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What is AI? Artificial intelligence (AI) is the ability of a machine or computer to imitate the capabilities of the human mind. AI taps into multiple technologies to equip machines in planning, acting, comprehending, learning, and sensing with human-like intelligence. AI systems may perceive environments, recognize objects, make decisions, solve problems, learn from experience, and imitate examples. These abilities are combined to accomplish actions that would otherwise require humans to do, such as driving a car or greeting a guest.

Artificial intelligence may have entered everyday conversation over the last decade or so but it has been around much longer (see the History of AI section below). The relatively recent rise in its prominence is not by accident.

AI technology, and especially machine learning, relies on the availability of vast volumes of information. The proliferation of the Internet, the expansion of cloud computing, the rise of smartphones, and the growth of the Internet of Things has created enormous quantities of data that grows every day. This treasure trove of information combined with the huge gains made in computing power have made the rapid and accurate processing of enormous data possible.

Today, AI is completing our chat conversations, suggesting email responses, providing driving directions, recommending the next movie we should stream, vacuuming our floors, and performing complex medical image analyses.

The history of artificial intelligence goes as far back as ancient Greece. However, its the rise of electronic computing that made AI a real possibility. Note that what is considered AI has changed as the technology evolves. For example, a few decades ago, machines that could perform optimal character recognition (OCR) or simple arithmetic were categorized as AI. Today, OCR and basic calculations are not considered AI but rather an elementary function of a computer system.

Artificial intelligence asserts that there are principles governing the actions of intelligent systems. It is based on reverse-engineering human capabilities and traits onto a machine. The system uses computational power to exceed what the average human is capable of doing. The machine must learn to respond to certain actions. It relies on historical data and algorithms to create a propensity model. Machines learn from experience to perform cognitive tasks that are ordinarily the preserve of the human brain. The system automatically learns from features or patterns in the data.

AI is founded on two pillars engineering and cognitive science. The engineering involves building the tools that rely on human-comparable intelligence. Large volumes of data are combined with series of instructions (algorithms) and rapid iterative processing. Cognitive science involves emulating how the human brain works, and brings to AI multiple fields including machine learning, deep learning, neural networks, cognitive computing, computer vision, natural language processing, and knowledge reasoning.

Artificial intelligence isnt one type of system. Its a diverse domain. Theres the simple, low-level AI systems focused on performing a specific task such as weather apps, business data analysis apps, taxi hailing apps, and digital assistants. This is the type of AI, called "Narrow AI", that the average person is most likely to interact with. Their main purpose is driving efficiency.

On the other end of the spectrum are advanced systems that emulate human intelligence at a more general level and can tackle complex tasks. These include thinking creatively, abstractly, and strategically. Strictly speaking, this kind of truly sentient machine, called "Artificial General Intelligence" or AGI, only exists on the silver screen for now, though the race toward its realization is accelerating.

Humans have pursued artificial intelligence in recognition of how invaluable it can be for business innovation and digital transformation. AI can cut costs and introduce levels of speed, scalability, and consistency that is otherwise out of reach. You probably interact with some form of AI multiple times each day. The applications of AI are too numerous to exhaustively cover here. Heres a high level look at some of the most significant ones.

As cyberattacks grow in scale, sophistication, and frequency, human-dependent cyber defenses are no longer adequate. Traditionally, anti-malware applications were built with specific threats in mind. Virus signatures would be updated as new malware was identified.

But keeping up with the sheer number and diversity of threats eventually becomes a near impossible task. This approach was reactive and depended on the identification of a specific malware for it to be added to the next update.

AI-based anti-spam, firewall, intrusion detection/prevention, and other cybersecurity systems go beyond the archaic rule-based strategy. Real-time threat identification, analysis, mitigation, and prevention is the name of the game. They deploy AI systems that detect malware traits and take remedial action even without the formal identification of the threat.

AI cybersecurity systems rely on the continuous feed of data to recognize patterns and backtrack attacks. By feeding algorithms large volumes of information, these systems learn how to detect anomalies, monitor behavior, respond to threats, adapt to attack, and issue alerts.

Also referred to as speech-to-text (STT), speech recognition is technology that recognizes speech and converts it into digital text. Its at the heart of computer dictation apps, as well as voice-enabled GPS and voice-driven call answering menus.

Natural language processing (NLP) relies on a software application to decipher, interpret, and generate human-readable text. NLP is the technology behind Alexa, Siri, chatbots, and other forms of text-based assistants. Some NLP systems use sentiment analysis to make out the attitude, mood, and subjective qualities in a language.

Also known as machine vision or computer vision, image recognition is artificial intelligence that allows one to classify and identify people, objects, text, actions, and writing occurring within moving or still images. Usually powered by deep neural networks, image recognition has found application in self-driving cars, medical image/video analysis, fingerprint identification systems, check deposit apps, and more.

E-commerce and entertainment websites/apps leverage neural networks to recommend products and media that will appeal to the customer based on their past activity, the activity of similar customers, the season, the weather, the time of day, and more. These real-time recommendations are customized to each user. For e-commerce sites, recommendations not only grow sales but also help optimize inventory, logistics, and store layout.

The stock market can be extremely volatile in times of crisis. Billions of dollars in market value may be wiped out in seconds. An investor who was in a highly profitable position one minute could find themselves deep in the red shortly thereafter. Yet, its near impossible for a human to react quick enough to market-influencing events. High-frequency trading (HFT) systems are AI-driven platforms that make thousands or millions of automated trades per day to maintain stock portfolio optimization for large institutions.

Lyft, Uber, and other ride-share apps use AI to connect requesting riders to available drivers. AI technology minimizes detours and wait times, provides realistic ETAs, and deploys surge-pricing during spikes in demand.

Self-driving cars are not yet standard in most of the world but theres already been a concerted push to embed AI-based safety functions to detect dangerous scenarios and prevent accidents.

Unlike land-based vehicles, the margin for error in aircraft is extremely narrow. Given the altitude, a small miscalculation may lead to hundreds of fatalities. Aircraft manufacturers had to push safety systems and become one of the earliest adopters of artificial intelligence.

To minimize the likelihood and impact of human error, autopilot systems have been flying military and commercial aircraft for decades. They use a combination of GPS technology, sensors, robotics, image recognition, and collision avoidance to navigate planes safely through the sky while keeping pilots and ground crew updated as needed.

Artificial Intelligence accelerates and simplifies test creation, execution, and maintenance through AI-powered intelligent test automation. AI-based machine learning and advanced optical character recognition (OCR) provide for advanced object recognition, and when combined with AI-based mockup identification, AI-based recording, AI-based text matching, and image-based automation, teams can reduce test creation time and test maintenance efforts,and boost test coverage and resilience of testing assets.

Artificial intelligence allows you to test earlier and faster with functional testing solutions. Combine extensive technology support with AI-driven capabilities. Deliver the speed and resiliency that supports rapid application changes within a continuous delivery pipeline.

Both IT and business face the challenges of too many manual, error-prone workflows, an ever-increasing volume of requests, employees dissatisfied with the level and quality of service, and more. Artificial Intelligence and machine learning technology can take service management to the next level:

Read How AI Is Enabling Enterprise Service Management from the resource list below for more thoughts and information on the role of artificial intelligence (AI) in the adoption and expansion of enterprise service management (ESM).

What is true of IT support, is also true for ESM; AI makes operations and outcomes better. To find out more read Ten Tips for Empowering Your IT Support with AI.

Robotic process automation (RPA) uses software robots that mimic screen-based human actions to perform repetitive tasks and extend automation to interfaces with difficult or no application programming interfaces (APIs). Thats why RPA is perfect for automating processes typically completed by humans or that require human intervention. Resilient robots adapt to screen changes and keep processes flowing when change happens. When powered by AI-based machine learning, RPA robots identify screen objects even ones they havent seen before and emulate human intuition to determine their functions. They use OCR to read text (for example, text boxes and links) and computer vision to read visual elements (for example, shopping cart icons and login buttons). When a screen object changes, robots adapt. Machine learning drives them to continuously improve how they see and interact with screen objects just like a human would.

There are plenty of ways you could leverage artificial intelligence for your business to stay competitive, drive growth, and unlock value. Nevertheless, your organization doesnt possess infinite resources. You must prioritize. Begin by defining what your organizations values and strategic objectives are. From that point, assess the possible applications of AI against these values and objectives. Choose the AI technology that is bound to deliver the biggest impact for the business.

The world is only going to grow more AI-dependent. Its no longer about whether to adopt AI but when. Organizations that tap into AI ahead of their peers could gain a significant competitive advantage. Developing and pursuing a well-defined AI strategy is where it all begins. It may take a bit of experimenting before you know what will work for you.

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What Is Artificial Intelligence (AI)? | Micro Focus

Artificial intelligence (AI) has taken on many new shapes and use cases as experts learn more about whats possible with big data and smart algorithms.

Todays AI market, then, consists of a mixture of tried-and-true smart technologies with new optimizations and advanced AI that is slowly transforming the way we do work and live daily life.

Read on to learn about some artificial intelligence trends that are making experts most excited for the future of AI:

More on the AI market: Artificial Intelligence Market

With its ability to follow basic tasks and routines based on smart programming and algorithms, artificial intelligence is becoming embedded in the way organizations automate their business processes.

AIOps and MLops are common use cases for AI and automation, but the breadth and depth of what AI can automate in the enterprise is quickly growing.

Bali D.R., SVP at Infosys, a global digital services and consulting firm, believes that AI is moving toward a certain level of hyper-automation, partially in response to the unexpected changes in manual data and procedures caused by the pandemic.

We are in the second inflection point for AI as it graduates from consumer AI, towards enterprise-grade AI, D.R. said. Being exposed to an over-reliance on manual procedures, such as mass rescheduling in the airline industry, unprecedented loan applications in banks, etc., the industries are now turning to hyper-automation that combines robotic process automation with modern machine learning to ensure they can better handle surges in the future.

Although AI automation is still mostly limited to interval and task-oriented automation that requires little imagination or guesswork on the part of the tool, some experts believe we are moving closer to more applications for intelligent automation.

David Tareen, director for artificial intelligence at SAS, a top analytics and AI software company, had this to say about the future of intelligent automation:

Intelligent automation is an area I expect to grow, Tareen said. Just like we automated manufacturing work, we will use AI heavily to automate knowledge work.

The complexity comes in because knowledge work has a high degree of variability. For example, an organization will receive feedback on their products or services in different ways and often in different languages as well. AI will need to ingest, understand, and modify processes in real-time before we can automate knowledge work at large.

AI, automation, and the job market: Artificial Intelligence and Automation

Because of the depth of big data and AIs reliance on it, theres always the possibility that unethical or ill-prepared data will make it into an AI training data set or model.

As more companies recognize the importance of creating AI that conducts its operations in a compliant and ethical manner, a number of AI developers and service providers are starting to offer responsible AI solutions to their customers.

Read Maloney, SVP of marketing at H2O.ai, a top AI and hybrid cloud company, explained what exactly responsible AI is and some of the different initiatives that companies are undertaking to improve their AI ethics.

AI creates incredible new opportunities to improve the lives of people around the world, Maloney said. We take the responsibility to mitigate risks as core to our work, so building fairness, interpretability, security, and privacy into our AI solutions is key.

Maloney said the market is seeing an increased adoption of the core pillars of responsible AI, which he shared with Datamation:

Companies are exploring several ways to make their AI more responsible, and most are starting with cleaning and assessing both data sets and existing AI models.

Brian Gilmore, director of IoT product management at InfluxData, a database solutions company, believes that one of the top options for model and data set management is distributed ledger technology (DLT).

As attention builds around the ethical and cultural impact of AI, some organizations are beginning to invest in ancillary but important technologies that utilize consensus and other trust-ensuring systems as a part of the AI framework, Gilmore said. For example, distributed ledger technology provides a sidecar platform for auditable proof of integrity for models and training data.

The decentralized ownership, distribution of access, and shared accountability of DLT can bring significant transparency to AI development and application across the board. The dilemma is whether for-profit corporations are willing to participate in a community model, trading transparency for consumer trust in something as mission critical as AI.

See more: The Ethics of Artificial Intelligence (AI)

Up to this point, AI has most frequently been used to optimize business processes and automate some home routines for consumers.

However, some experts are beginning to realize the potential that AI-powered models can have for solving global issues.

Read Maloney at H2O.ai has worked with people from a variety of industries to brainstorm how AI can be used for the greater good.

We work with many like-minded customers, partners, and organizations tackling issues from education, conservation, health care, and more, Maloney said. AI for good is fundamental to not only our work, including current work on climate change, wildfires, and hurricane predictions, but we are seeing more and more AI for good work to make the world a better place across the AI industry.

Some of the most exciting applications of altruistic AI are being implemented in early education right now.

For instance, Helen Thomas, CEO ofDMAI, an AI-powered health care and education company, offers an AI-powered product to ensure that preschool-aged children are getting the education they need, despite potential pandemic setbacks:

On top of pre-existing barriers to preschool education, including cost and access, recent research findings suggest children born during the COVID-19 pandemic display lower IQ scores than those born before January 2020, which means toddlers are less prepared for school than ever before.

DMAI DBA Animal Island Learning Adventure (AILA) is changing this with AI. [Our product] harnesses cognitive AI to deliver appropriate lessons in a consistent and repetitious format, supportive of natural learning patterns

Recognizing learning patterns that parents might miss, the AI creates an adaptive learning journey and doesnt allow the child to move forward until theyve mastered the skills and concepts presented. This intentional delivery also increases attention span over time, ensuring children step into the classroom with the social-emotional intelligence to succeed.

More on this topic: How AI is Being Used in Education

Internet of Things (IoT) devices have become incredibly widespread among both enterprise and personal users, but what many tech companies still struggle with is how to gather actionable insights from the constant inflow of data from these devices.

AIoT, or the idea of combining artificial intelligence with IoT products, is one field that is starting to address these pools of unused data, giving AI the power to translate that data quickly and intelligently.

Bill Scudder, SVP and AIoT general manager at AspenTech, an industrial AI solutions company, believes that AIoT is one of the most crucial fields for enabling more intelligent, real-time business decisions.

Forrester has noted that up to 73% of all data collected within the enterprise goes unused, which highlights a critical challenge with IoT, Scudder said. As the volume of connected devices for example, in industrial IoT settings continues to increase, so does the volume of data collected from these devices.

This has resulted in a trend seen across many industries: the need to marry AI and IoT. And heres why: where IoT allows connected devices to create and transmit data from various sources, AI can take that data one step further, translating data into actionable insights to fuel faster, more intelligent business decisions. This is giving way to the rising trend of artificial intelligence of things or AIoT.

Decision intelligence (DI) is one of the newest artificial intelligence concepts that takes many current business optimizations a step farther, by using AI models to analyze wide-ranging sets of commercial data. These analyses are used to predict future outcomes for everything from products to customers to supply chains.

Sorcha Gilroy, data science team lead at Peak, a commercial AI solutions provider, explained that although decision intelligence is a fairly new concept, its already gaining traction with larger enterprises because of its detailed business intelligence (BI) offerings.

Decision intelligence is a new category of software that facilitates the commercial application of artificial intelligence, providing predictive insight and recommended actions to users, Gilroy said. It is outcome focused, meaning a solution must deliver against a business need before it can be classed as DI.

Recognized by Gartner and IDC, it has the potential to be the biggest software category in the world and is already being utilized by businesses across a variety of use cases, from personalizing shopper experiences to streamlining complex supply chains. Brands such as Nike, PepsiCo, and ASOS are known to be using DI already.

Read next: Top Performing Artificial Intelligence Companies

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Artificial Intelligence Trends & Predictions for 2022 | Datamation

Many have suggested that AI-based algorithms represent the greatest current opportunity for human progress. But their unpredictability represents the greatest threat as well, and it has not been precisely clear what steps should be taken by us as end users. Kartik Hosanagar, John C. Hower Professor; Professor of Operations, Information, and Decisions, The Wharton School

Artificial Intelligence for Business is an online program for learners seeking the competitive edge in emerging business technology. Technology-oriented professionals, online marketers, statisticians, automation innovators and data professionals will benefit from this 4-week certificate.

In the artificial intelligence course, youll learn the fundamentals of Big Data, Artificial Intelligence, and Machine Learning, and how to deploy these technologies to support your organizations strategy. Professor Kartik Hosanagar of the Wharton School has designed this course to help you gain a better understanding of AI and Machine Learning, using real-life examples. Youll learn about the different types and methods of Machine Learning, and how businesses have applied Machine Learning successfully. Youll also cover the ethics and risks of AI in business management, and how to design governance frameworks for proper implementation. By the end of this course, youll have a foundational understanding of artificial intelligence in business and be able to incorporate these technologies into your business strategy.

The Artificial Intelligence for Business program is designed to provide learners with insights into the established and emerging developments in AI for business. This includes Big Data, Machine Learning in finance, and the operational changes AI will bring. The lessons within this course are applicable to multiple industries and dynamic markets. This course is taught by internationally-recognized internet marketing and media business professor, Kartik Hosanagar, PhD, and takes into account the latest data and insights in the AI realm.

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Artificial Intelligence (AI) for Business Course Wharton

The Internal Revenue Service unveiled a new artificial intelligence system it says will cut wait times to resolve simple tasks and improve customer service.

The technology enables the new phone system to authenticate callers by asking them basic questions, IRS officials said during a call with reporters Friday. The new system can understand complete and natural ways of speaking, they said.

For the first time in 160 years, this agency is able to successfully interact with a taxpayer using artificial intelligence to access their account and resolve it, in certain situations, without any wait on hold, IRS Deputy Commissioner Darren Guillot said during the call.

When taxpayers receive a mailed letter stating they owe money, they can use an ID number from the letter to call in and access the improved system, agency officials explained.

Frederick Schindler, the agency's director of collection, said his team staggered the generation and mailing of over 3 million letters so they will arrive in mailboxes in the coming days, enabling callers to make use of the new system.

In this photo illustration an IRS logo seen displayed on a smartphone.

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The IRS efforts to improve its phone system come roughly three months after the statutory body said it would hire 10,000 additional employees to cut through a pandemic-related backlog.

Expanding the phone bot with artificial intelligence demonstrates an improvement over the previous phone system, the IRS officials said. The previous unauthenticated phone bot could only answer basic questions and allowed callers to set up one-time payments, they said.

That more basic technology, which does not allow the system to pull up a person's IRS account, is also the technology behind an online chatbox the agency uses.

Because of the authentication capability of the new bot, it can access a callers IRS account. From there, callers can discuss and set up a payment plan with the bot without spending time on hold a process that would typically take 17-20 minutes with a human operator, IRS officials said.

By allowing the phone bot to handle more simple issues, it frees up human operators for more complex matters, the IRS officials said.

Treasury Department Deputy Secretary Wally Adeyemo recently told ABC News that the IRS received over 200 million calls and only had 15,000 people to answer those calls last year.

Even with the intelligent phone bot, callers will still have the option to speak with a human for additional support, IRS officials said.

Many callers owe less than $25,000, and can name their price, or the monthly amount they will commit to paying. The artificial intelligence system then computes that amount to determine whether it falls within the agency's deadline for repayment.

The Internal Revenue Service building is seen in Washington, D.C, April 5, 2022.

Stefani Reynolds/AFP via Getty Images, FILE

The new bot will not guide callers to pay more than the price they name, the officials explained.

While officials on the call admitted the new phone bot will offer a return on investment through expanded compliance, he said increasing government revenue was not the primary focus of developing the system.

Service is part of our name, Guillot said. This is all about the taxpayer experience, helping customers, he said later.

But not all callers will enjoy the no-wait time the authenticated phone bot offers. It launched only on the automated collection system and accounts management phone lines Tuesday, the IRS officials said.

For now, it is operating at 25% of its intended capacity, which saw the bot answer over 13,000 calls Thursday. The IRS plans to bring more of the system online through the end of next week, IRS officials said.

We have phone lines to deal with specific issues like liens or settlement proposals, Schindler said. In the future, theres use cases for taking this technology, particularly as we learn more about it, to any one of our collection processes.

The bot currently operates in English and Spanish, with IRS officials hoping to expand its language offerings in the future, they said.

More immediate expansion plans include programming the authenticated bot to ask questions of callers who name their monthly payments to ensure it is within their financial means, the officials said.

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IRS rolls out artificial intelligence to help callers make payments ...

Due to hype and popular fiction, the dangers of artificial intelligence (AI) are typically associated in the public eye with Sci-Fi horror scenarios. These often involve killer robots and hyper-intelligent computer systems which consider humanity a nuisance that needs to be gotten rid of for the good of the planet. While nightmares like this often play out as overblown and silly in comic books and on-screen, the risks of artificial intelligence cannot be dismissed so lightly and AI dangers do exist.

In this article, well be looking at some of the real risks of artificial intelligence, and why AI is dangerous when looked at in certain contexts or wrongly applied.

Artificial intelligence encompasses a range of technologies and systems ranging from Googles search algorithms, through smart home gadgets, to military-grade autonomous weapons. So issuing a blanket confirmation or denial to the question Is Artificial Intelligence Dangerous? isnt that simple the issue is much more nuanced than that.

Most artificial intelligence systems today qualify as weak or narrow AI technologies designed to perform specific tasks such as searching the internet, responding to environmental changes like temperature, or facial recognition. Generally speaking, narrow AI performs better than humans at those specific tasks.

For some AI developers, however, the Holy Grail is strong AI or artificial general intelligence (AGI), a level of technology at which machines would have a much greater degree of autonomy and versatility, enabling them to outperform humans in almost all cognitive tasks.

While the super intelligence of strong AI has the potential to help us eradicate war, disease, and poverty, there are significant dangers of artificial intelligence at this level. However, there are those who question whether strong AI will ever be achieved, and others who maintain that if and when it does arrive, it can only be beneficial.

Optimism aside, the increasing sophistication of technologies and algorithms may have the result that AI is dangerous if its goals and implementation run contrary to our own expectations or objectives. The risks of AI in this context may hold even at the level of narrow or weak AI. If, for example, a home or in-vehicle thermostat system is poorly configured or hacked, its operation could pose a serious hazard to human health through over-heating or freezing. The same would apply to smart city management systems or autonomous vehicle steering mechanisms.

Most researchers agree that a strong or AGI system would be unlikely to exhibit human emotions such as love or hate, and would therefore not pose AI dangers through benevolent or malevolent intentions. However, even the strongest AI must be programmed by humans initially, and its in this context that the danger lies. Specifically, artificial intelligence analysts highlight two scenarios where the underlying programming or human intent of a system design could cause problems:

This threat covers all existing and future autonomous weapons systems (military drones, robots, missile defenses, etc.), or technologies capable of intentionally or unintentionally causing massive harm or physical destruction due to misuse, hacking, or sabotage.

Besides the prospect of an AI arms race and the possibility of AI-enabled warfare in the case of autonomous weaponry, there are AI risks posed by the design and deployment of the technology itself. With high stakes activity an inherent part of military design, such systems would probably have fail-safes that make them extremely difficult to deactivate once started and their human owners could conceivably lose control of them, in escalating situations.

The classic illustration of this AI danger comes in the example of a self-driving car. If you ask such a vehicle to take you to the airport as quickly as possible, it could quite literally do so breaking every traffic law in the book, causing accidents, and freaking you out completely, in the process.

At the super intelligence level of AGI, imagine a geo-engineering or climate control system thats given free rein to implement its programming in the most efficient manner possible. The damage it could cause to infrastructure and ecosystems could be catastrophic.

How dangerous is AI? At its current rate of development, artificial intelligence has already exceeded the expectations of many observers, with milestones having been achieved that were considered decades away, just a few years ago.

While some experts still estimate that the development of human-level AI is still centuries away, most researchers are coming round to the opinion that it could happen before 2060. And the prevailing view amongst all observers is that, as long as were not 100% sure that artificial general intelligence wont happen this century, its a good idea to start safety research now, to prepare for its arrival.

Many of the safety problems associated with super intelligent AI are so complex that they may require decades to solve. A super intelligent AI will, by definition, be very good at achieving its goals whatever they may be. As humans, well need to ensure that its goals are completely aligned with ours. The same holds for weaker artificial intelligence systems as the technology continues to evolve.

Intelligence enables control, and as technology becomes smarter, the greatest danger of artificial intelligence lies in its capacity to exceed human intelligence. Once that milestone is achieved, we run the danger of losing our control over the technology. And this danger becomes even more severe if the goals of that technology dont align with our own objectives.

A scenario whereby an AGI whose goals run counter to our own uses the internet to enforce the implementation of its internal directives illustrates why AI is dangerous in this respect. Such a system could potentially impact the financial markets, manipulate social and political discourse, or introduce technological innovations that we can barely imagine, much less keep up with.

The keys to determining why artificial intelligence is dangerous or not lie in its underlying programming, the method of its deployment, and whether or not its goals are in alignment with our own.

As technology continues its march toward artificial general intelligence, AI has the potential to become more intelligent than any human, and we currently have no way of predicting how it will behave. What we can do is everything in our power to ensure that the goals of that intelligence remain compatible with ours and the research and design to implement systems that keep them that way.

Summary:

Artificial intelligence encompasses a range of technologies and systems ranging from Googles search algorithms, through smart home gadgets, to military-grade autonomous weapons. So issuing a blanket confirmation or denial to the question Is Artificial Intelligence Dangerous? isnt that simple. For some AI developers, the Holy Grail is strong AI or artificial general intelligence (AGI), a level of technology at which machines would have a much greater degree of autonomy and versatility, enabling them to outperform humans in almost all cognitive tasks. While the super intelligence of strong AI has the potential to help us eradicate war, disease, and poverty, there are significant dangers of artificial intelligence at this level. The keys to determining why artificial intelligence is dangerous or not lie in its underlying programming, the method of its deployment, and whether or not its goals are in alignment with our own.

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Dangers & Risks of Artificial Intelligence - ITChronicles