAI publishes GPT-3, a natural language processing model capable of producing text in the manner in which humans speak and write.
  • (2021) OpenAI builds on GPT-3 to create DALL-E, which can generate graphics based on text prompts.
  • (2022) The National Institute of Standards and Technology publishes the first draft of its AI Risk Management Framework, a voluntary United States guide “to better manage risks to individuals, organizations, and society associated with artificial intelligence.”
  • (2022) OpenAI introduces ChatGPT, a chatbot powered by a massive language model that quickly gets more than 100 million users.
  • (2023) Microsoft releases an AI-powered version of its search engine, Bing, based on the same technology that powers ChatGPT.
  • (2023) Google announces Bard, a rival conversational AI.

Strong AI Vs. Weak AI

Because intelligence is difficult to define, AI experts often distinguish between strong AI and weak AI.

Strong AI

Strong AI, sometimes known as artificial general intelligence, is a system that, like humans, can solve problems it has never been trained to solve. This is the type of AI we see in movies, such as Westworld’s robots or Star Trek: The Next Generation’s Data. This form of AI does not yet exist.

Many AI researchers consider the creation of a machine with human-level intelligence that can be applied to any task to be the Holy Grail, yet the path to artificial general intelligence has proved challenging. Some argue that strong AI development should be regulated owing to the risks of developing a powerful AI without sufficient safeguards.

In contrast to weak AI, strong AI depicts a machine with a full range of cognitive abilities — and an equally diverse collection of application cases — but time hasn’t made such a feat any easier.

Weak AI

Weak AI, also known as narrow AI or specialized AI, is a simulation of human intelligence applied to a tightly defined problem (such as driving a car, transcribing human speech, or curating material on a website).

Weak AI is frequently focused on executing a specific task exceptionally well. While these robots appear to be clever, they are constrained and limited in ways that even the most rudimentary human intelligence is not.

Examples of weak AI include:

• Siri, Alexa and other Smart Assistants
• Self-Driving Cars
• Google Search
• Conversational Bots
• Email Spam Filters
• Netflix’s Recommendations

Machine Learning Vs. Deep Learning

Although “Deep Learning” and “Machine Learning” are frequently used interchangeably in discussions of AI, they should not be. Machine learning, which includes deep learning, is a branch of artificial intelligence.

Machine Learning

A machine learning algorithm is given data by a computer and employs statistical techniques to “learn” how to get increasingly better at a task without being particularly programmed for that activity. ML algorithms, on the other hand, use historical data as input to anticipate new output values. To that purpose, machine learning (ML) includes both supervised learning (where the expected output for the input is known due to labeled data sets) and unsupervised learning (where the expected outputs are unknown due to the use of unlabeled data sets).

Deep Learning

Deep learning is a sort of machine learning that uses a biologically inspired neural network design to process data. The neural networks have a number of hidden layers that analyze the data, allowing the computer to go “deep” in its learning, creating connections and weighing input for the best results.

Types of Artificial intelligence (AI)

AI is classified into four categories based on the type and complexity of jobs that a system can execute. They are as follows:

1. Reactive Machines
2. Limited Memory
3. Theory of Mind
4. Self Awareness

1. Reactive Machines

A Reactive Machine adheres to the most fundamental AI principles and, as the name suggests, is only capable of using its intellect to observe and react to the world in front of it. Because a reactive machine lacks memory, it cannot depend on prior experiences to influence real-time decision making.

Because reactive machines perceive the world immediately, they are only designed to do a few specialized tasks. However, intentionally restricting a reactive machine’s vision has advantages: This sort of AI will be more trustworthy and reliable, and it will respond consistently to the same stimuli.

Reactive Machine Examples

• Deep Blue was created by IBM in the 1990s as a chess-playing supercomputer and defeated international grandmaster Gary Kasparov in a game. Deep Blue was only capable of detecting the pieces on a chess board and knowing how each moves according to the rules of chess, acknowledging each piece’s current position, and determining what the most logical move would be at that time. The machine was not anticipating future moves by its opponent or attempting to better place its own pieces. Every turn was considered as its own reality, distinct from any previous movement.
• Google’s AlphaGo is superior to Deep Blue in more complicated games despite not being capable of judging future plays and instead relying on its own neural network to assess game developments in the present. In 2016, champion Go player Lee Sedol was defeated by AlphaGo, which has already defeated other top-tier opponents in the game.

2. Limited Memory

When gathering information and assessing options, limited memory AI has the capacity to store earlier facts and forecasts, effectively looking back in time for hints on what might happen next. Reactive machines lack the complexity and potential that limited memory AI offers.

restricted memory An AI environment is developed so that models can be automatically taught and refreshed, or AI is created when a team continuously teaches a model in how to understand and use new data.

When utilizing limited memory AI in ML, six steps must be followed:

1. Establish training data
2. Create the machine learning model
3. Ensure the model can make predictions
4. Ensure the model can receive human or environmental feedback
5. Store human and environmental feedback as data
6. Reiterate the steps above as a cycle

3. Theory of Mind

It is only speculative to have a theory of mind. The technological and scientific advancements required to reach this advanced level of AI have not yet been attained.

The idea is founded on the psychological knowledge that one’s own behavior is influenced by the thoughts and feelings of other living creatures. This would imply that AI computers may understand how people, animals, and other machines feel and make decisions through introspection and determination, and then use that knowledge to make their own decisions. In order to create a two-way relationship between humans and AI, robots would essentially need to be able to understand and interpret the concept of “mind,” the fluctuations of emotions in decision-making, and a litany of other psychological concepts in real time.

4. Self Awareness

The final stage of AI development will be for it to become self-aware after theory of mind has been created, which will likely take a very long time. This sort of AI is conscious on a par with humans and is aware of both its own presence and the presence and emotional states of others. It would be able to comprehend what other people could need based on both what they say to them and how they say it.

AI self-awareness depends on human researchers being able to comprehend the basis of consciousness and then figure out how to reproduce it in machines.

Artificial Intelligence Examples

Chatbots, navigation apps, and wearable fitness trackers are just a few examples of the many applications of artificial intelligence technology. The examples below show the range of possible uses for AI.

1. ChatGPT

ChatGPT is an artificial intelligence chatbot that can generate textual content in a variety of formats, including essays, code, and replies to simple queries. ChatGPT, which will be released by OpenAI in November 2022, is powered by a vast language model that allows it to closely mimic human writing.

2. Google Maps

Google Maps monitors the ebb and flow of traffic and determines the shortest route using location data from smartphones as well as user-reported data on things like construction and car accidents.

3. MuZero

MuZero, a DeepMind computer program, is a promising frontrunner in the race to achieve true artificial general intelligence. It has mastered games it was never taught to play, such as chess and a whole suite of Atari games, by using brute force and replaying games millions of times.

4. Smart Assistants

Natural language processing, or NLP, is used by personal assistants such as Siri, Alexa, and Cortana to receive user commands to set reminders, search for internet information, and operate lights in people’s homes. Many of these assistants are supposed to learn a user’s preferences and improve their experience over time by making better suggestions and providing more personalized responses.

5. Self-Driving Cars

Self-driving cars are a well-known example of deep learning since they employ deep neural networks to detect items in their surroundings, assess their distance from other cars, recognize traffic signals, and much more.

6. Wearables

Deep learning is also employed in wearable sensors and gadgets used in the healthcare business to measure the patient’s health status, including blood sugar levels, blood pressure, and heart rate. They can also extract patterns from a patient’s prior medical data and utilize them to predict future health problems.

7. Snapchat Filters

Snapchat filters employ machine learning algorithms to distinguish between the foreground and background of an image, track facial movements, and modify the image on the screen based on what the user is doing.

Benefits of Artificial Intelligence

AI has numerous applications, ranging from accelerating vaccine development to automating the identification of potential fraud. According to CB Insights data, AI startups raised $66.8 billion in funding in 2022, more than tripling the amount raised in 2020. AI is creating waves in a multitude of areas due to its rapid adoption.

1. Safer Banking

According to Business Insider Intelligence’s AI in Banking 2022 research, more than half of financial services organizations now utilize AI solutions for risk management and revenue generation. The use of AI in banking could result in savings of up to $400 billion.

2. Better Medicine

In terms of medicine, a 2021 World Health Organization research stated that, while incorporating AI into the healthcare industry presents hurdles, the technology “holds great promise,” since it might lead to benefits such as more informed health policy and improved patient diagnosis accuracy.

3. Innovative Media

AI has also made an impact in the entertainment industry. According to Grand View Research, the global market for AI in media and entertainment is expected to reach $99.48 billion by 2030, up from $10.87 billion in 2021. This upgrade contains AI applications such as detecting plagiarism and creating high-definition visuals.

Challenges and Limitations of Artificial Intelligence (AI)

Although AI is undoubtedly seen as a valuable and rapidly developing asset, this young area is not without its drawbacks.

In 2021, the Pew Research Center polled 10,260 Americans about their views on AI. According to the findings, 37% of respondents are more concerned than excited, while 45% of respondents are both excited and concerned. Furthermore, more than 40% of respondents said they believed driverless automobiles will be detrimental to society. Even still, more respondents to the survey (almost 40%) thought it was a good idea to use AI to track the spread of incorrect information on social media.

AI is a blessing for increasing efficiency and productivity while also lowering the possibility of human error. However, there are some drawbacks as well, such as the expense of development and the potential for robots to take over human occupations. It’s important to remember, though, that the artificial intelligence sector has the potential to provide a variety of occupations, some of which haven’t even been imagined yet.

Future of Artificial Intelligence (AI)

When one takes into account the computing costs and the technological data infrastructure that support artificial intelligence, putting AI into practice is a difficult and expensive endeavor. Fortunately, there have been significant advances in computing technology, as demonstrated by Moore’s Law, which claims that the price of computers is cut in half while the number of transistors on a microchip doubles roughly every two years.

Moore’s Law has had a significant impact on present AI approaches, and without it, deep learning wouldn’t be feasible from a financial standpoint until the 2020s, according to several experts. According to recent study, Moore’s Law has actually been outpaced by AI innovation, which doubles roughly every six months as opposed to every two years.

According to such reasoning, over the past few years, artificial intelligence has significantly advanced a number of industries. Over the coming decades, there is a strong possibility for an even bigger influence.

What are the Applications of Artificial Intelligence (AI)?

Artificial intelligence has found its way into a wide range of industries.

1. AI in Healthcare

The most money is being bet on improving patient outcomes and lowering expenses. Machine learning is being used by businesses to make better and faster medical diagnosis than people. IBM Watson is a well-known healthcare technology. It understands natural language and can react to inquiries. The system mines patient data as well as other available data sources to generate a hypothesis, which it then provides with a confidence grading schema. Other AI applications include the use of online virtual health assistants and chatbots to aid patients and healthcare customers in locating medical information, scheduling appointments, understanding the billing process, and completing other administrative tasks. AI technologies are also being utilized to anticipate, battle, and comprehend pandemics like COVID-19.

2. AI in Education

Grading can be automated with AI, providing educators more time for other duties. It is capable of assessing students and adapting to their needs, allowing them to work at their own pace. AI tutors can help students stay on track by providing extra assistance. Technology may also alter where and how kids study, possibly even replacing certain professors. As demonstrated by ChatGPT, Bard, and other big language models, generative AI may assist instructors in creating course work and other instructional materials, as well as engaging students in novel ways. The introduction of these tools also forces instructors to reconsider student assignments and assessment, as well as alter plagiarism policies.

3. AI in Law

In law, the discovery procedure (sifting through documents) can be daunting for humans. Using AI to help automate labor-intensive operations in the legal business saves time and improves client experience. Machine learning is used by law firms to characterize data and anticipate outcomes, computer vision is used to classify and extract information from documents, and natural language processing (NLP) is used to interpret information requests.

4. Security

Buyers should proceed with caution because AI and machine learning are at the top of the list of buzzwords used by security companies to sell their solutions. Nonetheless, AI techniques are being successfully used to a variety of facets of cybersecurity, including anomaly detection, false-positive detection, and behavioral threat analytics. Machine learning is used by organizations in security information and event management (SIEM) software and related domains to detect abnormalities and suspicious actions that suggest dangers. AI can detect new and developing attacks far faster than human employees or prior technology iterations by evaluating data and using reasoning to spot similarities to known harmful code.

5. AI in Business

Machine learning algorithms are being integrated into analytics and customer relationship management (CRM) platforms to understand how to better service clients. Chatbots have been integrated into websites to give customers with immediate support. The rapid progress of generative AI technologies, such as ChatGPT, is projected to have far-reaching repercussions, such as employment loss, product redesign revolution, and business model disruption.

6. AI in Entertainment and Media

The entertainment industry use AI approaches for targeted advertising, content recommendation, distribution, fraud detection, script creation, and film production. Automated journalism assists newsrooms in streamlining media workflows, so saving time, money, and complexity. AI is used in newsrooms to automate regular jobs such as data entry and proofreading, as well as to research themes and assist with headlines. It’s unclear how journalism can rely on ChatGPT and other generative AI to generate content on a consistent basis.

7. AI in Finance

AI in personal finance apps like Intuit Mint and TurboTax is upending financial institutions. These kind of applications capture personal information and offer financial advise. Other programs, including as IBM Watson, have been used in the home-buying process. Today, artificial intelligence software handles the majority of Wall Street trading.

8. AI in Software Coding and IT Processes

New generative AI tools can be used to generate application code based on natural language cues, but these technologies are still in their early stages and are unlikely to replace software engineers anytime soon. Many IT tasks, including data entry, fraud detection, customer service, and predictive maintenance and security, are also being automated with AI.

9. AI in Manufacturing

Manufacturing has been a pioneer in integrating robots into the workflow. For example, industrial robots that were once programmed to perform single tasks while being separated from human workers are increasingly being used as cobots: smaller, multitasking robots that collaborate with humans and take on more responsibilities in warehouses, factory floors, and other workspaces.

10. AI in Transportation

Aside from playing a critical role in autonomous vehicle operation, AI technologies are utilized in transportation to control traffic, predict airline delays, and make ocean freight safer and more efficient. AI is replacing traditional techniques of anticipating demand and predicting disruptions in supply chains, a tendency hastened by COVID-19, when many corporations were caught off guard by the consequences of a global pandemic on products supply and demand.

11. AI in Banking

Banks are successfully using chatbots to inform clients about services and opportunities, as well as to manage transactions that do not require human participation. AI virtual assistants are utilized to improve and reduce the expenses of banking regulatory compliance. AI is used by banking firms to improve loan decision-making, set credit limits, and identify investment opportunities.

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AI Tools and Services

AI tools and services are rapidly evolving. Current advances in AI tools and services may be traced back to the AlexNet neural network, which debuted in 2012, ushering in a new era of high-performance AI built on GPUs and big data sets. The capacity to train neural networks on enormous volumes of data across several GPU cores in parallel in a more scalable manner was the significant advance.

The symbiotic link between AI discoveries at Google, Microsoft, and OpenAI, and hardware innovations pioneered by Nvidia, has enabled running ever-larger AI models on more connected GPUs, delivering game-changing increases in performance and scalability over the last several years.

Collaboration among these AI luminaries was critical to the recent success of ChatGPT, as well as hundreds of other game-changing AI services. Here is a list of significant advancements in AI tools and services.

1. Transformers

Google, for example, pioneered a more effective method of delivering AI training over a huge cluster of commodity PCs equipped with GPUs. This cleared the path for the development of transformers, which automate many parts of AI training on unlabeled data.

2. AI Cloud Services

The data engineering and data science efforts required to weave AI capabilities into new apps or develop new ones are among the most significant hurdles that prohibit firms from effectively employing AI in their businesses. All of the major cloud providers are launching their own branded AI as a service products to simplify data preparation, model creation, and application deployment. AWS AI Services, Google Cloud AI, Microsoft Azure AI platform, IBM AI solutions, and Oracle Cloud Infrastructure AI Services are just a few examples.

3. Cutting-Edge AI Models as a Service

On top of these cloud services, leading AI model developers provide cutting-edge AI models. OpenAI provides thousands of large language models designed for conversation, NLP, image generation, and code generation via Azure. Nvidia has taken a cloud-agnostic strategy, delivering AI infrastructure and fundamental models suited for text, pictures, and medical data to all cloud providers. Hundreds of other firms are now offering models tailored to specific industries and use cases.

4. Hardware Optimization

Equally important, hardware makers such as Nvidia are optimizing the microcode for the most popular algorithms to execute across several GPU cores in parallel. Nvidia claims that a combination of faster hardware, more efficient AI algorithms, fine-tuned GPU instructions, and improved data center integration is resulting in a million-fold gain in AI performance. Nvidia is also collaborating with all cloud service providers to make this capacity more widely available as AI-as-a-Service via IaaS, SaaS, and PaaS models.

5. Generative Pre-Trained Transformers

The AI stack has also advanced significantly in recent years. Previously, businesses had to train their AI models from scratch. Vendors such as OpenAI, Nvidia, Microsoft, Google, and others are increasingly offering generative pre-trained transformers (GPTs), which can be fine-tuned for a specific purpose at a significantly lower cost, expertise, and time. While some of the most complex models are expected to cost $5 million to $10 million per run, firms can fine-tune the generated models for a few thousand dollars. This eliminates risk and accelerates time to market.

Conclusion

Artificial intelligence (AI) is a game-changing technology that has revolutionized several sectors and the way we live and work. It refers to the creation of computer systems capable of doing tasks that normally require human intelligence, such as learning, problem solving, and decision making. AI has paved the way for virtual personal assistants, recommendation systems, picture and speech recognition, self-driving cars, and many more applications. Its uses are numerous and growing, providing intriguing future possibilities. As AI advances, it has the ability to solve complicated problems, improve efficiency, and improve our daily lives in remarkable ways. Accepting and comprehending AI will be critical in navigating the quickly changing digital landscape and realizing its full potential for societal benefit.